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25469 lines
1.2 MiB
25469 lines
1.2 MiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": [
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"exclude"
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]
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},
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"source": [
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"#### Imports"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"tags": [
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"exclude"
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]
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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" <script type=\"text/javascript\">\n",
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" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
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" if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
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" if (typeof require !== 'undefined') {\n",
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" require.undef(\"plotly\");\n",
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" requirejs.config({\n",
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" paths: {\n",
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" 'plotly': ['https://cdn.plot.ly/plotly-2.30.0.min']\n",
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" }\n",
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" });\n",
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" require(['plotly'], function(Plotly) {\n",
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" window._Plotly = Plotly;\n",
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" });\n",
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" }\n",
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" </script>\n",
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" "
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\__init__.py\n"
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]
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}
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],
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"source": [
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"from datetime import timedelta, datetime\n",
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"import json\n",
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"import pprint\n",
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"from copy import deepcopy\n",
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"import pytz\n",
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"\n",
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"import cufflinks\n",
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"cufflinks.go_offline()\n",
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"import numpy as np\n",
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"from numpy.polynomial import Polynomial\n",
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"import pandas as pd\n",
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"from tqdm.notebook import tqdm\n",
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"\n",
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"import sys\n",
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"\n",
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"import os\n",
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" \n",
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"# Path to the folder containing the alternate version of PyRecoy\n",
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"\n",
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"alternate_pyrecoy_path = 'C:\\\\Users\\\\shahla.huseynova\\\\source\\\\repos\\\\Mooi-Kickstart\\\\pyrecoy\\\\pyrecoy'\n",
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" \n",
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"# Add the path to sys.path\n",
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"\n",
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"if alternate_pyrecoy_path not in sys.path:\n",
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"\n",
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" sys.path.insert(0, alternate_pyrecoy_path)\n",
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" \n",
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"# Now import PyRecoy\n",
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"\n",
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"import pyrecoy2\n",
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"\n",
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"# Check the version or path to confirm\n",
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"\n",
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"print(pyrecoy2.__file__)\n",
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"\n",
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"\n",
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"from pyrecoy2.assets import Heatpump, Eboiler, GasBoiler, HotWaterStorage\n",
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"from pyrecoy2.colors import *\n",
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"from pyrecoy2.converters import *\n",
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"from pyrecoy2.financial import calculate_eb_ode, get_tax_tables, get_tax_rate, get_grid_tariffs_electricity\n",
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"from pyrecoy2.framework import TimeFramework\n",
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"from pyrecoy2.casestudy import CaseStudy\n",
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"from pyrecoy2.plotting import ebitda_bar_chart, npv_bar_chart\n",
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"from pyrecoy2.reports import CaseReport, ComparisonReport, BusinessCaseReport, SingleFigureComparison\n",
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"from pyrecoy2.sensitivity import SensitivityAnalysis\n",
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"from pyrecoy2.prices import get_tennet_data, get_afrr_capacity_fees_nl\n",
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"from pyrecoy2.forecasts import Mipf, Forecast\n",
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"\n",
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"%load_ext autoreload\n",
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"%autoreload 2"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": [
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"exclude"
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]
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},
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"source": [
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"#### Development backlog"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": [
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"exclude"
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]
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},
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"source": [
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"* aFRR (can improve the optimisation case)\n",
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"* Report\n",
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"\n",
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"--\n",
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"* Create strategy on imbalance POS (buy 100% day-ahead, and respond to high prices)\n",
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"* Graphs\n",
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" * EBITDA vs. baseline (earnings vs baseline)\n",
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"* Show COP curves in different cases, just for illustration\n",
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"* Energy report --> Check + add gas\n",
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"* Fix comparison reports\n",
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"* Model verification"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": [
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"exclude"
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]
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},
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"source": [
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"#### Meeting Notes"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": [
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"exclude"
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]
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},
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"source": [
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"##### Meeting 25-11-2020"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": [
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"exclude"
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]
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},
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"source": [
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"* aFRR can help optimisation case\n",
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"* SDE++ should be included\n",
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"* Tsource sensitivity really gives interesting insights\n",
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"* Sensitivities should be verified (especially CO2, Tsink, Tsource, time period)\n",
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"* AP TNO: Update/verify COP curve\n",
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"* AP TNO: Update CAPEX\n",
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"* AP Mark: \n",
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" * Create graphs on COP curve with different Tsource, Tsink\n",
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" * Generate table and output in .csv (send it to Andrew)\n",
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"* Investigate opportunity to lower the COP and negative electricity prices\n",
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" * Technically feasible, but not really needed/possible to do it in this project\n",
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"* Could be interesting to run this model on a usecase with higher delta T\n",
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"* Conclusion: Finalize this model, but not add too much extra complexity, next steps is to go towards industrial partners with the results"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": [
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"exclude"
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]
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},
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"source": [
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"# ENCORE : Heatpump Optimisation Framework\n",
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"\n",
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"***\n",
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"© Mark Kremer \n",
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"July 2020\n",
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"##### Cases\n",
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"In this model, 3 cases are compared:\n",
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"* **Baseline** : All heat is supplied by steam turbine\n",
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"* **Heatpump case** : All heat is supplied by heatpump\n",
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"* **Hybrid case** : Steam turbine and heatpump run in hybrid mode, and are optimised on costs in real-time"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": [
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"exclude"
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]
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},
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"source": [
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"#### Loading config"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"tags": [
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"exclude"
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]
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},
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"outputs": [],
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"source": [
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"class Config():\n",
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" start = '2023-01-01'\n",
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" end = '2023-12-31'\n",
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" # year = '2019'\n",
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" # startdate = year + '-01-01'\n",
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" # enddate = str(year) + '-12-31'\n",
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" # start = datetime.strptime(startdate, \"%Y-%m-%d\").astimezone(pytz.timezone('Europe/Amsterdam'))\n",
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" # end = datetime.strptime(enddate, \"%Y-%m-%d\").astimezone(pytz.timezone('Europe/Amsterdam'))\n",
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" # start = start.astimezone(pytz.UTC)\n",
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" # end = end.astimezone(pytz.UTC)\n",
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" \n",
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" hp_vdg_e_power = 23.3 # MW\n",
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" hp_ndg_e_power = 7.7 # MW\n",
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" hp_min_load = 0\n",
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" hp_lifetime = 25\n",
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" hp_capex = 200_000 # EUR/MWth\n",
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" hp_opex = 0.01 # in % of CAPEX\n",
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" hp_devex = 0.005 # in % of CAPEX\n",
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" \n",
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" gb_power = 35 # MW\n",
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" gb_efficiency = 0.9\n",
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" \n",
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" storage_power = 25\n",
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" storage_volume = 1000\n",
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" storage_cap_per_volume = 50 * 1e-3\n",
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" storage_lifetime = 25\n",
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" storage_temperature = 95\n",
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" storage_min_level = storage_volume * storage_cap_per_volume * 0.05\n",
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" storage_capex_per_MW = 7_000\n",
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" storage_capex_per_MWh = 5_000\n",
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" storage_opex_perc_of_capex = 0.02\n",
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" # storage_initial_level = 5\n",
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" threshold = 20\n",
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" \n",
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" tax_bracket_g = 4 \n",
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" tax_bracket_e = 4\n",
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" \n",
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" include_transport_costs = False\n",
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" grid_operator = 'Liander'\n",
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" connection_type = 'TS/MS'\n",
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" \n",
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" discount_rate = 0.1\n",
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" project_duration = 12\n",
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"\n",
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" forecast = 'ForeNeg'\n",
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" gas_price_multiplier = 1\n",
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" e_price_multiplier = 1\n",
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" e_price_volatility_multiplier = 1\n",
|
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" co2_price_multiplier = 1\n",
|
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" tsource_delta = 0\n",
|
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" tsink_delta = 0\n",
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" energy_tax_multiplier = 1\n",
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" \n",
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" # Review the SDE implementation\n",
|
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" sde_base_amount = 81\n",
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" longterm_gas_price = 24.00\n",
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" longterm_co2_price = 37.90\n",
|
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" sde_switch_price_correction = 40\n",
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" \n",
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" day_ahead_buying_perc = 0.3\n",
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" \n",
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" afrr_capacity_fee = 25_000\n",
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"\n",
|
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"c = Config()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"tags": [
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"exclude"
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]
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},
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"outputs": [],
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"source": [
|
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"class Store():\n",
|
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" pass"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": [
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"exclude"
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]
|
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},
|
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"source": [
|
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"## Model set-up"
|
|
]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
|
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Tsource (VDG) 0\n",
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"Tsink (VDG) 0\n",
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"MW (VDG) 0\n",
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"Tsource (NDG) 0\n",
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"Tsink (NDG) 0\n",
|
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"MW (NDG) 0\n",
|
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"dtype: int64\n"
|
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]
|
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}
|
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],
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"source": [
|
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"# Define the file paths\n",
|
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"excel_file_path = r'C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\Kickstart\\V2\\Demand_Data_Smurfit_Preprocessed2.xlsx'\n",
|
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"output_directory = r'C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\Kickstart\\V2\\data'\n",
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"output_file_name = 'smurfit_demand_preprocessed2.csv'\n",
|
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"csv_file_path = os.path.join(output_directory, output_file_name)\n",
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"\n",
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"df = pd.read_excel(excel_file_path)\n",
|
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"# df = df.fillna(0) \n",
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"\n",
|
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"# # Save the DataFrame to CSV\n",
|
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"df.to_csv(csv_file_path, index=False)"
|
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]
|
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 5,
|
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"metadata": {
|
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"tags": [
|
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"exclude"
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]
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},
|
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"outputs": [],
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"source": [
|
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"def load_demand_data(c, s):\n",
|
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" demand = pd.read_csv('data/smurfit_demand_preprocessed2.csv', delimiter=',', decimal=';')\n",
|
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" dt_index = pd.date_range(\n",
|
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" start=s.time_fw.start,\n",
|
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" end=s.time_fw.start + timedelta(days=365), \n",
|
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" freq='1T',\n",
|
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" tz='Europe/Amsterdam')\n",
|
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" dt_index = dt_index[:len(demand)]\n",
|
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" demand.index = dt_index\n",
|
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" demand['Total demand'] = demand['MW (VDG)'] + demand['MW (NDG)']\n",
|
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" demand = demand[c.start:c.end]\n",
|
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" return demand"
|
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]
|
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 6,
|
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"metadata": {},
|
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"outputs": [],
|
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"source": [
|
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"s = Store()"
|
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]
|
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 7,
|
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"metadata": {
|
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"tags": [
|
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"exclude"
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]
|
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},
|
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"outputs": [
|
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{
|
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"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
|
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"testets\n"
|
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]
|
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},
|
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{
|
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"name": "stderr",
|
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"output_type": "stream",
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"text": [
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"C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:41: UserWarning:\n",
|
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"\n",
|
|
"The chosen timeperiod spans 365.99930555555557 days, which is not a full year. Beware that certain functions that use yearly rates might return incorrect values.\n",
|
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"\n",
|
|
"C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:62: FutureWarning:\n",
|
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"\n",
|
|
"'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
|
|
"\n",
|
|
"C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:115: FutureWarning:\n",
|
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"\n",
|
|
"'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
|
|
"\n",
|
|
"C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
|
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"\n",
|
|
"'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
|
|
"\n",
|
|
"C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
|
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"\n",
|
|
"'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
|
|
"\n",
|
|
"C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
|
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"\n",
|
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"'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
|
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"\n",
|
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"C:\\Users\\shahla.huseynova\\AppData\\Local\\Temp\\ipykernel_15092\\3886619535.py:3: FutureWarning:\n",
|
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"\n",
|
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"'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
|
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"\n"
|
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]
|
|
}
|
|
],
|
|
"source": [
|
|
"def setup_model(c):\n",
|
|
" s = Store()\n",
|
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" \n",
|
|
" s.time_fw = TimeFramework(start=c.start, end=c.end)\n",
|
|
" # s.minute_ix = s.time_fw.dt_index(freq='1T')\n",
|
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" mipf = Mipf(None, start=s.time_fw.start, end=s.time_fw.end, from_database=True, add_days_to_start_end=False).data\n",
|
|
" s.mipf = mipf\n",
|
|
" s.baseline = CaseStudy(time_fw=s.time_fw, freq='1T', name='Baseline')\n",
|
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" s.hpcase = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump only', data=mipf)\n",
|
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" #s.hpcase_sde = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + SDE', data=mipf)\n",
|
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" #s.optcase1 = CaseStudy(time_fw=s.time_fw, freq='1T', name='Optimisation', data=mipf)\n",
|
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" #s.afrr_case = CaseStudy(time_fw=s.time_fw, freq='1T', name='Optimisation + aFRR', data=mipf)\n",
|
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" s.storage_case_PCM = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage_PCM', data=mipf)\n",
|
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" # s.storage_case_TCM = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage_TCM', data=mipf)\n",
|
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" # s.storage_case_battery = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage_battery', data=mipf)\n",
|
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" s.cases = list(CaseStudy.instances.values())\n",
|
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" #s.optcases = [s.hpcase, s.hpcase_sde, s.optcase1, s.afrr_case]\n",
|
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" s.optcases = [s.hpcase, s.storage_case_PCM]\n",
|
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" #s.sde_cases = [s.hpcase_sde, s.optcase1, s.afrr_case]\n",
|
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" s.sde_cases = []\n",
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" \n",
|
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" s.demand = load_demand_data(c, s)\n",
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" return s\n",
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"\n",
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"s = setup_model(c)"
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]
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},
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{
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"text": [
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"C:\\Users\\shahla.huseynova\\AppData\\Local\\Temp\\ipykernel_15092\\2657160476.py:1: FutureWarning:\n",
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"\n",
|
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"'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
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"C:\\Users\\shahla.huseynova\\AppData\\Roaming\\Python\\Python310\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
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"\n",
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"DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n",
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"2023-01-04 02:45:00+01:00",
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"2023-01-04 03:00:00+01:00",
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"2023-01-04 03:15:00+01:00",
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"2023-01-04 03:45:00+01:00",
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"2023-01-04 08:15:00+01:00",
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"2023-01-04 09:15:00+01:00",
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"2023-01-04 11:15:00+01:00",
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"2023-01-04 11:30:00+01:00",
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"2023-01-04 11:45:00+01:00",
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"2023-01-04 12:00:00+01:00",
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"2023-01-04 12:15:00+01:00",
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"2023-01-04 19:45:00+01:00",
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"2023-01-04 21:45:00+01:00",
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"2023-01-05 02:15:00+01:00",
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"2023-01-05 03:15:00+01:00",
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"2023-01-05 03:45:00+01:00",
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"2023-01-05 04:15:00+01:00",
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"2023-01-05 05:15:00+01:00",
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"2023-01-05 06:15:00+01:00",
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"2023-01-05 06:45:00+01:00",
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"2023-01-05 07:15:00+01:00",
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"2023-01-05 07:30:00+01:00",
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"2023-01-05 07:45:00+01:00",
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"2023-01-05 08:15:00+01:00",
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"2023-01-05 08:30:00+01:00",
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"2023-01-05 09:15:00+01:00",
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"2023-01-05 09:30:00+01:00",
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"2023-01-05 09:45:00+01:00",
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"2023-01-05 10:15:00+01:00",
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"2023-01-05 10:45:00+01:00",
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"2023-01-05 11:00:00+01:00",
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"2023-01-05 11:15:00+01:00",
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"2023-01-05 11:30:00+01:00",
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"2023-01-05 11:45:00+01:00",
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"2023-01-05 12:00:00+01:00",
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"2023-01-05 12:15:00+01:00",
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"2023-01-05 12:30:00+01:00",
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"2023-01-05 12:45:00+01:00",
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"2023-01-05 13:00:00+01:00",
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"2023-01-05 13:15:00+01:00",
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"2023-01-05 13:30:00+01:00",
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"2023-01-05 13:45:00+01:00",
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"2023-01-05 14:00:00+01:00",
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"2023-01-05 14:15:00+01:00",
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"2023-01-05 14:30:00+01:00",
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"2023-01-05 14:45:00+01:00",
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"2023-01-05 15:00:00+01:00",
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"2023-01-05 15:15:00+01:00",
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"2023-01-05 15:30:00+01:00",
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"2023-01-05 15:45:00+01:00",
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"2023-01-05 16:15:00+01:00",
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"2023-01-05 16:30:00+01:00",
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"2023-01-05 16:45:00+01:00",
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"2023-01-05 17:00:00+01:00",
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"2023-01-05 17:15:00+01:00",
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"2023-01-05 17:30:00+01:00",
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"2023-01-05 17:45:00+01:00",
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"2023-01-05 18:00:00+01:00",
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"2023-01-05 18:15:00+01:00",
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"2023-01-05 18:30:00+01:00",
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"2023-01-05 18:45:00+01:00",
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"2023-01-05 19:00:00+01:00",
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"2023-01-05 19:15:00+01:00",
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"2023-01-05 19:30:00+01:00",
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"2023-01-05 19:45:00+01:00",
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"2023-01-05 20:00:00+01:00",
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"2023-01-05 20:15:00+01:00",
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"2023-01-05 20:30:00+01:00",
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"2023-01-05 20:45:00+01:00",
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"2023-01-05 21:00:00+01:00",
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"2023-01-05 21:15:00+01:00",
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"2023-01-05 21:30:00+01:00",
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"2023-01-05 21:45:00+01:00",
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"2023-01-05 22:00:00+01:00",
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"2023-01-05 22:15:00+01:00",
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"2023-01-05 22:30:00+01:00",
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"2023-01-05 22:45:00+01:00",
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"2023-01-05 23:00:00+01:00",
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"2023-01-05 23:15:00+01:00",
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"2023-01-05 23:30:00+01:00",
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"2023-01-05 23:45:00+01:00",
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"2023-01-06 00:00:00+01:00",
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"2023-01-06 00:15:00+01:00",
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"2023-01-06 00:30:00+01:00",
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"2023-01-06 00:45:00+01:00",
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"2023-01-06 01:00:00+01:00",
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"2023-01-06 01:15:00+01:00",
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"2023-01-06 01:30:00+01:00",
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"2023-01-06 01:45:00+01:00",
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"2023-01-06 02:00:00+01:00",
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"2023-01-06 02:15:00+01:00",
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"2023-01-06 02:30:00+01:00",
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"2023-01-06 02:45:00+01:00",
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"2023-01-06 03:00:00+01:00",
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"2023-01-06 03:15:00+01:00",
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"2023-01-06 03:30:00+01:00",
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"2023-01-06 03:45:00+01:00",
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"2023-01-06 04:00:00+01:00",
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"2023-01-06 04:15:00+01:00",
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"2023-01-06 04:30:00+01:00",
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"2023-01-06 04:45:00+01:00",
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"2023-01-06 05:00:00+01:00",
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"2023-01-06 05:15:00+01:00",
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"2023-01-06 05:30:00+01:00",
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"2023-01-06 05:45:00+01:00",
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"2023-01-06 06:00:00+01:00",
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"2023-01-06 06:15:00+01:00",
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"2023-01-06 06:30:00+01:00",
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"2023-01-06 06:45:00+01:00",
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"2023-01-06 07:00:00+01:00",
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"2023-01-06 07:15:00+01:00",
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"2023-01-06 07:30:00+01:00",
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"2023-01-06 07:45:00+01:00",
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"2023-01-06 08:00:00+01:00",
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"2023-01-06 08:15:00+01:00",
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"2023-01-06 08:30:00+01:00",
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"2023-01-06 08:45:00+01:00",
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"2023-01-06 09:00:00+01:00",
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"2023-01-06 09:15:00+01:00",
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"2023-01-06 09:30:00+01:00",
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"2023-01-06 09:45:00+01:00",
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"2023-01-06 10:00:00+01:00",
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"2023-01-06 10:15:00+01:00",
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"2023-01-06 10:30:00+01:00",
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"2023-01-06 10:45:00+01:00",
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"2023-01-06 11:00:00+01:00",
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"2023-01-06 11:15:00+01:00",
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"2023-01-06 11:30:00+01:00",
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"2023-01-06 11:45:00+01:00",
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"2023-01-06 12:00:00+01:00",
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"2023-01-06 12:15:00+01:00",
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"2023-01-06 12:30:00+01:00",
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"2023-01-06 12:45:00+01:00",
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"2023-01-06 13:00:00+01:00",
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"2023-01-06 13:15:00+01:00",
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"2023-01-06 13:30:00+01:00",
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"2023-01-06 13:45:00+01:00",
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"2023-01-06 14:00:00+01:00",
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"2023-01-06 14:15:00+01:00",
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"2023-01-06 14:30:00+01:00",
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"2023-01-06 14:45:00+01:00",
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"2023-01-06 15:00:00+01:00",
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"2023-01-06 15:15:00+01:00",
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"2023-01-06 15:30:00+01:00",
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"2023-01-06 15:45:00+01:00",
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"2023-01-06 16:00:00+01:00",
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"2023-01-06 16:15:00+01:00",
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"2023-01-06 16:30:00+01:00",
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"2023-01-06 16:45:00+01:00",
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"2023-01-06 17:00:00+01:00",
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"2023-01-06 17:15:00+01:00",
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"2023-01-06 17:30:00+01:00",
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"2023-01-06 17:45:00+01:00",
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"2023-01-06 18:00:00+01:00",
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"2023-01-06 18:15:00+01:00",
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"2023-01-06 18:30:00+01:00",
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"2023-01-06 18:45:00+01:00",
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"2023-01-06 19:00:00+01:00",
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"2023-01-06 19:15:00+01:00",
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"2023-01-06 19:30:00+01:00",
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"2023-01-06 19:45:00+01:00",
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"2023-01-06 20:00:00+01:00",
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"2023-01-06 20:15:00+01:00",
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"2023-01-06 20:30:00+01:00",
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"2023-01-06 20:45:00+01:00",
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"2023-01-06 21:00:00+01:00",
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"2023-01-06 21:15:00+01:00",
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"2023-01-06 21:30:00+01:00",
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"2023-01-06 21:45:00+01:00",
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"2023-01-06 22:00:00+01:00",
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"2023-01-06 22:15:00+01:00",
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"2023-01-06 22:30:00+01:00",
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"2023-01-06 22:45:00+01:00",
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"2023-01-06 23:00:00+01:00",
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"2023-01-06 23:15:00+01:00",
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"2023-01-06 23:30:00+01:00",
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"2023-01-06 23:45:00+01:00",
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"2023-01-07 00:00:00+01:00",
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"2023-01-07 00:15:00+01:00",
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"2023-01-07 00:30:00+01:00",
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"2023-01-07 00:45:00+01:00",
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"2023-01-07 01:00:00+01:00",
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"2023-01-07 01:15:00+01:00",
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"2023-01-07 01:30:00+01:00",
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"2023-01-07 01:45:00+01:00",
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"2023-01-07 02:00:00+01:00",
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"2023-01-07 02:15:00+01:00",
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"2023-01-07 02:30:00+01:00",
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"2023-01-07 02:45:00+01:00",
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"2023-01-07 03:00:00+01:00",
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"2023-01-07 03:15:00+01:00",
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"2023-01-07 03:30:00+01:00",
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"2023-01-07 03:45:00+01:00",
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"2023-01-07 04:00:00+01:00",
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"2023-01-07 04:15:00+01:00",
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"2023-01-07 04:30:00+01:00",
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"2023-01-07 04:45:00+01:00",
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"2023-01-07 05:00:00+01:00",
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"2023-01-07 05:15:00+01:00",
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"2023-01-07 05:30:00+01:00",
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"2023-01-07 05:45:00+01:00",
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"2023-01-07 06:00:00+01:00",
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"2023-01-07 06:15:00+01:00",
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"2023-01-07 06:30:00+01:00",
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"2023-01-07 06:45:00+01:00",
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"2023-01-07 07:00:00+01:00",
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"2023-01-07 07:15:00+01:00",
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"2023-01-07 07:30:00+01:00",
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"2023-01-07 07:45:00+01:00",
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"2023-01-07 08:00:00+01:00",
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"2023-01-07 08:15:00+01:00",
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"2023-01-07 08:30:00+01:00",
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"2023-01-07 08:45:00+01:00",
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"2023-01-07 09:00:00+01:00",
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"2023-01-07 09:15:00+01:00",
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"2023-01-07 09:30:00+01:00",
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"2023-01-07 09:45:00+01:00",
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"2023-01-07 10:00:00+01:00",
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"2023-01-07 10:15:00+01:00",
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"2023-01-07 10:30:00+01:00",
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"2023-01-07 10:45:00+01:00",
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"2023-01-07 11:00:00+01:00",
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"2023-01-07 11:15:00+01:00",
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"2023-01-07 11:30:00+01:00",
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"2023-01-07 11:45:00+01:00",
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"2023-01-07 12:00:00+01:00",
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"2023-01-07 12:15:00+01:00",
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"2023-01-07 12:30:00+01:00",
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"2023-01-07 12:45:00+01:00",
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"2023-01-07 13:00:00+01:00",
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"2023-01-07 13:15:00+01:00",
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"2023-01-07 13:30:00+01:00",
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"2023-01-07 13:45:00+01:00",
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"2023-01-07 14:00:00+01:00",
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"2023-01-07 14:15:00+01:00",
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"2023-01-07 14:30:00+01:00",
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"2023-01-07 14:45:00+01:00",
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"2023-01-07 15:00:00+01:00",
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"2023-01-07 15:15:00+01:00",
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"2023-01-07 15:30:00+01:00",
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"2023-01-07 15:45:00+01:00",
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"2023-01-07 16:00:00+01:00",
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"2023-01-07 16:15:00+01:00",
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"2023-01-07 16:30:00+01:00",
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"2023-01-07 16:45:00+01:00",
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"2023-01-07 17:00:00+01:00",
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"2023-01-07 17:15:00+01:00",
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"2023-01-07 17:30:00+01:00",
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"2023-01-07 17:45:00+01:00",
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"2023-01-07 18:00:00+01:00",
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"2023-01-07 18:15:00+01:00",
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"2023-01-07 18:30:00+01:00",
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"2023-01-07 18:45:00+01:00",
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"2023-01-07 19:00:00+01:00",
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"2023-01-07 19:15:00+01:00",
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"2023-01-07 19:30:00+01:00",
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"2023-01-07 19:45:00+01:00",
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"2023-01-07 20:00:00+01:00",
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"2023-01-07 20:15:00+01:00",
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"2023-01-07 20:30:00+01:00",
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"2023-01-07 20:45:00+01:00",
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"2023-01-07 21:00:00+01:00",
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"2023-01-07 21:15:00+01:00",
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"2023-01-07 21:30:00+01:00",
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"2023-01-07 21:45:00+01:00",
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"2023-01-07 22:00:00+01:00",
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"2023-01-07 22:15:00+01:00",
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"2023-01-07 22:30:00+01:00",
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"2023-01-07 22:45:00+01:00",
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"2023-01-07 23:00:00+01:00",
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"2023-01-07 23:15:00+01:00",
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"2023-01-07 23:30:00+01:00",
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"2023-01-07 23:45:00+01:00",
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"2023-01-08 00:00:00+01:00",
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"2023-01-08 00:15:00+01:00",
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"2023-01-08 00:30:00+01:00",
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"2023-01-08 00:45:00+01:00",
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"2023-01-08 01:00:00+01:00",
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"2023-01-08 01:15:00+01:00",
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"2023-01-08 01:30:00+01:00",
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"2023-01-08 01:45:00+01:00",
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"2023-01-08 02:00:00+01:00",
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"2023-01-08 02:15:00+01:00",
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"2023-01-08 02:30:00+01:00",
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"2023-01-08 02:45:00+01:00",
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"2023-01-08 03:00:00+01:00",
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"2023-01-08 03:15:00+01:00",
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"2023-01-08 03:30:00+01:00",
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"2023-01-08 03:45:00+01:00",
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"2023-01-08 04:00:00+01:00",
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"2023-01-08 04:15:00+01:00",
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"2023-01-08 04:30:00+01:00",
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"2023-01-08 04:45:00+01:00",
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"2023-01-08 05:00:00+01:00",
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"2023-01-08 05:15:00+01:00",
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"2023-01-08 05:30:00+01:00",
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"2023-01-08 05:45:00+01:00",
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"2023-01-08 06:00:00+01:00",
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"2023-01-08 06:15:00+01:00",
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"2023-01-08 06:30:00+01:00",
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"2023-01-08 06:45:00+01:00",
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"2023-01-08 07:00:00+01:00",
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"2023-01-08 07:15:00+01:00",
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"2023-01-08 07:30:00+01:00",
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"2023-01-08 07:45:00+01:00",
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"2023-01-08 08:00:00+01:00",
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"2023-01-08 08:15:00+01:00",
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"2023-01-08 08:30:00+01:00",
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"2023-01-08 08:45:00+01:00",
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"2023-01-08 09:00:00+01:00",
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"2023-01-08 09:15:00+01:00",
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"2023-01-08 09:30:00+01:00",
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"2023-01-08 09:45:00+01:00",
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"2023-01-08 10:00:00+01:00",
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"2023-01-08 10:15:00+01:00",
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"2023-01-08 10:30:00+01:00",
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"2023-01-08 10:45:00+01:00",
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"2023-01-08 11:00:00+01:00",
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"2023-01-08 11:15:00+01:00",
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"2023-01-08 11:30:00+01:00",
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"2023-01-08 11:45:00+01:00",
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"2023-01-08 12:00:00+01:00",
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"2023-01-08 12:15:00+01:00",
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"2023-01-08 12:30:00+01:00",
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"2023-01-08 12:45:00+01:00",
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"2023-01-08 13:00:00+01:00",
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"2023-01-08 13:15:00+01:00",
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"2023-01-08 13:30:00+01:00",
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"2023-01-08 13:45:00+01:00",
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"2023-01-08 14:00:00+01:00",
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"2023-01-08 14:15:00+01:00",
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"2023-01-08 14:30:00+01:00",
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"2023-01-08 14:45:00+01:00",
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"2023-01-08 15:00:00+01:00",
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"2023-01-08 15:15:00+01:00",
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"2023-01-08 15:30:00+01:00",
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"2023-01-08 15:45:00+01:00",
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"2023-01-08 16:00:00+01:00",
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"2023-01-08 16:15:00+01:00",
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"2023-01-08 16:30:00+01:00",
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"2023-01-08 16:45:00+01:00",
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"2023-01-08 17:00:00+01:00",
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"2023-01-08 17:15:00+01:00",
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"2023-01-08 17:30:00+01:00",
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"2023-01-08 17:45:00+01:00",
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"2023-01-08 18:00:00+01:00",
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"2023-01-08 18:15:00+01:00",
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"2023-01-08 18:30:00+01:00",
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"2023-01-08 18:45:00+01:00",
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"2023-01-08 19:00:00+01:00",
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"2023-01-08 19:15:00+01:00",
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"2023-01-08 19:30:00+01:00",
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"2023-01-08 19:45:00+01:00",
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"2023-01-08 20:00:00+01:00",
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"2023-01-08 20:15:00+01:00",
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"2023-01-08 20:30:00+01:00",
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"2023-01-08 20:45:00+01:00",
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"2023-01-08 21:00:00+01:00",
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"2023-01-08 21:15:00+01:00",
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"2023-01-08 21:30:00+01:00",
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"2023-01-08 21:45:00+01:00",
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"2023-01-08 22:00:00+01:00",
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"2023-01-08 22:15:00+01:00",
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"2023-01-08 22:30:00+01:00",
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"2023-01-08 22:45:00+01:00",
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"2023-01-08 23:00:00+01:00",
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"2023-01-08 23:15:00+01:00",
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"2023-01-08 23:30:00+01:00",
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"2023-01-08 23:45:00+01:00",
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"2023-01-09 00:00:00+01:00",
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"2023-01-09 00:15:00+01:00",
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"2023-01-09 00:30:00+01:00",
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"2023-01-09 00:45:00+01:00",
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"2023-01-09 01:00:00+01:00",
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"2023-01-09 01:15:00+01:00",
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"2023-01-09 01:30:00+01:00",
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"2023-01-09 01:45:00+01:00",
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"2023-01-09 02:00:00+01:00",
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"2023-01-09 02:15:00+01:00",
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"2023-01-09 02:30:00+01:00",
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"2023-01-09 02:45:00+01:00",
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"2023-01-09 03:00:00+01:00",
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"2023-01-09 03:15:00+01:00",
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"2023-01-09 03:30:00+01:00",
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"2023-01-09 03:45:00+01:00",
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"2023-01-09 04:00:00+01:00",
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"2023-01-09 04:15:00+01:00",
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"2023-01-09 04:30:00+01:00",
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"2023-01-09 04:45:00+01:00",
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"2023-01-09 05:00:00+01:00",
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"2023-01-09 05:15:00+01:00",
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"2023-01-09 05:30:00+01:00",
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"2023-01-09 05:45:00+01:00",
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"2023-01-09 06:00:00+01:00",
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"2023-01-09 06:15:00+01:00",
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"2023-01-09 06:30:00+01:00",
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"2023-01-09 06:45:00+01:00",
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"2023-01-09 07:00:00+01:00",
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"2023-01-09 07:15:00+01:00",
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"2023-01-09 07:30:00+01:00",
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"2023-01-09 07:45:00+01:00",
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"2023-01-09 08:00:00+01:00",
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"2023-01-09 08:15:00+01:00",
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"2023-01-09 08:30:00+01:00",
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"2023-01-09 08:45:00+01:00",
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"2023-01-09 09:00:00+01:00",
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"2023-01-09 09:15:00+01:00",
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"2023-01-09 09:30:00+01:00",
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"2023-01-09 09:45:00+01:00",
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"2023-01-09 10:00:00+01:00",
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"2023-01-09 10:15:00+01:00",
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"2023-01-09 10:30:00+01:00",
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"2023-01-09 10:45:00+01:00",
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"2023-01-09 11:00:00+01:00",
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"2023-01-09 11:15:00+01:00",
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"2023-01-09 11:30:00+01:00",
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"2023-01-09 11:45:00+01:00",
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"2023-01-09 12:00:00+01:00",
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"2023-01-09 12:15:00+01:00",
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"2023-01-09 12:30:00+01:00",
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"2023-01-09 12:45:00+01:00",
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"2023-01-09 13:00:00+01:00",
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"2023-01-09 13:15:00+01:00",
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"2023-01-09 13:30:00+01:00",
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"2023-01-09 13:45:00+01:00",
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"2023-01-09 14:00:00+01:00",
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"2023-01-09 14:15:00+01:00",
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"2023-01-09 14:30:00+01:00",
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"2023-01-09 14:45:00+01:00",
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"2023-01-09 15:00:00+01:00",
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"2023-01-09 15:15:00+01:00",
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"2023-01-09 15:30:00+01:00",
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"2023-01-09 15:45:00+01:00",
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"2023-01-09 16:00:00+01:00",
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"2023-01-09 16:15:00+01:00",
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"2023-01-09 16:30:00+01:00",
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"2023-01-09 16:45:00+01:00",
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"2023-01-09 17:00:00+01:00",
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"2023-01-09 17:15:00+01:00",
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"2023-01-09 17:30:00+01:00",
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"2023-01-09 17:45:00+01:00",
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"2023-01-09 18:00:00+01:00",
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"2023-01-09 18:15:00+01:00",
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"2023-01-09 18:30:00+01:00",
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"2023-01-09 18:45:00+01:00",
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"2023-01-09 19:00:00+01:00",
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"2023-01-09 19:15:00+01:00",
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"2023-01-09 19:30:00+01:00",
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"2023-01-09 19:45:00+01:00",
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"2023-01-09 20:00:00+01:00",
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"2023-01-09 20:15:00+01:00",
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"2023-01-09 20:30:00+01:00",
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"2023-01-09 20:45:00+01:00",
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"2023-01-09 21:00:00+01:00",
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"2023-01-09 21:15:00+01:00",
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"2023-01-09 21:30:00+01:00",
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"2023-01-09 21:45:00+01:00",
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"2023-01-09 22:00:00+01:00",
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"2023-01-09 22:15:00+01:00",
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"2023-01-09 22:30:00+01:00",
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"2023-01-09 22:45:00+01:00",
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"2023-01-09 23:00:00+01:00",
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"2023-01-09 23:15:00+01:00",
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"2023-01-09 23:30:00+01:00",
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"2023-01-09 23:45:00+01:00",
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"2023-01-10 00:00:00+01:00",
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"2023-01-10 00:15:00+01:00",
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"2023-01-10 00:30:00+01:00",
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"2023-01-10 00:45:00+01:00",
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"2023-01-10 01:00:00+01:00",
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"2023-01-10 01:15:00+01:00",
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"2023-01-10 01:30:00+01:00",
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"2023-01-10 01:45:00+01:00",
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"2023-01-10 02:00:00+01:00",
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"2023-01-10 02:15:00+01:00",
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"2023-01-10 02:30:00+01:00",
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"2023-01-10 02:45:00+01:00",
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"2023-01-10 03:00:00+01:00",
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"2023-01-10 03:15:00+01:00",
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"2023-01-10 03:30:00+01:00",
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"2023-01-10 03:45:00+01:00",
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"2023-01-10 04:00:00+01:00",
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"2023-01-10 04:15:00+01:00",
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"2023-01-10 04:30:00+01:00",
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"2023-01-10 04:45:00+01:00",
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"2023-01-10 05:00:00+01:00",
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"2023-01-10 05:15:00+01:00",
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"2023-01-10 05:30:00+01:00",
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"2023-01-10 05:45:00+01:00",
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"2023-01-10 06:00:00+01:00",
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"2023-01-10 06:15:00+01:00",
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"2023-01-10 06:30:00+01:00",
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"2023-01-10 06:45:00+01:00",
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"2023-01-10 07:00:00+01:00",
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"2023-01-10 07:15:00+01:00",
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"2023-01-10 07:30:00+01:00",
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"2023-01-10 07:45:00+01:00",
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"2023-01-10 08:00:00+01:00",
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"2023-01-10 08:15:00+01:00",
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"2023-01-10 08:30:00+01:00",
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"2023-01-10 08:45:00+01:00",
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"2023-01-10 09:00:00+01:00",
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"2023-01-10 09:15:00+01:00",
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"2023-01-10 09:30:00+01:00",
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"2023-01-10 09:45:00+01:00",
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"2023-01-10 10:00:00+01:00",
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"2023-01-10 10:15:00+01:00",
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"2023-01-10 10:30:00+01:00",
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"2023-01-10 10:45:00+01:00",
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"2023-01-10 11:00:00+01:00",
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"2023-01-10 11:15:00+01:00",
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"2023-01-10 11:30:00+01:00",
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"2023-01-10 11:45:00+01:00",
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"2023-01-10 12:00:00+01:00",
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"2023-01-10 12:15:00+01:00",
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"2023-01-10 12:30:00+01:00",
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"2023-01-10 12:45:00+01:00",
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"2023-01-10 13:00:00+01:00",
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"2023-01-10 13:15:00+01:00",
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"2023-01-10 13:30:00+01:00",
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"2023-01-10 13:45:00+01:00",
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"2023-01-10 14:00:00+01:00",
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"2023-01-10 14:15:00+01:00",
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"2023-01-10 14:30:00+01:00",
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"2023-01-10 14:45:00+01:00",
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"2023-01-10 15:00:00+01:00",
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"2023-01-10 15:15:00+01:00",
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"2023-01-10 15:30:00+01:00",
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"2023-01-10 15:45:00+01:00",
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"2023-01-10 16:00:00+01:00",
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"2023-01-10 16:15:00+01:00",
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"2023-01-10 16:30:00+01:00",
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"2023-01-10 16:45:00+01:00",
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"2023-01-10 17:00:00+01:00",
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"2023-01-10 17:15:00+01:00",
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"2023-01-10 17:30:00+01:00",
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"2023-01-10 17:45:00+01:00",
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"2023-01-10 18:00:00+01:00",
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"2023-01-10 18:15:00+01:00",
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"2023-01-10 18:30:00+01:00",
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"2023-01-10 18:45:00+01:00",
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"2023-01-10 19:00:00+01:00",
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"2023-01-10 19:15:00+01:00",
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"2023-01-10 19:30:00+01:00",
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"2023-01-10 19:45:00+01:00",
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"2023-01-10 20:00:00+01:00",
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"2023-01-10 20:15:00+01:00",
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"2023-01-10 20:30:00+01:00",
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"2023-01-10 20:45:00+01:00",
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"2023-01-10 21:00:00+01:00",
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"2023-01-10 21:15:00+01:00",
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"2023-01-10 21:30:00+01:00",
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"2023-01-10 21:45:00+01:00",
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"2023-01-10 22:00:00+01:00",
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"2023-01-10 22:15:00+01:00",
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"2023-01-10 22:30:00+01:00",
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"2023-01-10 22:45:00+01:00",
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"2023-01-10 23:00:00+01:00",
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"2023-01-10 23:15:00+01:00",
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"2023-01-10 23:30:00+01:00",
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"2023-01-10 23:45:00+01:00",
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"2023-01-11 00:00:00+01:00",
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"2023-01-11 00:15:00+01:00",
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"2023-01-11 00:30:00+01:00",
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"2023-01-11 00:45:00+01:00",
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"2023-01-11 01:00:00+01:00",
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"2023-01-11 01:15:00+01:00",
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"2023-01-11 01:30:00+01:00",
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"2023-01-11 01:45:00+01:00",
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"2023-01-11 02:00:00+01:00",
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"2023-01-11 02:15:00+01:00",
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"2023-01-11 02:30:00+01:00",
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"2023-01-11 02:45:00+01:00",
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"2023-01-11 03:00:00+01:00",
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"2023-01-11 03:15:00+01:00",
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"2023-01-11 03:30:00+01:00",
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"2023-01-11 03:45:00+01:00",
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"2023-01-11 04:00:00+01:00",
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"2023-01-11 04:15:00+01:00",
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"2023-01-11 04:30:00+01:00",
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"2023-01-11 04:45:00+01:00",
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"2023-01-11 05:00:00+01:00",
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"2023-01-11 05:15:00+01:00",
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"2023-01-11 05:30:00+01:00",
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"2023-01-11 05:45:00+01:00",
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"2023-01-11 06:00:00+01:00",
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"2023-01-11 06:15:00+01:00",
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"2023-01-11 06:30:00+01:00",
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"2023-01-11 06:45:00+01:00",
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"2023-01-11 07:00:00+01:00",
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"2023-01-11 07:15:00+01:00",
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"2023-01-11 07:30:00+01:00",
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"2023-01-11 07:45:00+01:00",
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"2023-01-11 08:00:00+01:00",
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"2023-01-11 08:15:00+01:00",
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"2023-01-11 08:30:00+01:00",
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"2023-01-11 08:45:00+01:00",
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"2023-01-11 09:00:00+01:00",
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"2023-01-11 09:15:00+01:00",
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"2023-01-11 09:30:00+01:00",
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"2023-01-11 09:45:00+01:00",
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"2023-01-11 10:00:00+01:00",
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"2023-01-11 10:15:00+01:00",
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"2023-01-11 10:30:00+01:00",
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"2023-01-11 10:45:00+01:00",
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"2023-01-11 11:00:00+01:00",
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"2023-01-11 11:15:00+01:00",
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"2023-01-11 11:30:00+01:00",
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"2023-01-11 11:45:00+01:00",
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"2023-01-11 12:00:00+01:00",
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"2023-01-11 12:15:00+01:00",
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"2023-01-11 12:30:00+01:00",
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"2023-01-11 12:45:00+01:00",
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"2023-01-11 13:00:00+01:00",
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"2023-01-11 13:15:00+01:00",
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"2023-01-11 13:30:00+01:00",
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"2023-01-11 13:45:00+01:00",
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"2023-01-11 14:00:00+01:00",
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"2023-01-11 14:15:00+01:00",
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"2023-01-11 14:30:00+01:00",
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"2023-01-11 14:45:00+01:00",
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"2023-01-11 15:00:00+01:00",
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"2023-01-11 15:15:00+01:00",
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"2023-01-11 15:30:00+01:00",
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"2023-01-11 15:45:00+01:00",
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"2023-01-11 16:00:00+01:00",
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"2023-01-11 16:15:00+01:00",
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"2023-01-11 16:30:00+01:00",
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"2023-01-11 16:45:00+01:00",
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"2023-01-11 17:15:00+01:00",
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"2023-01-11 17:30:00+01:00",
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"2023-01-11 17:45:00+01:00",
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"2023-01-11 18:00:00+01:00",
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"2023-01-11 18:15:00+01:00",
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"2023-01-11 18:30:00+01:00",
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"2023-01-11 18:45:00+01:00",
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"2023-01-11 19:00:00+01:00",
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"2023-01-11 19:15:00+01:00",
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"2023-01-11 19:30:00+01:00",
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"2023-01-11 19:45:00+01:00",
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"2023-01-11 20:00:00+01:00",
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"2023-01-11 20:15:00+01:00",
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"2023-01-11 20:30:00+01:00",
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"2023-01-11 20:45:00+01:00",
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"2023-01-11 21:00:00+01:00",
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"2023-01-11 21:15:00+01:00",
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"2023-01-11 21:30:00+01:00",
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"2023-01-11 21:45:00+01:00",
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"2023-01-11 22:15:00+01:00",
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"2023-01-11 22:30:00+01:00",
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"2023-01-11 22:45:00+01:00",
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"2023-01-11 23:00:00+01:00",
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"2023-01-11 23:15:00+01:00",
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"2023-01-11 23:30:00+01:00",
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"2023-01-11 23:45:00+01:00",
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"2023-01-12 00:00:00+01:00",
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"2023-01-12 00:15:00+01:00",
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"2023-01-12 00:30:00+01:00",
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"2023-01-12 00:45:00+01:00",
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"2023-01-12 01:00:00+01:00",
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"2023-01-12 01:15:00+01:00",
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"2023-01-12 01:30:00+01:00",
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"2023-01-12 01:45:00+01:00",
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"2023-01-12 02:00:00+01:00",
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"2023-01-12 02:15:00+01:00",
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"2023-01-12 02:30:00+01:00",
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"2023-01-12 02:45:00+01:00",
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"2023-01-12 03:00:00+01:00",
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"2023-01-12 03:15:00+01:00",
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"2023-01-12 03:30:00+01:00",
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"2023-01-12 03:45:00+01:00",
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"2023-01-12 04:00:00+01:00",
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"2023-01-12 04:15:00+01:00",
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"2023-01-12 04:30:00+01:00",
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"2023-01-12 04:45:00+01:00",
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"2023-01-12 05:00:00+01:00",
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"2023-01-12 05:15:00+01:00",
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"2023-01-12 05:30:00+01:00",
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"2023-01-12 05:45:00+01:00",
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"2023-01-12 06:00:00+01:00",
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"2023-01-12 06:15:00+01:00",
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"2023-01-12 06:30:00+01:00",
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"2023-01-12 06:45:00+01:00",
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"2023-01-12 07:00:00+01:00",
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"2023-01-12 07:15:00+01:00",
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"2023-01-12 07:30:00+01:00",
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"2023-01-12 07:45:00+01:00",
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"2023-01-12 08:00:00+01:00",
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"2023-01-12 08:15:00+01:00",
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"2023-01-12 08:30:00+01:00",
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"2023-01-12 08:45:00+01:00",
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"2023-01-12 09:00:00+01:00",
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"2023-01-12 09:15:00+01:00",
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"2023-01-12 09:30:00+01:00",
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"2023-01-12 09:45:00+01:00",
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"2023-01-12 10:00:00+01:00",
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"2023-01-12 10:15:00+01:00",
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"2023-01-12 10:30:00+01:00",
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"2023-01-12 10:45:00+01:00",
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"2023-01-12 11:00:00+01:00",
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"2023-01-12 11:15:00+01:00",
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"2023-01-12 11:30:00+01:00",
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"2023-01-12 11:45:00+01:00",
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"2023-01-12 12:00:00+01:00",
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"2023-01-12 12:15:00+01:00",
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"2023-01-12 12:30:00+01:00",
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"2023-01-12 12:45:00+01:00",
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"2023-01-12 13:00:00+01:00",
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"2023-01-12 13:15:00+01:00",
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"2023-01-12 13:30:00+01:00",
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"2023-01-12 13:45:00+01:00",
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"2023-01-12 14:00:00+01:00",
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"2023-01-12 14:15:00+01:00",
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"2023-01-12 14:30:00+01:00",
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"2023-01-12 14:45:00+01:00",
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"2023-01-12 15:00:00+01:00",
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"2023-01-12 15:15:00+01:00",
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"2023-01-12 15:30:00+01:00",
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"2023-01-12 15:45:00+01:00",
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"2023-01-12 16:00:00+01:00",
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"2023-01-12 16:15:00+01:00",
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"2023-01-12 16:30:00+01:00",
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"2023-01-12 16:45:00+01:00",
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"2023-01-12 17:00:00+01:00",
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"2023-01-12 17:15:00+01:00",
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"2023-01-12 17:30:00+01:00",
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"2023-01-12 17:45:00+01:00",
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"2023-01-12 18:00:00+01:00",
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"2023-01-12 18:15:00+01:00",
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"2023-01-12 18:30:00+01:00",
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"2023-01-12 18:45:00+01:00",
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"2023-01-12 19:00:00+01:00",
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"2023-01-12 19:15:00+01:00",
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"2023-01-12 19:30:00+01:00",
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"2023-01-12 19:45:00+01:00",
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"2023-01-12 20:00:00+01:00",
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"2023-01-12 20:15:00+01:00",
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"2023-01-12 20:30:00+01:00",
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"2023-01-12 20:45:00+01:00",
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"2023-01-12 21:00:00+01:00",
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"2023-01-12 21:15:00+01:00",
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"2023-01-12 21:30:00+01:00",
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"2023-01-12 21:45:00+01:00",
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"2023-01-12 22:00:00+01:00",
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"2023-01-12 22:15:00+01:00",
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"2023-01-12 22:30:00+01:00",
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"2023-01-12 22:45:00+01:00",
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"2023-01-12 23:00:00+01:00",
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"2023-01-12 23:15:00+01:00",
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"2023-01-12 23:30:00+01:00",
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"2023-01-12 23:45:00+01:00",
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"2023-01-13 00:00:00+01:00",
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"2023-01-13 00:15:00+01:00",
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"2023-01-13 00:30:00+01:00",
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"2023-01-13 00:45:00+01:00",
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"2023-01-13 01:00:00+01:00",
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"2023-01-13 01:15:00+01:00",
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"2023-01-13 01:30:00+01:00",
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"2023-01-13 01:45:00+01:00",
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"2023-01-13 02:00:00+01:00",
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"2023-01-13 02:15:00+01:00",
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"2023-01-13 02:30:00+01:00",
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"2023-01-13 02:45:00+01:00",
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"2023-01-13 03:00:00+01:00",
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"2023-01-13 03:15:00+01:00",
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"2023-01-13 03:30:00+01:00",
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"2023-01-13 03:45:00+01:00",
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"2023-01-13 04:00:00+01:00",
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"2023-01-13 04:15:00+01:00",
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"2023-01-13 04:30:00+01:00",
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"2023-01-13 04:45:00+01:00",
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"2023-01-13 05:00:00+01:00",
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"2023-01-13 05:15:00+01:00",
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"2023-01-13 05:30:00+01:00",
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"2023-01-13 05:45:00+01:00",
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"2023-01-13 06:00:00+01:00",
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"2023-01-13 06:15:00+01:00",
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"2023-01-13 06:30:00+01:00",
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"2023-01-13 06:45:00+01:00",
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"2023-01-13 07:00:00+01:00",
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"2023-01-13 07:15:00+01:00",
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"2023-01-13 07:30:00+01:00",
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"2023-01-13 07:45:00+01:00",
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"2023-01-13 08:00:00+01:00",
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"2023-01-13 08:15:00+01:00",
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"2023-01-13 08:30:00+01:00",
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"2023-01-13 08:45:00+01:00",
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"2023-01-13 09:00:00+01:00",
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"2023-01-13 09:15:00+01:00",
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"2023-01-13 09:30:00+01:00",
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"2023-01-13 09:45:00+01:00",
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"2023-01-13 10:00:00+01:00",
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"2023-01-13 10:15:00+01:00",
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"2023-01-13 10:30:00+01:00",
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"2023-01-13 10:45:00+01:00",
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"2023-01-13 11:00:00+01:00",
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"2023-01-13 11:15:00+01:00",
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"2023-01-13 11:30:00+01:00",
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"2023-01-13 11:45:00+01:00",
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"2023-01-13 12:00:00+01:00",
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"2023-01-13 12:15:00+01:00",
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"2023-01-13 12:30:00+01:00",
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"2023-01-13 12:45:00+01:00",
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"2023-01-13 13:00:00+01:00",
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"2023-01-13 13:15:00+01:00",
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"2023-01-13 13:30:00+01:00",
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"2023-01-13 13:45:00+01:00",
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"2023-01-13 14:00:00+01:00",
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"2023-01-13 14:15:00+01:00",
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"2023-01-13 14:30:00+01:00",
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"2023-01-13 14:45:00+01:00",
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"2023-01-13 15:00:00+01:00",
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"2023-01-13 15:15:00+01:00",
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"2023-01-13 15:30:00+01:00",
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"2023-01-13 15:45:00+01:00",
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"2023-01-13 16:00:00+01:00",
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"2023-01-13 16:15:00+01:00",
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"2023-01-13 16:30:00+01:00",
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"2023-01-13 16:45:00+01:00",
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"2023-01-13 17:00:00+01:00",
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"2023-01-13 17:15:00+01:00",
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"2023-01-13 17:30:00+01:00",
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"2023-01-13 17:45:00+01:00",
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"2023-01-13 18:00:00+01:00",
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"2023-01-13 18:15:00+01:00",
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"2023-01-13 18:30:00+01:00",
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"2023-01-13 18:45:00+01:00",
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"2023-01-13 19:00:00+01:00",
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"2023-01-13 19:15:00+01:00",
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"2023-01-13 19:30:00+01:00",
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"2023-01-13 19:45:00+01:00",
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"2023-01-13 20:00:00+01:00",
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"2023-01-13 20:15:00+01:00",
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"2023-01-13 20:30:00+01:00",
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"2023-01-13 20:45:00+01:00",
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"2023-01-13 21:00:00+01:00",
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"2023-01-13 21:15:00+01:00",
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"2023-01-13 21:30:00+01:00",
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"2023-01-13 21:45:00+01:00",
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"2023-01-13 22:00:00+01:00",
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"2023-01-13 22:15:00+01:00",
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"2023-01-13 22:30:00+01:00",
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"2023-01-13 22:45:00+01:00",
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"2023-01-13 23:00:00+01:00",
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"2023-01-13 23:15:00+01:00",
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"2023-01-13 23:30:00+01:00",
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"2023-01-13 23:45:00+01:00",
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"2023-01-14 00:00:00+01:00",
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"2023-01-14 00:15:00+01:00",
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"2023-01-14 00:30:00+01:00",
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"2023-01-14 00:45:00+01:00",
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"2023-01-14 01:00:00+01:00",
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"2023-01-14 01:15:00+01:00",
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"2023-01-14 01:30:00+01:00",
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"2023-01-14 01:45:00+01:00",
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"2023-01-14 02:00:00+01:00",
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"2023-01-14 02:15:00+01:00",
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"2023-01-14 02:30:00+01:00",
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"2023-01-14 02:45:00+01:00",
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"2023-01-14 03:00:00+01:00",
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"2023-01-14 03:15:00+01:00",
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"2023-01-14 03:30:00+01:00",
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"2023-01-14 03:45:00+01:00",
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"2023-01-14 04:00:00+01:00",
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"2023-01-14 04:15:00+01:00",
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"2023-01-14 04:30:00+01:00",
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"2023-01-14 04:45:00+01:00",
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"2023-01-14 05:00:00+01:00",
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"2023-01-14 05:15:00+01:00",
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"2023-01-14 05:30:00+01:00",
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"2023-01-14 05:45:00+01:00",
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"2023-01-14 06:00:00+01:00",
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"2023-01-14 06:15:00+01:00",
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"2023-01-14 06:30:00+01:00",
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"2023-01-14 06:45:00+01:00",
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"2023-01-14 07:00:00+01:00",
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"2023-01-14 07:15:00+01:00",
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"2023-01-14 07:30:00+01:00",
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"2023-01-14 07:45:00+01:00",
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"2023-01-14 08:00:00+01:00",
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"2023-01-14 08:15:00+01:00",
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"2023-01-14 08:30:00+01:00",
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"2023-01-14 08:45:00+01:00",
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"2023-01-14 09:00:00+01:00",
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"2023-01-14 09:15:00+01:00",
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"2023-01-14 09:30:00+01:00",
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"2023-01-14 09:45:00+01:00",
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"2023-01-14 10:00:00+01:00",
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"2023-01-14 10:15:00+01:00",
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"2023-01-14 10:30:00+01:00",
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"2023-01-14 10:45:00+01:00",
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"2023-01-14 11:00:00+01:00",
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"2023-01-14 11:15:00+01:00",
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"2023-01-14 11:30:00+01:00",
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"2023-01-14 11:45:00+01:00",
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"2023-01-14 12:00:00+01:00",
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"2023-01-14 12:15:00+01:00",
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"2023-01-14 12:30:00+01:00",
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"2023-01-14 12:45:00+01:00",
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"2023-01-14 13:00:00+01:00",
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"2023-01-14 13:15:00+01:00",
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"2023-01-14 13:30:00+01:00",
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"2023-01-14 13:45:00+01:00",
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"2023-01-14 14:00:00+01:00",
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"2023-01-14 14:15:00+01:00",
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"2023-01-14 14:30:00+01:00",
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"2023-01-14 14:45:00+01:00",
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"2023-01-14 15:00:00+01:00",
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"2023-01-14 15:15:00+01:00",
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"2023-01-14 15:30:00+01:00",
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"2023-01-14 15:45:00+01:00",
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"2023-01-14 16:00:00+01:00",
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"2023-01-14 16:15:00+01:00",
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"2023-01-14 16:30:00+01:00",
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"2023-01-14 16:45:00+01:00",
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"2023-01-14 17:00:00+01:00",
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"2023-01-14 17:15:00+01:00",
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"2023-01-14 17:30:00+01:00",
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"2023-01-14 17:45:00+01:00",
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"2023-01-14 18:00:00+01:00",
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"2023-01-14 18:15:00+01:00",
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"2023-01-14 18:30:00+01:00",
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"2023-01-14 18:45:00+01:00",
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"2023-01-14 19:00:00+01:00",
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"2023-01-14 19:15:00+01:00",
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"2023-01-14 19:30:00+01:00",
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"2023-01-14 19:45:00+01:00",
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"2023-01-14 20:00:00+01:00",
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"2023-01-14 20:15:00+01:00",
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"2023-01-14 20:30:00+01:00",
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"2023-01-14 20:45:00+01:00",
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"2023-01-14 21:00:00+01:00",
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"2023-01-14 21:15:00+01:00",
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"2023-01-14 21:30:00+01:00",
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"2023-01-14 21:45:00+01:00",
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"2023-01-14 22:00:00+01:00",
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"2023-01-14 22:15:00+01:00",
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"2023-01-14 22:30:00+01:00",
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"2023-01-14 22:45:00+01:00",
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"2023-01-14 23:00:00+01:00",
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"2023-01-14 23:15:00+01:00",
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"2023-01-14 23:30:00+01:00",
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"2023-01-14 23:45:00+01:00",
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"2023-01-15 00:00:00+01:00",
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"2023-01-15 00:15:00+01:00",
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"2023-01-15 00:30:00+01:00",
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"2023-01-15 00:45:00+01:00",
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"2023-01-15 01:00:00+01:00",
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"2023-01-15 01:15:00+01:00",
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"2023-01-15 01:30:00+01:00",
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"2023-01-15 01:45:00+01:00",
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"2023-01-15 02:00:00+01:00",
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"2023-01-15 02:15:00+01:00",
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"2023-01-15 02:30:00+01:00",
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"2023-01-15 02:45:00+01:00",
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"2023-01-15 03:00:00+01:00",
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"2023-01-15 03:15:00+01:00",
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"2023-01-15 03:30:00+01:00",
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"2023-01-15 03:45:00+01:00",
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"2023-01-15 04:00:00+01:00",
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"2023-01-15 04:15:00+01:00",
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"2023-01-15 04:30:00+01:00",
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"2023-01-15 04:45:00+01:00",
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"2023-01-15 05:00:00+01:00",
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"2023-01-15 05:15:00+01:00",
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"2023-01-15 05:30:00+01:00",
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"2023-01-15 05:45:00+01:00",
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"2023-01-15 06:00:00+01:00",
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"2023-01-15 06:15:00+01:00",
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"2023-01-15 06:30:00+01:00",
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"2023-01-15 06:45:00+01:00",
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"2023-01-15 07:00:00+01:00",
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"2023-01-15 07:15:00+01:00",
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"2023-01-15 07:30:00+01:00",
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"2023-01-15 07:45:00+01:00",
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"2023-01-15 08:00:00+01:00",
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"2023-01-15 08:15:00+01:00",
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"2023-01-15 08:30:00+01:00",
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"2023-01-15 08:45:00+01:00",
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"2023-01-15 09:00:00+01:00",
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"2023-01-15 09:15:00+01:00",
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"2023-01-15 09:30:00+01:00",
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"2023-01-15 09:45:00+01:00",
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"2023-01-15 10:00:00+01:00",
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"2023-01-15 10:15:00+01:00",
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"2023-01-15 10:30:00+01:00",
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"2023-01-15 10:45:00+01:00",
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"2023-01-15 11:00:00+01:00",
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"2023-01-15 11:15:00+01:00",
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"2023-01-15 11:30:00+01:00",
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"2023-01-15 11:45:00+01:00",
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"2023-01-15 12:00:00+01:00",
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"2023-01-15 12:15:00+01:00",
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"2023-01-15 12:30:00+01:00",
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"2023-01-15 12:45:00+01:00",
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"2023-01-15 13:00:00+01:00",
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"2023-01-15 13:15:00+01:00",
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"2023-01-15 13:30:00+01:00",
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"2023-01-15 13:45:00+01:00",
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"2023-01-15 14:00:00+01:00",
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"2023-01-15 14:15:00+01:00",
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"2023-01-15 14:30:00+01:00",
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"2023-01-15 14:45:00+01:00",
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"2023-01-15 15:00:00+01:00",
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"2023-01-15 15:15:00+01:00",
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"2023-01-15 15:30:00+01:00",
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"2023-01-15 15:45:00+01:00",
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"2023-01-15 16:00:00+01:00",
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"2023-01-15 16:15:00+01:00",
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"2023-01-15 16:30:00+01:00",
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"2023-01-15 16:45:00+01:00",
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"2023-01-15 17:00:00+01:00",
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"2023-01-15 17:15:00+01:00",
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"2023-01-15 17:30:00+01:00",
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"2023-01-15 17:45:00+01:00",
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"2023-01-15 18:00:00+01:00",
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"2023-01-15 18:15:00+01:00",
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"2023-01-15 18:30:00+01:00",
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"2023-01-15 18:45:00+01:00",
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"2023-01-15 19:00:00+01:00",
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"2023-01-15 19:15:00+01:00",
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"2023-01-15 19:30:00+01:00",
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"2023-01-15 19:45:00+01:00",
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"2023-01-15 20:00:00+01:00",
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"2023-01-15 20:15:00+01:00",
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"2023-01-15 20:30:00+01:00",
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"2023-01-15 20:45:00+01:00",
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"2023-01-15 21:00:00+01:00",
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"2023-01-15 21:15:00+01:00",
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"2023-01-15 21:30:00+01:00",
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"2023-01-15 21:45:00+01:00",
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"2023-01-15 22:00:00+01:00",
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"2023-01-15 22:15:00+01:00",
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"2023-01-15 22:30:00+01:00",
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"2023-01-15 22:45:00+01:00",
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"2023-01-15 23:00:00+01:00",
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"2023-01-15 23:15:00+01:00",
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"2023-01-15 23:30:00+01:00",
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"2023-01-15 23:45:00+01:00",
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"2023-01-16 00:00:00+01:00",
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"2023-01-16 00:15:00+01:00",
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"2023-01-16 00:30:00+01:00",
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"2023-01-16 00:45:00+01:00",
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"2023-01-16 01:00:00+01:00",
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"2023-01-16 01:15:00+01:00",
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"2023-01-16 01:30:00+01:00",
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"2023-01-16 01:45:00+01:00",
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"2023-01-16 02:00:00+01:00",
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"2023-01-16 02:15:00+01:00",
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"2023-01-16 02:30:00+01:00",
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"2023-01-16 02:45:00+01:00",
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"2023-01-16 03:00:00+01:00",
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"2023-01-16 03:15:00+01:00",
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"2023-01-16 03:30:00+01:00",
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"2023-01-16 03:45:00+01:00",
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"2023-01-16 04:00:00+01:00",
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"2023-01-16 04:15:00+01:00",
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"2023-01-16 04:30:00+01:00",
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"2023-01-16 04:45:00+01:00",
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"2023-01-16 05:00:00+01:00",
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"2023-01-16 05:15:00+01:00",
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"2023-01-16 05:30:00+01:00",
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"2023-01-16 05:45:00+01:00",
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"2023-01-16 06:00:00+01:00",
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"2023-01-16 06:15:00+01:00",
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"2023-01-16 06:30:00+01:00",
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"2023-01-16 06:45:00+01:00",
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"2023-01-16 07:00:00+01:00",
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"2023-01-16 07:15:00+01:00",
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"2023-01-16 07:30:00+01:00",
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"2023-01-16 07:45:00+01:00",
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"2023-01-16 08:00:00+01:00",
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"2023-01-16 08:15:00+01:00",
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"2023-01-16 08:30:00+01:00",
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"2023-01-16 08:45:00+01:00",
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"2023-01-16 09:00:00+01:00",
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"2023-01-16 09:15:00+01:00",
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"2023-01-16 09:30:00+01:00",
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"2023-01-16 09:45:00+01:00",
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"2023-01-16 10:00:00+01:00",
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"2023-01-16 10:15:00+01:00",
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"2023-01-16 10:30:00+01:00",
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"2023-01-16 10:45:00+01:00",
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"2023-01-16 11:00:00+01:00",
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"2023-01-16 11:15:00+01:00",
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"2023-01-16 11:30:00+01:00",
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"2023-01-16 11:45:00+01:00",
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"2023-01-16 12:00:00+01:00",
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"2023-01-16 12:15:00+01:00",
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"2023-01-16 12:30:00+01:00",
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"2023-01-16 12:45:00+01:00",
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"2023-01-16 13:00:00+01:00",
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"2023-01-16 13:15:00+01:00",
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"2023-01-16 13:30:00+01:00",
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"2023-01-16 13:45:00+01:00",
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"2023-01-16 14:00:00+01:00",
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"2023-01-16 14:15:00+01:00",
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"2023-01-16 14:30:00+01:00",
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"2023-01-16 14:45:00+01:00",
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"2023-01-16 15:00:00+01:00",
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"2023-01-16 15:15:00+01:00",
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"2023-01-16 15:30:00+01:00",
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"2023-01-16 15:45:00+01:00",
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"2023-01-16 16:00:00+01:00",
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"2023-01-16 16:15:00+01:00",
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"2023-01-16 16:30:00+01:00",
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"2023-01-16 16:45:00+01:00",
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"2023-01-16 17:00:00+01:00",
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"2023-01-16 17:15:00+01:00",
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"2023-01-16 17:30:00+01:00",
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"2023-01-16 17:45:00+01:00",
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"2023-01-16 18:00:00+01:00",
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"2023-01-16 18:15:00+01:00",
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"2023-01-16 18:30:00+01:00",
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"2023-01-16 18:45:00+01:00",
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"2023-01-16 19:00:00+01:00",
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"2023-01-16 19:15:00+01:00",
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"2023-01-16 19:30:00+01:00",
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"2023-01-16 19:45:00+01:00",
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"2023-01-16 20:00:00+01:00",
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"2023-01-16 20:15:00+01:00",
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"2023-01-16 20:30:00+01:00",
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"2023-01-16 20:45:00+01:00",
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"2023-01-16 21:00:00+01:00",
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"2023-01-16 21:15:00+01:00",
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"2023-01-16 21:30:00+01:00",
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"2023-01-16 21:45:00+01:00",
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"2023-01-16 22:00:00+01:00",
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"2023-01-16 22:15:00+01:00",
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"2023-01-16 22:30:00+01:00",
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"2023-01-16 22:45:00+01:00",
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"2023-01-16 23:00:00+01:00",
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"2023-01-16 23:15:00+01:00",
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"2023-01-16 23:30:00+01:00",
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"2023-01-16 23:45:00+01:00",
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"2023-01-17 00:00:00+01:00",
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"2023-01-17 00:15:00+01:00",
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"2023-01-17 00:30:00+01:00",
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"2023-01-17 00:45:00+01:00",
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"2023-01-17 01:00:00+01:00",
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"2023-01-17 01:15:00+01:00",
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"2023-01-17 01:30:00+01:00",
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"2023-01-17 01:45:00+01:00",
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"2023-01-17 02:00:00+01:00",
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"2023-01-17 02:15:00+01:00",
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"2023-01-17 02:30:00+01:00",
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"2023-01-17 02:45:00+01:00",
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"2023-01-17 03:00:00+01:00",
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"2023-01-17 03:15:00+01:00",
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"2023-01-17 03:30:00+01:00",
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"2023-01-17 03:45:00+01:00",
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"2023-01-17 04:00:00+01:00",
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"2023-01-17 04:15:00+01:00",
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"2023-01-17 04:30:00+01:00",
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"2023-01-17 04:45:00+01:00",
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"2023-01-17 05:00:00+01:00",
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"2023-01-17 05:15:00+01:00",
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"2023-01-17 05:30:00+01:00",
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"2023-01-17 05:45:00+01:00",
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"2023-01-17 06:00:00+01:00",
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"2023-01-17 06:15:00+01:00",
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"2023-01-17 06:30:00+01:00",
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"2023-01-17 06:45:00+01:00",
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"2023-01-17 07:00:00+01:00",
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"2023-01-17 07:15:00+01:00",
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"2023-01-17 07:30:00+01:00",
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"2023-01-17 07:45:00+01:00",
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"2023-01-17 08:00:00+01:00",
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"2023-01-17 08:15:00+01:00",
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"2023-01-17 08:30:00+01:00",
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"2023-01-17 08:45:00+01:00",
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"2023-01-17 09:00:00+01:00",
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"2023-01-17 09:15:00+01:00",
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"2023-01-17 09:30:00+01:00",
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"2023-01-17 09:45:00+01:00",
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"2023-01-17 10:00:00+01:00",
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"2023-01-17 10:15:00+01:00",
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"2023-01-17 10:30:00+01:00",
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"2023-01-17 10:45:00+01:00",
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"2023-01-17 11:00:00+01:00",
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"2023-01-17 11:15:00+01:00",
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"2023-01-17 11:30:00+01:00",
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"2023-01-17 11:45:00+01:00",
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"2023-01-17 12:00:00+01:00",
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"2023-01-17 12:15:00+01:00",
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"2023-01-17 12:30:00+01:00",
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"2023-01-17 12:45:00+01:00",
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"2023-01-17 13:00:00+01:00",
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"2023-01-17 13:15:00+01:00",
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"2023-01-17 13:30:00+01:00",
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"2023-01-17 13:45:00+01:00",
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"2023-01-17 14:00:00+01:00",
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"2023-01-17 14:15:00+01:00",
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"2023-01-17 14:30:00+01:00",
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"2023-01-17 14:45:00+01:00",
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"2023-01-17 15:00:00+01:00",
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"2023-01-17 15:15:00+01:00",
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"2023-01-17 15:30:00+01:00",
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"2023-01-17 15:45:00+01:00",
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"2023-01-17 16:00:00+01:00",
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"2023-01-17 16:15:00+01:00",
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"2023-01-17 16:30:00+01:00",
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"2023-01-17 16:45:00+01:00",
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"2023-01-17 17:00:00+01:00",
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"2023-01-17 17:15:00+01:00",
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"2023-01-17 17:30:00+01:00",
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"2023-01-17 17:45:00+01:00",
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"2023-01-17 18:00:00+01:00",
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"2023-01-17 18:15:00+01:00",
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"2023-01-17 18:30:00+01:00",
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"2023-01-17 18:45:00+01:00",
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"2023-01-17 19:00:00+01:00",
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"2023-01-17 19:15:00+01:00",
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"2023-01-17 19:30:00+01:00",
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"2023-01-17 19:45:00+01:00",
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"2023-01-17 20:15:00+01:00",
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"2023-01-17 20:30:00+01:00",
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"2023-01-17 20:45:00+01:00",
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"2023-01-17 21:00:00+01:00",
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"2023-01-17 21:15:00+01:00",
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"2023-01-17 21:30:00+01:00",
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"2023-01-17 21:45:00+01:00",
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"2023-01-17 22:15:00+01:00",
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"2023-01-17 23:15:00+01:00",
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"2023-01-17 23:30:00+01:00",
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"2023-01-17 23:45:00+01:00",
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"2023-01-18 00:00:00+01:00",
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"2023-01-18 00:15:00+01:00",
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"2023-01-18 00:30:00+01:00",
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"2023-01-18 01:15:00+01:00",
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"2023-01-18 01:45:00+01:00",
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"2023-01-18 02:00:00+01:00",
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"2023-01-18 02:15:00+01:00",
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"2023-01-18 02:30:00+01:00",
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"2023-01-18 02:45:00+01:00",
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"2023-01-18 03:00:00+01:00",
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"2023-01-18 03:15:00+01:00",
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"2023-01-18 03:30:00+01:00",
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"2023-01-18 03:45:00+01:00",
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"2023-01-18 04:15:00+01:00",
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"2023-01-18 05:15:00+01:00",
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"2023-01-18 05:30:00+01:00",
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"2023-01-18 06:15:00+01:00",
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"2023-01-18 06:45:00+01:00",
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"2023-01-18 07:00:00+01:00",
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"2023-01-18 07:15:00+01:00",
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"2023-01-18 07:30:00+01:00",
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"2023-01-18 07:45:00+01:00",
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"2023-01-18 08:15:00+01:00",
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"2023-01-18 08:30:00+01:00",
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"2023-01-18 08:45:00+01:00",
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"2023-01-18 09:00:00+01:00",
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"2023-01-18 09:15:00+01:00",
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"2023-01-18 09:30:00+01:00",
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"2023-01-18 09:45:00+01:00",
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"2023-01-18 10:15:00+01:00",
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"2023-01-18 10:30:00+01:00",
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"2023-01-18 10:45:00+01:00",
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"2023-01-18 11:00:00+01:00",
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"2023-01-18 11:15:00+01:00",
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"2023-01-18 11:30:00+01:00",
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"2023-01-18 11:45:00+01:00",
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"2023-01-18 12:00:00+01:00",
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"2023-01-18 12:15:00+01:00",
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"2023-01-18 12:30:00+01:00",
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"2023-01-18 12:45:00+01:00",
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"2023-01-18 13:00:00+01:00",
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"2023-01-18 13:15:00+01:00",
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"2023-01-18 13:30:00+01:00",
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"2023-01-18 13:45:00+01:00",
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"2023-01-18 14:15:00+01:00",
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"2023-01-18 14:30:00+01:00",
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"2023-01-18 14:45:00+01:00",
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"2023-01-18 15:15:00+01:00",
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"2023-01-18 15:30:00+01:00",
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"2023-01-18 15:45:00+01:00",
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"2023-01-18 16:30:00+01:00",
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"2023-01-18 16:45:00+01:00",
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"2023-01-18 17:00:00+01:00",
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"2023-01-18 17:15:00+01:00",
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"2023-01-18 17:30:00+01:00",
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"2023-01-18 17:45:00+01:00",
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"2023-01-18 18:00:00+01:00",
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"2023-01-18 18:15:00+01:00",
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"2023-01-18 18:30:00+01:00",
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"2023-01-18 18:45:00+01:00",
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"2023-01-18 19:00:00+01:00",
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"2023-01-18 19:15:00+01:00",
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"2023-01-18 19:30:00+01:00",
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"2023-01-18 19:45:00+01:00",
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"2023-01-18 20:15:00+01:00",
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"2023-01-18 20:30:00+01:00",
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"2023-01-18 20:45:00+01:00",
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"2023-01-18 21:00:00+01:00",
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"2023-01-18 21:15:00+01:00",
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"2023-01-18 21:30:00+01:00",
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"2023-01-18 21:45:00+01:00",
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"2023-01-18 22:00:00+01:00",
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"2023-01-18 22:15:00+01:00",
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"2023-01-18 22:30:00+01:00",
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"2023-01-18 22:45:00+01:00",
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"2023-01-18 23:00:00+01:00",
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"2023-01-18 23:15:00+01:00",
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"2023-01-18 23:30:00+01:00",
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"2023-01-18 23:45:00+01:00",
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"2023-01-19 00:00:00+01:00",
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"2023-01-19 00:15:00+01:00",
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"2023-01-19 00:30:00+01:00",
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"2023-01-19 00:45:00+01:00",
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"2023-01-19 01:00:00+01:00",
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"2023-01-19 01:15:00+01:00",
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"2023-01-19 01:30:00+01:00",
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"2023-01-19 01:45:00+01:00",
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"2023-01-19 02:00:00+01:00",
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"2023-01-19 02:15:00+01:00",
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"2023-01-19 02:30:00+01:00",
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"2023-01-19 02:45:00+01:00",
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"2023-01-19 03:00:00+01:00",
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"2023-01-19 03:15:00+01:00",
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"2023-01-19 03:30:00+01:00",
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"2023-01-19 03:45:00+01:00",
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"2023-01-19 04:00:00+01:00",
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"2023-01-19 04:15:00+01:00",
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"2023-01-19 04:30:00+01:00",
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"2023-01-19 04:45:00+01:00",
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"2023-01-19 05:00:00+01:00",
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"2023-01-19 05:15:00+01:00",
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"2023-01-19 05:30:00+01:00",
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"2023-01-19 05:45:00+01:00",
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"2023-01-19 06:00:00+01:00",
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"2023-01-19 06:15:00+01:00",
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"2023-01-19 06:30:00+01:00",
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"2023-01-19 06:45:00+01:00",
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"2023-01-19 07:00:00+01:00",
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"2023-01-19 07:15:00+01:00",
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"2023-01-19 07:30:00+01:00",
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"2023-01-19 07:45:00+01:00",
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"2023-01-19 08:00:00+01:00",
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"2023-01-19 08:15:00+01:00",
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"2023-01-19 08:30:00+01:00",
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"2023-01-19 08:45:00+01:00",
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"2023-01-19 09:00:00+01:00",
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"2023-01-19 09:15:00+01:00",
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"2023-01-19 09:30:00+01:00",
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"2023-01-19 09:45:00+01:00",
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"2023-01-19 10:00:00+01:00",
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"2023-01-19 10:15:00+01:00",
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"2023-01-19 10:30:00+01:00",
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"2023-01-19 10:45:00+01:00",
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"2023-01-19 11:00:00+01:00",
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"2023-01-19 11:15:00+01:00",
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"2023-01-19 11:30:00+01:00",
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"2023-01-19 11:45:00+01:00",
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"2023-01-19 12:00:00+01:00",
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"2023-01-19 12:15:00+01:00",
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"2023-01-19 12:30:00+01:00",
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"2023-01-19 12:45:00+01:00",
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"2023-01-19 13:00:00+01:00",
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"2023-01-19 13:15:00+01:00",
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"2023-01-19 13:30:00+01:00",
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"2023-01-19 13:45:00+01:00",
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"2023-01-19 14:00:00+01:00",
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"2023-01-19 14:15:00+01:00",
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"2023-01-19 14:30:00+01:00",
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"2023-01-19 14:45:00+01:00",
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"2023-01-19 15:00:00+01:00",
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"2023-01-19 15:15:00+01:00",
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"2023-01-19 15:30:00+01:00",
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"2023-01-19 15:45:00+01:00",
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"2023-01-19 16:00:00+01:00",
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"2023-01-19 16:15:00+01:00",
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"2023-01-19 16:30:00+01:00",
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"2023-01-19 16:45:00+01:00",
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"2023-01-19 17:00:00+01:00",
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"2023-01-19 17:15:00+01:00",
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"2023-01-19 17:30:00+01:00",
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"2023-01-19 17:45:00+01:00",
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"2023-01-19 18:00:00+01:00",
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"2023-01-19 18:15:00+01:00",
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"2023-01-19 18:30:00+01:00",
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"2023-01-19 18:45:00+01:00",
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"2023-01-19 19:00:00+01:00",
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"2023-01-19 19:15:00+01:00",
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"2023-01-19 19:30:00+01:00",
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"2023-01-19 19:45:00+01:00",
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"2023-01-19 20:00:00+01:00",
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"2023-01-19 20:15:00+01:00",
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"2023-01-19 20:30:00+01:00",
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"2023-01-19 20:45:00+01:00",
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"2023-01-19 21:00:00+01:00",
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"2023-01-19 21:15:00+01:00",
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"2023-01-19 21:30:00+01:00",
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"2023-01-19 21:45:00+01:00",
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"2023-01-19 22:00:00+01:00",
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"2023-01-19 22:15:00+01:00",
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"2023-01-19 22:30:00+01:00",
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"2023-01-19 22:45:00+01:00",
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"2023-01-19 23:00:00+01:00",
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"2023-01-19 23:15:00+01:00",
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"2023-01-19 23:30:00+01:00",
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"2023-01-19 23:45:00+01:00",
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"2023-01-20 00:00:00+01:00",
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"2023-01-20 00:15:00+01:00",
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"2023-01-20 00:30:00+01:00",
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"2023-01-20 00:45:00+01:00",
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"2023-01-20 01:00:00+01:00",
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"2023-01-20 01:15:00+01:00",
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"2023-01-20 01:30:00+01:00",
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"2023-01-20 01:45:00+01:00",
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"2023-01-20 02:00:00+01:00",
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"2023-01-20 02:15:00+01:00",
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"2023-01-20 02:30:00+01:00",
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"2023-01-20 02:45:00+01:00",
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"2023-01-20 03:00:00+01:00",
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"2023-01-20 03:15:00+01:00",
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"2023-01-20 03:30:00+01:00",
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"2023-01-20 03:45:00+01:00",
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"2023-01-20 04:00:00+01:00",
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"2023-01-20 04:15:00+01:00",
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"2023-01-20 04:30:00+01:00",
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"2023-01-20 04:45:00+01:00",
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"2023-01-20 05:00:00+01:00",
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"2023-01-20 05:15:00+01:00",
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"2023-01-20 05:30:00+01:00",
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"2023-01-20 05:45:00+01:00",
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"2023-01-20 06:00:00+01:00",
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"2023-01-20 06:15:00+01:00",
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"2023-01-20 06:30:00+01:00",
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"2023-01-20 06:45:00+01:00",
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"2023-01-20 07:00:00+01:00",
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"2023-01-20 07:15:00+01:00",
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"2023-01-20 07:30:00+01:00",
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"2023-01-20 07:45:00+01:00",
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"2023-01-20 08:00:00+01:00",
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"2023-01-20 08:15:00+01:00",
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"2023-01-20 08:30:00+01:00",
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"2023-01-20 08:45:00+01:00",
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"2023-01-20 09:00:00+01:00",
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"2023-01-20 09:15:00+01:00",
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"2023-01-20 09:30:00+01:00",
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"2023-01-20 09:45:00+01:00",
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"2023-01-20 10:00:00+01:00",
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"2023-01-20 10:15:00+01:00",
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"2023-01-20 10:30:00+01:00",
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"2023-01-20 10:45:00+01:00",
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"2023-01-20 11:00:00+01:00",
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"2023-01-20 11:15:00+01:00",
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"2023-01-20 11:30:00+01:00",
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"2023-01-20 11:45:00+01:00",
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"2023-01-20 12:00:00+01:00",
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"2023-01-20 12:15:00+01:00",
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"2023-01-20 12:30:00+01:00",
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"2023-01-20 12:45:00+01:00",
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"2023-01-20 13:00:00+01:00",
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"2023-01-20 13:15:00+01:00",
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"2023-01-20 13:30:00+01:00",
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"2023-01-20 13:45:00+01:00",
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"2023-01-20 14:00:00+01:00",
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"2023-01-20 14:15:00+01:00",
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"2023-01-20 14:30:00+01:00",
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"2023-01-20 14:45:00+01:00",
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"2023-01-20 15:00:00+01:00",
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"2023-01-20 15:15:00+01:00",
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"2023-01-20 15:30:00+01:00",
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"2023-01-20 15:45:00+01:00",
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"2023-01-20 16:00:00+01:00",
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"2023-01-20 16:15:00+01:00",
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"2023-01-20 16:30:00+01:00",
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"2023-01-20 16:45:00+01:00",
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"2023-01-20 17:00:00+01:00",
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"2023-01-20 17:15:00+01:00",
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"2023-01-20 17:30:00+01:00",
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"2023-01-20 17:45:00+01:00",
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"2023-01-20 18:00:00+01:00",
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"2023-01-20 18:15:00+01:00",
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"2023-01-20 18:30:00+01:00",
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"2023-01-20 18:45:00+01:00",
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"2023-01-20 19:00:00+01:00",
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"2023-01-20 19:15:00+01:00",
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"2023-01-20 19:30:00+01:00",
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"2023-01-20 19:45:00+01:00",
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"2023-01-20 20:00:00+01:00",
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"2023-01-20 20:15:00+01:00",
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"2023-01-20 20:30:00+01:00",
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"2023-01-20 20:45:00+01:00",
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"2023-01-20 21:00:00+01:00",
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"2023-01-20 21:15:00+01:00",
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"2023-01-20 21:30:00+01:00",
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"2023-01-20 21:45:00+01:00",
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"2023-01-20 22:00:00+01:00",
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"2023-01-20 22:15:00+01:00",
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"2023-01-20 22:30:00+01:00",
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"2023-01-20 22:45:00+01:00",
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"2023-01-20 23:00:00+01:00",
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"2023-01-20 23:15:00+01:00",
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"2023-01-20 23:30:00+01:00",
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"2023-01-20 23:45:00+01:00",
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"2023-01-21 00:00:00+01:00",
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"2023-01-21 00:15:00+01:00",
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"2023-01-21 00:30:00+01:00",
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"2023-01-21 00:45:00+01:00",
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"2023-01-21 01:00:00+01:00",
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"2023-01-21 01:15:00+01:00",
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"2023-01-21 01:30:00+01:00",
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"2023-01-21 01:45:00+01:00",
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"2023-01-21 02:00:00+01:00",
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"2023-01-21 02:15:00+01:00",
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"2023-01-21 02:30:00+01:00",
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"2023-01-21 02:45:00+01:00",
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"2023-01-21 03:00:00+01:00",
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"2023-01-21 03:15:00+01:00",
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"2023-01-21 03:30:00+01:00",
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"2023-01-21 03:45:00+01:00",
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"2023-01-21 04:00:00+01:00",
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"2023-01-21 04:15:00+01:00",
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"2023-01-21 04:30:00+01:00",
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"2023-01-21 04:45:00+01:00",
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"2023-01-21 05:00:00+01:00",
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"2023-01-21 05:15:00+01:00",
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"2023-01-21 05:30:00+01:00",
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"2023-01-21 05:45:00+01:00",
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"2023-01-21 06:15:00+01:00",
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"2023-01-21 06:30:00+01:00",
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"2023-01-21 07:15:00+01:00",
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"2023-01-21 07:30:00+01:00",
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"2023-01-21 08:15:00+01:00",
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"2023-01-21 09:15:00+01:00",
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"2023-01-21 09:30:00+01:00",
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"2023-01-21 09:45:00+01:00",
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"2023-01-21 10:15:00+01:00",
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"2023-01-21 11:15:00+01:00",
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"2023-01-21 11:30:00+01:00",
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"2023-01-21 11:45:00+01:00",
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"2023-01-21 12:15:00+01:00",
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"2023-01-21 12:30:00+01:00",
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"2023-01-21 12:45:00+01:00",
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"2023-01-21 13:15:00+01:00",
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"2023-01-21 13:30:00+01:00",
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"2023-01-21 16:45:00+01:00",
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"2023-01-21 17:15:00+01:00",
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"2023-01-21 17:30:00+01:00",
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"2023-01-21 19:15:00+01:00",
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"2023-01-21 20:15:00+01:00",
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"2023-01-21 20:30:00+01:00",
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"2023-01-21 20:45:00+01:00",
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"2023-01-21 21:15:00+01:00",
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"2023-01-21 21:30:00+01:00",
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"2023-01-21 23:15:00+01:00",
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"2023-01-21 23:30:00+01:00",
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"2023-01-22 00:15:00+01:00",
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"2023-01-22 01:00:00+01:00",
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"2023-01-22 01:15:00+01:00",
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"2023-01-22 01:30:00+01:00",
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"2023-01-22 01:45:00+01:00",
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"2023-01-22 02:15:00+01:00",
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"2023-01-22 02:30:00+01:00",
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"2023-01-22 03:00:00+01:00",
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"2023-01-22 03:15:00+01:00",
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"2023-01-22 03:30:00+01:00",
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"2023-01-22 03:45:00+01:00",
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"2023-01-22 04:00:00+01:00",
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"2023-01-22 04:15:00+01:00",
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"2023-01-22 04:30:00+01:00",
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"2023-01-22 04:45:00+01:00",
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"2023-01-22 05:00:00+01:00",
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"2023-01-22 05:15:00+01:00",
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"2023-01-22 05:30:00+01:00",
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"2023-01-22 05:45:00+01:00",
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"2023-01-22 06:15:00+01:00",
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"2023-01-22 06:30:00+01:00",
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"2023-01-22 06:45:00+01:00",
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"2023-01-22 07:15:00+01:00",
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"2023-01-22 07:30:00+01:00",
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"2023-01-22 07:45:00+01:00",
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"2023-01-22 08:15:00+01:00",
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"2023-01-22 08:30:00+01:00",
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"2023-01-22 08:45:00+01:00",
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"2023-01-22 09:15:00+01:00",
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"2023-01-22 09:30:00+01:00",
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"2023-01-22 09:45:00+01:00",
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"2023-01-22 10:15:00+01:00",
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"2023-01-22 10:30:00+01:00",
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"2023-01-22 10:45:00+01:00",
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"2023-01-22 11:15:00+01:00",
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"2023-01-22 11:30:00+01:00",
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"2023-01-22 11:45:00+01:00",
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"2023-01-22 12:00:00+01:00",
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"2023-01-22 12:15:00+01:00",
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"2023-01-22 12:30:00+01:00",
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"2023-01-22 12:45:00+01:00",
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"2023-01-22 13:00:00+01:00",
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"2023-01-22 13:15:00+01:00",
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"2023-01-22 13:30:00+01:00",
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"2023-01-22 13:45:00+01:00",
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"2023-01-22 14:15:00+01:00",
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"2023-01-22 14:30:00+01:00",
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"2023-01-22 14:45:00+01:00",
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"2023-01-22 15:00:00+01:00",
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"2023-01-22 15:15:00+01:00",
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"2023-01-22 15:30:00+01:00",
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"2023-01-22 15:45:00+01:00",
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"2023-01-22 16:00:00+01:00",
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"2023-01-22 16:15:00+01:00",
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"2023-01-22 16:30:00+01:00",
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"2023-01-22 16:45:00+01:00",
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"2023-01-22 17:00:00+01:00",
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"2023-01-22 17:15:00+01:00",
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"2023-01-22 17:30:00+01:00",
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"2023-01-22 17:45:00+01:00",
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"2023-01-22 18:00:00+01:00",
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"2023-01-22 18:15:00+01:00",
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"2023-01-22 18:30:00+01:00",
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"2023-01-22 18:45:00+01:00",
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"2023-01-22 19:00:00+01:00",
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"2023-01-22 19:15:00+01:00",
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"2023-01-22 19:30:00+01:00",
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"2023-01-22 19:45:00+01:00",
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"2023-01-22 20:00:00+01:00",
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"2023-01-22 20:15:00+01:00",
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"2023-01-22 20:30:00+01:00",
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"2023-01-22 20:45:00+01:00",
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"2023-01-22 21:00:00+01:00",
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"2023-01-22 21:15:00+01:00",
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"2023-01-22 21:30:00+01:00",
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"2023-01-22 21:45:00+01:00",
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"2023-01-22 22:00:00+01:00",
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"2023-01-22 22:15:00+01:00",
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"2023-01-22 22:30:00+01:00",
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"2023-01-22 22:45:00+01:00",
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"2023-01-22 23:00:00+01:00",
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"2023-01-22 23:15:00+01:00",
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"2023-01-22 23:30:00+01:00",
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"2023-01-22 23:45:00+01:00",
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"2023-01-23 00:00:00+01:00",
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"2023-01-23 00:15:00+01:00",
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"2023-01-23 00:30:00+01:00",
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"2023-01-23 00:45:00+01:00",
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"2023-01-23 01:00:00+01:00",
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"2023-01-23 01:15:00+01:00",
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"2023-01-23 01:30:00+01:00",
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"2023-01-23 01:45:00+01:00",
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"2023-01-23 02:00:00+01:00",
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"2023-01-23 02:15:00+01:00",
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"2023-01-23 02:30:00+01:00",
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"2023-01-23 02:45:00+01:00",
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"2023-01-23 03:00:00+01:00",
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"2023-01-23 03:15:00+01:00",
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"2023-01-23 03:30:00+01:00",
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"2023-01-23 03:45:00+01:00",
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"2023-01-23 04:00:00+01:00",
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"2023-01-23 04:15:00+01:00",
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"2023-01-23 04:30:00+01:00",
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"2023-01-23 04:45:00+01:00",
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"2023-01-23 05:00:00+01:00",
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"2023-01-23 05:15:00+01:00",
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"2023-01-23 05:30:00+01:00",
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"2023-01-23 05:45:00+01:00",
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"2023-01-23 06:00:00+01:00",
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"2023-01-23 06:15:00+01:00",
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"2023-01-23 06:30:00+01:00",
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"2023-01-23 06:45:00+01:00",
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"2023-01-23 07:00:00+01:00",
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"2023-01-23 07:15:00+01:00",
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"2023-01-23 07:30:00+01:00",
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"2023-01-23 07:45:00+01:00",
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"2023-01-23 08:00:00+01:00",
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"2023-01-23 08:15:00+01:00",
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"2023-01-23 08:30:00+01:00",
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"2023-01-23 08:45:00+01:00",
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"2023-01-23 09:00:00+01:00",
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"2023-01-23 09:15:00+01:00",
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"2023-01-23 09:30:00+01:00",
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"2023-01-23 09:45:00+01:00",
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"2023-01-23 10:00:00+01:00",
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"2023-01-23 10:15:00+01:00",
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"2023-01-23 10:30:00+01:00",
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"2023-01-23 10:45:00+01:00",
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"2023-01-23 11:00:00+01:00",
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"2023-01-23 11:15:00+01:00",
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"2023-01-23 11:30:00+01:00",
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"2023-01-23 11:45:00+01:00",
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"2023-01-23 12:00:00+01:00",
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"2023-01-23 12:15:00+01:00",
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"2023-01-23 12:30:00+01:00",
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"2023-01-23 12:45:00+01:00",
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"2023-01-23 13:00:00+01:00",
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"2023-01-23 13:15:00+01:00",
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"2023-01-23 13:30:00+01:00",
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"2023-01-23 13:45:00+01:00",
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"2023-01-23 14:00:00+01:00",
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"2023-01-23 14:15:00+01:00",
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"2023-01-23 14:30:00+01:00",
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"2023-01-23 14:45:00+01:00",
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"2023-01-23 15:00:00+01:00",
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"2023-01-23 15:15:00+01:00",
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"2023-01-23 15:30:00+01:00",
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"2023-01-23 15:45:00+01:00",
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"2023-01-23 16:00:00+01:00",
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"2023-01-23 16:15:00+01:00",
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"2023-01-23 16:30:00+01:00",
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"2023-01-23 16:45:00+01:00",
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"2023-01-23 17:00:00+01:00",
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"2023-01-23 17:15:00+01:00",
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"2023-01-23 17:30:00+01:00",
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"2023-01-23 17:45:00+01:00",
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"2023-01-23 18:00:00+01:00",
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"2023-01-23 18:15:00+01:00",
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"2023-01-23 18:30:00+01:00",
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"2023-01-23 18:45:00+01:00",
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"2023-01-23 19:00:00+01:00",
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"2023-01-23 19:15:00+01:00",
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"2023-01-23 19:30:00+01:00",
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"2023-01-23 19:45:00+01:00",
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"2023-01-23 20:00:00+01:00",
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"2023-01-23 20:15:00+01:00",
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"2023-01-23 20:30:00+01:00",
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"2023-01-23 20:45:00+01:00",
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"2023-01-23 21:00:00+01:00",
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"2023-01-23 21:15:00+01:00",
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"2023-01-23 21:30:00+01:00",
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"2023-01-23 21:45:00+01:00",
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"2023-01-23 22:00:00+01:00",
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"2023-01-23 22:15:00+01:00",
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"2023-01-23 22:30:00+01:00",
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"2023-01-23 22:45:00+01:00",
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"2023-01-23 23:00:00+01:00",
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"2023-01-23 23:15:00+01:00",
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"2023-01-23 23:30:00+01:00",
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"2023-01-23 23:45:00+01:00",
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"2023-01-24 00:00:00+01:00",
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"2023-01-24 00:15:00+01:00",
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"2023-01-24 00:30:00+01:00",
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"2023-01-24 00:45:00+01:00",
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"2023-01-24 01:00:00+01:00",
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"2023-01-24 01:15:00+01:00",
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"2023-01-24 01:30:00+01:00",
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"2023-01-24 01:45:00+01:00",
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"2023-01-24 02:00:00+01:00",
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"2023-01-24 02:15:00+01:00",
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"2023-01-24 02:30:00+01:00",
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"2023-01-24 02:45:00+01:00",
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"2023-01-24 03:00:00+01:00",
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"2023-01-24 03:15:00+01:00",
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"2023-01-24 03:30:00+01:00",
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"2023-01-24 03:45:00+01:00",
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"2023-01-24 04:00:00+01:00",
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"2023-01-24 04:15:00+01:00",
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"2023-01-24 04:30:00+01:00",
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"2023-01-24 04:45:00+01:00",
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"2023-01-24 05:00:00+01:00",
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"2023-01-24 05:15:00+01:00",
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"2023-01-24 05:30:00+01:00",
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"2023-01-24 05:45:00+01:00",
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"2023-01-24 06:00:00+01:00",
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"2023-01-24 06:15:00+01:00",
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"2023-01-24 06:30:00+01:00",
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"2023-01-24 06:45:00+01:00",
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"2023-01-24 07:00:00+01:00",
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"2023-01-24 07:15:00+01:00",
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"2023-01-24 07:30:00+01:00",
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"2023-01-24 07:45:00+01:00",
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"2023-01-24 08:00:00+01:00",
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"2023-01-24 08:15:00+01:00",
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"2023-01-24 08:30:00+01:00",
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"2023-01-24 08:45:00+01:00",
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"2023-01-24 09:00:00+01:00",
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"2023-01-24 09:15:00+01:00",
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"2023-01-24 09:30:00+01:00",
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"2023-01-24 09:45:00+01:00",
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"2023-01-24 10:00:00+01:00",
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"2023-01-24 10:15:00+01:00",
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"2023-01-24 10:30:00+01:00",
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"2023-01-24 10:45:00+01:00",
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"2023-01-24 11:00:00+01:00",
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"2023-01-24 11:15:00+01:00",
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"2023-01-24 11:30:00+01:00",
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"2023-01-24 11:45:00+01:00",
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"2023-01-24 12:00:00+01:00",
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"2023-01-24 12:15:00+01:00",
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"2023-01-24 12:30:00+01:00",
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"2023-01-24 12:45:00+01:00",
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"2023-01-24 13:00:00+01:00",
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"2023-01-27 23:00:00+01:00",
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"2023-01-27 23:15:00+01:00",
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"2023-01-27 23:30:00+01:00",
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"2023-01-27 23:45:00+01:00",
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"2023-01-28 00:00:00+01:00",
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"2023-01-28 00:15:00+01:00",
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"2023-01-28 00:30:00+01:00",
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"2023-01-28 00:45:00+01:00",
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"2023-01-28 01:00:00+01:00",
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"2023-01-28 01:15:00+01:00",
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"2023-01-28 01:30:00+01:00",
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"2023-01-28 01:45:00+01:00",
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"2023-01-28 02:00:00+01:00",
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"2023-01-28 02:15:00+01:00",
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"2023-01-28 02:30:00+01:00",
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"2023-01-28 02:45:00+01:00",
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"2023-01-28 03:00:00+01:00",
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"2023-01-28 03:15:00+01:00",
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"2023-01-28 03:30:00+01:00",
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"2023-01-28 03:45:00+01:00",
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"2023-01-28 04:00:00+01:00",
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"2023-01-28 04:15:00+01:00",
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"2023-01-28 04:30:00+01:00",
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"2023-01-28 04:45:00+01:00",
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"2023-01-28 05:00:00+01:00",
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"2023-01-28 05:15:00+01:00",
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"2023-01-28 05:30:00+01:00",
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"2023-01-28 05:45:00+01:00",
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"2023-01-28 06:00:00+01:00",
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"2023-01-28 06:15:00+01:00",
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"2023-01-28 06:30:00+01:00",
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"2023-01-28 06:45:00+01:00",
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"2023-01-28 07:00:00+01:00",
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"2023-01-28 07:15:00+01:00",
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"2023-01-28 07:30:00+01:00",
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"2023-01-28 07:45:00+01:00",
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"2023-01-28 08:00:00+01:00",
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"2023-01-28 08:15:00+01:00",
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"2023-01-28 08:30:00+01:00",
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"2023-01-28 08:45:00+01:00",
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"2023-01-28 09:00:00+01:00",
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"2023-01-28 09:15:00+01:00",
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"2023-01-28 09:30:00+01:00",
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"2023-01-28 09:45:00+01:00",
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"2023-01-28 10:00:00+01:00",
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"2023-01-28 10:15:00+01:00",
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"2023-01-28 10:30:00+01:00",
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"2023-01-28 10:45:00+01:00",
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"2023-01-28 11:00:00+01:00",
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"2023-01-28 11:15:00+01:00",
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"2023-01-28 11:30:00+01:00",
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"2023-01-28 11:45:00+01:00",
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"2023-01-28 12:00:00+01:00",
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"2023-01-28 12:15:00+01:00",
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"2023-01-28 12:30:00+01:00",
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"2023-01-28 12:45:00+01:00",
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"2023-01-28 13:00:00+01:00",
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"2023-01-28 13:15:00+01:00",
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"2023-01-28 13:30:00+01:00",
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"2023-01-28 13:45:00+01:00",
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"2023-01-28 14:00:00+01:00",
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"2023-01-28 14:15:00+01:00",
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"2023-01-28 14:30:00+01:00",
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"2023-01-28 14:45:00+01:00",
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"2023-01-28 15:00:00+01:00",
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"2023-01-28 15:15:00+01:00",
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"2023-01-28 15:30:00+01:00",
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"2023-01-28 15:45:00+01:00",
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"2023-01-28 16:00:00+01:00",
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"2023-01-28 16:15:00+01:00",
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"2023-01-28 16:30:00+01:00",
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"2023-01-28 16:45:00+01:00",
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"2023-01-28 17:00:00+01:00",
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"2023-01-28 17:15:00+01:00",
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"2023-01-28 17:30:00+01:00",
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"2023-01-28 17:45:00+01:00",
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"2023-01-28 18:00:00+01:00",
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"2023-01-28 18:15:00+01:00",
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"2023-01-28 18:30:00+01:00",
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"2023-01-28 18:45:00+01:00",
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"2023-01-28 19:00:00+01:00",
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"2023-01-28 19:15:00+01:00",
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"2023-01-28 19:30:00+01:00",
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"2023-01-28 19:45:00+01:00",
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"2023-01-28 20:00:00+01:00",
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"2023-01-28 20:15:00+01:00",
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"2023-01-28 20:30:00+01:00",
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"2023-01-28 20:45:00+01:00",
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"2023-01-28 21:00:00+01:00",
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"2023-01-28 21:15:00+01:00",
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"2023-01-28 21:30:00+01:00",
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"2023-01-28 21:45:00+01:00",
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"2023-01-28 22:00:00+01:00",
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"2023-01-28 22:15:00+01:00",
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"2023-01-28 22:30:00+01:00",
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"2023-01-28 22:45:00+01:00",
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"2023-01-28 23:00:00+01:00",
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"2023-01-28 23:15:00+01:00",
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"2023-01-28 23:30:00+01:00",
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"2023-01-28 23:45:00+01:00",
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"2023-01-29 00:00:00+01:00",
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"2023-01-29 00:15:00+01:00",
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"2023-01-29 00:30:00+01:00",
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"2023-01-29 00:45:00+01:00",
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"2023-01-29 01:00:00+01:00",
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"2023-01-29 01:15:00+01:00",
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"2023-01-29 01:30:00+01:00",
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"2023-01-29 01:45:00+01:00",
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"2023-01-29 02:00:00+01:00",
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"2023-01-29 02:15:00+01:00",
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"2023-01-29 02:30:00+01:00",
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"2023-01-29 02:45:00+01:00",
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"2023-01-29 03:00:00+01:00",
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"2023-01-29 03:15:00+01:00",
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"2023-01-29 03:30:00+01:00",
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"2023-01-29 03:45:00+01:00",
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"2023-01-29 04:00:00+01:00",
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"2023-01-29 04:15:00+01:00",
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"2023-01-29 04:30:00+01:00",
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"2023-01-29 04:45:00+01:00",
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"2023-01-29 05:00:00+01:00",
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"2023-01-29 05:15:00+01:00",
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"2023-01-29 05:30:00+01:00",
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"2023-01-29 05:45:00+01:00",
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"2023-01-29 06:00:00+01:00",
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"2023-01-29 06:15:00+01:00",
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"2023-01-29 06:30:00+01:00",
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"2023-01-29 06:45:00+01:00",
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"2023-01-29 07:00:00+01:00",
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"2023-01-29 07:15:00+01:00",
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"2023-01-29 07:30:00+01:00",
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"2023-01-29 07:45:00+01:00",
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"2023-01-29 08:00:00+01:00",
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"2023-01-29 08:15:00+01:00",
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"2023-01-29 08:30:00+01:00",
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"2023-01-29 08:45:00+01:00",
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"2023-01-29 09:00:00+01:00",
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"2023-01-29 09:15:00+01:00",
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"2023-01-29 09:30:00+01:00",
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"2023-01-29 09:45:00+01:00",
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"2023-01-29 10:00:00+01:00",
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"2023-01-29 10:15:00+01:00",
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"2023-01-29 10:30:00+01:00",
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"2023-01-29 10:45:00+01:00",
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"2023-01-29 11:00:00+01:00",
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"2023-01-29 11:15:00+01:00",
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"2023-01-29 11:30:00+01:00",
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"2023-01-29 11:45:00+01:00",
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"2023-01-29 12:00:00+01:00",
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"2023-01-29 12:15:00+01:00",
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"2023-01-29 12:30:00+01:00",
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"2023-01-29 12:45:00+01:00",
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"2023-01-29 13:00:00+01:00",
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"2023-01-29 13:15:00+01:00",
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"2023-01-29 13:30:00+01:00",
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"2023-01-29 13:45:00+01:00",
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"2023-01-29 14:00:00+01:00",
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"2023-01-29 14:15:00+01:00",
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"2023-01-29 14:30:00+01:00",
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"2023-01-29 14:45:00+01:00",
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"2023-01-29 15:00:00+01:00",
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"2023-01-29 15:15:00+01:00",
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"2023-01-29 15:30:00+01:00",
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"2023-01-29 15:45:00+01:00",
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"2023-01-29 16:00:00+01:00",
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"2023-01-29 16:15:00+01:00",
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"2023-01-29 16:30:00+01:00",
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"2023-01-29 16:45:00+01:00",
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"2023-01-29 17:00:00+01:00",
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"2023-01-29 17:15:00+01:00",
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"2023-01-29 17:30:00+01:00",
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"2023-01-29 17:45:00+01:00",
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"2023-01-29 18:00:00+01:00",
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"2023-01-29 18:15:00+01:00",
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"2023-01-29 18:30:00+01:00",
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"2023-01-29 18:45:00+01:00",
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"2023-01-29 19:00:00+01:00",
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"2023-01-29 19:15:00+01:00",
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"2023-01-29 19:30:00+01:00",
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"2023-01-29 19:45:00+01:00",
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"2023-01-29 20:00:00+01:00",
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"2023-01-29 20:15:00+01:00",
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"2023-01-29 20:30:00+01:00",
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"2023-01-29 20:45:00+01:00",
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"2023-01-29 21:00:00+01:00",
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"2023-01-29 21:15:00+01:00",
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"2023-01-29 21:30:00+01:00",
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"2023-01-29 21:45:00+01:00",
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"2023-01-29 22:00:00+01:00",
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"2023-01-29 22:15:00+01:00",
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"2023-01-29 22:30:00+01:00",
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"2023-01-29 22:45:00+01:00",
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"2023-01-29 23:00:00+01:00",
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"2023-01-29 23:15:00+01:00",
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"2023-01-29 23:30:00+01:00",
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"2023-01-29 23:45:00+01:00",
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"2023-01-30 00:00:00+01:00",
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"2023-01-30 00:15:00+01:00",
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"2023-01-30 00:30:00+01:00",
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"2023-01-30 00:45:00+01:00",
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"2023-01-30 01:00:00+01:00",
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"2023-01-30 01:15:00+01:00",
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"2023-01-30 01:30:00+01:00",
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"2023-01-30 01:45:00+01:00",
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"2023-01-30 02:00:00+01:00",
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"2023-01-30 02:15:00+01:00",
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"2023-01-30 02:30:00+01:00",
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"2023-01-30 02:45:00+01:00",
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"2023-01-30 03:00:00+01:00",
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"2023-01-30 03:15:00+01:00",
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"2023-01-30 03:30:00+01:00",
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"2023-01-30 03:45:00+01:00",
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"2023-01-30 04:00:00+01:00",
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"2023-01-30 04:15:00+01:00",
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"2023-01-30 04:30:00+01:00",
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"2023-01-30 04:45:00+01:00",
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"2023-01-30 05:00:00+01:00",
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"2023-01-30 05:15:00+01:00",
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"2023-01-30 05:30:00+01:00",
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"2023-01-30 05:45:00+01:00",
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"2023-01-30 06:00:00+01:00",
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"2023-01-30 06:15:00+01:00",
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"2023-01-30 06:30:00+01:00",
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"2023-01-30 06:45:00+01:00",
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"2023-01-30 07:00:00+01:00",
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"2023-01-30 07:15:00+01:00",
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"2023-01-30 07:30:00+01:00",
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"2023-01-30 07:45:00+01:00",
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"2023-01-30 08:00:00+01:00",
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"2023-01-30 08:15:00+01:00",
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"2023-01-30 08:30:00+01:00",
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"2023-01-30 08:45:00+01:00",
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"2023-01-30 09:00:00+01:00",
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"2023-01-30 09:15:00+01:00",
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"2023-01-30 09:30:00+01:00",
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"2023-01-30 09:45:00+01:00",
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"2023-01-30 10:00:00+01:00",
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"2023-01-30 10:15:00+01:00",
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"2023-01-30 10:30:00+01:00",
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"2023-01-30 10:45:00+01:00",
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"2023-01-30 11:00:00+01:00",
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"2023-01-30 11:15:00+01:00",
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"2023-01-30 11:30:00+01:00",
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"2023-01-30 11:45:00+01:00",
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"2023-01-30 12:00:00+01:00",
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"2023-01-30 12:15:00+01:00",
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"2023-01-30 12:30:00+01:00",
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"2023-01-30 12:45:00+01:00",
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"2023-01-30 13:00:00+01:00",
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"2023-01-30 13:15:00+01:00",
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"2023-01-30 13:30:00+01:00",
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"2023-01-30 13:45:00+01:00",
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"2023-01-30 14:00:00+01:00",
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"2023-01-30 14:15:00+01:00",
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"2023-01-30 14:30:00+01:00",
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"2023-01-30 14:45:00+01:00",
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"2023-01-30 15:00:00+01:00",
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"2023-01-30 15:15:00+01:00",
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"2023-01-30 15:30:00+01:00",
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"2023-01-30 15:45:00+01:00",
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"2023-01-30 16:00:00+01:00",
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"2023-01-30 16:15:00+01:00",
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"2023-01-30 16:30:00+01:00",
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"2023-01-30 16:45:00+01:00",
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"2023-01-30 17:00:00+01:00",
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"2023-01-30 17:15:00+01:00",
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"2023-01-30 17:30:00+01:00",
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"2023-01-30 17:45:00+01:00",
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"2023-01-30 18:00:00+01:00",
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"2023-01-30 18:15:00+01:00",
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"2023-01-30 18:30:00+01:00",
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"2023-01-30 18:45:00+01:00",
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"2023-01-30 19:00:00+01:00",
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"2023-01-30 19:15:00+01:00",
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"2023-01-30 19:30:00+01:00",
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"2023-01-30 19:45:00+01:00",
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"2023-01-30 20:00:00+01:00",
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"2023-01-30 20:15:00+01:00",
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"2023-01-30 20:30:00+01:00",
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"2023-01-30 20:45:00+01:00",
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"2023-01-30 21:00:00+01:00",
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"2023-01-30 21:15:00+01:00",
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"2023-01-30 21:30:00+01:00",
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"2023-01-30 21:45:00+01:00",
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"2023-01-30 22:00:00+01:00",
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"2023-01-30 22:15:00+01:00",
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"2023-01-30 22:30:00+01:00",
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"2023-01-30 22:45:00+01:00",
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"2023-01-30 23:00:00+01:00",
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"2023-01-30 23:15:00+01:00",
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"2023-01-30 23:30:00+01:00",
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"2023-01-30 23:45:00+01:00",
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"2023-01-31 00:00:00+01:00",
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"2023-01-31 00:15:00+01:00",
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"2023-01-31 00:30:00+01:00",
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"2023-01-31 00:45:00+01:00",
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"2023-01-31 01:00:00+01:00",
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"2023-01-31 01:15:00+01:00",
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"2023-01-31 01:30:00+01:00",
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"2023-01-31 01:45:00+01:00",
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"2023-01-31 02:00:00+01:00",
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"2023-01-31 02:15:00+01:00",
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"2023-01-31 02:30:00+01:00",
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"2023-01-31 02:45:00+01:00",
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"2023-01-31 03:00:00+01:00",
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"2023-01-31 03:15:00+01:00",
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"2023-01-31 03:30:00+01:00",
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"2023-01-31 03:45:00+01:00",
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"2023-01-31 04:00:00+01:00",
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"2023-01-31 04:15:00+01:00",
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"2023-01-31 04:30:00+01:00",
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"2023-01-31 04:45:00+01:00",
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"2023-01-31 05:00:00+01:00",
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"2023-01-31 05:15:00+01:00",
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"2023-01-31 05:30:00+01:00",
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"2023-01-31 05:45:00+01:00",
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"2023-01-31 06:00:00+01:00",
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"2023-01-31 06:15:00+01:00",
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"2023-01-31 06:30:00+01:00",
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"2023-01-31 06:45:00+01:00",
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"2023-01-31 07:00:00+01:00",
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"2023-01-31 07:15:00+01:00",
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"2023-01-31 07:30:00+01:00",
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"2023-01-31 07:45:00+01:00",
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"2023-01-31 08:00:00+01:00",
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"2023-01-31 08:15:00+01:00",
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"2023-01-31 08:30:00+01:00",
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"2023-01-31 08:45:00+01:00",
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"2023-01-31 09:00:00+01:00",
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"2023-01-31 09:15:00+01:00",
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"2023-01-31 09:30:00+01:00",
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"2023-01-31 09:45:00+01:00",
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"2023-01-31 10:00:00+01:00",
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"2023-01-31 10:15:00+01:00",
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"2023-01-31 10:30:00+01:00",
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"2023-01-31 10:45:00+01:00",
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"2023-01-31 11:00:00+01:00",
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"2023-01-31 11:15:00+01:00",
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"2023-01-31 11:30:00+01:00",
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"2023-01-31 11:45:00+01:00",
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"2023-01-31 12:00:00+01:00",
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"2023-01-31 12:15:00+01:00",
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"2023-01-31 12:30:00+01:00",
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"2023-01-31 12:45:00+01:00",
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"2023-01-31 13:00:00+01:00",
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"2023-01-31 13:15:00+01:00",
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"2023-01-31 13:30:00+01:00",
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"2023-01-31 13:45:00+01:00",
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"2023-01-31 14:00:00+01:00",
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"2023-01-31 14:15:00+01:00",
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"2023-01-31 14:30:00+01:00",
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"2023-01-31 14:45:00+01:00",
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"2023-01-31 15:00:00+01:00",
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"2023-01-31 15:15:00+01:00",
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"2023-01-31 15:30:00+01:00",
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"2023-01-31 15:45:00+01:00",
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"2023-01-31 16:00:00+01:00",
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"2023-01-31 16:15:00+01:00",
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"2023-01-31 16:30:00+01:00",
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"2023-01-31 16:45:00+01:00",
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"2023-01-31 17:00:00+01:00",
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"2023-01-31 17:15:00+01:00",
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"2023-01-31 17:30:00+01:00",
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"2023-01-31 17:45:00+01:00",
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"2023-01-31 18:00:00+01:00",
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"2023-01-31 18:15:00+01:00",
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"2023-01-31 18:30:00+01:00",
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"2023-01-31 18:45:00+01:00",
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"2023-01-31 19:15:00+01:00",
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"2023-01-31 19:30:00+01:00",
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"2023-01-31 19:45:00+01:00",
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"2023-01-31 20:00:00+01:00",
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"2023-01-31 20:15:00+01:00",
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"2023-01-31 20:30:00+01:00",
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"2023-01-31 20:45:00+01:00",
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"2023-01-31 21:00:00+01:00",
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"2023-01-31 21:15:00+01:00",
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"2023-01-31 21:30:00+01:00",
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"2023-01-31 21:45:00+01:00",
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"2023-01-31 22:00:00+01:00",
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"2023-01-31 22:15:00+01:00",
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"2023-01-31 22:30:00+01:00",
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"2023-01-31 22:45:00+01:00",
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"2023-01-31 23:00:00+01:00",
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"2023-01-31 23:15:00+01:00",
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"2023-01-31 23:30:00+01:00",
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"2023-01-31 23:45:00+01:00",
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"2023-02-01 00:00:00+01:00",
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"2023-02-01 00:15:00+01:00",
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"2023-02-01 00:30:00+01:00",
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"2023-02-01 00:45:00+01:00",
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"2023-02-01 01:00:00+01:00",
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"2023-02-01 01:15:00+01:00",
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"2023-02-01 01:30:00+01:00",
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"2023-02-01 01:45:00+01:00",
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"2023-02-01 02:00:00+01:00",
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"2023-02-01 02:15:00+01:00",
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"2023-02-01 02:30:00+01:00",
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"2023-02-01 02:45:00+01:00",
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"2023-02-01 03:00:00+01:00",
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"2023-02-01 03:15:00+01:00",
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"2023-02-01 03:30:00+01:00",
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"2023-02-01 03:45:00+01:00",
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"2023-02-01 04:00:00+01:00",
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"2023-02-01 04:15:00+01:00",
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"2023-02-01 04:30:00+01:00",
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"2023-02-01 04:45:00+01:00",
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"2023-02-01 05:00:00+01:00",
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"2023-02-01 05:15:00+01:00",
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"2023-02-01 05:30:00+01:00",
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"2023-02-01 05:45:00+01:00",
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"2023-02-01 06:00:00+01:00",
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"2023-02-01 06:15:00+01:00",
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"2023-02-01 06:30:00+01:00",
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"2023-02-01 06:45:00+01:00",
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"2023-02-01 07:00:00+01:00",
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"2023-02-01 07:15:00+01:00",
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"2023-02-01 07:30:00+01:00",
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"2023-02-01 07:45:00+01:00",
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"2023-02-01 08:00:00+01:00",
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"2023-02-01 08:15:00+01:00",
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"2023-02-01 08:30:00+01:00",
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"2023-02-01 08:45:00+01:00",
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"2023-02-01 09:00:00+01:00",
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"2023-02-01 09:15:00+01:00",
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"2023-02-01 09:30:00+01:00",
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"2023-02-01 09:45:00+01:00",
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"2023-02-01 10:00:00+01:00",
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"2023-02-01 10:15:00+01:00",
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"2023-02-01 10:30:00+01:00",
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"2023-02-01 10:45:00+01:00",
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"2023-02-01 11:00:00+01:00",
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"2023-02-01 11:15:00+01:00",
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"2023-02-01 11:30:00+01:00",
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"2023-02-01 11:45:00+01:00",
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"2023-02-01 12:00:00+01:00",
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"2023-02-01 12:15:00+01:00",
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"2023-02-01 12:30:00+01:00",
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"2023-02-01 12:45:00+01:00",
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"2023-02-01 13:00:00+01:00",
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"2023-02-01 13:15:00+01:00",
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"2023-02-01 13:30:00+01:00",
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"2023-02-01 13:45:00+01:00",
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"2023-02-01 14:00:00+01:00",
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"2023-02-01 14:15:00+01:00",
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"2023-02-01 14:30:00+01:00",
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"2023-02-01 14:45:00+01:00",
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"2023-02-01 15:00:00+01:00",
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"2023-02-01 15:15:00+01:00",
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"2023-02-01 15:30:00+01:00",
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"2023-02-01 15:45:00+01:00",
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"2023-02-01 16:00:00+01:00",
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"2023-02-01 16:15:00+01:00",
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"2023-02-01 16:30:00+01:00",
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"2023-02-01 16:45:00+01:00",
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"2023-02-01 17:00:00+01:00",
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"2023-02-01 17:15:00+01:00",
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"2023-02-01 17:30:00+01:00",
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"2023-02-01 17:45:00+01:00",
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"2023-02-01 18:00:00+01:00",
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"2023-02-01 18:15:00+01:00",
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"2023-02-01 18:30:00+01:00",
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"2023-02-01 18:45:00+01:00",
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"2023-02-01 19:00:00+01:00",
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"2023-02-01 19:15:00+01:00",
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"2023-02-01 19:30:00+01:00",
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"2023-02-01 19:45:00+01:00",
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"2023-02-01 20:00:00+01:00",
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"2023-02-01 20:15:00+01:00",
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"2023-02-01 20:30:00+01:00",
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"2023-02-01 20:45:00+01:00",
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"2023-02-01 21:00:00+01:00",
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"2023-02-01 21:15:00+01:00",
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"2023-02-01 21:30:00+01:00",
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"2023-02-01 21:45:00+01:00",
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"2023-02-01 22:00:00+01:00",
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"2023-02-01 22:15:00+01:00",
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"2023-02-01 22:30:00+01:00",
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"2023-02-01 22:45:00+01:00",
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"2023-02-01 23:00:00+01:00",
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"2023-02-01 23:15:00+01:00",
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"2023-02-01 23:30:00+01:00",
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"2023-02-01 23:45:00+01:00",
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"2023-02-02 00:00:00+01:00",
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"2023-02-02 00:15:00+01:00",
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"2023-02-02 00:30:00+01:00",
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"2023-02-02 00:45:00+01:00",
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"2023-02-02 01:00:00+01:00",
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"2023-02-02 01:15:00+01:00",
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"2023-02-02 01:30:00+01:00",
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"2023-02-02 01:45:00+01:00",
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"2023-02-02 02:00:00+01:00",
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"2023-02-02 02:15:00+01:00",
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"2023-02-02 02:30:00+01:00",
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"2023-02-02 02:45:00+01:00",
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"2023-02-02 03:00:00+01:00",
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"2023-02-02 03:15:00+01:00",
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"2023-02-02 03:30:00+01:00",
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"2023-02-02 03:45:00+01:00",
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"2023-02-02 04:00:00+01:00",
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"2023-02-02 04:15:00+01:00",
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"2023-02-02 04:30:00+01:00",
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"2023-02-02 04:45:00+01:00",
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"2023-02-02 05:00:00+01:00",
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"2023-02-02 05:15:00+01:00",
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"2023-02-02 05:30:00+01:00",
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"2023-02-02 05:45:00+01:00",
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"2023-02-02 06:00:00+01:00",
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"2023-02-02 06:15:00+01:00",
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"2023-02-02 06:30:00+01:00",
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"2023-02-02 06:45:00+01:00",
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"2023-02-02 07:00:00+01:00",
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"2023-02-02 07:15:00+01:00",
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"2023-02-02 07:30:00+01:00",
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"2023-02-02 07:45:00+01:00",
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"2023-02-02 08:00:00+01:00",
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"2023-02-02 08:15:00+01:00",
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"2023-02-02 08:30:00+01:00",
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"2023-02-02 08:45:00+01:00",
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"2023-02-02 09:00:00+01:00",
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"2023-02-02 09:15:00+01:00",
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"2023-02-02 09:30:00+01:00",
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"2023-02-02 09:45:00+01:00",
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"2023-02-02 10:00:00+01:00",
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"2023-02-02 10:15:00+01:00",
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"2023-02-02 10:30:00+01:00",
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"2023-02-02 10:45:00+01:00",
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"2023-02-02 11:00:00+01:00",
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"2023-02-02 11:15:00+01:00",
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"2023-02-02 11:30:00+01:00",
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"2023-02-02 11:45:00+01:00",
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"2023-02-02 12:00:00+01:00",
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"2023-02-02 12:15:00+01:00",
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"2023-02-02 12:30:00+01:00",
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"2023-02-02 12:45:00+01:00",
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"2023-02-02 13:00:00+01:00",
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"2023-02-02 13:15:00+01:00",
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"2023-02-02 13:30:00+01:00",
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"2023-02-02 13:45:00+01:00",
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"2023-02-02 14:00:00+01:00",
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"2023-02-02 14:15:00+01:00",
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"2023-02-02 14:30:00+01:00",
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"2023-02-02 14:45:00+01:00",
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"2023-02-02 15:00:00+01:00",
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"2023-02-02 15:15:00+01:00",
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"2023-02-02 15:30:00+01:00",
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"2023-02-02 15:45:00+01:00",
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"2023-02-02 16:00:00+01:00",
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"2023-02-02 16:15:00+01:00",
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"2023-02-02 16:30:00+01:00",
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"2023-02-02 16:45:00+01:00",
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"2023-02-02 17:00:00+01:00",
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"2023-02-02 17:15:00+01:00",
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"2023-02-02 17:30:00+01:00",
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"2023-02-02 17:45:00+01:00",
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"2023-02-02 18:00:00+01:00",
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"2023-02-02 18:15:00+01:00",
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"2023-02-02 18:30:00+01:00",
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"2023-02-02 18:45:00+01:00",
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"2023-02-02 19:00:00+01:00",
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"2023-02-02 19:15:00+01:00",
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"2023-02-02 19:30:00+01:00",
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"2023-02-02 19:45:00+01:00",
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"2023-02-02 20:00:00+01:00",
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"2023-02-02 20:15:00+01:00",
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"2023-02-02 20:30:00+01:00",
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"2023-02-02 20:45:00+01:00",
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"2023-02-02 21:00:00+01:00",
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"2023-02-02 21:15:00+01:00",
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"2023-02-02 21:30:00+01:00",
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"2023-02-02 21:45:00+01:00",
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"2023-02-02 22:00:00+01:00",
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"2023-02-02 22:15:00+01:00",
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"2023-02-02 22:30:00+01:00",
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"2023-02-02 22:45:00+01:00",
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"2023-02-02 23:00:00+01:00",
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"2023-02-02 23:15:00+01:00",
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"2023-02-02 23:30:00+01:00",
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"2023-02-02 23:45:00+01:00",
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"2023-02-03 00:00:00+01:00",
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"2023-02-03 00:15:00+01:00",
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"2023-02-03 00:30:00+01:00",
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"2023-02-03 00:45:00+01:00",
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"2023-02-03 01:00:00+01:00",
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"2023-02-03 01:15:00+01:00",
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"2023-02-03 01:30:00+01:00",
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"2023-02-03 01:45:00+01:00",
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"2023-02-03 02:00:00+01:00",
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"2023-02-03 02:15:00+01:00",
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"2023-02-03 02:30:00+01:00",
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"2023-02-03 02:45:00+01:00",
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"2023-02-03 03:00:00+01:00",
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"2023-02-03 03:15:00+01:00",
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"2023-02-03 03:30:00+01:00",
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"2023-02-03 03:45:00+01:00",
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"2023-02-03 04:00:00+01:00",
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"2023-02-03 04:15:00+01:00",
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"2023-02-03 04:30:00+01:00",
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"2023-02-03 04:45:00+01:00",
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"2023-02-03 05:00:00+01:00",
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"2023-02-03 05:15:00+01:00",
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"2023-02-03 05:30:00+01:00",
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"2023-02-03 05:45:00+01:00",
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"2023-02-03 06:00:00+01:00",
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"2023-02-03 06:15:00+01:00",
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"2023-02-03 06:30:00+01:00",
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"2023-02-03 06:45:00+01:00",
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"2023-02-03 07:00:00+01:00",
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"2023-02-03 07:15:00+01:00",
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"2023-02-03 07:30:00+01:00",
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"2023-02-03 07:45:00+01:00",
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"2023-02-03 08:00:00+01:00",
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"2023-02-03 08:15:00+01:00",
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"2023-02-03 08:30:00+01:00",
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"2023-02-03 08:45:00+01:00",
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"2023-02-03 09:00:00+01:00",
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"2023-02-03 09:15:00+01:00",
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"2023-02-03 09:30:00+01:00",
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"2023-02-03 09:45:00+01:00",
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"2023-02-03 10:00:00+01:00",
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"2023-02-03 10:15:00+01:00",
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"2023-02-03 10:30:00+01:00",
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"2023-02-03 10:45:00+01:00",
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"2023-02-03 11:00:00+01:00",
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"2023-02-03 11:15:00+01:00",
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"2023-02-03 11:30:00+01:00",
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"2023-02-03 11:45:00+01:00",
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"2023-02-03 12:00:00+01:00",
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"2023-02-03 12:15:00+01:00",
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"2023-02-03 12:30:00+01:00",
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"2023-02-03 12:45:00+01:00",
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"2023-02-03 13:00:00+01:00",
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"2023-02-03 13:15:00+01:00",
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"2023-02-03 13:30:00+01:00",
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"2023-02-03 13:45:00+01:00",
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"2023-02-03 14:00:00+01:00",
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"2023-02-03 14:15:00+01:00",
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"2023-02-03 14:30:00+01:00",
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"2023-02-03 14:45:00+01:00",
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"2023-02-03 15:00:00+01:00",
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"2023-02-03 15:15:00+01:00",
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"2023-02-03 15:30:00+01:00",
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"2023-02-03 15:45:00+01:00",
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"2023-02-03 16:00:00+01:00",
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"2023-02-03 16:15:00+01:00",
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"2023-02-03 16:30:00+01:00",
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"2023-02-03 16:45:00+01:00",
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"2023-02-03 17:00:00+01:00",
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"2023-02-03 17:15:00+01:00",
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"2023-02-03 17:30:00+01:00",
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"2023-02-03 17:45:00+01:00",
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"2023-02-03 18:00:00+01:00",
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"2023-02-03 18:15:00+01:00",
|
|
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"2023-01-04 01:00:00+01:00",
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"2023-01-04 01:15:00+01:00",
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"2023-01-04 01:30:00+01:00",
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"2023-01-04 01:45:00+01:00",
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"2023-01-04 02:00:00+01:00",
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"2023-01-04 02:15:00+01:00",
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"2023-01-04 02:30:00+01:00",
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"2023-01-04 02:45:00+01:00",
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"2023-01-04 03:00:00+01:00",
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"2023-01-04 03:15:00+01:00",
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"2023-01-04 03:30:00+01:00",
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"2023-01-04 03:45:00+01:00",
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"2023-01-04 04:00:00+01:00",
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"2023-01-04 04:15:00+01:00",
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"2023-01-04 04:30:00+01:00",
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"2023-01-04 04:45:00+01:00",
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"2023-01-04 05:15:00+01:00",
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"2023-01-04 05:30:00+01:00",
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"2023-01-04 05:45:00+01:00",
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"2023-01-04 06:30:00+01:00",
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"2023-01-04 06:45:00+01:00",
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"2023-01-04 07:15:00+01:00",
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"2023-01-04 07:45:00+01:00",
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"2023-01-04 09:15:00+01:00",
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"2023-01-04 09:30:00+01:00",
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"2023-01-04 09:45:00+01:00",
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"2023-01-04 10:15:00+01:00",
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"2023-01-04 10:30:00+01:00",
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"2023-01-04 10:45:00+01:00",
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"2023-01-04 11:00:00+01:00",
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"2023-01-04 11:15:00+01:00",
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"2023-01-04 11:30:00+01:00",
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"2023-01-04 11:45:00+01:00",
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"2023-01-04 12:00:00+01:00",
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"2023-01-04 12:15:00+01:00",
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"2023-01-04 12:30:00+01:00",
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"2023-01-04 12:45:00+01:00",
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"2023-01-04 13:15:00+01:00",
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"2023-01-04 13:30:00+01:00",
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"2023-01-04 13:45:00+01:00",
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"2023-01-04 14:15:00+01:00",
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"2023-01-04 14:30:00+01:00",
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"2023-01-04 14:45:00+01:00",
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"2023-01-04 15:30:00+01:00",
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"2023-01-04 15:45:00+01:00",
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"2023-01-04 17:30:00+01:00",
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"2023-01-04 17:45:00+01:00",
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"2023-01-04 18:15:00+01:00",
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"2023-01-04 18:30:00+01:00",
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"2023-01-04 18:45:00+01:00",
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"2023-01-04 19:15:00+01:00",
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"2023-01-04 19:30:00+01:00",
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"2023-01-04 19:45:00+01:00",
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"2023-01-04 20:15:00+01:00",
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"2023-01-04 20:30:00+01:00",
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"2023-01-04 20:45:00+01:00",
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"2023-01-04 21:15:00+01:00",
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"2023-01-04 21:30:00+01:00",
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"2023-01-04 21:45:00+01:00",
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"2023-01-04 23:15:00+01:00",
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"2023-01-05 00:15:00+01:00",
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"2023-01-05 01:00:00+01:00",
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"2023-01-05 01:15:00+01:00",
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"2023-01-05 01:30:00+01:00",
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"2023-01-05 01:45:00+01:00",
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"2023-01-05 02:00:00+01:00",
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"2023-01-05 02:15:00+01:00",
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"2023-01-05 02:30:00+01:00",
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"2023-01-05 02:45:00+01:00",
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"2023-01-05 03:00:00+01:00",
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"2023-01-05 03:15:00+01:00",
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"2023-01-05 03:30:00+01:00",
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"2023-01-05 03:45:00+01:00",
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"2023-01-05 04:00:00+01:00",
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"2023-01-05 04:15:00+01:00",
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"2023-01-05 04:30:00+01:00",
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"2023-01-05 04:45:00+01:00",
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"2023-01-05 05:00:00+01:00",
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"2023-01-05 05:15:00+01:00",
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"2023-01-05 05:30:00+01:00",
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"2023-01-05 05:45:00+01:00",
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"2023-01-05 06:00:00+01:00",
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"2023-01-05 06:15:00+01:00",
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"2023-01-05 06:30:00+01:00",
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"2023-01-05 06:45:00+01:00",
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"2023-01-05 07:15:00+01:00",
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"2023-01-05 07:30:00+01:00",
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"2023-01-05 07:45:00+01:00",
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"2023-01-05 08:15:00+01:00",
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"2023-01-05 08:45:00+01:00",
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"2023-01-05 09:15:00+01:00",
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"2023-01-05 09:30:00+01:00",
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"2023-01-05 09:45:00+01:00",
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"2023-01-05 10:15:00+01:00",
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"2023-01-05 10:30:00+01:00",
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"2023-01-05 10:45:00+01:00",
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"2023-01-05 11:00:00+01:00",
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"2023-01-05 11:15:00+01:00",
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"2023-01-05 11:30:00+01:00",
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"2023-01-05 11:45:00+01:00",
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"2023-01-05 12:00:00+01:00",
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"2023-01-05 12:15:00+01:00",
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"2023-01-05 12:30:00+01:00",
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"2023-01-05 12:45:00+01:00",
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"2023-01-05 13:00:00+01:00",
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"2023-01-05 13:15:00+01:00",
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"2023-01-05 13:30:00+01:00",
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"2023-01-05 13:45:00+01:00",
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"2023-01-05 14:00:00+01:00",
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"2023-01-05 14:15:00+01:00",
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"2023-01-05 14:30:00+01:00",
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"2023-01-05 14:45:00+01:00",
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"2023-01-05 15:00:00+01:00",
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"2023-01-05 15:15:00+01:00",
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"2023-01-05 15:30:00+01:00",
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"2023-01-05 15:45:00+01:00",
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"2023-01-05 16:00:00+01:00",
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"2023-01-05 16:15:00+01:00",
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"2023-01-05 16:30:00+01:00",
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"2023-01-05 16:45:00+01:00",
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"2023-01-05 17:00:00+01:00",
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"2023-01-05 17:15:00+01:00",
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"2023-01-05 17:30:00+01:00",
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"2023-01-05 17:45:00+01:00",
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"2023-01-05 18:00:00+01:00",
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"2023-01-05 18:15:00+01:00",
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"2023-01-05 18:30:00+01:00",
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"2023-01-05 18:45:00+01:00",
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"2023-01-05 19:00:00+01:00",
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"2023-01-05 19:15:00+01:00",
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"2023-01-05 19:30:00+01:00",
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"2023-01-05 19:45:00+01:00",
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"2023-01-05 20:00:00+01:00",
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"2023-01-05 20:15:00+01:00",
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"2023-01-05 20:30:00+01:00",
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"2023-01-05 20:45:00+01:00",
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"2023-01-05 21:00:00+01:00",
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"2023-01-05 21:15:00+01:00",
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"2023-01-05 21:30:00+01:00",
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"2023-01-05 21:45:00+01:00",
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"2023-01-05 22:00:00+01:00",
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"2023-01-05 22:15:00+01:00",
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"2023-01-05 22:30:00+01:00",
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"2023-01-05 22:45:00+01:00",
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"2023-01-05 23:00:00+01:00",
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"2023-01-05 23:15:00+01:00",
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"2023-01-05 23:30:00+01:00",
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"2023-01-05 23:45:00+01:00",
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"2023-01-06 00:00:00+01:00",
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"2023-01-06 00:15:00+01:00",
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"2023-01-06 00:30:00+01:00",
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"2023-01-06 00:45:00+01:00",
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"2023-01-06 01:00:00+01:00",
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"2023-01-06 01:15:00+01:00",
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"2023-01-06 01:30:00+01:00",
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"2023-01-06 01:45:00+01:00",
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"2023-01-06 02:00:00+01:00",
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"2023-01-06 02:15:00+01:00",
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"2023-01-06 02:30:00+01:00",
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"2023-01-06 02:45:00+01:00",
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"2023-01-06 03:00:00+01:00",
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"2023-01-06 03:15:00+01:00",
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"2023-01-06 03:30:00+01:00",
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"2023-01-06 03:45:00+01:00",
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"2023-01-06 04:00:00+01:00",
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"2023-01-06 04:15:00+01:00",
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"2023-01-06 04:30:00+01:00",
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"2023-01-06 04:45:00+01:00",
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"2023-01-06 05:00:00+01:00",
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"2023-01-06 05:15:00+01:00",
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"2023-01-06 05:30:00+01:00",
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"2023-01-06 05:45:00+01:00",
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"2023-01-06 06:00:00+01:00",
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"2023-01-06 06:15:00+01:00",
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"2023-01-06 06:30:00+01:00",
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"2023-01-06 06:45:00+01:00",
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"2023-01-06 07:00:00+01:00",
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"2023-01-06 07:15:00+01:00",
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"2023-01-06 07:30:00+01:00",
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"2023-01-06 07:45:00+01:00",
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"2023-01-06 08:00:00+01:00",
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"2023-01-06 08:15:00+01:00",
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"2023-01-06 08:30:00+01:00",
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"2023-01-06 08:45:00+01:00",
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"2023-01-06 09:00:00+01:00",
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"2023-01-06 09:15:00+01:00",
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"2023-01-06 09:30:00+01:00",
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"2023-01-06 09:45:00+01:00",
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"2023-01-06 10:00:00+01:00",
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"2023-01-06 10:15:00+01:00",
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"2023-01-06 10:30:00+01:00",
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"2023-01-06 10:45:00+01:00",
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"2023-01-06 11:00:00+01:00",
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"2023-01-06 11:15:00+01:00",
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"2023-01-06 11:30:00+01:00",
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"2023-01-06 11:45:00+01:00",
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"2023-01-06 12:00:00+01:00",
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"2023-01-06 12:15:00+01:00",
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"2023-01-06 12:30:00+01:00",
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"2023-01-06 12:45:00+01:00",
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"2023-01-06 13:00:00+01:00",
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"2023-01-06 13:15:00+01:00",
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"2023-01-06 13:30:00+01:00",
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"2023-01-06 13:45:00+01:00",
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"2023-01-06 14:00:00+01:00",
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"2023-01-06 14:15:00+01:00",
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"2023-01-06 14:30:00+01:00",
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"2023-01-06 14:45:00+01:00",
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"2023-01-06 15:00:00+01:00",
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"2023-01-06 15:15:00+01:00",
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"2023-01-06 15:30:00+01:00",
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"2023-01-06 15:45:00+01:00",
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"2023-01-06 16:00:00+01:00",
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"2023-01-06 16:15:00+01:00",
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"2023-01-06 16:30:00+01:00",
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"2023-01-06 16:45:00+01:00",
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"2023-01-06 17:00:00+01:00",
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"2023-01-06 17:15:00+01:00",
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"2023-01-06 17:30:00+01:00",
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"2023-01-06 17:45:00+01:00",
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"2023-01-06 18:00:00+01:00",
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"2023-01-06 18:15:00+01:00",
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"2023-01-06 18:30:00+01:00",
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"2023-01-06 18:45:00+01:00",
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"2023-01-06 19:00:00+01:00",
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"2023-01-06 19:15:00+01:00",
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"2023-01-06 19:30:00+01:00",
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"2023-01-06 19:45:00+01:00",
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"2023-01-06 20:00:00+01:00",
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"2023-01-06 20:15:00+01:00",
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"2023-01-06 20:30:00+01:00",
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"2023-01-06 20:45:00+01:00",
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"2023-01-06 21:00:00+01:00",
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"2023-01-06 21:15:00+01:00",
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"2023-01-06 21:30:00+01:00",
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"2023-01-06 21:45:00+01:00",
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"2023-01-06 22:00:00+01:00",
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"2023-01-06 22:15:00+01:00",
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"2023-01-06 22:30:00+01:00",
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"2023-01-06 22:45:00+01:00",
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"2023-01-06 23:00:00+01:00",
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"2023-01-06 23:15:00+01:00",
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"2023-01-06 23:30:00+01:00",
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"2023-01-06 23:45:00+01:00",
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"2023-01-07 00:00:00+01:00",
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"2023-01-07 00:15:00+01:00",
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"2023-01-07 00:30:00+01:00",
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"2023-01-07 00:45:00+01:00",
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"2023-01-07 01:00:00+01:00",
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"2023-01-07 01:15:00+01:00",
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"2023-01-07 01:30:00+01:00",
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"2023-01-07 01:45:00+01:00",
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"2023-01-07 02:00:00+01:00",
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"2023-01-07 02:15:00+01:00",
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"2023-01-07 02:30:00+01:00",
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"2023-01-07 02:45:00+01:00",
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"2023-01-07 03:00:00+01:00",
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"2023-01-07 03:15:00+01:00",
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"2023-01-07 03:30:00+01:00",
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"2023-01-07 03:45:00+01:00",
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"2023-01-07 04:00:00+01:00",
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"2023-01-07 04:15:00+01:00",
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"2023-01-07 04:30:00+01:00",
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"2023-01-07 04:45:00+01:00",
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"2023-01-07 05:00:00+01:00",
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"2023-01-07 05:15:00+01:00",
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"2023-01-07 05:30:00+01:00",
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"2023-01-07 05:45:00+01:00",
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"2023-01-07 06:00:00+01:00",
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"2023-01-07 06:15:00+01:00",
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"2023-01-07 06:30:00+01:00",
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"2023-01-07 06:45:00+01:00",
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"2023-01-07 07:00:00+01:00",
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"2023-01-07 07:15:00+01:00",
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"2023-01-07 07:30:00+01:00",
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"2023-01-07 07:45:00+01:00",
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"2023-01-07 08:00:00+01:00",
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"2023-01-07 08:15:00+01:00",
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"2023-01-07 08:30:00+01:00",
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"2023-01-07 08:45:00+01:00",
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"2023-01-07 09:00:00+01:00",
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"2023-01-07 09:15:00+01:00",
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"2023-01-07 09:30:00+01:00",
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"2023-01-07 09:45:00+01:00",
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"2023-01-07 10:00:00+01:00",
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"2023-01-07 10:15:00+01:00",
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"2023-01-07 10:30:00+01:00",
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"2023-01-07 10:45:00+01:00",
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"2023-01-07 11:00:00+01:00",
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"2023-01-07 11:15:00+01:00",
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"2023-01-07 11:30:00+01:00",
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"2023-01-07 11:45:00+01:00",
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"2023-01-07 12:00:00+01:00",
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"2023-01-07 12:15:00+01:00",
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"2023-01-07 12:30:00+01:00",
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"2023-01-07 12:45:00+01:00",
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"2023-01-07 13:00:00+01:00",
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"2023-01-07 13:15:00+01:00",
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"2023-01-07 13:30:00+01:00",
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"2023-01-07 13:45:00+01:00",
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"2023-01-07 14:00:00+01:00",
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"2023-01-07 14:15:00+01:00",
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"2023-01-07 14:30:00+01:00",
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"2023-01-07 14:45:00+01:00",
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"2023-01-07 15:00:00+01:00",
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"2023-01-07 15:15:00+01:00",
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"2023-01-07 15:30:00+01:00",
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"2023-01-07 15:45:00+01:00",
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"2023-01-07 16:00:00+01:00",
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"2023-01-07 16:15:00+01:00",
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"2023-01-07 16:30:00+01:00",
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"2023-01-07 16:45:00+01:00",
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"2023-01-07 17:00:00+01:00",
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"2023-01-07 17:15:00+01:00",
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"2023-01-07 17:30:00+01:00",
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"2023-01-07 17:45:00+01:00",
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"2023-01-07 18:00:00+01:00",
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"2023-01-07 18:15:00+01:00",
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"2023-01-07 18:30:00+01:00",
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"2023-01-07 18:45:00+01:00",
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"2023-01-07 19:00:00+01:00",
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"2023-01-07 19:15:00+01:00",
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"2023-01-07 19:30:00+01:00",
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"2023-01-07 19:45:00+01:00",
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"2023-01-07 20:00:00+01:00",
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"2023-01-07 20:15:00+01:00",
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"2023-01-07 20:30:00+01:00",
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"2023-01-07 20:45:00+01:00",
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"2023-01-07 21:00:00+01:00",
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"2023-01-07 21:15:00+01:00",
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"2023-01-07 21:30:00+01:00",
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"2023-01-07 21:45:00+01:00",
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"2023-01-07 22:00:00+01:00",
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"2023-01-07 22:15:00+01:00",
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"2023-01-07 22:30:00+01:00",
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"2023-01-07 22:45:00+01:00",
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"2023-01-07 23:00:00+01:00",
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"2023-01-07 23:15:00+01:00",
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"2023-01-07 23:30:00+01:00",
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"2023-01-07 23:45:00+01:00",
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"2023-01-08 00:00:00+01:00",
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"2023-01-08 00:15:00+01:00",
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"2023-01-08 00:30:00+01:00",
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"2023-01-08 00:45:00+01:00",
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"2023-01-08 01:00:00+01:00",
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"2023-01-08 01:15:00+01:00",
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"2023-01-08 01:30:00+01:00",
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"2023-01-08 01:45:00+01:00",
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"2023-01-08 02:00:00+01:00",
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"2023-01-08 02:15:00+01:00",
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"2023-01-08 02:30:00+01:00",
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"2023-01-08 02:45:00+01:00",
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"2023-01-08 03:00:00+01:00",
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"2023-01-08 03:15:00+01:00",
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"2023-01-08 03:30:00+01:00",
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"2023-01-08 03:45:00+01:00",
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"2023-01-08 04:00:00+01:00",
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"2023-01-08 04:15:00+01:00",
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"2023-01-08 04:30:00+01:00",
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"2023-01-08 04:45:00+01:00",
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"2023-01-08 05:00:00+01:00",
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"2023-01-08 05:15:00+01:00",
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"2023-01-08 05:30:00+01:00",
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"2023-01-08 05:45:00+01:00",
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"2023-01-08 06:00:00+01:00",
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"2023-01-08 06:15:00+01:00",
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"2023-01-08 06:30:00+01:00",
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"2023-01-08 06:45:00+01:00",
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"2023-01-08 07:00:00+01:00",
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"2023-01-08 07:15:00+01:00",
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"2023-01-08 07:30:00+01:00",
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"2023-01-08 07:45:00+01:00",
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"2023-01-08 08:00:00+01:00",
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"2023-01-08 08:15:00+01:00",
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"2023-01-08 08:30:00+01:00",
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"2023-01-08 08:45:00+01:00",
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"2023-01-08 09:00:00+01:00",
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"2023-01-08 09:15:00+01:00",
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"2023-01-08 09:30:00+01:00",
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"2023-01-08 09:45:00+01:00",
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"2023-01-08 10:00:00+01:00",
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"2023-01-08 10:15:00+01:00",
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"2023-01-08 10:30:00+01:00",
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"2023-01-08 10:45:00+01:00",
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"2023-01-08 11:00:00+01:00",
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"2023-01-08 11:15:00+01:00",
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"2023-01-08 11:30:00+01:00",
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"2023-01-08 11:45:00+01:00",
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"2023-01-08 12:00:00+01:00",
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"2023-01-08 12:15:00+01:00",
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"2023-01-08 12:30:00+01:00",
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"2023-01-08 12:45:00+01:00",
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"2023-01-08 13:00:00+01:00",
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"2023-01-08 13:15:00+01:00",
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"2023-01-08 13:30:00+01:00",
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"2023-01-08 13:45:00+01:00",
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"2023-01-08 14:00:00+01:00",
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"2023-01-08 14:15:00+01:00",
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"2023-01-08 14:30:00+01:00",
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"2023-01-08 14:45:00+01:00",
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"2023-01-08 15:00:00+01:00",
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"2023-01-08 15:15:00+01:00",
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"2023-01-08 15:30:00+01:00",
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"2023-01-08 15:45:00+01:00",
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"2023-01-08 16:00:00+01:00",
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"2023-01-08 16:15:00+01:00",
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"2023-01-08 16:30:00+01:00",
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"2023-01-08 16:45:00+01:00",
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"2023-01-08 17:00:00+01:00",
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"2023-01-08 17:15:00+01:00",
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"2023-01-08 17:30:00+01:00",
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"2023-01-08 17:45:00+01:00",
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"2023-01-08 18:00:00+01:00",
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"2023-01-08 18:15:00+01:00",
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"2023-01-08 18:30:00+01:00",
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"2023-01-08 18:45:00+01:00",
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"2023-01-08 19:00:00+01:00",
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"2023-01-08 19:15:00+01:00",
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"2023-01-08 19:30:00+01:00",
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"2023-01-08 19:45:00+01:00",
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"2023-01-08 20:00:00+01:00",
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"2023-01-08 20:15:00+01:00",
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"2023-01-08 20:30:00+01:00",
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"2023-01-08 20:45:00+01:00",
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"2023-01-08 21:00:00+01:00",
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"2023-01-08 21:15:00+01:00",
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"2023-01-08 21:30:00+01:00",
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"2023-01-08 21:45:00+01:00",
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"2023-01-08 22:00:00+01:00",
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"2023-01-08 22:15:00+01:00",
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"2023-01-08 22:30:00+01:00",
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"2023-01-08 22:45:00+01:00",
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"2023-01-08 23:00:00+01:00",
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"2023-01-08 23:15:00+01:00",
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"2023-01-08 23:30:00+01:00",
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"2023-01-08 23:45:00+01:00",
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"2023-01-09 00:00:00+01:00",
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"2023-01-09 00:15:00+01:00",
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"2023-01-09 00:30:00+01:00",
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"2023-01-09 00:45:00+01:00",
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"2023-01-09 01:00:00+01:00",
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"2023-01-09 01:15:00+01:00",
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"2023-01-09 01:30:00+01:00",
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"2023-01-09 01:45:00+01:00",
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"2023-01-09 02:00:00+01:00",
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"2023-01-09 02:15:00+01:00",
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"2023-01-09 02:30:00+01:00",
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"2023-01-09 02:45:00+01:00",
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"2023-01-09 03:00:00+01:00",
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"2023-01-09 03:15:00+01:00",
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"2023-01-09 03:30:00+01:00",
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"2023-01-09 03:45:00+01:00",
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"2023-01-09 04:00:00+01:00",
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"2023-01-09 04:15:00+01:00",
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"2023-01-09 04:30:00+01:00",
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"2023-01-09 04:45:00+01:00",
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"2023-01-09 05:00:00+01:00",
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"2023-01-09 05:15:00+01:00",
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"2023-01-09 05:30:00+01:00",
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"2023-01-09 05:45:00+01:00",
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"2023-01-09 06:00:00+01:00",
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"2023-01-09 06:15:00+01:00",
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"2023-01-09 06:30:00+01:00",
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"2023-01-09 06:45:00+01:00",
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"2023-01-09 07:00:00+01:00",
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"2023-01-09 07:15:00+01:00",
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"2023-01-09 07:30:00+01:00",
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"2023-01-09 07:45:00+01:00",
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"2023-01-09 08:00:00+01:00",
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"2023-01-09 08:15:00+01:00",
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"2023-01-09 08:30:00+01:00",
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"2023-01-09 08:45:00+01:00",
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"2023-01-09 09:00:00+01:00",
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"2023-01-09 09:15:00+01:00",
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"2023-01-09 09:30:00+01:00",
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"2023-01-09 09:45:00+01:00",
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"2023-01-09 10:00:00+01:00",
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"2023-01-09 10:15:00+01:00",
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"2023-01-09 10:30:00+01:00",
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"2023-01-09 10:45:00+01:00",
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"2023-01-09 11:00:00+01:00",
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"2023-01-09 11:15:00+01:00",
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"2023-01-09 11:30:00+01:00",
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"2023-01-09 11:45:00+01:00",
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"2023-01-09 12:00:00+01:00",
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"2023-01-09 12:15:00+01:00",
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"2023-01-09 12:30:00+01:00",
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"2023-01-09 12:45:00+01:00",
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"2023-01-09 13:00:00+01:00",
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"2023-01-09 13:15:00+01:00",
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"2023-01-09 13:30:00+01:00",
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"2023-01-09 13:45:00+01:00",
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"2023-01-09 14:00:00+01:00",
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"2023-01-09 14:15:00+01:00",
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"2023-01-09 14:30:00+01:00",
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"2023-01-09 14:45:00+01:00",
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"2023-01-09 15:00:00+01:00",
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"2023-01-09 15:15:00+01:00",
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"2023-01-09 15:30:00+01:00",
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"2023-01-09 15:45:00+01:00",
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"2023-01-09 16:00:00+01:00",
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"2023-01-09 16:15:00+01:00",
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"2023-01-09 16:30:00+01:00",
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"2023-01-09 16:45:00+01:00",
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"2023-01-09 17:00:00+01:00",
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"2023-01-09 17:15:00+01:00",
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"2023-01-09 17:30:00+01:00",
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"2023-01-09 17:45:00+01:00",
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"2023-01-09 18:00:00+01:00",
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"2023-01-09 18:15:00+01:00",
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"2023-01-09 18:30:00+01:00",
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"2023-01-09 18:45:00+01:00",
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"2023-01-09 19:00:00+01:00",
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"2023-01-09 19:15:00+01:00",
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"2023-01-09 19:30:00+01:00",
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"2023-01-09 19:45:00+01:00",
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"2023-01-09 20:00:00+01:00",
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"2023-01-09 20:15:00+01:00",
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"2023-01-09 20:30:00+01:00",
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"2023-01-09 20:45:00+01:00",
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"2023-01-09 21:00:00+01:00",
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"2023-01-09 21:15:00+01:00",
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"2023-01-09 21:30:00+01:00",
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"2023-01-09 21:45:00+01:00",
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"2023-01-09 22:00:00+01:00",
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"2023-01-09 22:15:00+01:00",
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"2023-01-09 22:30:00+01:00",
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"2023-01-09 22:45:00+01:00",
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"2023-01-09 23:00:00+01:00",
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"2023-01-09 23:15:00+01:00",
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"2023-01-09 23:30:00+01:00",
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"2023-01-09 23:45:00+01:00",
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"2023-01-10 00:00:00+01:00",
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"2023-01-10 00:15:00+01:00",
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"2023-01-10 00:30:00+01:00",
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"2023-01-10 00:45:00+01:00",
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"2023-01-10 01:00:00+01:00",
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"2023-01-10 01:15:00+01:00",
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"2023-01-10 01:30:00+01:00",
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"2023-01-10 01:45:00+01:00",
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"2023-01-10 02:00:00+01:00",
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"2023-01-10 02:15:00+01:00",
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"2023-01-10 02:30:00+01:00",
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"2023-01-10 02:45:00+01:00",
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"2023-01-10 03:00:00+01:00",
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"2023-01-10 03:15:00+01:00",
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"2023-01-10 03:30:00+01:00",
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"2023-01-10 03:45:00+01:00",
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"2023-01-10 04:00:00+01:00",
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"2023-01-10 04:15:00+01:00",
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"2023-01-10 04:30:00+01:00",
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"2023-01-10 04:45:00+01:00",
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"2023-01-10 05:00:00+01:00",
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"2023-01-10 05:15:00+01:00",
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"2023-01-10 05:30:00+01:00",
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"2023-01-10 05:45:00+01:00",
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"2023-01-10 06:00:00+01:00",
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"2023-01-10 06:15:00+01:00",
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"2023-01-10 06:30:00+01:00",
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"2023-01-10 06:45:00+01:00",
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"2023-01-10 07:00:00+01:00",
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"2023-01-10 07:15:00+01:00",
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"2023-01-10 07:30:00+01:00",
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"2023-01-10 07:45:00+01:00",
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"2023-01-10 08:00:00+01:00",
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"2023-01-10 08:15:00+01:00",
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"2023-01-10 08:30:00+01:00",
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"2023-01-10 08:45:00+01:00",
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"2023-01-10 09:00:00+01:00",
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"2023-01-10 09:15:00+01:00",
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"2023-01-10 09:30:00+01:00",
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"2023-01-10 09:45:00+01:00",
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"2023-01-10 10:00:00+01:00",
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"2023-01-10 10:15:00+01:00",
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"2023-01-10 10:30:00+01:00",
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"2023-01-10 10:45:00+01:00",
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"2023-01-10 11:00:00+01:00",
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"2023-01-10 11:15:00+01:00",
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"2023-01-10 11:30:00+01:00",
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"2023-01-10 11:45:00+01:00",
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"2023-01-10 12:00:00+01:00",
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"2023-01-10 12:15:00+01:00",
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"2023-01-10 12:30:00+01:00",
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"2023-01-10 12:45:00+01:00",
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"2023-01-10 13:00:00+01:00",
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"2023-01-10 13:15:00+01:00",
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"2023-01-10 13:30:00+01:00",
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"2023-01-10 13:45:00+01:00",
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"2023-01-10 14:00:00+01:00",
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"2023-01-10 14:15:00+01:00",
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"2023-01-10 14:30:00+01:00",
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"2023-01-10 14:45:00+01:00",
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"2023-01-10 15:00:00+01:00",
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"2023-01-10 15:15:00+01:00",
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"2023-01-10 15:30:00+01:00",
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"2023-01-10 15:45:00+01:00",
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"2023-01-10 16:00:00+01:00",
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"2023-01-10 16:15:00+01:00",
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"2023-01-10 16:30:00+01:00",
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"2023-01-10 16:45:00+01:00",
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"2023-01-10 17:00:00+01:00",
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"2023-01-10 17:15:00+01:00",
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"2023-01-10 17:30:00+01:00",
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"2023-01-10 17:45:00+01:00",
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"2023-01-10 18:00:00+01:00",
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"2023-01-10 18:15:00+01:00",
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"2023-01-10 18:30:00+01:00",
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"2023-01-10 18:45:00+01:00",
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"2023-01-10 19:00:00+01:00",
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"2023-01-10 19:15:00+01:00",
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"2023-01-10 19:30:00+01:00",
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"2023-01-10 19:45:00+01:00",
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"2023-01-10 20:00:00+01:00",
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"2023-01-10 20:15:00+01:00",
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"2023-01-10 20:30:00+01:00",
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"2023-01-10 20:45:00+01:00",
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"2023-01-10 21:00:00+01:00",
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"2023-01-10 21:15:00+01:00",
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"2023-01-10 21:30:00+01:00",
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"2023-01-10 21:45:00+01:00",
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"2023-01-10 22:00:00+01:00",
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"2023-01-10 22:15:00+01:00",
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"2023-01-10 22:30:00+01:00",
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"2023-01-10 22:45:00+01:00",
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"2023-01-10 23:00:00+01:00",
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"2023-01-10 23:15:00+01:00",
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"2023-01-10 23:30:00+01:00",
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"2023-01-10 23:45:00+01:00",
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"2023-01-11 00:00:00+01:00",
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"2023-01-11 00:15:00+01:00",
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"2023-01-11 00:30:00+01:00",
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"2023-01-11 00:45:00+01:00",
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"2023-01-11 01:00:00+01:00",
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"2023-01-11 01:15:00+01:00",
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"2023-01-11 01:30:00+01:00",
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"2023-01-11 01:45:00+01:00",
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"2023-01-11 02:00:00+01:00",
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"2023-01-11 02:15:00+01:00",
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"2023-01-11 02:30:00+01:00",
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"2023-01-11 02:45:00+01:00",
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"2023-01-11 03:00:00+01:00",
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"2023-01-11 03:15:00+01:00",
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"2023-01-11 03:30:00+01:00",
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"2023-01-11 03:45:00+01:00",
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"2023-01-11 04:00:00+01:00",
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"2023-01-11 04:15:00+01:00",
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"2023-01-11 04:30:00+01:00",
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"2023-01-11 04:45:00+01:00",
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"2023-01-11 05:00:00+01:00",
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"2023-01-11 05:15:00+01:00",
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"2023-01-11 05:30:00+01:00",
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"2023-01-11 05:45:00+01:00",
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"2023-01-11 06:00:00+01:00",
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"2023-01-11 06:15:00+01:00",
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"2023-01-11 06:30:00+01:00",
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"2023-01-11 06:45:00+01:00",
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"2023-01-11 07:00:00+01:00",
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"2023-01-11 07:15:00+01:00",
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"2023-01-11 07:30:00+01:00",
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"2023-01-11 07:45:00+01:00",
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"2023-01-11 08:00:00+01:00",
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"2023-01-11 08:15:00+01:00",
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"2023-01-11 08:30:00+01:00",
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"2023-01-11 08:45:00+01:00",
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"2023-01-11 09:00:00+01:00",
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"2023-01-11 09:15:00+01:00",
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"2023-01-11 09:30:00+01:00",
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"2023-01-11 09:45:00+01:00",
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"2023-01-11 10:00:00+01:00",
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"2023-01-11 10:15:00+01:00",
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"2023-01-11 10:30:00+01:00",
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"2023-01-11 10:45:00+01:00",
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"2023-01-11 11:00:00+01:00",
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"2023-01-11 11:15:00+01:00",
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"2023-01-11 11:30:00+01:00",
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"2023-01-11 11:45:00+01:00",
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"2023-01-11 12:00:00+01:00",
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"2023-01-11 12:15:00+01:00",
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"2023-01-11 12:30:00+01:00",
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"2023-01-11 12:45:00+01:00",
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"2023-01-11 13:00:00+01:00",
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"2023-01-11 13:15:00+01:00",
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"2023-01-11 13:30:00+01:00",
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"2023-01-11 13:45:00+01:00",
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"2023-01-11 14:00:00+01:00",
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"2023-01-11 14:15:00+01:00",
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"2023-01-11 14:30:00+01:00",
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"2023-01-11 14:45:00+01:00",
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"2023-01-11 15:00:00+01:00",
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"2023-01-11 15:15:00+01:00",
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"2023-01-11 15:30:00+01:00",
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"2023-01-11 15:45:00+01:00",
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"2023-01-11 16:00:00+01:00",
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"2023-01-11 16:15:00+01:00",
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"2023-01-11 16:30:00+01:00",
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"2023-01-11 16:45:00+01:00",
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"2023-01-11 17:00:00+01:00",
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"2023-01-11 17:15:00+01:00",
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"2023-01-11 17:30:00+01:00",
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"2023-01-11 17:45:00+01:00",
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"2023-01-11 18:00:00+01:00",
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"2023-01-11 18:15:00+01:00",
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"2023-01-11 18:30:00+01:00",
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"2023-01-11 18:45:00+01:00",
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"2023-01-11 19:00:00+01:00",
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"2023-01-11 19:15:00+01:00",
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"2023-01-11 19:30:00+01:00",
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"2023-01-11 19:45:00+01:00",
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"2023-01-11 20:00:00+01:00",
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"2023-01-11 20:15:00+01:00",
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"2023-01-11 20:30:00+01:00",
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"2023-01-11 20:45:00+01:00",
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"2023-01-11 21:00:00+01:00",
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"2023-01-11 21:15:00+01:00",
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"2023-01-11 21:30:00+01:00",
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"2023-01-11 21:45:00+01:00",
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"2023-01-11 22:00:00+01:00",
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"2023-01-11 22:15:00+01:00",
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"2023-01-11 22:30:00+01:00",
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"2023-01-11 22:45:00+01:00",
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"2023-01-11 23:00:00+01:00",
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"2023-01-11 23:15:00+01:00",
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"2023-01-11 23:30:00+01:00",
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"2023-01-11 23:45:00+01:00",
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"2023-01-12 00:00:00+01:00",
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"2023-01-12 00:15:00+01:00",
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"2023-01-12 00:30:00+01:00",
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"2023-01-12 00:45:00+01:00",
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"2023-01-12 01:00:00+01:00",
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"2023-01-12 01:15:00+01:00",
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"2023-01-12 01:30:00+01:00",
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"2023-01-12 01:45:00+01:00",
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"2023-01-12 02:00:00+01:00",
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"2023-01-12 02:15:00+01:00",
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"2023-01-12 02:30:00+01:00",
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"2023-01-12 02:45:00+01:00",
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"2023-01-12 03:00:00+01:00",
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"2023-01-12 03:15:00+01:00",
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"2023-01-12 03:30:00+01:00",
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"2023-01-12 03:45:00+01:00",
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"2023-01-12 04:00:00+01:00",
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"2023-01-12 04:15:00+01:00",
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"2023-01-12 04:30:00+01:00",
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"2023-01-12 04:45:00+01:00",
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"2023-01-12 05:00:00+01:00",
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"2023-01-12 05:15:00+01:00",
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"2023-01-12 05:30:00+01:00",
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"2023-01-12 05:45:00+01:00",
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"2023-01-12 06:00:00+01:00",
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"2023-01-12 06:15:00+01:00",
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"2023-01-12 06:30:00+01:00",
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"2023-01-12 06:45:00+01:00",
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"2023-01-12 07:00:00+01:00",
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"2023-01-12 07:15:00+01:00",
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"2023-01-12 07:30:00+01:00",
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"2023-01-12 07:45:00+01:00",
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"2023-01-12 08:00:00+01:00",
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"2023-01-12 08:15:00+01:00",
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"2023-01-12 08:30:00+01:00",
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"2023-01-12 08:45:00+01:00",
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"2023-01-12 09:00:00+01:00",
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"2023-01-12 09:15:00+01:00",
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"2023-01-12 09:30:00+01:00",
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"2023-01-12 09:45:00+01:00",
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"2023-01-12 10:00:00+01:00",
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"2023-01-12 10:15:00+01:00",
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"2023-01-12 10:30:00+01:00",
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"2023-01-12 10:45:00+01:00",
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"2023-01-12 11:00:00+01:00",
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"2023-01-12 11:15:00+01:00",
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"2023-01-12 11:30:00+01:00",
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"2023-01-12 11:45:00+01:00",
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"2023-01-12 12:00:00+01:00",
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"2023-01-12 12:15:00+01:00",
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"2023-01-12 12:30:00+01:00",
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"2023-01-12 12:45:00+01:00",
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"2023-01-12 13:00:00+01:00",
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"2023-01-12 13:15:00+01:00",
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"2023-01-12 13:30:00+01:00",
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"2023-01-12 13:45:00+01:00",
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"2023-01-12 14:00:00+01:00",
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"2023-01-12 14:15:00+01:00",
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"2023-01-12 14:30:00+01:00",
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"2023-01-12 14:45:00+01:00",
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"2023-01-12 15:00:00+01:00",
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"2023-01-12 15:15:00+01:00",
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"2023-01-12 15:30:00+01:00",
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"2023-01-12 15:45:00+01:00",
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"2023-01-12 16:00:00+01:00",
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"2023-01-12 16:15:00+01:00",
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"2023-01-12 16:30:00+01:00",
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"2023-01-12 16:45:00+01:00",
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"2023-01-12 17:00:00+01:00",
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"2023-01-12 17:15:00+01:00",
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"2023-01-12 17:30:00+01:00",
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"2023-01-12 17:45:00+01:00",
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"2023-01-12 18:00:00+01:00",
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"2023-01-12 18:15:00+01:00",
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"2023-01-12 18:30:00+01:00",
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"2023-01-12 18:45:00+01:00",
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"2023-01-12 19:00:00+01:00",
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"2023-01-12 19:15:00+01:00",
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"2023-01-12 19:30:00+01:00",
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"2023-01-12 19:45:00+01:00",
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"2023-01-12 20:00:00+01:00",
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"2023-01-12 20:15:00+01:00",
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"2023-01-12 20:30:00+01:00",
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"2023-01-12 20:45:00+01:00",
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"2023-01-12 21:00:00+01:00",
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"2023-01-12 21:15:00+01:00",
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"2023-01-12 21:30:00+01:00",
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"2023-01-12 21:45:00+01:00",
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"2023-01-12 22:00:00+01:00",
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"2023-01-12 22:15:00+01:00",
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"2023-01-12 22:30:00+01:00",
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"2023-01-12 22:45:00+01:00",
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"2023-01-12 23:00:00+01:00",
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"2023-01-12 23:15:00+01:00",
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"2023-01-12 23:30:00+01:00",
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"2023-01-12 23:45:00+01:00",
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"2023-01-13 00:00:00+01:00",
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"2023-01-13 00:15:00+01:00",
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"2023-01-13 00:30:00+01:00",
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"2023-01-13 00:45:00+01:00",
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"2023-01-13 01:00:00+01:00",
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"2023-01-13 01:15:00+01:00",
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"2023-01-13 01:30:00+01:00",
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"2023-01-13 01:45:00+01:00",
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"2023-01-13 02:00:00+01:00",
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"2023-01-13 02:15:00+01:00",
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"2023-01-13 02:30:00+01:00",
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"2023-01-13 02:45:00+01:00",
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"2023-01-13 03:00:00+01:00",
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"2023-01-13 03:15:00+01:00",
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"2023-01-13 03:30:00+01:00",
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"2023-01-13 03:45:00+01:00",
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"2023-01-13 04:00:00+01:00",
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"2023-01-13 04:15:00+01:00",
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"2023-01-13 04:30:00+01:00",
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"2023-01-13 04:45:00+01:00",
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"2023-01-13 05:00:00+01:00",
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"2023-01-13 05:15:00+01:00",
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"2023-01-13 05:30:00+01:00",
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"2023-01-13 05:45:00+01:00",
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"2023-01-13 06:00:00+01:00",
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"2023-01-13 06:15:00+01:00",
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"2023-01-13 06:30:00+01:00",
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"2023-01-13 06:45:00+01:00",
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"2023-01-13 07:00:00+01:00",
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"2023-01-13 07:15:00+01:00",
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"2023-01-13 07:30:00+01:00",
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"2023-01-13 07:45:00+01:00",
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"2023-01-13 08:00:00+01:00",
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"2023-01-13 08:15:00+01:00",
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"2023-01-13 08:30:00+01:00",
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"2023-01-13 08:45:00+01:00",
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"2023-01-13 09:00:00+01:00",
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"2023-01-13 09:15:00+01:00",
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"2023-01-13 09:30:00+01:00",
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"2023-01-13 09:45:00+01:00",
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"2023-01-13 10:00:00+01:00",
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"2023-01-13 10:15:00+01:00",
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"2023-01-13 10:30:00+01:00",
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"2023-01-13 10:45:00+01:00",
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"2023-01-13 11:00:00+01:00",
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"2023-01-13 11:15:00+01:00",
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"2023-01-13 11:30:00+01:00",
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"2023-01-13 11:45:00+01:00",
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"2023-01-13 12:00:00+01:00",
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"2023-01-13 12:15:00+01:00",
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"2023-01-13 12:30:00+01:00",
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"2023-01-13 12:45:00+01:00",
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"2023-01-13 13:00:00+01:00",
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"2023-01-13 13:15:00+01:00",
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"2023-01-13 13:30:00+01:00",
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"2023-01-13 13:45:00+01:00",
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"2023-01-13 14:00:00+01:00",
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"2023-01-13 14:15:00+01:00",
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"2023-01-13 14:30:00+01:00",
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"2023-01-13 14:45:00+01:00",
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"2023-01-13 15:00:00+01:00",
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"2023-01-13 15:15:00+01:00",
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"2023-01-13 15:30:00+01:00",
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"2023-01-13 15:45:00+01:00",
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"2023-01-13 16:00:00+01:00",
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"2023-01-13 16:15:00+01:00",
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"2023-01-13 16:30:00+01:00",
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"2023-01-13 16:45:00+01:00",
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"2023-01-13 17:00:00+01:00",
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"2023-01-13 17:15:00+01:00",
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"2023-01-13 17:30:00+01:00",
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"2023-01-13 17:45:00+01:00",
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"2023-01-13 18:00:00+01:00",
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"2023-01-13 18:15:00+01:00",
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"2023-01-13 18:30:00+01:00",
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"2023-01-13 18:45:00+01:00",
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"2023-01-13 19:00:00+01:00",
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"2023-01-13 19:15:00+01:00",
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"2023-01-13 19:30:00+01:00",
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"2023-01-13 19:45:00+01:00",
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"2023-01-13 20:00:00+01:00",
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"2023-01-13 20:15:00+01:00",
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"2023-01-13 20:30:00+01:00",
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"2023-01-13 20:45:00+01:00",
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"2023-01-13 21:00:00+01:00",
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"2023-01-13 21:15:00+01:00",
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"2023-01-13 21:30:00+01:00",
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"2023-01-13 21:45:00+01:00",
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"2023-01-13 22:00:00+01:00",
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"2023-01-13 22:15:00+01:00",
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"2023-01-13 22:30:00+01:00",
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"2023-01-13 22:45:00+01:00",
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"2023-01-13 23:00:00+01:00",
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"2023-01-13 23:15:00+01:00",
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"2023-01-13 23:30:00+01:00",
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"2023-01-13 23:45:00+01:00",
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"2023-01-14 00:00:00+01:00",
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"2023-01-14 00:15:00+01:00",
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"2023-01-14 00:30:00+01:00",
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"2023-01-14 00:45:00+01:00",
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"2023-01-14 01:00:00+01:00",
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"2023-01-14 01:15:00+01:00",
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"2023-01-14 01:30:00+01:00",
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"2023-01-14 01:45:00+01:00",
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"2023-01-14 02:00:00+01:00",
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"2023-01-14 02:15:00+01:00",
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"2023-01-14 02:30:00+01:00",
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"2023-01-14 02:45:00+01:00",
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"2023-01-14 03:00:00+01:00",
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"2023-01-14 03:15:00+01:00",
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"2023-01-14 03:30:00+01:00",
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"2023-01-14 03:45:00+01:00",
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"2023-01-14 04:00:00+01:00",
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"2023-01-14 04:15:00+01:00",
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"2023-01-14 04:30:00+01:00",
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"2023-01-14 04:45:00+01:00",
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"2023-01-14 05:00:00+01:00",
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"2023-01-14 05:15:00+01:00",
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"2023-01-14 05:30:00+01:00",
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"2023-01-14 05:45:00+01:00",
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"2023-01-14 06:00:00+01:00",
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"2023-01-14 06:15:00+01:00",
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"2023-01-14 06:30:00+01:00",
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"2023-01-14 06:45:00+01:00",
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"2023-01-14 07:00:00+01:00",
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"2023-01-14 07:15:00+01:00",
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"2023-01-14 07:30:00+01:00",
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"2023-01-14 07:45:00+01:00",
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"2023-01-14 08:00:00+01:00",
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"2023-01-14 08:15:00+01:00",
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"2023-01-14 08:30:00+01:00",
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"2023-01-14 08:45:00+01:00",
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"2023-01-14 09:00:00+01:00",
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"2023-01-14 09:15:00+01:00",
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"2023-01-14 09:30:00+01:00",
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"2023-01-14 09:45:00+01:00",
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"2023-01-14 10:00:00+01:00",
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"2023-01-14 10:15:00+01:00",
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"2023-01-14 10:30:00+01:00",
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"2023-01-14 10:45:00+01:00",
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"2023-01-14 11:00:00+01:00",
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"2023-01-14 11:15:00+01:00",
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"2023-01-14 11:30:00+01:00",
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"2023-01-14 11:45:00+01:00",
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"2023-01-14 12:00:00+01:00",
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"2023-01-14 12:15:00+01:00",
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"2023-01-14 12:30:00+01:00",
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"2023-01-14 12:45:00+01:00",
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"2023-01-14 13:00:00+01:00",
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"2023-01-14 13:15:00+01:00",
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"2023-01-14 13:30:00+01:00",
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"2023-01-14 13:45:00+01:00",
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"2023-01-14 14:00:00+01:00",
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"2023-01-14 14:15:00+01:00",
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"2023-01-14 14:30:00+01:00",
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"2023-01-14 14:45:00+01:00",
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"2023-01-14 15:00:00+01:00",
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"2023-01-14 15:15:00+01:00",
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"2023-01-14 15:30:00+01:00",
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"2023-01-14 15:45:00+01:00",
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"2023-01-14 16:00:00+01:00",
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"2023-01-14 16:15:00+01:00",
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"2023-01-14 16:30:00+01:00",
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"2023-01-14 16:45:00+01:00",
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"2023-01-14 17:00:00+01:00",
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"2023-01-14 17:15:00+01:00",
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"2023-01-14 17:30:00+01:00",
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"2023-01-14 17:45:00+01:00",
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"2023-01-14 18:00:00+01:00",
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"2023-01-14 18:15:00+01:00",
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"2023-01-14 18:30:00+01:00",
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"2023-01-14 18:45:00+01:00",
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"2023-01-14 19:00:00+01:00",
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"2023-01-14 19:15:00+01:00",
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"2023-01-14 19:30:00+01:00",
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"2023-01-14 19:45:00+01:00",
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"2023-01-14 20:00:00+01:00",
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"2023-01-14 20:15:00+01:00",
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"2023-01-14 20:30:00+01:00",
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"2023-01-14 20:45:00+01:00",
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"2023-01-14 21:00:00+01:00",
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"2023-01-14 21:15:00+01:00",
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"2023-01-14 21:30:00+01:00",
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"2023-01-14 21:45:00+01:00",
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"2023-01-14 22:00:00+01:00",
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"2023-01-14 22:15:00+01:00",
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"2023-01-14 22:30:00+01:00",
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"2023-01-14 22:45:00+01:00",
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"2023-01-14 23:00:00+01:00",
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"2023-01-14 23:15:00+01:00",
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"2023-01-14 23:30:00+01:00",
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"2023-01-14 23:45:00+01:00",
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"2023-01-15 00:00:00+01:00",
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"2023-01-15 00:15:00+01:00",
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"2023-01-15 00:30:00+01:00",
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"2023-01-15 00:45:00+01:00",
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"2023-01-15 01:00:00+01:00",
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"2023-01-15 01:15:00+01:00",
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"2023-01-15 01:30:00+01:00",
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"2023-01-15 01:45:00+01:00",
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"2023-01-15 02:00:00+01:00",
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"2023-01-15 02:15:00+01:00",
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"2023-01-15 02:30:00+01:00",
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"2023-01-15 02:45:00+01:00",
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"2023-01-15 03:00:00+01:00",
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"2023-01-15 03:15:00+01:00",
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"2023-01-15 03:30:00+01:00",
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"2023-01-15 03:45:00+01:00",
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"2023-01-15 04:00:00+01:00",
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"2023-01-15 04:15:00+01:00",
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"2023-01-15 04:30:00+01:00",
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"2023-01-15 04:45:00+01:00",
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"2023-01-15 05:00:00+01:00",
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"2023-01-15 05:15:00+01:00",
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"2023-01-15 05:30:00+01:00",
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"2023-01-15 05:45:00+01:00",
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"2023-01-15 06:00:00+01:00",
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"2023-01-15 06:15:00+01:00",
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"2023-01-15 06:30:00+01:00",
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"2023-01-15 06:45:00+01:00",
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"2023-01-15 07:00:00+01:00",
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"2023-01-15 07:15:00+01:00",
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"2023-01-15 07:30:00+01:00",
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"2023-01-15 07:45:00+01:00",
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"2023-01-15 08:00:00+01:00",
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"2023-01-15 08:15:00+01:00",
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"2023-01-15 08:30:00+01:00",
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"2023-01-15 08:45:00+01:00",
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"2023-01-15 09:00:00+01:00",
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"2023-01-15 09:15:00+01:00",
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"2023-01-15 09:30:00+01:00",
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"2023-01-15 09:45:00+01:00",
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"2023-01-15 10:00:00+01:00",
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"2023-01-15 10:15:00+01:00",
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"2023-01-15 10:30:00+01:00",
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"2023-01-15 10:45:00+01:00",
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"2023-01-15 11:00:00+01:00",
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"2023-01-15 11:15:00+01:00",
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"2023-01-15 11:30:00+01:00",
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"2023-01-15 11:45:00+01:00",
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"2023-01-15 12:00:00+01:00",
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"2023-01-15 12:15:00+01:00",
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"2023-01-15 12:30:00+01:00",
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"2023-01-15 12:45:00+01:00",
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"2023-01-15 13:00:00+01:00",
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"2023-01-15 13:15:00+01:00",
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"2023-01-15 13:30:00+01:00",
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"2023-01-15 13:45:00+01:00",
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"2023-01-15 14:00:00+01:00",
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"2023-01-15 14:15:00+01:00",
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"2023-01-15 14:30:00+01:00",
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"2023-01-15 14:45:00+01:00",
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"2023-01-15 15:00:00+01:00",
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"2023-01-15 15:15:00+01:00",
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"2023-01-15 15:30:00+01:00",
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"2023-01-15 15:45:00+01:00",
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"2023-01-15 16:00:00+01:00",
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"2023-01-15 16:15:00+01:00",
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"2023-01-15 16:30:00+01:00",
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"2023-01-15 16:45:00+01:00",
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"2023-01-15 17:00:00+01:00",
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"2023-01-15 17:15:00+01:00",
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"2023-01-15 17:30:00+01:00",
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"2023-01-15 17:45:00+01:00",
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"2023-01-15 18:00:00+01:00",
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"2023-01-15 18:15:00+01:00",
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"2023-01-15 18:30:00+01:00",
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"2023-01-15 18:45:00+01:00",
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"2023-01-15 19:00:00+01:00",
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"2023-01-15 19:15:00+01:00",
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"2023-01-15 19:30:00+01:00",
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"2023-01-15 19:45:00+01:00",
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"2023-01-15 20:00:00+01:00",
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"2023-01-15 20:15:00+01:00",
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"2023-01-15 20:30:00+01:00",
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"2023-01-15 20:45:00+01:00",
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"2023-01-15 21:00:00+01:00",
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"2023-01-15 21:15:00+01:00",
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"2023-01-15 21:30:00+01:00",
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"2023-01-15 21:45:00+01:00",
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"2023-01-15 22:00:00+01:00",
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"2023-01-15 22:15:00+01:00",
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"2023-01-15 22:30:00+01:00",
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"2023-01-15 22:45:00+01:00",
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"2023-01-15 23:00:00+01:00",
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"2023-01-15 23:15:00+01:00",
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"2023-01-15 23:30:00+01:00",
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"2023-01-15 23:45:00+01:00",
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"2023-01-16 00:00:00+01:00",
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"2023-01-16 00:15:00+01:00",
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"2023-01-16 00:30:00+01:00",
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"2023-01-16 00:45:00+01:00",
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"2023-01-16 01:00:00+01:00",
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"2023-01-16 01:15:00+01:00",
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"2023-01-16 01:30:00+01:00",
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"2023-01-16 01:45:00+01:00",
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"2023-01-16 02:00:00+01:00",
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"2023-01-16 02:15:00+01:00",
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"2023-01-16 02:30:00+01:00",
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"2023-01-16 02:45:00+01:00",
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"2023-01-16 03:00:00+01:00",
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"2023-01-16 03:15:00+01:00",
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"2023-01-16 03:30:00+01:00",
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"2023-01-16 03:45:00+01:00",
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"2023-01-16 04:00:00+01:00",
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"2023-01-16 04:15:00+01:00",
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"2023-01-16 04:30:00+01:00",
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"2023-01-16 04:45:00+01:00",
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"2023-01-16 05:00:00+01:00",
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"2023-01-16 05:15:00+01:00",
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"2023-01-16 05:30:00+01:00",
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"2023-01-16 05:45:00+01:00",
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"2023-01-16 06:00:00+01:00",
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"2023-01-16 06:15:00+01:00",
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"2023-01-16 06:30:00+01:00",
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"2023-01-16 06:45:00+01:00",
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"2023-01-16 07:00:00+01:00",
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"2023-01-16 07:15:00+01:00",
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"2023-01-16 07:30:00+01:00",
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"2023-01-16 07:45:00+01:00",
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"2023-01-16 08:00:00+01:00",
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"2023-01-16 08:15:00+01:00",
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"2023-01-16 08:30:00+01:00",
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"2023-01-16 08:45:00+01:00",
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"2023-01-16 09:00:00+01:00",
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"2023-01-16 09:15:00+01:00",
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"2023-01-16 09:30:00+01:00",
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"2023-01-16 09:45:00+01:00",
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"2023-01-16 10:00:00+01:00",
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"2023-01-16 10:15:00+01:00",
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"2023-01-16 10:30:00+01:00",
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"2023-01-16 10:45:00+01:00",
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"2023-01-16 11:00:00+01:00",
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"2023-01-16 11:15:00+01:00",
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"2023-01-16 11:30:00+01:00",
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"2023-01-16 11:45:00+01:00",
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"2023-01-16 12:00:00+01:00",
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"2023-01-16 12:15:00+01:00",
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"2023-01-16 12:30:00+01:00",
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"2023-01-16 12:45:00+01:00",
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"2023-01-16 13:00:00+01:00",
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"2023-01-16 13:15:00+01:00",
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"2023-01-16 13:30:00+01:00",
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"2023-01-16 13:45:00+01:00",
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"2023-01-16 14:00:00+01:00",
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"2023-01-16 14:15:00+01:00",
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"2023-01-16 14:30:00+01:00",
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"2023-01-16 14:45:00+01:00",
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"2023-01-16 15:00:00+01:00",
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"2023-01-16 15:15:00+01:00",
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"2023-01-16 15:30:00+01:00",
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"2023-01-16 15:45:00+01:00",
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"2023-01-16 16:00:00+01:00",
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"2023-01-16 16:15:00+01:00",
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"2023-01-16 16:30:00+01:00",
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"2023-01-16 16:45:00+01:00",
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"2023-01-16 17:00:00+01:00",
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"2023-01-16 17:15:00+01:00",
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"2023-01-16 17:30:00+01:00",
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"2023-01-16 17:45:00+01:00",
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"2023-01-16 18:00:00+01:00",
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"2023-01-16 18:15:00+01:00",
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"2023-01-16 18:30:00+01:00",
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"2023-01-16 18:45:00+01:00",
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"2023-01-16 19:00:00+01:00",
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"2023-01-16 19:15:00+01:00",
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"2023-01-16 19:30:00+01:00",
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"2023-01-16 19:45:00+01:00",
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"2023-01-16 20:00:00+01:00",
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"2023-01-16 20:15:00+01:00",
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"2023-01-16 20:30:00+01:00",
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"2023-01-16 20:45:00+01:00",
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"2023-01-16 21:00:00+01:00",
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"2023-01-16 21:15:00+01:00",
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"2023-01-16 21:30:00+01:00",
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"2023-01-16 21:45:00+01:00",
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"2023-01-16 22:00:00+01:00",
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"2023-01-16 22:15:00+01:00",
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"2023-01-16 22:30:00+01:00",
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"2023-01-16 22:45:00+01:00",
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"2023-01-16 23:00:00+01:00",
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"2023-01-16 23:15:00+01:00",
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"2023-01-16 23:30:00+01:00",
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"2023-01-16 23:45:00+01:00",
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"2023-01-17 00:00:00+01:00",
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"2023-01-17 00:15:00+01:00",
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"2023-01-17 00:30:00+01:00",
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"2023-01-17 00:45:00+01:00",
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"2023-01-17 01:00:00+01:00",
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"2023-01-17 01:15:00+01:00",
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"2023-01-17 01:30:00+01:00",
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"2023-01-17 01:45:00+01:00",
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"2023-01-17 02:00:00+01:00",
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"2023-01-17 02:15:00+01:00",
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"2023-01-17 02:30:00+01:00",
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"2023-01-17 02:45:00+01:00",
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"2023-01-17 03:00:00+01:00",
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"2023-01-17 03:15:00+01:00",
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"2023-01-17 03:30:00+01:00",
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"2023-01-17 03:45:00+01:00",
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"2023-01-17 04:00:00+01:00",
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"2023-01-17 04:15:00+01:00",
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"2023-01-17 04:30:00+01:00",
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"2023-01-17 04:45:00+01:00",
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"2023-01-17 05:00:00+01:00",
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"2023-01-17 05:15:00+01:00",
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"2023-01-17 05:30:00+01:00",
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"2023-01-17 05:45:00+01:00",
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"2023-01-17 06:00:00+01:00",
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"2023-01-17 06:15:00+01:00",
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"2023-01-17 06:30:00+01:00",
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"2023-01-17 06:45:00+01:00",
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"2023-01-17 07:00:00+01:00",
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"2023-01-17 07:15:00+01:00",
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"2023-01-17 07:30:00+01:00",
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"2023-01-17 07:45:00+01:00",
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"2023-01-17 08:00:00+01:00",
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"2023-01-17 08:15:00+01:00",
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"2023-01-17 08:30:00+01:00",
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"2023-01-17 08:45:00+01:00",
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"2023-01-17 09:00:00+01:00",
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"2023-01-17 09:15:00+01:00",
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"2023-01-17 09:30:00+01:00",
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"2023-01-17 09:45:00+01:00",
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"2023-01-17 10:00:00+01:00",
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"2023-01-17 10:15:00+01:00",
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"2023-01-17 10:30:00+01:00",
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"2023-01-17 10:45:00+01:00",
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"2023-01-17 11:00:00+01:00",
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"2023-01-17 11:15:00+01:00",
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"2023-01-17 11:30:00+01:00",
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"2023-01-17 11:45:00+01:00",
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"2023-01-17 12:00:00+01:00",
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"2023-01-17 12:15:00+01:00",
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"2023-01-17 12:30:00+01:00",
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"2023-01-17 12:45:00+01:00",
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"2023-01-17 13:00:00+01:00",
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"2023-01-17 13:15:00+01:00",
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"2023-01-17 13:30:00+01:00",
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"2023-01-17 13:45:00+01:00",
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"2023-01-17 14:00:00+01:00",
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"2023-01-17 14:15:00+01:00",
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"2023-01-17 14:30:00+01:00",
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"2023-01-17 14:45:00+01:00",
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"2023-01-17 15:00:00+01:00",
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"2023-01-17 15:15:00+01:00",
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"2023-01-17 15:30:00+01:00",
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"2023-01-17 15:45:00+01:00",
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"2023-01-17 16:00:00+01:00",
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"2023-01-17 16:15:00+01:00",
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"2023-01-17 16:30:00+01:00",
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"2023-01-17 16:45:00+01:00",
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"2023-01-17 17:00:00+01:00",
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"2023-01-17 17:15:00+01:00",
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"2023-01-17 17:30:00+01:00",
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"2023-01-17 17:45:00+01:00",
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"2023-01-17 18:00:00+01:00",
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"2023-01-17 18:15:00+01:00",
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"2023-01-17 18:30:00+01:00",
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"2023-01-17 18:45:00+01:00",
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"2023-01-17 19:00:00+01:00",
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"2023-01-17 19:15:00+01:00",
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"2023-01-17 19:30:00+01:00",
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"2023-01-17 19:45:00+01:00",
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"2023-01-17 20:15:00+01:00",
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"2023-01-17 20:30:00+01:00",
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"2023-01-17 20:45:00+01:00",
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"2023-01-17 21:15:00+01:00",
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"2023-01-17 21:30:00+01:00",
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"2023-01-17 21:45:00+01:00",
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"2023-01-17 22:15:00+01:00",
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"2023-01-17 22:45:00+01:00",
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"2023-01-17 23:15:00+01:00",
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"2023-01-17 23:30:00+01:00",
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"2023-01-17 23:45:00+01:00",
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"2023-01-18 00:00:00+01:00",
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"2023-01-18 00:15:00+01:00",
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"2023-01-18 01:00:00+01:00",
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"2023-01-18 01:15:00+01:00",
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"2023-01-18 01:30:00+01:00",
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"2023-01-18 01:45:00+01:00",
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"2023-01-18 02:00:00+01:00",
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"2023-01-18 02:15:00+01:00",
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"2023-01-18 02:30:00+01:00",
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"2023-01-18 02:45:00+01:00",
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"2023-01-18 03:15:00+01:00",
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"2023-01-18 03:45:00+01:00",
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"2023-01-18 04:15:00+01:00",
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"2023-01-18 04:30:00+01:00",
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"2023-01-18 04:45:00+01:00",
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"2023-01-18 05:15:00+01:00",
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"2023-01-18 05:30:00+01:00",
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"2023-01-18 05:45:00+01:00",
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"2023-01-18 06:00:00+01:00",
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"2023-01-18 06:15:00+01:00",
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"2023-01-18 06:30:00+01:00",
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"2023-01-18 07:15:00+01:00",
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"2023-01-18 07:30:00+01:00",
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"2023-01-18 07:45:00+01:00",
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"2023-01-18 08:15:00+01:00",
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"2023-01-18 08:30:00+01:00",
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"2023-01-18 08:45:00+01:00",
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"2023-01-18 09:00:00+01:00",
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"2023-01-18 09:15:00+01:00",
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"2023-01-18 09:30:00+01:00",
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"2023-01-18 09:45:00+01:00",
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"2023-01-18 10:15:00+01:00",
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"2023-01-18 10:30:00+01:00",
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"2023-01-18 10:45:00+01:00",
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"2023-01-18 11:00:00+01:00",
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"2023-01-18 11:15:00+01:00",
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"2023-01-18 11:30:00+01:00",
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"2023-01-18 11:45:00+01:00",
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"2023-01-18 12:00:00+01:00",
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"2023-01-18 12:15:00+01:00",
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"2023-01-18 12:30:00+01:00",
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"2023-01-18 12:45:00+01:00",
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"2023-01-18 13:00:00+01:00",
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"2023-01-18 13:15:00+01:00",
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"2023-01-18 13:30:00+01:00",
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"2023-01-18 13:45:00+01:00",
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"2023-01-18 14:00:00+01:00",
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"2023-01-18 14:15:00+01:00",
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"2023-01-18 14:30:00+01:00",
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"2023-01-18 14:45:00+01:00",
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"2023-01-18 15:30:00+01:00",
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"2023-01-18 15:45:00+01:00",
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"2023-01-18 16:00:00+01:00",
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"2023-01-18 16:15:00+01:00",
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"2023-01-18 16:30:00+01:00",
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"2023-01-18 16:45:00+01:00",
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"2023-01-18 17:15:00+01:00",
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"2023-01-18 17:30:00+01:00",
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"2023-01-18 17:45:00+01:00",
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"2023-01-18 18:00:00+01:00",
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"2023-01-18 18:15:00+01:00",
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"2023-01-18 18:30:00+01:00",
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"2023-01-18 18:45:00+01:00",
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"2023-01-18 19:00:00+01:00",
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"2023-01-18 19:15:00+01:00",
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"2023-01-18 19:30:00+01:00",
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"2023-01-18 19:45:00+01:00",
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"2023-01-18 20:00:00+01:00",
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"2023-01-18 20:15:00+01:00",
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"2023-01-18 20:30:00+01:00",
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"2023-01-18 20:45:00+01:00",
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"2023-01-18 21:00:00+01:00",
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"2023-01-18 21:15:00+01:00",
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"2023-01-18 21:30:00+01:00",
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"2023-01-18 21:45:00+01:00",
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"2023-01-18 22:00:00+01:00",
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"2023-01-18 22:15:00+01:00",
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"2023-01-18 22:30:00+01:00",
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"2023-01-18 22:45:00+01:00",
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"2023-01-18 23:00:00+01:00",
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"2023-01-18 23:15:00+01:00",
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"2023-01-18 23:30:00+01:00",
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"2023-01-18 23:45:00+01:00",
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"2023-01-19 00:00:00+01:00",
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"2023-01-19 00:15:00+01:00",
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"2023-01-19 00:30:00+01:00",
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"2023-01-19 00:45:00+01:00",
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"2023-01-19 01:00:00+01:00",
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"2023-01-19 01:15:00+01:00",
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"2023-01-19 01:30:00+01:00",
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"2023-01-19 01:45:00+01:00",
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"2023-01-19 02:00:00+01:00",
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"2023-01-19 02:15:00+01:00",
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"2023-01-19 02:30:00+01:00",
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"2023-01-19 02:45:00+01:00",
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"2023-01-19 03:00:00+01:00",
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"2023-01-19 03:15:00+01:00",
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"2023-01-19 03:30:00+01:00",
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"2023-01-19 03:45:00+01:00",
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"2023-01-19 04:00:00+01:00",
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"2023-01-19 04:15:00+01:00",
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"2023-01-19 04:30:00+01:00",
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"2023-01-19 04:45:00+01:00",
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"2023-01-19 05:00:00+01:00",
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"2023-01-19 05:15:00+01:00",
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"2023-01-19 05:30:00+01:00",
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"2023-01-19 05:45:00+01:00",
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"2023-01-19 06:00:00+01:00",
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"2023-01-19 06:15:00+01:00",
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"2023-01-19 06:30:00+01:00",
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"2023-01-19 06:45:00+01:00",
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"2023-01-19 07:00:00+01:00",
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"2023-01-19 07:15:00+01:00",
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"2023-01-19 07:30:00+01:00",
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"2023-01-19 07:45:00+01:00",
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"2023-01-19 08:00:00+01:00",
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"2023-01-19 08:15:00+01:00",
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"2023-01-19 08:30:00+01:00",
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"2023-01-19 08:45:00+01:00",
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"2023-01-19 09:00:00+01:00",
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"2023-01-19 09:15:00+01:00",
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"2023-01-19 09:30:00+01:00",
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"2023-01-19 09:45:00+01:00",
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"2023-01-19 10:00:00+01:00",
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"2023-01-19 10:15:00+01:00",
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"2023-01-19 10:30:00+01:00",
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"2023-01-19 10:45:00+01:00",
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"2023-01-19 11:00:00+01:00",
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"2023-01-19 11:15:00+01:00",
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"2023-01-19 11:30:00+01:00",
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"2023-01-19 11:45:00+01:00",
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"2023-01-19 12:00:00+01:00",
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"2023-01-19 12:15:00+01:00",
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"2023-01-19 12:30:00+01:00",
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"2023-01-19 12:45:00+01:00",
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"2023-01-19 13:00:00+01:00",
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"2023-01-19 13:15:00+01:00",
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"2023-01-19 13:30:00+01:00",
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"2023-01-19 13:45:00+01:00",
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"2023-01-19 14:00:00+01:00",
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"2023-01-19 14:15:00+01:00",
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"2023-01-19 14:30:00+01:00",
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"2023-01-19 14:45:00+01:00",
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"2023-01-19 15:00:00+01:00",
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"2023-01-19 15:15:00+01:00",
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"2023-01-19 15:30:00+01:00",
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"2023-01-19 15:45:00+01:00",
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"2023-01-19 16:00:00+01:00",
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"2023-01-19 16:15:00+01:00",
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"2023-01-19 16:30:00+01:00",
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"2023-01-19 16:45:00+01:00",
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"2023-01-19 17:00:00+01:00",
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"2023-01-19 17:15:00+01:00",
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"2023-01-19 17:30:00+01:00",
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"2023-01-19 17:45:00+01:00",
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"2023-01-19 18:00:00+01:00",
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"2023-01-19 18:15:00+01:00",
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"2023-01-19 18:30:00+01:00",
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"2023-01-19 18:45:00+01:00",
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"2023-01-19 19:00:00+01:00",
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"2023-01-19 19:15:00+01:00",
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"2023-01-19 19:30:00+01:00",
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"2023-01-19 19:45:00+01:00",
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"2023-01-19 20:00:00+01:00",
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"2023-01-19 20:15:00+01:00",
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"2023-01-19 20:30:00+01:00",
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"2023-01-19 20:45:00+01:00",
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"2023-01-19 21:00:00+01:00",
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"2023-01-19 21:15:00+01:00",
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"2023-01-19 21:30:00+01:00",
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"2023-01-19 21:45:00+01:00",
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"2023-01-19 22:00:00+01:00",
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"2023-01-19 22:15:00+01:00",
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"2023-01-19 22:30:00+01:00",
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"2023-01-19 22:45:00+01:00",
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"2023-01-19 23:00:00+01:00",
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"2023-01-19 23:15:00+01:00",
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"2023-01-19 23:30:00+01:00",
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"2023-01-19 23:45:00+01:00",
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"2023-01-20 00:00:00+01:00",
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"2023-01-20 00:15:00+01:00",
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"2023-01-20 00:30:00+01:00",
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"2023-01-20 00:45:00+01:00",
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"2023-01-20 01:00:00+01:00",
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"2023-01-20 01:15:00+01:00",
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"2023-01-20 01:30:00+01:00",
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"2023-01-20 01:45:00+01:00",
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"2023-01-20 02:00:00+01:00",
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"2023-01-20 02:15:00+01:00",
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"2023-01-20 02:30:00+01:00",
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"2023-01-20 02:45:00+01:00",
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"2023-01-20 03:00:00+01:00",
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"2023-01-20 03:15:00+01:00",
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"2023-01-20 03:30:00+01:00",
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"2023-01-20 03:45:00+01:00",
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"2023-01-20 04:00:00+01:00",
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"2023-01-20 04:15:00+01:00",
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"2023-01-20 04:30:00+01:00",
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"2023-01-20 04:45:00+01:00",
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"2023-01-20 05:00:00+01:00",
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"2023-01-20 05:15:00+01:00",
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"2023-01-20 05:30:00+01:00",
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"2023-01-20 05:45:00+01:00",
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"2023-01-20 06:00:00+01:00",
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"2023-01-20 06:15:00+01:00",
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"2023-01-20 06:30:00+01:00",
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"2023-01-20 06:45:00+01:00",
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"2023-01-20 07:00:00+01:00",
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"2023-01-20 07:15:00+01:00",
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"2023-01-20 07:30:00+01:00",
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"2023-01-20 07:45:00+01:00",
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"2023-01-20 08:00:00+01:00",
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"2023-01-20 08:15:00+01:00",
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"2023-01-20 08:30:00+01:00",
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"2023-01-20 08:45:00+01:00",
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"2023-01-20 09:00:00+01:00",
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"2023-01-20 09:15:00+01:00",
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"2023-01-20 09:30:00+01:00",
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"2023-01-20 09:45:00+01:00",
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"2023-01-20 10:00:00+01:00",
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"2023-01-20 10:15:00+01:00",
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"2023-01-20 10:30:00+01:00",
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"2023-01-20 10:45:00+01:00",
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"2023-01-20 11:00:00+01:00",
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"2023-01-20 11:15:00+01:00",
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"2023-01-20 11:30:00+01:00",
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"2023-01-20 11:45:00+01:00",
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"2023-01-20 12:00:00+01:00",
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"2023-01-20 12:15:00+01:00",
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"2023-01-20 12:30:00+01:00",
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"2023-01-20 12:45:00+01:00",
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"2023-01-20 13:00:00+01:00",
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"2023-01-20 13:15:00+01:00",
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"2023-01-20 13:30:00+01:00",
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"2023-01-20 13:45:00+01:00",
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"2023-01-20 14:00:00+01:00",
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"2023-01-20 14:15:00+01:00",
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"2023-01-20 14:30:00+01:00",
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"2023-01-20 14:45:00+01:00",
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"2023-01-20 15:00:00+01:00",
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"2023-01-20 15:15:00+01:00",
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"2023-01-20 15:30:00+01:00",
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"2023-01-20 15:45:00+01:00",
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"2023-01-20 16:00:00+01:00",
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"2023-01-20 16:15:00+01:00",
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"2023-01-20 16:30:00+01:00",
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"2023-01-20 16:45:00+01:00",
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"2023-01-20 17:00:00+01:00",
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"2023-01-20 17:15:00+01:00",
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"2023-01-20 17:30:00+01:00",
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"2023-01-20 17:45:00+01:00",
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"2023-01-20 18:00:00+01:00",
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"2023-01-20 18:15:00+01:00",
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"2023-01-20 18:30:00+01:00",
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"2023-01-20 18:45:00+01:00",
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"2023-01-20 19:00:00+01:00",
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"2023-01-20 19:15:00+01:00",
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"2023-01-20 19:30:00+01:00",
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"2023-01-20 19:45:00+01:00",
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"2023-01-20 20:00:00+01:00",
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"2023-01-20 20:15:00+01:00",
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"2023-01-20 20:30:00+01:00",
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"2023-01-20 20:45:00+01:00",
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"2023-01-20 21:00:00+01:00",
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"2023-01-20 21:15:00+01:00",
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"2023-01-20 21:30:00+01:00",
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"2023-01-20 21:45:00+01:00",
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"2023-01-20 22:00:00+01:00",
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"2023-01-20 22:15:00+01:00",
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"2023-01-20 22:30:00+01:00",
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"2023-01-20 22:45:00+01:00",
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"2023-01-20 23:00:00+01:00",
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"2023-01-20 23:15:00+01:00",
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"2023-01-20 23:30:00+01:00",
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"2023-01-20 23:45:00+01:00",
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"2023-01-21 00:00:00+01:00",
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"2023-01-21 00:15:00+01:00",
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"2023-01-21 00:30:00+01:00",
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"2023-01-21 00:45:00+01:00",
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"2023-01-21 01:00:00+01:00",
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"2023-01-21 01:15:00+01:00",
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"2023-01-21 01:30:00+01:00",
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"2023-01-21 01:45:00+01:00",
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"2023-01-21 02:00:00+01:00",
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"2023-01-21 02:15:00+01:00",
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"2023-01-21 02:45:00+01:00",
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"2023-01-21 03:15:00+01:00",
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"2023-01-21 05:15:00+01:00",
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"2023-01-21 06:15:00+01:00",
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"2023-01-21 07:15:00+01:00",
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"2023-01-21 11:15:00+01:00",
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"2023-01-21 11:30:00+01:00",
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"2023-01-21 12:15:00+01:00",
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"2023-01-21 19:15:00+01:00",
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"2023-01-21 19:45:00+01:00",
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"2023-01-21 20:45:00+01:00",
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"2023-01-21 21:15:00+01:00",
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"2023-01-22 02:15:00+01:00",
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"2023-01-22 02:45:00+01:00",
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"2023-01-22 03:15:00+01:00",
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"2023-01-22 03:45:00+01:00",
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"2023-01-22 04:15:00+01:00",
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"2023-01-22 05:15:00+01:00",
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"2023-01-22 05:45:00+01:00",
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"2023-01-22 06:15:00+01:00",
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"2023-01-22 06:30:00+01:00",
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"2023-01-22 06:45:00+01:00",
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"2023-01-22 07:15:00+01:00",
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"2023-01-22 07:30:00+01:00",
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"2023-01-22 07:45:00+01:00",
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"2023-01-22 08:15:00+01:00",
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"2023-01-22 08:30:00+01:00",
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"2023-01-22 08:45:00+01:00",
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"2023-01-22 09:15:00+01:00",
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"2023-01-22 09:45:00+01:00",
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"2023-01-22 10:15:00+01:00",
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"2023-01-22 10:30:00+01:00",
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"2023-01-22 10:45:00+01:00",
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"2023-01-22 11:15:00+01:00",
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"2023-01-22 11:30:00+01:00",
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"2023-01-22 11:45:00+01:00",
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"2023-01-22 12:00:00+01:00",
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"2023-01-22 12:15:00+01:00",
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"2023-01-22 12:30:00+01:00",
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"2023-01-22 12:45:00+01:00",
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"2023-01-22 13:15:00+01:00",
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"2023-01-22 13:30:00+01:00",
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"2023-01-22 13:45:00+01:00",
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"2023-01-22 14:15:00+01:00",
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"2023-01-22 14:30:00+01:00",
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"2023-01-22 14:45:00+01:00",
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"2023-01-22 15:00:00+01:00",
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"2023-01-22 15:15:00+01:00",
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"2023-01-22 15:30:00+01:00",
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"2023-01-22 15:45:00+01:00",
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"2023-01-22 16:15:00+01:00",
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"2023-01-22 16:30:00+01:00",
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"2023-01-22 16:45:00+01:00",
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"2023-01-22 17:15:00+01:00",
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"2023-01-22 17:30:00+01:00",
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"2023-01-22 17:45:00+01:00",
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"2023-01-22 18:00:00+01:00",
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"2023-01-22 18:15:00+01:00",
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"2023-01-22 18:30:00+01:00",
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"2023-01-22 18:45:00+01:00",
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"2023-01-22 19:00:00+01:00",
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"2023-01-22 19:15:00+01:00",
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"2023-01-22 19:30:00+01:00",
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"2023-01-22 19:45:00+01:00",
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"2023-01-22 20:00:00+01:00",
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"2023-01-22 20:15:00+01:00",
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"2023-01-22 20:30:00+01:00",
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"2023-01-22 20:45:00+01:00",
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"2023-01-22 21:00:00+01:00",
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"2023-01-22 21:15:00+01:00",
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"2023-01-22 21:30:00+01:00",
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"2023-01-22 21:45:00+01:00",
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"2023-01-22 22:00:00+01:00",
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"2023-01-22 22:15:00+01:00",
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"2023-01-22 22:30:00+01:00",
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"2023-01-22 22:45:00+01:00",
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"2023-01-22 23:00:00+01:00",
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"2023-01-22 23:15:00+01:00",
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"2023-01-22 23:30:00+01:00",
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"2023-01-22 23:45:00+01:00",
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"2023-01-23 00:00:00+01:00",
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"2023-01-23 00:15:00+01:00",
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"2023-01-23 00:30:00+01:00",
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"2023-01-23 00:45:00+01:00",
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"2023-01-23 01:00:00+01:00",
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"2023-01-23 01:15:00+01:00",
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"2023-01-23 01:30:00+01:00",
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"2023-01-23 01:45:00+01:00",
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"2023-01-23 02:00:00+01:00",
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"2023-01-23 02:15:00+01:00",
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"2023-01-23 02:30:00+01:00",
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"2023-01-23 02:45:00+01:00",
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"2023-01-23 03:00:00+01:00",
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"2023-01-23 03:15:00+01:00",
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"2023-01-23 03:30:00+01:00",
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"2023-01-23 03:45:00+01:00",
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"2023-01-23 04:00:00+01:00",
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"2023-01-23 04:15:00+01:00",
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"2023-01-23 04:30:00+01:00",
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"2023-01-23 04:45:00+01:00",
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"2023-01-23 05:00:00+01:00",
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"2023-01-23 05:15:00+01:00",
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"2023-01-23 05:30:00+01:00",
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"2023-01-23 05:45:00+01:00",
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"2023-01-23 06:00:00+01:00",
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"2023-01-23 06:15:00+01:00",
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"2023-01-23 06:30:00+01:00",
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"2023-01-23 06:45:00+01:00",
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"2023-01-23 07:00:00+01:00",
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"2023-01-23 07:15:00+01:00",
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"2023-01-23 07:30:00+01:00",
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"2023-01-23 07:45:00+01:00",
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"2023-01-23 08:00:00+01:00",
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"2023-01-23 08:15:00+01:00",
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"2023-01-23 08:30:00+01:00",
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"2023-01-23 08:45:00+01:00",
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"2023-01-23 09:00:00+01:00",
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"2023-01-23 09:15:00+01:00",
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"2023-01-23 09:30:00+01:00",
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"2023-01-23 09:45:00+01:00",
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"2023-01-23 10:00:00+01:00",
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"2023-01-23 10:15:00+01:00",
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"2023-01-23 10:30:00+01:00",
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"2023-01-23 10:45:00+01:00",
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"2023-01-23 11:00:00+01:00",
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"2023-01-23 11:15:00+01:00",
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"2023-01-23 11:30:00+01:00",
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"2023-01-23 11:45:00+01:00",
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"2023-01-23 12:00:00+01:00",
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"2023-01-23 12:15:00+01:00",
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"2023-01-23 12:30:00+01:00",
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"2023-01-23 12:45:00+01:00",
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"2023-01-23 13:00:00+01:00",
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"2023-01-23 13:15:00+01:00",
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"2023-01-23 13:30:00+01:00",
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"2023-01-23 13:45:00+01:00",
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"2023-01-23 14:00:00+01:00",
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"2023-01-23 14:15:00+01:00",
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"2023-01-23 14:30:00+01:00",
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"2023-01-23 14:45:00+01:00",
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"2023-01-23 15:00:00+01:00",
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"2023-01-23 15:15:00+01:00",
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"2023-01-23 15:30:00+01:00",
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"2023-01-23 15:45:00+01:00",
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"2023-01-23 16:00:00+01:00",
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"2023-01-23 16:15:00+01:00",
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"2023-01-23 16:30:00+01:00",
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"2023-01-23 16:45:00+01:00",
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"2023-01-23 17:00:00+01:00",
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"2023-01-23 17:15:00+01:00",
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"2023-01-23 17:30:00+01:00",
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"2023-01-23 17:45:00+01:00",
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"2023-01-23 18:00:00+01:00",
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"2023-01-23 18:15:00+01:00",
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"2023-01-23 18:30:00+01:00",
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"2023-01-23 18:45:00+01:00",
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"2023-01-23 19:00:00+01:00",
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"2023-01-23 19:15:00+01:00",
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"2023-01-23 19:30:00+01:00",
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"2023-01-23 19:45:00+01:00",
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"2023-01-23 20:00:00+01:00",
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"2023-01-23 20:15:00+01:00",
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"2023-01-23 20:30:00+01:00",
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"2023-01-23 20:45:00+01:00",
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"2023-01-23 21:00:00+01:00",
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"2023-01-23 21:15:00+01:00",
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"2023-01-23 21:30:00+01:00",
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"2023-01-23 21:45:00+01:00",
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"2023-01-23 22:00:00+01:00",
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"2023-01-23 22:15:00+01:00",
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"2023-01-23 22:30:00+01:00",
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"2023-01-23 22:45:00+01:00",
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"2023-01-23 23:00:00+01:00",
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"2023-01-23 23:15:00+01:00",
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"2023-01-23 23:30:00+01:00",
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"2023-01-23 23:45:00+01:00",
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"2023-01-24 00:00:00+01:00",
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"2023-01-24 00:15:00+01:00",
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"2023-01-24 00:30:00+01:00",
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"2023-01-24 00:45:00+01:00",
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"2023-01-24 01:00:00+01:00",
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"2023-01-24 01:15:00+01:00",
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"2023-01-24 01:30:00+01:00",
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"2023-01-24 01:45:00+01:00",
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"2023-01-24 02:00:00+01:00",
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"2023-01-24 02:15:00+01:00",
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"2023-01-24 02:30:00+01:00",
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"2023-01-24 02:45:00+01:00",
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"2023-01-24 03:00:00+01:00",
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"2023-01-24 03:15:00+01:00",
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"2023-01-24 03:30:00+01:00",
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"2023-01-24 03:45:00+01:00",
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"2023-01-24 04:00:00+01:00",
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"2023-01-24 04:15:00+01:00",
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"2023-01-24 04:30:00+01:00",
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"2023-01-24 04:45:00+01:00",
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"2023-01-24 05:00:00+01:00",
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"2023-01-24 05:15:00+01:00",
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"2023-01-24 05:30:00+01:00",
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"2023-01-24 05:45:00+01:00",
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"2023-01-24 06:00:00+01:00",
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"2023-01-24 06:15:00+01:00",
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"2023-01-24 06:30:00+01:00",
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"2023-01-24 06:45:00+01:00",
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"2023-01-24 07:00:00+01:00",
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"2023-01-24 07:15:00+01:00",
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"2023-01-24 07:30:00+01:00",
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"2023-01-24 07:45:00+01:00",
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"2023-01-24 08:00:00+01:00",
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"2023-01-24 08:15:00+01:00",
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"2023-01-24 08:30:00+01:00",
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"2023-01-24 08:45:00+01:00",
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"2023-01-24 09:00:00+01:00",
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"2023-01-24 09:15:00+01:00",
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"2023-01-24 09:30:00+01:00",
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"2023-01-24 09:45:00+01:00",
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"2023-01-24 10:00:00+01:00",
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"2023-01-24 10:15:00+01:00",
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"2023-01-24 10:30:00+01:00",
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"2023-01-24 10:45:00+01:00",
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"2023-01-24 11:00:00+01:00",
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"2023-01-24 11:15:00+01:00",
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"2023-01-26 01:15:00+01:00",
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"2023-01-26 01:30:00+01:00",
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"2023-01-26 02:15:00+01:00",
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"2023-01-26 02:30:00+01:00",
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"2023-01-26 03:00:00+01:00",
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"2023-01-26 03:15:00+01:00",
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"2023-01-26 03:45:00+01:00",
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"2023-01-26 04:15:00+01:00",
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"2023-01-26 04:30:00+01:00",
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"2023-01-26 04:45:00+01:00",
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"2023-01-26 05:15:00+01:00",
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"2023-01-26 05:30:00+01:00",
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"2023-01-26 05:45:00+01:00",
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"2023-01-26 07:15:00+01:00",
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"2023-01-26 07:30:00+01:00",
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"2023-01-26 08:15:00+01:00",
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"2023-01-26 09:15:00+01:00",
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"2023-01-26 09:30:00+01:00",
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"2023-01-26 09:45:00+01:00",
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"2023-01-26 10:15:00+01:00",
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"2023-01-26 10:30:00+01:00",
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"2023-01-26 10:45:00+01:00",
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"2023-01-26 11:15:00+01:00",
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"2023-01-26 11:30:00+01:00",
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"2023-01-26 11:45:00+01:00",
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"2023-01-26 12:15:00+01:00",
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"2023-01-26 12:30:00+01:00",
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"2023-01-26 13:15:00+01:00",
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"2023-01-26 20:15:00+01:00",
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"2023-01-26 23:30:00+01:00",
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"2023-01-27 00:00:00+01:00",
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"2023-01-27 00:15:00+01:00",
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"2023-01-27 01:30:00+01:00",
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"2023-01-27 01:45:00+01:00",
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"2023-01-27 02:15:00+01:00",
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"2023-01-27 02:45:00+01:00",
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"2023-01-27 03:15:00+01:00",
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"2023-01-27 21:15:00+01:00",
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"2023-01-27 21:30:00+01:00",
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"2023-01-27 21:45:00+01:00",
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"2023-01-27 22:15:00+01:00",
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"2023-01-27 22:45:00+01:00",
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"2023-01-27 23:15:00+01:00",
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"2023-01-27 23:30:00+01:00",
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"2023-01-27 23:45:00+01:00",
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"2023-01-28 00:00:00+01:00",
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"2023-01-28 00:15:00+01:00",
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"2023-01-28 00:30:00+01:00",
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"2023-01-28 00:45:00+01:00",
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"2023-01-28 01:00:00+01:00",
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"2023-01-28 01:15:00+01:00",
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"2023-01-28 01:30:00+01:00",
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"2023-01-28 01:45:00+01:00",
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"2023-01-28 02:00:00+01:00",
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"2023-01-28 02:15:00+01:00",
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"2023-01-28 02:30:00+01:00",
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"2023-01-28 02:45:00+01:00",
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"2023-01-28 03:00:00+01:00",
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"2023-01-28 03:15:00+01:00",
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"2023-01-28 03:30:00+01:00",
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"2023-01-28 03:45:00+01:00",
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"2023-01-28 04:00:00+01:00",
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"2023-01-28 04:15:00+01:00",
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"2023-01-28 04:30:00+01:00",
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"2023-01-28 04:45:00+01:00",
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"2023-01-28 05:15:00+01:00",
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"2023-01-28 05:30:00+01:00",
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"2023-01-28 05:45:00+01:00",
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"2023-01-28 06:15:00+01:00",
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"2023-01-28 06:30:00+01:00",
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"2023-01-28 06:45:00+01:00",
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"2023-01-28 07:15:00+01:00",
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"2023-01-28 07:30:00+01:00",
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"2023-01-28 07:45:00+01:00",
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"2023-01-28 08:15:00+01:00",
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"2023-01-28 08:30:00+01:00",
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"2023-01-28 08:45:00+01:00",
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"2023-01-28 09:00:00+01:00",
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"2023-01-28 09:15:00+01:00",
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"2023-01-28 09:30:00+01:00",
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"2023-01-28 09:45:00+01:00",
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"2023-01-28 10:00:00+01:00",
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"2023-01-28 10:15:00+01:00",
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"2023-01-28 10:30:00+01:00",
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"2023-01-28 10:45:00+01:00",
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"2023-01-28 11:00:00+01:00",
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"2023-01-28 11:15:00+01:00",
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"2023-01-28 11:30:00+01:00",
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"2023-01-28 11:45:00+01:00",
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"2023-01-28 12:00:00+01:00",
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"2023-01-28 12:15:00+01:00",
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"2023-01-28 12:30:00+01:00",
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"2023-01-28 12:45:00+01:00",
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"2023-01-28 13:00:00+01:00",
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"2023-01-28 13:15:00+01:00",
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"2023-01-28 13:30:00+01:00",
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"2023-01-28 13:45:00+01:00",
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"2023-01-28 14:00:00+01:00",
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"2023-01-28 14:15:00+01:00",
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"2023-01-28 14:30:00+01:00",
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"2023-01-28 14:45:00+01:00",
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"2023-01-28 15:00:00+01:00",
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"2023-01-28 15:15:00+01:00",
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"2023-01-28 15:30:00+01:00",
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"2023-01-28 15:45:00+01:00",
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"2023-01-28 16:15:00+01:00",
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"2023-01-28 16:30:00+01:00",
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"2023-01-28 16:45:00+01:00",
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"2023-01-28 17:00:00+01:00",
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"2023-01-28 17:15:00+01:00",
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"2023-01-28 17:30:00+01:00",
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"2023-01-28 17:45:00+01:00",
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"2023-01-28 18:15:00+01:00",
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"2023-01-28 18:30:00+01:00",
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"2023-01-28 18:45:00+01:00",
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"2023-01-28 19:15:00+01:00",
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"2023-01-28 19:30:00+01:00",
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"2023-01-28 19:45:00+01:00",
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"2023-01-28 20:00:00+01:00",
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"2023-01-28 20:15:00+01:00",
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"2023-01-28 20:30:00+01:00",
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"2023-01-28 20:45:00+01:00",
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"2023-01-28 21:00:00+01:00",
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"2023-01-28 21:15:00+01:00",
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"2023-01-28 21:30:00+01:00",
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"2023-01-28 21:45:00+01:00",
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"2023-01-28 22:00:00+01:00",
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"2023-01-28 22:15:00+01:00",
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"2023-01-28 22:30:00+01:00",
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"2023-01-28 22:45:00+01:00",
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"2023-01-28 23:00:00+01:00",
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"2023-01-28 23:15:00+01:00",
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"2023-01-28 23:30:00+01:00",
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"2023-01-28 23:45:00+01:00",
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"2023-01-29 00:00:00+01:00",
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"2023-01-29 00:15:00+01:00",
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"2023-01-29 00:30:00+01:00",
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"2023-01-29 00:45:00+01:00",
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"2023-01-29 01:00:00+01:00",
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"2023-01-29 01:15:00+01:00",
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"2023-01-29 01:30:00+01:00",
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"2023-01-29 01:45:00+01:00",
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"2023-01-29 02:00:00+01:00",
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"2023-01-29 02:15:00+01:00",
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"2023-01-29 02:30:00+01:00",
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"2023-01-29 02:45:00+01:00",
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"2023-01-29 03:00:00+01:00",
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"2023-01-29 03:15:00+01:00",
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"2023-01-29 03:30:00+01:00",
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"2023-01-29 03:45:00+01:00",
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"2023-01-29 04:00:00+01:00",
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"2023-01-29 04:15:00+01:00",
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"2023-01-29 04:30:00+01:00",
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"2023-01-29 04:45:00+01:00",
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"2023-01-29 05:00:00+01:00",
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"2023-01-29 05:15:00+01:00",
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"2023-01-29 05:30:00+01:00",
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"2023-01-29 05:45:00+01:00",
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"2023-01-29 06:00:00+01:00",
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"2023-01-29 06:15:00+01:00",
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"2023-01-29 06:30:00+01:00",
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"2023-01-29 06:45:00+01:00",
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"2023-01-29 07:00:00+01:00",
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"2023-01-29 07:15:00+01:00",
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"2023-01-29 07:30:00+01:00",
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"2023-01-29 07:45:00+01:00",
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"2023-01-29 08:00:00+01:00",
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"2023-01-29 08:15:00+01:00",
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"2023-01-29 08:30:00+01:00",
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"2023-01-29 08:45:00+01:00",
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"2023-01-29 09:00:00+01:00",
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"2023-01-29 09:15:00+01:00",
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"2023-01-29 09:30:00+01:00",
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"2023-01-29 09:45:00+01:00",
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"2023-01-29 10:00:00+01:00",
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"2023-01-29 10:15:00+01:00",
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"2023-01-29 10:30:00+01:00",
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"2023-01-29 10:45:00+01:00",
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"2023-01-29 11:00:00+01:00",
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"2023-01-29 11:15:00+01:00",
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"2023-01-29 11:30:00+01:00",
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"2023-01-29 11:45:00+01:00",
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"2023-01-29 12:00:00+01:00",
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"2023-01-29 12:15:00+01:00",
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"2023-01-29 12:30:00+01:00",
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"2023-01-29 12:45:00+01:00",
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"2023-01-29 13:00:00+01:00",
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"2023-01-29 13:15:00+01:00",
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"2023-01-29 13:30:00+01:00",
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"2023-01-29 13:45:00+01:00",
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"2023-01-29 14:00:00+01:00",
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"2023-01-29 14:15:00+01:00",
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"2023-01-29 14:30:00+01:00",
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"2023-01-29 14:45:00+01:00",
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"2023-01-29 15:00:00+01:00",
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"2023-01-29 15:15:00+01:00",
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"2023-01-29 15:30:00+01:00",
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"2023-01-29 15:45:00+01:00",
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"2023-01-29 16:00:00+01:00",
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"2023-01-29 16:15:00+01:00",
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"2023-01-29 16:30:00+01:00",
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"2023-01-29 16:45:00+01:00",
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"2023-01-29 17:00:00+01:00",
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"2023-01-29 17:15:00+01:00",
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"2023-01-29 17:30:00+01:00",
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"2023-01-29 17:45:00+01:00",
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"2023-01-29 18:00:00+01:00",
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"2023-01-29 18:15:00+01:00",
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"2023-01-29 18:30:00+01:00",
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"2023-01-29 18:45:00+01:00",
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"2023-01-29 19:00:00+01:00",
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"2023-01-29 19:15:00+01:00",
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"2023-01-29 19:30:00+01:00",
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"2023-01-29 19:45:00+01:00",
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"2023-01-29 20:00:00+01:00",
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"2023-01-29 20:15:00+01:00",
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"2023-01-29 20:30:00+01:00",
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"2023-01-29 20:45:00+01:00",
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"2023-01-29 21:00:00+01:00",
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"2023-01-29 21:15:00+01:00",
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"2023-01-29 21:30:00+01:00",
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"2023-01-29 21:45:00+01:00",
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"2023-01-29 22:00:00+01:00",
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"2023-01-29 22:15:00+01:00",
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"2023-01-29 22:30:00+01:00",
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"2023-01-29 22:45:00+01:00",
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"2023-01-29 23:00:00+01:00",
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"2023-01-29 23:15:00+01:00",
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"2023-01-29 23:30:00+01:00",
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"2023-01-29 23:45:00+01:00",
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"2023-01-30 00:00:00+01:00",
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"2023-01-30 00:15:00+01:00",
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"2023-01-30 00:30:00+01:00",
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"2023-01-30 00:45:00+01:00",
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"2023-01-30 01:00:00+01:00",
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"2023-01-30 01:15:00+01:00",
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"2023-01-30 01:30:00+01:00",
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"2023-01-30 01:45:00+01:00",
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"2023-01-30 02:00:00+01:00",
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"2023-01-30 02:15:00+01:00",
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"2023-01-30 02:30:00+01:00",
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"2023-01-30 02:45:00+01:00",
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"2023-01-30 03:00:00+01:00",
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"2023-01-30 03:15:00+01:00",
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"2023-01-30 03:30:00+01:00",
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"2023-01-30 03:45:00+01:00",
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"2023-01-30 04:00:00+01:00",
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"2023-01-30 04:15:00+01:00",
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"2023-01-30 04:30:00+01:00",
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"2023-01-30 04:45:00+01:00",
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"2023-01-30 05:00:00+01:00",
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"2023-01-30 05:15:00+01:00",
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"2023-01-30 05:30:00+01:00",
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"2023-01-30 05:45:00+01:00",
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"2023-01-30 06:00:00+01:00",
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"2023-01-30 06:15:00+01:00",
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"2023-01-30 06:30:00+01:00",
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"2023-01-30 06:45:00+01:00",
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"2023-01-30 07:00:00+01:00",
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"2023-01-30 07:15:00+01:00",
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"2023-01-30 07:30:00+01:00",
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"2023-01-30 07:45:00+01:00",
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"2023-01-30 08:00:00+01:00",
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"2023-01-30 08:15:00+01:00",
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"2023-01-30 08:30:00+01:00",
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"2023-01-30 08:45:00+01:00",
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"2023-01-30 09:00:00+01:00",
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"2023-01-30 09:15:00+01:00",
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"2023-01-30 09:30:00+01:00",
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"2023-01-30 09:45:00+01:00",
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"2023-01-30 10:00:00+01:00",
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"2023-01-30 10:15:00+01:00",
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"2023-01-30 10:30:00+01:00",
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"2023-01-30 10:45:00+01:00",
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"2023-01-30 11:00:00+01:00",
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"2023-01-30 11:15:00+01:00",
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"2023-01-30 11:30:00+01:00",
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"2023-01-30 11:45:00+01:00",
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"2023-01-30 12:00:00+01:00",
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"2023-01-30 12:15:00+01:00",
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"2023-01-30 12:30:00+01:00",
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"2023-01-30 12:45:00+01:00",
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"2023-01-30 13:00:00+01:00",
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"2023-01-30 13:15:00+01:00",
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"2023-01-30 13:30:00+01:00",
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"2023-01-30 13:45:00+01:00",
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"2023-01-30 14:00:00+01:00",
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"2023-01-30 14:15:00+01:00",
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"2023-01-30 14:30:00+01:00",
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"2023-01-30 14:45:00+01:00",
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"2023-01-30 15:00:00+01:00",
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"2023-01-30 15:15:00+01:00",
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"2023-01-30 15:30:00+01:00",
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"2023-01-30 15:45:00+01:00",
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"2023-01-30 16:00:00+01:00",
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"2023-01-30 16:15:00+01:00",
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"2023-01-30 16:30:00+01:00",
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"2023-01-30 16:45:00+01:00",
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"2023-01-30 17:00:00+01:00",
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"2023-01-30 17:15:00+01:00",
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"2023-01-30 17:30:00+01:00",
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"2023-01-30 17:45:00+01:00",
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"2023-01-30 18:00:00+01:00",
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"2023-01-30 18:15:00+01:00",
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"2023-01-30 18:30:00+01:00",
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"2023-01-30 18:45:00+01:00",
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"2023-01-30 19:00:00+01:00",
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"2023-01-30 19:15:00+01:00",
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"2023-01-30 19:30:00+01:00",
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"2023-01-30 19:45:00+01:00",
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"2023-01-30 20:00:00+01:00",
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"2023-01-30 20:15:00+01:00",
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"2023-01-30 20:30:00+01:00",
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"2023-01-30 20:45:00+01:00",
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"2023-01-30 21:00:00+01:00",
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"2023-01-30 21:15:00+01:00",
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"2023-01-30 21:30:00+01:00",
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"2023-01-30 21:45:00+01:00",
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"2023-01-30 22:00:00+01:00",
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"2023-01-30 22:15:00+01:00",
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"2023-01-30 22:30:00+01:00",
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"2023-01-30 22:45:00+01:00",
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"2023-01-30 23:00:00+01:00",
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"2023-01-30 23:15:00+01:00",
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"2023-01-30 23:30:00+01:00",
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"2023-01-30 23:45:00+01:00",
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"2023-01-31 00:00:00+01:00",
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"2023-01-31 00:15:00+01:00",
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"2023-01-31 00:30:00+01:00",
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"2023-01-31 00:45:00+01:00",
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"2023-01-31 01:00:00+01:00",
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"2023-01-31 01:15:00+01:00",
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"2023-01-31 01:30:00+01:00",
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"2023-01-31 01:45:00+01:00",
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"2023-01-31 02:00:00+01:00",
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"2023-01-31 02:15:00+01:00",
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"2023-01-31 02:30:00+01:00",
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"2023-01-31 02:45:00+01:00",
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"2023-01-31 03:00:00+01:00",
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"2023-01-31 03:15:00+01:00",
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"2023-01-31 03:30:00+01:00",
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"2023-01-31 03:45:00+01:00",
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"2023-01-31 04:00:00+01:00",
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"2023-01-31 04:15:00+01:00",
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"2023-01-31 04:30:00+01:00",
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"2023-01-31 04:45:00+01:00",
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"2023-01-31 05:00:00+01:00",
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"2023-01-31 05:15:00+01:00",
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"2023-01-31 05:30:00+01:00",
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"2023-01-31 05:45:00+01:00",
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"2023-01-31 06:00:00+01:00",
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"2023-01-31 06:15:00+01:00",
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"2023-01-31 06:30:00+01:00",
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"2023-01-31 06:45:00+01:00",
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"2023-01-31 07:00:00+01:00",
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"2023-01-31 07:15:00+01:00",
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"2023-01-31 07:30:00+01:00",
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"2023-01-31 07:45:00+01:00",
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"2023-01-31 08:00:00+01:00",
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"2023-01-31 08:15:00+01:00",
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"2023-01-31 08:30:00+01:00",
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"2023-01-31 08:45:00+01:00",
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"2023-01-31 09:00:00+01:00",
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"2023-01-31 09:15:00+01:00",
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"2023-01-31 09:30:00+01:00",
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"2023-01-31 09:45:00+01:00",
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"2023-01-31 10:00:00+01:00",
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"2023-01-31 10:15:00+01:00",
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"2023-01-31 10:30:00+01:00",
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"2023-01-31 10:45:00+01:00",
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"2023-01-31 11:00:00+01:00",
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"2023-01-31 11:15:00+01:00",
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"2023-01-31 11:30:00+01:00",
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"2023-01-31 11:45:00+01:00",
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"2023-01-31 12:00:00+01:00",
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"2023-01-31 12:15:00+01:00",
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"2023-01-31 12:30:00+01:00",
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"2023-01-31 12:45:00+01:00",
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"2023-01-31 13:00:00+01:00",
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"2023-01-31 13:15:00+01:00",
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"2023-01-31 13:30:00+01:00",
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"2023-01-31 13:45:00+01:00",
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"2023-01-31 14:00:00+01:00",
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"2023-01-31 14:15:00+01:00",
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"2023-01-31 14:30:00+01:00",
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"2023-01-31 14:45:00+01:00",
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"2023-01-31 15:00:00+01:00",
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"2023-01-31 15:15:00+01:00",
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"2023-01-31 15:30:00+01:00",
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"2023-01-31 15:45:00+01:00",
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"2023-01-31 16:00:00+01:00",
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"2023-01-31 16:15:00+01:00",
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"2023-01-31 16:30:00+01:00",
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"2023-01-31 16:45:00+01:00",
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"2023-01-31 17:00:00+01:00",
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"2023-01-31 17:15:00+01:00",
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"2023-01-31 17:30:00+01:00",
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"2023-01-31 17:45:00+01:00",
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"2023-01-31 18:15:00+01:00",
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"2023-01-31 18:30:00+01:00",
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"2023-01-31 18:45:00+01:00",
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"2023-01-31 19:15:00+01:00",
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"2023-01-31 19:30:00+01:00",
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"2023-01-31 19:45:00+01:00",
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"2023-01-31 20:00:00+01:00",
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"2023-01-31 20:15:00+01:00",
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"2023-01-31 20:30:00+01:00",
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"2023-01-31 20:45:00+01:00",
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"2023-01-31 21:00:00+01:00",
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"2023-01-31 21:15:00+01:00",
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"2023-01-31 21:30:00+01:00",
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"2023-01-31 21:45:00+01:00",
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"2023-01-31 22:00:00+01:00",
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"2023-01-31 22:15:00+01:00",
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"2023-01-31 22:45:00+01:00",
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"2023-01-31 23:15:00+01:00",
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"2023-01-31 23:30:00+01:00",
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"2023-01-31 23:45:00+01:00",
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"2023-02-01 00:00:00+01:00",
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"2023-02-01 00:15:00+01:00",
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"2023-02-01 00:30:00+01:00",
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"2023-02-01 00:45:00+01:00",
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"2023-02-01 01:00:00+01:00",
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"2023-02-01 01:15:00+01:00",
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"2023-02-01 01:30:00+01:00",
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"2023-02-01 01:45:00+01:00",
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"2023-02-01 02:00:00+01:00",
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"2023-02-01 02:15:00+01:00",
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"2023-02-01 02:30:00+01:00",
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"2023-02-01 02:45:00+01:00",
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"2023-02-01 03:00:00+01:00",
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"2023-02-01 03:15:00+01:00",
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"2023-02-01 03:30:00+01:00",
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"2023-02-01 03:45:00+01:00",
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"2023-02-01 04:00:00+01:00",
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"2023-02-01 04:15:00+01:00",
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"2023-02-01 04:30:00+01:00",
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"2023-02-01 04:45:00+01:00",
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"2023-02-01 05:00:00+01:00",
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"2023-02-01 05:15:00+01:00",
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"2023-02-01 05:30:00+01:00",
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"2023-02-01 05:45:00+01:00",
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"2023-02-01 06:00:00+01:00",
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"2023-02-01 06:15:00+01:00",
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"2023-02-01 06:30:00+01:00",
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"2023-02-01 06:45:00+01:00",
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"2023-02-01 07:00:00+01:00",
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"2023-02-01 07:15:00+01:00",
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"2023-02-01 07:30:00+01:00",
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"2023-02-01 07:45:00+01:00",
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"2023-02-01 08:00:00+01:00",
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"2023-02-01 08:15:00+01:00",
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"2023-02-01 08:30:00+01:00",
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"2023-02-01 08:45:00+01:00",
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"2023-02-01 09:00:00+01:00",
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"2023-02-01 09:15:00+01:00",
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"2023-02-01 09:30:00+01:00",
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"2023-02-01 09:45:00+01:00",
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"2023-02-01 10:00:00+01:00",
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"2023-02-01 10:15:00+01:00",
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"2023-02-01 10:30:00+01:00",
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"2023-02-01 10:45:00+01:00",
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"2023-02-01 11:00:00+01:00",
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"2023-02-01 11:15:00+01:00",
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"2023-02-01 11:30:00+01:00",
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"2023-02-01 11:45:00+01:00",
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"2023-02-01 12:00:00+01:00",
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"2023-02-01 12:15:00+01:00",
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"2023-02-01 12:30:00+01:00",
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"2023-02-01 12:45:00+01:00",
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"2023-02-01 13:00:00+01:00",
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"2023-02-01 13:15:00+01:00",
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"2023-02-01 13:30:00+01:00",
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"2023-02-01 13:45:00+01:00",
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"2023-02-01 14:00:00+01:00",
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"2023-02-01 14:15:00+01:00",
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"2023-02-01 14:30:00+01:00",
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"2023-02-01 14:45:00+01:00",
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"2023-02-01 15:00:00+01:00",
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"2023-02-01 15:15:00+01:00",
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"2023-02-01 15:30:00+01:00",
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"2023-02-01 15:45:00+01:00",
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"2023-02-01 16:00:00+01:00",
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"2023-02-01 16:15:00+01:00",
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"2023-02-01 16:30:00+01:00",
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"2023-02-01 16:45:00+01:00",
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"2023-02-01 17:00:00+01:00",
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"2023-02-01 17:15:00+01:00",
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"2023-02-01 17:30:00+01:00",
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"2023-02-01 17:45:00+01:00",
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"2023-02-01 18:00:00+01:00",
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"2023-02-01 18:15:00+01:00",
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"2023-02-01 18:30:00+01:00",
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"2023-02-01 18:45:00+01:00",
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"2023-02-01 19:00:00+01:00",
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"2023-02-01 19:15:00+01:00",
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"2023-02-01 19:30:00+01:00",
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"2023-02-01 19:45:00+01:00",
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"2023-02-01 20:00:00+01:00",
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"2023-02-01 20:15:00+01:00",
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"2023-02-01 20:30:00+01:00",
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"2023-02-01 20:45:00+01:00",
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"2023-02-01 21:00:00+01:00",
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"2023-02-01 21:15:00+01:00",
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"2023-02-01 21:30:00+01:00",
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"2023-02-01 21:45:00+01:00",
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"2023-02-01 22:00:00+01:00",
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"2023-02-01 22:15:00+01:00",
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"2023-02-01 22:30:00+01:00",
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"2023-02-01 22:45:00+01:00",
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"2023-02-01 23:00:00+01:00",
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"2023-02-01 23:15:00+01:00",
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"2023-02-01 23:30:00+01:00",
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"2023-02-01 23:45:00+01:00",
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"2023-02-02 00:00:00+01:00",
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"2023-02-02 00:15:00+01:00",
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"2023-02-02 00:30:00+01:00",
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"2023-02-02 00:45:00+01:00",
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"2023-02-02 01:00:00+01:00",
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"2023-02-02 01:15:00+01:00",
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"2023-02-02 01:30:00+01:00",
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"2023-02-02 01:45:00+01:00",
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"2023-02-02 02:00:00+01:00",
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"2023-02-02 02:15:00+01:00",
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"2023-02-02 02:30:00+01:00",
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"2023-02-02 02:45:00+01:00",
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"2023-02-02 03:00:00+01:00",
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"2023-02-02 03:15:00+01:00",
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"2023-02-02 03:30:00+01:00",
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"2023-02-02 03:45:00+01:00",
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"2023-02-02 04:00:00+01:00",
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"2023-02-02 04:15:00+01:00",
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"2023-02-02 04:30:00+01:00",
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"2023-02-02 04:45:00+01:00",
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"2023-02-02 05:00:00+01:00",
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"2023-02-02 05:15:00+01:00",
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"2023-02-02 05:30:00+01:00",
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"2023-02-02 05:45:00+01:00",
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"2023-02-02 06:00:00+01:00",
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"2023-02-02 06:15:00+01:00",
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"2023-02-02 06:30:00+01:00",
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"2023-02-02 06:45:00+01:00",
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"2023-02-02 07:00:00+01:00",
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"2023-02-02 07:15:00+01:00",
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"2023-02-02 07:30:00+01:00",
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"2023-02-02 07:45:00+01:00",
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"2023-02-02 08:00:00+01:00",
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"2023-02-02 08:15:00+01:00",
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"2023-02-02 08:30:00+01:00",
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"2023-02-02 08:45:00+01:00",
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"2023-02-02 09:00:00+01:00",
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"2023-02-02 09:15:00+01:00",
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"2023-02-02 09:30:00+01:00",
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"2023-02-02 09:45:00+01:00",
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"2023-02-02 10:00:00+01:00",
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"2023-02-02 10:15:00+01:00",
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"2023-02-02 10:30:00+01:00",
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"2023-02-02 10:45:00+01:00",
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"2023-02-02 11:00:00+01:00",
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"2023-02-02 11:15:00+01:00",
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"2023-02-02 11:30:00+01:00",
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"2023-02-02 11:45:00+01:00",
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"2023-02-02 12:00:00+01:00",
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"2023-02-02 12:15:00+01:00",
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"2023-02-02 12:30:00+01:00",
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"2023-02-02 12:45:00+01:00",
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"2023-02-02 13:00:00+01:00",
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"2023-02-02 13:15:00+01:00",
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"2023-02-02 13:30:00+01:00",
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"2023-02-02 13:45:00+01:00",
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"2023-02-02 14:00:00+01:00",
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"2023-02-02 14:15:00+01:00",
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"2023-02-02 14:30:00+01:00",
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"2023-02-02 14:45:00+01:00",
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"2023-02-02 15:00:00+01:00",
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"2023-02-02 15:15:00+01:00",
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"2023-02-02 15:30:00+01:00",
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"2023-02-02 15:45:00+01:00",
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"2023-02-02 16:00:00+01:00",
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"2023-02-02 16:15:00+01:00",
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"2023-02-02 16:30:00+01:00",
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"2023-02-02 16:45:00+01:00",
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"2023-02-02 17:00:00+01:00",
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"2023-02-02 17:15:00+01:00",
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"2023-02-02 17:30:00+01:00",
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"2023-02-02 17:45:00+01:00",
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"2023-02-02 18:00:00+01:00",
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"2023-02-02 18:15:00+01:00",
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"2023-02-02 18:30:00+01:00",
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"2023-02-02 18:45:00+01:00",
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"2023-02-02 19:00:00+01:00",
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"2023-02-02 19:15:00+01:00",
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"2023-02-02 19:30:00+01:00",
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"2023-02-02 19:45:00+01:00",
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"2023-02-02 20:00:00+01:00",
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"2023-02-02 20:15:00+01:00",
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"2023-02-02 20:30:00+01:00",
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"2023-02-02 20:45:00+01:00",
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"2023-02-02 21:00:00+01:00",
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"2023-02-02 21:15:00+01:00",
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"2023-02-02 21:30:00+01:00",
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"2023-02-02 21:45:00+01:00",
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"2023-02-02 22:00:00+01:00",
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"2023-02-02 22:15:00+01:00",
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"2023-02-02 22:30:00+01:00",
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"2023-02-02 22:45:00+01:00",
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"2023-02-02 23:00:00+01:00",
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"2023-02-02 23:15:00+01:00",
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"2023-02-02 23:30:00+01:00",
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"2023-02-02 23:45:00+01:00",
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"2023-02-03 00:00:00+01:00",
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"2023-02-03 00:15:00+01:00",
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"2023-02-03 00:30:00+01:00",
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"2023-02-03 00:45:00+01:00",
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"2023-02-03 01:00:00+01:00",
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"2023-02-03 01:15:00+01:00",
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"2023-02-03 01:30:00+01:00",
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"2023-02-03 01:45:00+01:00",
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"2023-02-03 02:00:00+01:00",
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"2023-02-03 02:15:00+01:00",
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"2023-02-03 02:30:00+01:00",
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"2023-02-03 02:45:00+01:00",
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"2023-02-03 03:00:00+01:00",
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"2023-02-03 03:15:00+01:00",
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"2023-02-03 03:30:00+01:00",
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"2023-02-03 03:45:00+01:00",
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"2023-02-03 04:00:00+01:00",
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"2023-02-03 04:15:00+01:00",
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"2023-02-03 04:30:00+01:00",
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"2023-02-03 04:45:00+01:00",
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"2023-02-03 05:00:00+01:00",
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"2023-02-03 05:15:00+01:00",
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"2023-02-03 05:30:00+01:00",
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"2023-02-03 05:45:00+01:00",
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"2023-02-03 06:00:00+01:00",
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"2023-02-03 06:15:00+01:00",
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"2023-02-03 06:30:00+01:00",
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"2023-02-03 06:45:00+01:00",
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"2023-02-03 07:00:00+01:00",
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"2023-02-03 07:15:00+01:00",
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"2023-02-03 07:30:00+01:00",
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"2023-02-03 07:45:00+01:00",
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"2023-02-03 08:00:00+01:00",
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"2023-02-03 08:15:00+01:00",
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"2023-02-03 08:30:00+01:00",
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"2023-02-03 08:45:00+01:00",
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"2023-02-03 09:00:00+01:00",
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"2023-02-03 09:15:00+01:00",
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"2023-02-03 09:30:00+01:00",
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"2023-02-03 09:45:00+01:00",
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"2023-02-03 10:00:00+01:00",
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"2023-02-03 10:15:00+01:00",
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"2023-02-03 10:30:00+01:00",
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"2023-02-03 10:45:00+01:00",
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"2023-02-03 11:00:00+01:00",
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"2023-02-03 11:15:00+01:00",
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"2023-02-03 11:30:00+01:00",
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"2023-02-03 11:45:00+01:00",
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"2023-02-03 12:00:00+01:00",
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"2023-02-03 12:15:00+01:00",
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"2023-02-03 12:30:00+01:00",
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"2023-02-03 12:45:00+01:00",
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"2023-02-03 13:00:00+01:00",
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"2023-02-03 13:15:00+01:00",
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"2023-02-03 13:30:00+01:00",
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"2023-02-03 13:45:00+01:00",
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"2023-02-03 14:00:00+01:00",
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"2023-02-03 14:15:00+01:00",
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"2023-02-03 14:30:00+01:00",
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"2023-02-03 14:45:00+01:00",
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"2023-02-03 15:00:00+01:00",
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"2023-02-03 15:15:00+01:00",
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"2023-02-03 15:30:00+01:00",
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"2023-02-03 15:45:00+01:00",
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"2023-02-03 16:00:00+01:00",
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"2023-02-03 16:15:00+01:00",
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"2023-02-03 16:30:00+01:00",
|
|
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"metadata": {},
|
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"output_type": "display_data"
|
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}
|
|
],
|
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"source": [
|
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"s.demand.resample('15T').mean()[['Tsource (VDG)', 'Tsink (VDG)']].iplot()"
|
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]
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},
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{
|
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"cell_type": "code",
|
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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"\n",
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" .dataframe tbody tr th {\n",
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"\n",
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" .dataframe thead th {\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
|
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" <th></th>\n",
|
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" <th>Tsource (VDG)</th>\n",
|
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" <th>Tsink (VDG)</th>\n",
|
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" <th>MW (VDG)</th>\n",
|
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" <th>Tsource (NDG)</th>\n",
|
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" <th>Tsink (NDG)</th>\n",
|
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" <th>MW (NDG)</th>\n",
|
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" <th>Total demand</th>\n",
|
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" </tr>\n",
|
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" </thead>\n",
|
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" <tbody>\n",
|
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" <tr>\n",
|
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" <th>count</th>\n",
|
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" <td>52560.0</td>\n",
|
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" <td>52560.0</td>\n",
|
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" <td>52560.0</td>\n",
|
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" <td>52560.0</td>\n",
|
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" <td>52560.0</td>\n",
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" <td>52560.0</td>\n",
|
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" <td>52560.0</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
|
" <th>mean</th>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>2.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>std</th>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>min</th>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>2.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25%</th>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>2.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50%</th>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>2.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>75%</th>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>2.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>max</th>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>2.0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Tsource (VDG) Tsink (VDG) MW (VDG) Tsource (NDG) Tsink (NDG) \\\n",
|
|
"count 52560.0 52560.0 52560.0 52560.0 52560.0 \n",
|
|
"mean 75.0 144.0 1.0 75.0 144.0 \n",
|
|
"std 0.0 0.0 0.0 0.0 0.0 \n",
|
|
"min 75.0 144.0 1.0 75.0 144.0 \n",
|
|
"25% 75.0 144.0 1.0 75.0 144.0 \n",
|
|
"50% 75.0 144.0 1.0 75.0 144.0 \n",
|
|
"75% 75.0 144.0 1.0 75.0 144.0 \n",
|
|
"max 75.0 144.0 1.0 75.0 144.0 \n",
|
|
"\n",
|
|
" MW (NDG) Total demand \n",
|
|
"count 52560.0 52560.0 \n",
|
|
"mean 1.0 2.0 \n",
|
|
"std 0.0 0.0 \n",
|
|
"min 1.0 2.0 \n",
|
|
"25% 1.0 2.0 \n",
|
|
"50% 1.0 2.0 \n",
|
|
"75% 1.0 2.0 \n",
|
|
"max 1.0 2.0 "
|
|
]
|
|
},
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"s.demand.describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"## Load in data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def add_afrr_prices(c, case):\n",
|
|
" try:\n",
|
|
" aFRR_signal = pd.read_csv(f'data/aFRR_{c.start}.csv', delimiter=';', decimal=',', index_col='datetime')\n",
|
|
" aFRR_signal.index = case.data.index\n",
|
|
" except:\n",
|
|
" data = get_tennet_data('balansdelta2017', pd.to_datetime(c.start), pd.to_datetime(c.end))\n",
|
|
" data.index = data[[\"datum\", \"tijd\"]].apply(lambda x: \" \".join(x), axis=1)\n",
|
|
" data.index = pd.to_datetime(data.index, format=\"%d-%m-%Y %H:%M\").tz_localize(\n",
|
|
" \"Europe/Amsterdam\", ambiguous=True\n",
|
|
" )\n",
|
|
" data = data[~data.index.duplicated(keep=\"first\")]\n",
|
|
" date_ix = pd.date_range(\n",
|
|
" data.index[0], data.index[-1], freq=\"1T\", tz=\"Europe/Amsterdam\"\n",
|
|
" )\n",
|
|
" data = data.reindex(date_ix)\n",
|
|
" aFRR_signal = data[['Hoogste_prijs_opregelen', 'Mid_prijs_opregelen', 'Laagste_prijs_afregelen']]\n",
|
|
" aFRR_signal.to_csv(f'data/aFRR_{c.start}.csv', sep=';', decimal=',', index_label='datetime')\n",
|
|
"\n",
|
|
" try:\n",
|
|
" aFRR_prices = pd.read_csv(f'data/aFRR_prices_{c.start}.csv', delimiter=';', decimal=',', index_col='datetime')\n",
|
|
" aFRR_prices.index = case.data.index\n",
|
|
" except:\n",
|
|
" data = get_aFRR_prices_nl(pd.to_datetime(c.start), pd.to_datetime(c.end))\n",
|
|
" data.index = pd.date_range(\n",
|
|
" start=c.start,\n",
|
|
" end=pd.to_datetime(c.end) + timedelta(days=1),\n",
|
|
" tz='Europe/Amsterdam', \n",
|
|
" freq='15T', \n",
|
|
" closed='left'\n",
|
|
" )\n",
|
|
" aFRR_prices = data.reindex(case.data.index, method='ffill')\n",
|
|
" aFRR_prices.to_csv(f'data/aFRR_prices_{c.start}.csv', sep=';', decimal=',', index_label='datetime')\n",
|
|
" \n",
|
|
" case.data = pd.concat([case.data, aFRR_signal, aFRR_prices], axis=1)\n",
|
|
" return case"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def increase_volatility_by_factor(col, factor):\n",
|
|
" mean = col.mean()\n",
|
|
" diff_to_mean = col - mean\n",
|
|
" new_diff = diff_to_mean * factor\n",
|
|
" return mean + new_diff\n",
|
|
"\n",
|
|
"def multiply_by_factor(col, factor):\n",
|
|
" mean = col.mean()\n",
|
|
" diff_to_mean = col - mean\n",
|
|
" \n",
|
|
" cond = diff_to_mean > 0\n",
|
|
" diff_to_mean[cond] *= factor\n",
|
|
" diff_to_mean[~cond] /= factor\n",
|
|
" return mean + diff_to_mean"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
|
|
"\n",
|
|
"'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
|
|
"\n",
|
|
"C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
|
|
"\n",
|
|
"'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
|
|
"\n",
|
|
"C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
|
|
"\n",
|
|
"'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
|
|
"\n",
|
|
"C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
|
|
"\n",
|
|
"'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
|
|
"\n",
|
|
"C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:531: FutureWarning:\n",
|
|
"\n",
|
|
"'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
|
|
"\n",
|
|
"C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\prices.py:443: FutureWarning:\n",
|
|
"\n",
|
|
"'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"def load_data(c, s):\n",
|
|
" if hasattr(s, 'afrr_case'):\n",
|
|
" s.afrr_case = add_afrr_prices(c, s.afrr_case)\n",
|
|
" \n",
|
|
" for case in s.cases:\n",
|
|
" case.add_gasprices()\n",
|
|
" case.add_co2prices(perMWh=True)\n",
|
|
" \n",
|
|
" case.data['Gas prices (€/MWh)'] *= c.gas_price_multiplier\n",
|
|
" case.data['CO2 prices (€/MWh)'] *= c.co2_price_multiplier\n",
|
|
" case.data['CO2 prices (€/ton)'] *= c.co2_price_multiplier\n",
|
|
" \n",
|
|
" for case in s.optcases:\n",
|
|
" case.data['NEG'] = multiply_by_factor(case.data['NEG'], c.e_price_multiplier)\n",
|
|
" case.data['ForeNeg'] = multiply_by_factor(case.data['ForeNeg'], c.e_price_multiplier)\n",
|
|
" case.data['DAM'] = multiply_by_factor(case.data['DAM'], c.e_price_multiplier)\n",
|
|
" \n",
|
|
" case.data['NEG'] = increase_volatility_by_factor(case.data['NEG'], c.e_price_volatility_multiplier)\n",
|
|
" case.data['ForeNeg'] = increase_volatility_by_factor(case.data['ForeNeg'], c.e_price_volatility_multiplier)\n",
|
|
" \n",
|
|
" if hasattr(s, 'afrr_case'):\n",
|
|
" for case in [s.afrr_case]:\n",
|
|
" case.data['Hoogste_prijs_opregelen'] = multiply_by_factor(case.data['Hoogste_prijs_opregelen'], c.e_price_multiplier)\n",
|
|
" case.data['Hoogste_prijs_opregelen'] = increase_volatility_by_factor(case.data['Hoogste_prijs_opregelen'], c.e_price_volatility_multiplier)\n",
|
|
" case.data['aFRR_up'] = multiply_by_factor(case.data['aFRR_up'], c.e_price_multiplier)\n",
|
|
" case.data['aFRR_up'] = increase_volatility_by_factor(case.data['aFRR_up'], c.e_price_volatility_multiplier)\n",
|
|
"\n",
|
|
" s.demand[['Tsource (VDG)', 'Tsource (NDG)']] += c.tsource_delta\n",
|
|
" s.demand[['Tsink (VDG)', 'Tsink (NDG)']] += c.tsink_delta\n",
|
|
" for case in s.cases:\n",
|
|
" case.data = pd.concat([case.data, s.demand], axis=1) \n",
|
|
"\n",
|
|
" s.eb_ode_g = get_tax_rate('gas', 2020, 4)['EB+ODE'] * c.energy_tax_multiplier\n",
|
|
" s.eb_ode_e = get_tax_rate('electricity', 2020, 4)['EB+ODE'] * c.energy_tax_multiplier\n",
|
|
" s.grid_fees = get_grid_tariffs_electricity(c.grid_operator, 2020, c.connection_type)\n",
|
|
" s.grid_fee_per_MWh = s.grid_fees['kWh tarief'] * 1000\n",
|
|
" return s\n",
|
|
"\n",
|
|
"s = load_data(c, s)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>DAM</th>\n",
|
|
" <th>POS</th>\n",
|
|
" <th>NEG</th>\n",
|
|
" <th>ForeNeg</th>\n",
|
|
" <th>ForePos</th>\n",
|
|
" <th>Gas prices (€/MWh)</th>\n",
|
|
" <th>CO2 prices (€/ton)</th>\n",
|
|
" <th>CO2 prices (€/MWh)</th>\n",
|
|
" <th>Tsource (VDG)</th>\n",
|
|
" <th>Tsink (VDG)</th>\n",
|
|
" <th>MW (VDG)</th>\n",
|
|
" <th>Tsource (NDG)</th>\n",
|
|
" <th>Tsink (NDG)</th>\n",
|
|
" <th>MW (NDG)</th>\n",
|
|
" <th>Total demand</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>2023-01-01 00:00:00+01:00</th>\n",
|
|
" <td>-3.61</td>\n",
|
|
" <td>-209.4</td>\n",
|
|
" <td>-209.4</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>63.998</td>\n",
|
|
" <td>5.23</td>\n",
|
|
" <td>1.01</td>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>2.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2023-01-01 00:01:00+01:00</th>\n",
|
|
" <td>-3.61</td>\n",
|
|
" <td>-209.4</td>\n",
|
|
" <td>-209.4</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>63.998</td>\n",
|
|
" <td>5.23</td>\n",
|
|
" <td>1.01</td>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>2.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2023-01-01 00:02:00+01:00</th>\n",
|
|
" <td>-3.61</td>\n",
|
|
" <td>-209.4</td>\n",
|
|
" <td>-209.4</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>63.998</td>\n",
|
|
" <td>5.23</td>\n",
|
|
" <td>1.01</td>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>2.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2023-01-01 00:03:00+01:00</th>\n",
|
|
" <td>-3.61</td>\n",
|
|
" <td>-209.4</td>\n",
|
|
" <td>-209.4</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>63.998</td>\n",
|
|
" <td>5.23</td>\n",
|
|
" <td>1.01</td>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>2.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2023-01-01 00:04:00+01:00</th>\n",
|
|
" <td>-3.61</td>\n",
|
|
" <td>-209.4</td>\n",
|
|
" <td>-209.4</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>63.998</td>\n",
|
|
" <td>5.23</td>\n",
|
|
" <td>1.01</td>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>75.0</td>\n",
|
|
" <td>144.0</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>2.0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" DAM POS NEG ForeNeg ForePos \\\n",
|
|
"2023-01-01 00:00:00+01:00 -3.61 -209.4 -209.4 NaN NaN \n",
|
|
"2023-01-01 00:01:00+01:00 -3.61 -209.4 -209.4 NaN NaN \n",
|
|
"2023-01-01 00:02:00+01:00 -3.61 -209.4 -209.4 NaN NaN \n",
|
|
"2023-01-01 00:03:00+01:00 -3.61 -209.4 -209.4 NaN NaN \n",
|
|
"2023-01-01 00:04:00+01:00 -3.61 -209.4 -209.4 NaN NaN \n",
|
|
"\n",
|
|
" Gas prices (€/MWh) CO2 prices (€/ton) \\\n",
|
|
"2023-01-01 00:00:00+01:00 63.998 5.23 \n",
|
|
"2023-01-01 00:01:00+01:00 63.998 5.23 \n",
|
|
"2023-01-01 00:02:00+01:00 63.998 5.23 \n",
|
|
"2023-01-01 00:03:00+01:00 63.998 5.23 \n",
|
|
"2023-01-01 00:04:00+01:00 63.998 5.23 \n",
|
|
"\n",
|
|
" CO2 prices (€/MWh) Tsource (VDG) Tsink (VDG) \\\n",
|
|
"2023-01-01 00:00:00+01:00 1.01 75.0 144.0 \n",
|
|
"2023-01-01 00:01:00+01:00 1.01 75.0 144.0 \n",
|
|
"2023-01-01 00:02:00+01:00 1.01 75.0 144.0 \n",
|
|
"2023-01-01 00:03:00+01:00 1.01 75.0 144.0 \n",
|
|
"2023-01-01 00:04:00+01:00 1.01 75.0 144.0 \n",
|
|
"\n",
|
|
" MW (VDG) Tsource (NDG) Tsink (NDG) MW (NDG) \\\n",
|
|
"2023-01-01 00:00:00+01:00 1.0 75.0 144.0 1.0 \n",
|
|
"2023-01-01 00:01:00+01:00 1.0 75.0 144.0 1.0 \n",
|
|
"2023-01-01 00:02:00+01:00 1.0 75.0 144.0 1.0 \n",
|
|
"2023-01-01 00:03:00+01:00 1.0 75.0 144.0 1.0 \n",
|
|
"2023-01-01 00:04:00+01:00 1.0 75.0 144.0 1.0 \n",
|
|
"\n",
|
|
" Total demand \n",
|
|
"2023-01-01 00:00:00+01:00 2.0 \n",
|
|
"2023-01-01 00:01:00+01:00 2.0 \n",
|
|
"2023-01-01 00:02:00+01:00 2.0 \n",
|
|
"2023-01-01 00:03:00+01:00 2.0 \n",
|
|
"2023-01-01 00:04:00+01:00 2.0 "
|
|
]
|
|
},
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"s.hpcase.data.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"## Assets"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"### COP curve"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def cop_curve(Tsink, Tsource):\n",
|
|
" Tsink += 273\n",
|
|
" Tsource += 273\n",
|
|
"\n",
|
|
" c1 = 0.267 * Tsink / (Tsink - Tsource)\n",
|
|
" c2 = 0.333 * Tsink / (Tsink - Tsource)\n",
|
|
" \n",
|
|
" return Polynomial([c2, c1])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/latex": [
|
|
"$x \\mapsto \\text{1.08290551} + \\text{0.86827559}\\,x$"
|
|
],
|
|
"text/plain": [
|
|
"Polynomial([1.08290551, 0.86827559], domain=[-1, 1], window=[-1, 1], symbol='x')"
|
|
]
|
|
},
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"cop_curve(140, 13)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/latex": [
|
|
"$x \\mapsto \\text{2.05267164} + \\text{1.64583582}\\,x$"
|
|
],
|
|
"text/plain": [
|
|
"Polynomial([2.05267164, 1.64583582], domain=[-1, 1], window=[-1, 1], symbol='x')"
|
|
]
|
|
},
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"cop_curve(140, 73)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"heatpump = Heatpump(\n",
|
|
" name='Heatpump',\n",
|
|
" max_th_power=1,\n",
|
|
" min_th_power=0,\n",
|
|
" cop_curve=cop_curve\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#heatpump.get_cop(heat_output=load, Tsink=140, Tsource=13)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#heatpump.get_cop(heat_output=load, Tsink=140, Tsource=99)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import itertools\n",
|
|
"\n",
|
|
"source_Ts = np.arange(25, 75) + 273\n",
|
|
"sink_Ts = np.arange(80, 170) + 273\n",
|
|
"\n",
|
|
"df = pd.DataFrame(columns=list(sink_Ts), index=list(source_Ts))\n",
|
|
"for sourceT, sinkT in itertools.product(source_Ts, sink_Ts):\n",
|
|
" df.loc[sourceT, sinkT] = heatpump.get_cop(heat_output=0.5, Tsink=sinkT, Tsource=sourceT)\n",
|
|
" \n",
|
|
"#df.to_csv('cops_at_50perc_load.csv', sep=';', decimal=',')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"sourceT = 63 + 273\n",
|
|
"sinkT = 140 + 273\n",
|
|
"loads = np.arange(0, 1, 0.01)\n",
|
|
"\n",
|
|
"cop_series = pd.Series(index=loads, dtype='float')\n",
|
|
"load_series = pd.Series(index=loads, dtype='float')\n",
|
|
"for load in loads:\n",
|
|
" cop = heatpump.get_cop(heat_output=load, Tsink=sinkT, Tsource=sourceT)\n",
|
|
" cop_series[load] = cop\n",
|
|
" load_series[load] = load / cop "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.plotly.v1+json": {
|
|
"config": {
|
|
"plotlyServerURL": "https://plot.ly"
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},
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"data": [
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{
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"line": {
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"shape": "linear",
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|
"width": 1.3
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},
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|
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"name": "None",
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],
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"layout": {
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[
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1,
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"#f0f921"
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]
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]
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},
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"colorway": [
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"#636efa",
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"#EF553B",
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"#00cc96",
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"#ab63fa",
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"#FFA15A",
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"#19d3f3",
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"#FF6692",
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"#B6E880",
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"#FF97FF",
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"#FECB52"
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],
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"font": {
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"color": "#2a3f5f"
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},
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"geo": {
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"bgcolor": "white",
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"lakecolor": "white",
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"landcolor": "#E5ECF6",
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"showlakes": true,
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"showland": true,
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"subunitcolor": "white"
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},
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"hoverlabel": {
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"align": "left"
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},
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"hovermode": "closest",
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"mapbox": {
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"style": "light"
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},
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"paper_bgcolor": "white",
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"plot_bgcolor": "#E5ECF6",
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"polar": {
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"angularaxis": {
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"gridcolor": "white",
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"linecolor": "white",
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"ticks": ""
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},
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"bgcolor": "#E5ECF6",
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"radialaxis": {
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"gridcolor": "white",
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"linecolor": "white",
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"ticks": ""
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}
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},
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"scene": {
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"xaxis": {
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"backgroundcolor": "#E5ECF6",
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"gridcolor": "white",
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"gridwidth": 2,
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"linecolor": "white",
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"showbackground": true,
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"ticks": "",
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"zerolinecolor": "white"
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},
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"yaxis": {
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"backgroundcolor": "#E5ECF6",
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"gridcolor": "white",
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"gridwidth": 2,
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"linecolor": "white",
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"showbackground": true,
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"ticks": "",
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"zerolinecolor": "white"
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},
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"zaxis": {
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"backgroundcolor": "#E5ECF6",
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"gridcolor": "white",
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"gridwidth": 2,
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"linecolor": "white",
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"showbackground": true,
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"ticks": "",
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"zerolinecolor": "white"
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}
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},
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"shapedefaults": {
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"line": {
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"color": "#2a3f5f"
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}
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},
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"ternary": {
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"aaxis": {
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"gridcolor": "white",
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"linecolor": "white",
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"ticks": ""
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},
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"baxis": {
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"gridcolor": "white",
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"linecolor": "white",
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"ticks": ""
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},
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"bgcolor": "#E5ECF6",
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"caxis": {
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"gridcolor": "white",
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"linecolor": "white",
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"ticks": ""
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}
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},
|
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"title": {
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"x": 0.05
|
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},
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"xaxis": {
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"automargin": true,
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|
"gridcolor": "white",
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"linecolor": "white",
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"ticks": "",
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"title": {
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|
"standoff": 15
|
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},
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|
"zerolinecolor": "white",
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"zerolinewidth": 2
|
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},
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|
"yaxis": {
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"automargin": true,
|
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"gridcolor": "white",
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"linecolor": "white",
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"ticks": "",
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"title": {
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|
"standoff": 15
|
|
},
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"zerolinecolor": "white",
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"zerolinewidth": 2
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}
|
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}
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},
|
|
"title": {
|
|
"font": {
|
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"color": "#4D5663"
|
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},
|
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"text": "Heatpump COP curve at Tsource=336 and Tsink=413"
|
|
},
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"width": 600,
|
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"xaxis": {
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"autorange": true,
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"gridcolor": "#E1E5ED",
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"range": [
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0,
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0.99
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],
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"showgrid": true,
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"tickfont": {
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"color": "#4D5663"
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},
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"title": {
|
|
"font": {
|
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"color": "#4D5663"
|
|
},
|
|
"text": "Thermal load in %"
|
|
},
|
|
"type": "linear",
|
|
"zerolinecolor": "#E1E5ED"
|
|
},
|
|
"yaxis": {
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"autorange": true,
|
|
"gridcolor": "#E1E5ED",
|
|
"range": [
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|
2.835897272727273,
|
|
5.452497272727274
|
|
],
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|
"showgrid": true,
|
|
"tickfont": {
|
|
"color": "#4D5663"
|
|
},
|
|
"title": {
|
|
"font": {
|
|
"color": "#4D5663"
|
|
},
|
|
"text": "COP"
|
|
},
|
|
"type": "linear",
|
|
"zerolinecolor": "#E1E5ED"
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|
}
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|
}
|
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},
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",
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"title": {
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"font": {
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},
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"text": "E-load"
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},
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}
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"fig_load_curve = load_series.iplot(title=f'Heatpump Load curve at Tsource={sourceT} and Tsink={sinkT}', yTitle='E-load', xTitle='Thermal load in %', colors=recoygreen, asFigure=True, dimensions=(600,400))\n",
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"def cop_curve_new(Tsink, Tsource):\n",
|
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" Tsink += 273\n",
|
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" Tsource += 273\n",
|
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" Tlift = Tsink - Tsource\n",
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"\n",
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" c0 = 0.0005426*Tlift**2 - 0.1178*Tlift + 6.962\n",
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" c1 = 6.7058 \n",
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" c2 = -1.79\n",
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" \n",
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"$x \\mapsto \\text{19.2496754} + \\text{6.7058}\\,x - \\text{1.79}\\,x^{2}$"
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"source": [
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"sourceT = 63 + 273\n",
|
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"sinkT = 140 + 273\n",
|
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"cop_curve_new(sourceT, sinkT)"
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]
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},
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"cell_type": "code",
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"execution_count": 26,
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},
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"outputs": [],
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"source": [
|
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"heatpump = Heatpump(\n",
|
|
" name='Heatpump',\n",
|
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" max_th_power=1,\n",
|
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" min_th_power=0,\n",
|
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" cop_curve=cop_curve_new\n",
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")"
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"outputs": [],
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"source": [
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"import itertools\n",
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"\n",
|
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"source_Ts = np.arange(25, 75) + 273\n",
|
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"sink_Ts = np.arange(80, 170) + 273\n",
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"\n",
|
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"df = pd.DataFrame(columns=list(sink_Ts), index=list(source_Ts))\n",
|
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"for sourceT, sinkT in itertools.product(source_Ts, sink_Ts):\n",
|
|
" df.loc[sourceT, sinkT] = heatpump.get_cop(heat_output=0.5, Tsink=sinkT, Tsource=sourceT)\n",
|
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" \n",
|
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"#df.to_csv('cops_at_50perc_load.csv', sep=';', decimal=',')"
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},
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"cell_type": "code",
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"metadata": {
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"outputs": [],
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"source": [
|
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"sourceT = 63 + 273\n",
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"sinkT = 140 + 273\n",
|
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"loads = np.arange(0, 1, 0.01)\n",
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"\n",
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"cop_series = pd.Series(index=loads, dtype='float')\n",
|
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"load_series = pd.Series(index=loads, dtype='float')\n",
|
|
"for load in loads:\n",
|
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" cop = heatpump.get_cop(heat_output=load, Tsink=sinkT, Tsource=sourceT)\n",
|
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" cop_series[load] = cop\n",
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" load_series[load] = load / cop "
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",
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"caxis": {
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"ticks": ""
|
|
}
|
|
},
|
|
"title": {
|
|
"x": 0.05
|
|
},
|
|
"xaxis": {
|
|
"automargin": true,
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"ticks": "",
|
|
"title": {
|
|
"standoff": 15
|
|
},
|
|
"zerolinecolor": "white",
|
|
"zerolinewidth": 2
|
|
},
|
|
"yaxis": {
|
|
"automargin": true,
|
|
"gridcolor": "white",
|
|
"linecolor": "white",
|
|
"ticks": "",
|
|
"title": {
|
|
"standoff": 15
|
|
},
|
|
"zerolinecolor": "white",
|
|
"zerolinewidth": 2
|
|
}
|
|
}
|
|
},
|
|
"title": {
|
|
"font": {
|
|
"color": "#4D5663"
|
|
},
|
|
"text": "Heatpump COP curve at Tsource=99 and Tsink=413"
|
|
},
|
|
"width": 600,
|
|
"xaxis": {
|
|
"autorange": true,
|
|
"gridcolor": "#E1E5ED",
|
|
"range": [
|
|
50,
|
|
99
|
|
],
|
|
"showgrid": true,
|
|
"tickfont": {
|
|
"color": "#4D5663"
|
|
},
|
|
"title": {
|
|
"font": {
|
|
"color": "#4D5663"
|
|
},
|
|
"text": "Thermal load in %"
|
|
},
|
|
"type": "linear",
|
|
"zerolinecolor": "#E1E5ED"
|
|
},
|
|
"yaxis": {
|
|
"autorange": true,
|
|
"gridcolor": "#E1E5ED",
|
|
"range": [
|
|
5.5436794111111105,
|
|
8.087291188888889
|
|
],
|
|
"showgrid": true,
|
|
"tickfont": {
|
|
"color": "#4D5663"
|
|
},
|
|
"title": {
|
|
"font": {
|
|
"color": "#4D5663"
|
|
},
|
|
"text": "COP"
|
|
},
|
|
"type": "linear",
|
|
"zerolinecolor": "#E1E5ED"
|
|
}
|
|
}
|
|
},
|
|
"image/png": 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",
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0.6666666666666666,
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],
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0.7777777777777778,
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"#fb9f3a"
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],
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0.8888888888888888,
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"#fdca26"
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],
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[
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1,
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"#f0f921"
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]
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]
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},
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"colorway": [
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"#636efa",
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"#EF553B",
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"#00cc96",
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"#ab63fa",
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"#FFA15A",
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"#19d3f3",
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"#FF6692",
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"#B6E880",
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"#FF97FF",
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"#FECB52"
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"font": {
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"geo": {
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"lakecolor": "white",
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"landcolor": "#E5ECF6",
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"showlakes": true,
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"showland": true,
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"subunitcolor": "white"
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"hoverlabel": {
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"polar": {
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"yaxis": {
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"backgroundcolor": "#E5ECF6",
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"gridcolor": "white",
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"gridwidth": 2,
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"gridwidth": 2,
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"showbackground": true,
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"ticks": "",
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"zerolinecolor": "white"
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}
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},
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"shapedefaults": {
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"line": {
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"color": "#2a3f5f"
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"title": {
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"x": 0.05
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},
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"title": {
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"standoff": 15
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},
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},
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"standoff": 15
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},
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}
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}
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},
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"title": {
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"font": {
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"color": "#4D5663"
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},
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"text": "Heatpump Load curve at Tsource=99 and Tsink=413"
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},
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},
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"title": {
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"font": {
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"color": "#4D5663"
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},
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"text": "Thermal load in %"
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},
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"type": "linear",
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"zerolinecolor": "#E1E5ED"
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},
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"range": [
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-0.009177621075182005,
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0.1743748004284581
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"tickfont": {
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"color": "#4D5663"
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},
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"title": {
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"font": {
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"color": "#4D5663"
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},
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"text": "E-load"
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},
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"type": "linear",
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"zerolinecolor": "#E1E5ED"
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}
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}
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",
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{\"responsive\": true} ).then(function(){\n",
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" \n",
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"var gd = document.getElementById('7a86609d-4662-4071-b76a-7da3b50b904e');\n",
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"var x = new MutationObserver(function (mutations, observer) {{\n",
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" var display = window.getComputedStyle(gd).display;\n",
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" if (!display || display === 'none') {{\n",
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" console.log([gd, 'removed!']);\n",
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" Plotly.purge(gd);\n",
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" observer.disconnect();\n",
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" }}\n",
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"}});\n",
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"\n",
|
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"// Listen for the removal of the full notebook cells\n",
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"var notebookContainer = gd.closest('#notebook-container');\n",
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"if (notebookContainer) {{\n",
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" x.observe(notebookContainer, {childList: true});\n",
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"}}\n",
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"\n",
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"// Listen for the clearing of the current output cell\n",
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"var outputEl = gd.closest('.output');\n",
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"if (outputEl) {{\n",
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" x.observe(outputEl, {childList: true});\n",
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"}}\n",
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"\n",
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" }) }; }); </script> </div>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
|
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"fig_load_curve = load_series.iplot(title=f'Heatpump Load curve at Tsource={sourceT} and Tsink={sinkT}', yTitle='E-load', xTitle='Thermal load in %', colors=recoygreen, asFigure=True, dimensions=(600,400))\n",
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"fig_load_curve"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
|
|
"tags": [
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"exclude"
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|
]
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},
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"source": [
|
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"### Create and assign assets"
|
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]
|
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},
|
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{
|
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"cell_type": "code",
|
|
"execution_count": 32,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
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},
|
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"outputs": [],
|
|
"source": [
|
|
"def create_and_assign_assets(c, s):\n",
|
|
" heatpump_vdg = Heatpump(\n",
|
|
" name='Heatpump VDG',\n",
|
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" max_th_power=c.hp_vdg_e_power,\n",
|
|
" min_th_power=c.hp_vdg_e_power * c.hp_min_load,\n",
|
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" cop_curve=cop_curve_new\n",
|
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" )\n",
|
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"\n",
|
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" heatpump_ndg = Heatpump(\n",
|
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" name='Heatpump NDG',\n",
|
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" max_th_power=c.hp_ndg_e_power,\n",
|
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" min_th_power=c.hp_ndg_e_power * c.hp_min_load,\n",
|
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" cop_curve=cop_curve_new\n",
|
|
" )\n",
|
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"\n",
|
|
" capex_vdg = c.hp_capex*(heatpump_vdg.max_th_power) \n",
|
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" capex_ndg = c.hp_capex*(heatpump_ndg.max_th_power)\n",
|
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" heatpump_vdg.set_financials(capex=capex_vdg, opex=c.hp_opex*capex_vdg, devex=c.hp_devex*capex_vdg, lifetime=25)\n",
|
|
" heatpump_ndg.set_financials(capex=capex_ndg, opex=c.hp_opex*capex_ndg, devex=c.hp_devex*capex_ndg, lifetime=25)\n",
|
|
"\n",
|
|
" gasboiler = GasBoiler(\n",
|
|
" name='Gasboiler',\n",
|
|
" max_th_output=c.gb_power,\n",
|
|
" efficiency=c.gb_efficiency\n",
|
|
" )\n",
|
|
" gasboiler.set_financials(capex=0, opex=0, devex=0, lifetime=25)\n",
|
|
" \n",
|
|
" waterstorage = HotWaterStorage(\n",
|
|
" name='HotWaterStorage',\n",
|
|
" rated_power=c.storage_power, \n",
|
|
" capacity_per_volume=c.storage_cap_per_volume,\n",
|
|
" volume=c.storage_volume, \n",
|
|
" temperature=c.storage_temperature,\n",
|
|
" min_storagelevel=c.storage_min_level,\n",
|
|
" # initial_storagelevel=c.storage_initial_level\n",
|
|
" )\n",
|
|
" capex_ws = c.storage_capex_per_MW * waterstorage.max_power + c.storage_capex_per_MWh * waterstorage.capacity\n",
|
|
" opex_ws = c.storage_opex_perc_of_capex * capex_ws\n",
|
|
" waterstorage.set_financials(capex=capex_ws, opex=opex_ws, devex=0, lifetime=c.storage_lifetime)\n",
|
|
" \n",
|
|
" s.baseline.add_asset(gasboiler)\n",
|
|
" s.hpcase.add_asset(heatpump_vdg)\n",
|
|
" s.hpcase.add_asset(heatpump_ndg)\n",
|
|
" s.storage_case_PCM.add_asset(heatpump_vdg)\n",
|
|
" s.storage_case_PCM.add_asset(heatpump_ndg)\n",
|
|
" s.storage_case_PCM.add_asset(waterstorage)\n",
|
|
"# s.hpcase_sde.add_asset(heatpump_vdg)\n",
|
|
"# s.hpcase_sde.add_asset(heatpump_ndg)\n",
|
|
"# s.optcase1.add_asset(heatpump_vdg)\n",
|
|
"# s.optcase1.add_asset(heatpump_ndg)\n",
|
|
"# s.optcase1.add_asset(gasboiler)\n",
|
|
"# s.afrr_case.add_asset(heatpump_vdg)\n",
|
|
"# s.afrr_case.add_asset(heatpump_ndg)\n",
|
|
"# s.afrr_case.add_asset(gasboiler)\n",
|
|
" return s\n",
|
|
"\n",
|
|
"s = create_and_assign_assets(c, s)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"HotWaterStorage(name=HotWaterStorage, rated_power=25, capacity=50.0, temperature=95, min_storagelevel=2.5)"
|
|
]
|
|
},
|
|
"execution_count": 33,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"s.storage_case_PCM.assets['HotWaterStorage']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"## Optimization"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"### Strategies"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def baseline_sim(case):\n",
|
|
" gasboiler = list(case.assets.values())[0]\n",
|
|
" data = case.data\n",
|
|
" demand = (data['MW (VDG)'] + data['MW (NDG)']).to_list()\n",
|
|
"\n",
|
|
" minutes = iter(range(len(case.data)))\n",
|
|
" th_output = [0] * len(case.data)\n",
|
|
" gas_input = [0] * len(case.data)\n",
|
|
"\n",
|
|
" for m in minutes:\n",
|
|
" th_output[m], gas_input[m] = gasboiler.set_heat_output(demand[m])\n",
|
|
"\n",
|
|
" data['output_MW_th'] = np.array(th_output)\n",
|
|
" data['output_MWh_th'] = np.array(data['output_MW_th']/60)\n",
|
|
" data['gb_input_MW'] = np.array(gas_input)\n",
|
|
" data['gb_input_MWh'] = np.array(data['gb_input_MW']/60)\n",
|
|
" case.data = data.round(5)\n",
|
|
" return case"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def hponly(case):\n",
|
|
" hp_vdg = case.assets['Heatpump VDG']\n",
|
|
" hp_ndg = case.assets['Heatpump NDG']\n",
|
|
" demand_vdg = case.data['MW (VDG)'].to_list()\n",
|
|
" demand_ndg = case.data['MW (NDG)'].to_list()\n",
|
|
" Tsink_vdg = case.data['Tsink (VDG)'].to_list()\n",
|
|
" Tsink_ndg = case.data['Tsink (NDG)'].to_list()\n",
|
|
" Tsource_vdg = case.data['Tsource (VDG)'].to_list()\n",
|
|
" Tsource_ndg = case.data['Tsource (NDG)'].to_list()\n",
|
|
"\n",
|
|
" hp_vdg_input = [0] * len(case.data)\n",
|
|
" hp_ndg_input = [0] * len(case.data)\n",
|
|
" hp_vdg_output = [0] * len(case.data)\n",
|
|
" hp_ndg_output = [0] * len(case.data)\n",
|
|
"\n",
|
|
" minutes = iter(range(len(case.data)))\n",
|
|
" for m in minutes:\n",
|
|
" demand = demand_vdg[m]\n",
|
|
" if demand != 0:\n",
|
|
" hp_vdg_input[m], hp_vdg_output[m] = hp_vdg.set_heat_output(\n",
|
|
" heat_output=demand,\n",
|
|
" Tsink=Tsink_vdg[m],\n",
|
|
" Tsource=Tsource_vdg[m]\n",
|
|
" )\n",
|
|
"\n",
|
|
" demand = demand_ndg[m]\n",
|
|
" if demand != 0:\n",
|
|
" hp_ndg_input[m], hp_ndg_output[m] = hp_ndg.set_heat_output(\n",
|
|
" heat_output=demand_ndg[m],\n",
|
|
" Tsink=Tsink_ndg[m],\n",
|
|
" Tsource=Tsource_ndg[m]\n",
|
|
" )\n",
|
|
"\n",
|
|
" case.data['hp_output_MW'] = np.array(hp_vdg_output) + np.array(hp_ndg_output)\n",
|
|
" case.data['hp_input_MW'] = np.array(hp_vdg_input) + np.array(hp_ndg_input)\n",
|
|
" case.data['cop'] = case.data['hp_output_MW'] / -case.data['hp_input_MW']\n",
|
|
" \n",
|
|
" for col in case.data.columns:\n",
|
|
" if col.endswith('MW'):\n",
|
|
" case.data[col + 'h'] = case.data[col] / 60\n",
|
|
"\n",
|
|
" case.data = case.data.round(3)\n",
|
|
" return case"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"Tref = 0\n",
|
|
"Cp = 4190 #J/kgK\n",
|
|
"MWtoJs = 1000_000\n",
|
|
"\n",
|
|
"def power_to_mass_flow(power_MW, Tsink, Tref, Cp):\n",
|
|
" return power_MW * MWtoJs /(Cp*(Tsink - Tref))\n",
|
|
"def energy_to_storage(hp_heat_output_MW, process_demand_MW):\n",
|
|
" return hp_heat_output_MW - process_demand_MW #MW\n",
|
|
"\n",
|
|
"def Tsource_calculation(Tstorage, discharge_power, Tsource, process_mass_flow): \n",
|
|
" discharge_mass_flow = power_to_mass_flow(discharge_power, Tstorage, Tref, Cp)\n",
|
|
" \n",
|
|
" combined_mass_flow = (discharge_mass_flow + process_mass_flow)\n",
|
|
" if combined_mass_flow == 0:\n",
|
|
" return Tsource\n",
|
|
" else: \n",
|
|
" return (Tstorage * discharge_mass_flow + Tsource * process_mass_flow) / combined_mass_flow"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 37,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def hp_storage_opt_enginge(c, ws, hp_vdg, hp_ndg, pos, neg, dam, demand_vdg, demand_ndg, tsource_vdg, tsink_vdg, tsource_ndg, tsink_ndg):\n",
|
|
" \n",
|
|
" from_storage_vdg_MW = 0\n",
|
|
" to_storage_vdg_MW = 0 \n",
|
|
" from_storage_ndg_MW = 0\n",
|
|
" to_storage_ndg_MW = 0 \n",
|
|
" \n",
|
|
" if neg < dam - c.threshold:\n",
|
|
" # overproduce\n",
|
|
" if demand_vdg != 0:\n",
|
|
" desired_hp_load_vdg = min(demand_vdg + ws.charging_power_limit, hp_vdg.max_th_power)\n",
|
|
" e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(desired_hp_load_vdg, tsink_vdg, tsource_vdg)\n",
|
|
"\n",
|
|
" to_storage_vdg_MW = th_load_vdg - demand_vdg\n",
|
|
" to_storage_vdg_MW = -ws.charge(round(to_storage_vdg_MW, 3))\n",
|
|
"\n",
|
|
" extra_charging_power_constraint = ws.max_power - to_storage_vdg_MW\n",
|
|
" else:\n",
|
|
" e_load_vdg, th_load_vdg = (0,0)\n",
|
|
" \n",
|
|
" if demand_ndg != 0:\n",
|
|
" desired_hp_load_ndg = min(\n",
|
|
" demand_ndg + min(ws.charging_power_limit, extra_charging_power_constraint), \n",
|
|
" hp_ndg.max_th_power\n",
|
|
" )\n",
|
|
" e_load_ndg, th_load_ndg = hp_ndg.set_heat_output(desired_hp_load_ndg, tsink_ndg, tsource_ndg)\n",
|
|
"\n",
|
|
" to_storage_ndg_MW = th_load_ndg - demand_ndg\n",
|
|
" to_storage_ndg_MW = -ws.charge(round(to_storage_ndg_MW, 3))\n",
|
|
" else:\n",
|
|
" e_load_ndg, th_load_ndg = (0,0)\n",
|
|
" \n",
|
|
"\n",
|
|
" elif pos > dam + c.threshold:\n",
|
|
" # take from storage\n",
|
|
" if demand_vdg != 0:\n",
|
|
" from_process_massflow_vdg = power_to_mass_flow(demand_vdg, tsink_vdg, Tref, Cp)\n",
|
|
" try:\n",
|
|
" from_storage_vdg_MW = ws.discharging_power_limit\n",
|
|
" tsource_vdg = Tsource_calculation(ws.temperature, from_storage_vdg_MW, tsource_vdg, from_process_massflow_vdg)\n",
|
|
" e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(demand_vdg, tsink_vdg, tsource_vdg)\n",
|
|
" except: \n",
|
|
" from_storage_vdg_MW = 0\n",
|
|
" tsource_vdg = Tsource_calculation(ws.temperature, from_storage_vdg_MW, tsource_vdg, from_process_massflow_vdg)\n",
|
|
" e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(demand_vdg, tsink_vdg, tsource_vdg)\n",
|
|
"\n",
|
|
" from_storage_vdg_MW = ws.discharge(round(from_storage_vdg_MW, 3))\n",
|
|
" else:\n",
|
|
" e_load_vdg, th_load_vdg = (0,0)\n",
|
|
" \n",
|
|
" #print({f'from_storage_vdg_MW='})\n",
|
|
" if demand_ndg != 0:\n",
|
|
" from_process_massflow_ndg = power_to_mass_flow(demand_ndg, tsink_ndg, Tref, Cp)\n",
|
|
" \n",
|
|
" try:\n",
|
|
" from_storage_ndg_MW = min(ws.max_power - from_storage_vdg_MW, ws.discharging_power_limit)\n",
|
|
" tsource_ndg = Tsource_calculation(ws.temperature, from_storage_ndg_MW, tsource_ndg, from_process_massflow_ndg)\n",
|
|
" e_load_ndg, th_load_ndg = hp_ndg.set_heat_output(demand_ndg, tsink_ndg, tsource_ndg)\n",
|
|
" except: \n",
|
|
" from_storage_vdg_MW = 0\n",
|
|
" tsource_ndg = Tsource_calculation(ws.temperature, from_storage_ndg_MW, tsource_ndg, from_process_massflow_ndg)\n",
|
|
" e_load_ndg, th_load_ndg = hp_ndg.set_heat_output(demand_ndg, tsink_ndg, tsource_ndg)\n",
|
|
"\n",
|
|
" from_storage_ndg_MW = ws.discharge(round(from_storage_ndg_MW))\n",
|
|
" else:\n",
|
|
" e_load_ndg, th_load_ndg = (0,0)\n",
|
|
" else:\n",
|
|
" e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(\n",
|
|
" heat_output=demand_vdg,\n",
|
|
" Tsink=tsink_vdg,\n",
|
|
" Tsource=tsource_vdg\n",
|
|
" )\n",
|
|
"\n",
|
|
" e_load_ndg, th_load_ndg = hp_ndg.set_heat_output(\n",
|
|
" heat_output=demand_ndg,\n",
|
|
" Tsink=tsink_ndg,\n",
|
|
" Tsource=tsource_ndg\n",
|
|
" )\n",
|
|
" return e_load_vdg, th_load_vdg, e_load_ndg, th_load_ndg, tsource_vdg, tsource_ndg, to_storage_vdg_MW, from_storage_vdg_MW, to_storage_ndg_MW, from_storage_ndg_MW, ws, hp_vdg, hp_ndg\n",
|
|
"# Neg-take price, Pos-feed price"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def hponly_with_storage(case):\n",
|
|
" hp_vdg = case.assets['Heatpump VDG']\n",
|
|
" hp_ndg = case.assets['Heatpump NDG']\n",
|
|
" ws = case.assets['HotWaterStorage']\n",
|
|
" demands_vdg = case.data['MW (VDG)'].to_list()\n",
|
|
" demands_ndg = case.data['MW (NDG)'].to_list()\n",
|
|
" Tsink_vdg = case.data['Tsink (VDG)'].to_list()\n",
|
|
" Tsink_ndg = case.data['Tsink (NDG)'].to_list()\n",
|
|
" Tsource_vdg = case.data['Tsource (VDG)'].to_list()\n",
|
|
" Tsource_ndg = case.data['Tsource (NDG)'].to_list()\n",
|
|
" dam_prices = case.data['DAM'].to_list()\n",
|
|
" pos_prices = case.data['POS'].to_list()\n",
|
|
" neg_prices = case.data['NEG'].to_list()\n",
|
|
"\n",
|
|
" hp_vdg_input = [0] * len(case.data)\n",
|
|
" hp_ndg_input = [0] * len(case.data)\n",
|
|
" hp_vdg_output = [0] * len(case.data)\n",
|
|
" hp_ndg_output = [0] * len(case.data)\n",
|
|
"\n",
|
|
" minutes = len(case.data)\n",
|
|
" storage_levels = [None] * minutes\n",
|
|
" to_storage_list = [0] * minutes\n",
|
|
" from_storage_vdg_list = [0] * minutes\n",
|
|
" to_storage_vdg_list = [0] * minutes\n",
|
|
" from_storage_ndg_list = [0] * minutes\n",
|
|
" to_storage_ndg_list = [0] * minutes\n",
|
|
" \n",
|
|
" new_tsources_vdg = case.data['Tsource (VDG)'].to_list()\n",
|
|
" new_tsources_ndg = case.data['Tsource (NDG)'].to_list()\n",
|
|
" \n",
|
|
" ws.set_chargelevel(ws.min_chargelevel)\n",
|
|
" \n",
|
|
" for m in range(minutes):\n",
|
|
" tsource_vdg = Tsource_vdg[m]\n",
|
|
" tsink_vdg = Tsink_vdg[m]\n",
|
|
" tsource_ndg = Tsource_ndg[m]\n",
|
|
" tsink_ndg = Tsink_ndg[m]\n",
|
|
" demand_vdg = demands_vdg[m]\n",
|
|
" demand_ndg = demands_ndg[m]\n",
|
|
" \n",
|
|
" e_load_vdg, th_load_vdg, e_load_ndg, th_load_ndg, tsource_vdg, tsource_ndg, to_storage_vdg_MW, from_storage_vdg_MW, to_storage_ndg_MW, from_storage_ndg_MW, ws, hp_vdg, hp_ndg = hp_storage_opt_enginge(\n",
|
|
" c, ws, hp_vdg, hp_ndg, pos_prices[m], neg_prices[m], dam_prices[m], demand_vdg, demand_ndg, \n",
|
|
" tsource_vdg, tsink_vdg, tsource_ndg, tsink_ndg\n",
|
|
" )\n",
|
|
" \n",
|
|
" hp_vdg_input[m] = e_load_vdg\n",
|
|
" hp_vdg_output[m] = th_load_vdg\n",
|
|
" hp_ndg_input[m] = e_load_ndg\n",
|
|
" hp_ndg_output[m] = th_load_ndg\n",
|
|
" \n",
|
|
" storage_levels[m] = ws.chargelevel\n",
|
|
" new_tsources_vdg[m] = tsource_vdg\n",
|
|
" new_tsources_ndg[m] = tsource_ndg\n",
|
|
" to_storage_vdg_list[m] = to_storage_vdg_MW\n",
|
|
" from_storage_vdg_list[m] = from_storage_vdg_MW\n",
|
|
" to_storage_ndg_list[m] = to_storage_ndg_MW\n",
|
|
" from_storage_ndg_list[m] = from_storage_ndg_MW\n",
|
|
" \n",
|
|
" case.data['hp_output_MW'] = np.array(hp_vdg_output) + np.array(hp_ndg_output)\n",
|
|
" case.data['hp_input_MW'] = np.array(hp_vdg_input) + np.array(hp_ndg_input)\n",
|
|
" case.data['cop'] = case.data['hp_output_MW'] / -case.data['hp_input_MW']\n",
|
|
" case.data['tsource_vdg'] = new_tsources_vdg\n",
|
|
" case.data['tsource_ndg'] = new_tsources_ndg\n",
|
|
" case.data['to_storage_ndg_MW'] = to_storage_ndg_list\n",
|
|
" case.data['from_storage_ndg_MW'] = from_storage_ndg_list\n",
|
|
" case.data['to_storage_vdg_MW'] = to_storage_vdg_list\n",
|
|
" case.data['from_storage_vdg_MW'] = from_storage_vdg_list\n",
|
|
" case.data['to_storage_MW'] = case.data['to_storage_vdg_MW'] + case.data['to_storage_ndg_MW']\n",
|
|
" case.data['from_storage_MW'] = case.data['from_storage_vdg_MW'] + case.data['from_storage_ndg_MW']\n",
|
|
" case.data['storage_level_MWh'] = storage_levels\n",
|
|
" \n",
|
|
" for col in case.data.columns:\n",
|
|
" if col.endswith('MW'):\n",
|
|
" case.data[col + 'h'] = case.data[col] / 60\n",
|
|
"\n",
|
|
" case.data = case.data.round(3)\n",
|
|
" return case\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 39,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# charge_times = print(sum(s.storage_case_PCM.data['to_storage_MW']!= 0))\n",
|
|
"# discharge_times = print(sum(s.storage_case_PCM.data['from_storage_MW']!= 0))\n",
|
|
"# # 154476 times out of 525600, the storage is used to charge/dischage\n",
|
|
"# # from 39 000 to 110 000 discharge time increases\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 40,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\assets.py:263: UserWarning:\n",
|
|
"\n",
|
|
"Chosen heat output is out of range [0.0 - 23.3]. Heat output is being limited to the closest boundary.\n",
|
|
"\n",
|
|
"C:\\Users\\shahla.huseynova\\source\\repos\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\assets.py:263: UserWarning:\n",
|
|
"\n",
|
|
"Chosen heat output is out of range [0.0 - 7.7]. Heat output is being limited to the closest boundary.\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"ename": "ValueError",
|
|
"evalue": "cannot convert float NaN to integer",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
|
|
"Cell \u001b[1;32mIn[40], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m s\u001b[38;5;241m.\u001b[39mstorage_case_PCM \u001b[38;5;241m=\u001b[39m \u001b[43mhponly_with_storage\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstorage_case_PCM\u001b[49m\u001b[43m)\u001b[49m\n",
|
|
"Cell \u001b[1;32mIn[38], line 41\u001b[0m, in \u001b[0;36mhponly_with_storage\u001b[1;34m(case)\u001b[0m\n\u001b[0;32m 38\u001b[0m demand_vdg \u001b[38;5;241m=\u001b[39m demands_vdg[m]\n\u001b[0;32m 39\u001b[0m demand_ndg \u001b[38;5;241m=\u001b[39m demands_ndg[m]\n\u001b[1;32m---> 41\u001b[0m e_load_vdg, th_load_vdg, e_load_ndg, th_load_ndg, tsource_vdg, tsource_ndg, to_storage_vdg_MW, from_storage_vdg_MW, to_storage_ndg_MW, from_storage_ndg_MW, ws, hp_vdg, hp_ndg \u001b[38;5;241m=\u001b[39m \u001b[43mhp_storage_opt_enginge\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 42\u001b[0m \u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mws\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhp_vdg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhp_ndg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpos_prices\u001b[49m\u001b[43m[\u001b[49m\u001b[43mm\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mneg_prices\u001b[49m\u001b[43m[\u001b[49m\u001b[43mm\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdam_prices\u001b[49m\u001b[43m[\u001b[49m\u001b[43mm\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdemand_vdg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdemand_ndg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[0;32m 43\u001b[0m \u001b[43m \u001b[49m\u001b[43mtsource_vdg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtsink_vdg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtsource_ndg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtsink_ndg\u001b[49m\n\u001b[0;32m 44\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 46\u001b[0m hp_vdg_input[m] \u001b[38;5;241m=\u001b[39m e_load_vdg\n\u001b[0;32m 47\u001b[0m hp_vdg_output[m] \u001b[38;5;241m=\u001b[39m th_load_vdg\n",
|
|
"Cell \u001b[1;32mIn[37], line 64\u001b[0m, in \u001b[0;36mhp_storage_opt_enginge\u001b[1;34m(c, ws, hp_vdg, hp_ndg, pos, neg, dam, demand_vdg, demand_ndg, tsource_vdg, tsink_vdg, tsource_ndg, tsink_ndg)\u001b[0m\n\u001b[0;32m 61\u001b[0m tsource_ndg \u001b[38;5;241m=\u001b[39m Tsource_calculation(ws\u001b[38;5;241m.\u001b[39mtemperature, from_storage_ndg_MW, tsource_ndg, from_process_massflow_ndg)\n\u001b[0;32m 62\u001b[0m e_load_ndg, th_load_ndg \u001b[38;5;241m=\u001b[39m hp_ndg\u001b[38;5;241m.\u001b[39mset_heat_output(demand_ndg, tsink_ndg, tsource_ndg)\n\u001b[1;32m---> 64\u001b[0m from_storage_ndg_MW \u001b[38;5;241m=\u001b[39m ws\u001b[38;5;241m.\u001b[39mdischarge(\u001b[38;5;28;43mround\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfrom_storage_ndg_MW\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m 65\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 66\u001b[0m e_load_ndg, th_load_ndg \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m0\u001b[39m)\n",
|
|
"\u001b[1;31mValueError\u001b[0m: cannot convert float NaN to integer"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"s.storage_case_PCM = hponly_with_storage(s.storage_case_PCM)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"s.storage_case_PCM.data.sample(2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"s.storage_case_PCM.data[['DAM', 'POS', 'NEG', 'hp_output_MW', 'to_storage_MW', 'Total demand']].describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"s.storage_case_PCM.data[['DAM', 'POS', 'NEG', 'hp_output_MW', 'to_storage_MW', 'from_storage_MW','Total demand']].sample(20)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"s.hpcase.data.columns"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"s.storage_case_PCM.data.columns"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# hp_selection = s.hpcase.data[['DAM', 'POS', 'NEG', 'hp_input_MW', 'hp_output_MW', 'cop', 'Total demand']]\n",
|
|
"hp_selection = s.hpcase.data[['DAM', 'POS', 'NEG', 'Total demand']]\n",
|
|
"storage_selection = s.storage_case_PCM.data[['hp_input_MW', 'hp_output_MW', 'cop', 'to_storage_MW', 'from_storage_MW']]\n",
|
|
"\n",
|
|
"selection = pd.concat([hp_selection, storage_selection], axis=1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"selection.sample(20)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"selection.mean()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"########################\n",
|
|
"\n",
|
|
"# This is where we left\n",
|
|
"\n",
|
|
"########################"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def cost_function(th_load, cop, electricity_cost, alt_heat_price, demand):\n",
|
|
" return (\n",
|
|
" th_load / cop * electricity_cost\n",
|
|
" + (demand - th_load) * alt_heat_price\n",
|
|
" )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def hybrid_imb_optimisation(case, decimals, s):\n",
|
|
" gb = case.assets['Gasboiler']\n",
|
|
" hp_vdg = case.assets['Heatpump VDG']\n",
|
|
" hp_ndg = case.assets['Heatpump NDG']\n",
|
|
" demand_vdg = case.data['MW (VDG)'].round(decimals).to_list()\n",
|
|
" demand_ndg = case.data['MW (NDG)'].round(decimals).to_list()\n",
|
|
" Tsink_vdg = case.data['Tsink (VDG)'].round(decimals).to_list()\n",
|
|
" Tsink_ndg = case.data['Tsink (NDG)'].round(decimals).to_list()\n",
|
|
" Tsource_vdg = case.data['Tsource (VDG)'].round(decimals).to_list()\n",
|
|
" Tsource_ndg = case.data['Tsource (NDG)'].round(decimals).to_list()\n",
|
|
" fore_neg = case.data[c.forecast].fillna(999).round(decimals).to_list()\n",
|
|
" gas_prices = case.data['Gas prices (€/MWh)'].round(decimals).to_list()\n",
|
|
" co2_prices = case.data['CO2 prices (€/MWh)'].round(decimals).to_list()\n",
|
|
" eb_ode_g = s.eb_ode_g\n",
|
|
" eb_ode_e = s.eb_ode_e\n",
|
|
" \n",
|
|
" gb_input = [0] * len(case.data)\n",
|
|
" gb_output = [0] * len(case.data)\n",
|
|
"\n",
|
|
" minutes = range(len(case.data))\n",
|
|
" hp_output = [0] * len(case.data)\n",
|
|
" hp_input = [0] * len(case.data)\n",
|
|
"\n",
|
|
" for m in tqdm(minutes):\n",
|
|
" dem_vgd = demand_vdg[m]\n",
|
|
" if dem_vgd != 0:\n",
|
|
" max_load = min(hp_vdg.max_th_power, dem_vgd)\n",
|
|
" min_load = hp_vdg.min_th_power\n",
|
|
" Tsink = Tsink_vdg[m]\n",
|
|
" Tsource = Tsource_vdg[m]\n",
|
|
" cop_max_load = hp_vdg.get_cop(heat_output=max_load, Tsink=Tsink, Tsource=Tsource)\n",
|
|
" cop_min_load = hp_vdg.get_cop(heat_output=min_load, Tsink=Tsink_vdg[m], Tsource=Tsource_vdg[m])\n",
|
|
" \n",
|
|
" cost_full_load = cost_function(\n",
|
|
" th_load=max_load,\n",
|
|
" cop=cop_max_load,\n",
|
|
" electricity_cost=fore_neg[m] + eb_ode_e - c.sde_switch_price_correction,\n",
|
|
" alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n",
|
|
" demand=dem_vgd\n",
|
|
" )\n",
|
|
" \n",
|
|
" cost_min_load = cost_function(\n",
|
|
" th_load=min_load,\n",
|
|
" cop=cop_min_load,\n",
|
|
" electricity_cost=fore_neg[m] + eb_ode_e,\n",
|
|
" alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n",
|
|
" demand=dem_vgd\n",
|
|
" )\n",
|
|
" \n",
|
|
" if cost_full_load < cost_min_load:\n",
|
|
" hp_vdg_input, hp_vdg_output = hp_vdg.set_heat_output(max_load, Tsink, Tsource)\n",
|
|
" else:\n",
|
|
" hp_vdg_input, hp_vdg_output = hp_vdg.set_heat_output(min_load, Tsink, Tsource)\n",
|
|
" else:\n",
|
|
" hp_vdg_input, hp_vdg_output = (0, 0)\n",
|
|
"\n",
|
|
" dem_ngd = demand_ndg[m]\n",
|
|
" if dem_ngd != 0:\n",
|
|
" max_load = min(hp_ndg.max_th_power, dem_ngd)\n",
|
|
" min_load = hp_ndg.min_th_power\n",
|
|
" Tsink = Tsink_ndg[m]\n",
|
|
" Tsource = Tsource_ndg[m]\n",
|
|
" cop_max_load = hp_ndg.get_cop(heat_output=max_load, Tsink=Tsink, Tsource=Tsource)\n",
|
|
" cop_min_load = hp_ndg.get_cop(heat_output=min_load, Tsink=Tsink, Tsource=Tsource)\n",
|
|
" \n",
|
|
" cost_full_load = cost_function(\n",
|
|
" th_load=max_load,\n",
|
|
" cop=cop_max_load,\n",
|
|
" electricity_cost=fore_neg[m] + eb_ode_e,\n",
|
|
" alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n",
|
|
" demand=dem_ngd\n",
|
|
" )\n",
|
|
" \n",
|
|
" cost_min_load = cost_function(\n",
|
|
" th_load=min_load,\n",
|
|
" cop=cop_min_load,\n",
|
|
" electricity_cost=fore_neg[m] + eb_ode_e,\n",
|
|
" alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n",
|
|
" demand=dem_vgd\n",
|
|
" )\n",
|
|
" \n",
|
|
" if cost_full_load <= cost_min_load:\n",
|
|
" hp_ndg_input, hp_ndg_output = hp_ndg.set_heat_output(max_load, Tsink=Tsink, Tsource=Tsource)\n",
|
|
" else:\n",
|
|
" hp_ndg_input, hp_ndg_output = hp_ndg.set_heat_output(min_load, Tsink=Tsink, Tsource=Tsource)\n",
|
|
" else:\n",
|
|
" hp_ndg_input, hp_ndg_output = (0, 0)\n",
|
|
"\n",
|
|
" hp_out = hp_vdg_output + hp_ndg_output\n",
|
|
" hp_output[m] = hp_out\n",
|
|
" hp_input[m] = hp_vdg_input + hp_ndg_input\n",
|
|
" remaining_demand = max(dem_vgd+dem_ngd-hp_out, 0)\n",
|
|
" gb_output[m], gb_input[m] = gb.set_heat_output(remaining_demand)\n",
|
|
"\n",
|
|
" case.data['hp_output_MW'] = np.array(hp_output)\n",
|
|
" case.data['hp_input_MW'] = np.array(hp_input)\n",
|
|
" case.data['gb_output_MW'] = np.array(gb_output)\n",
|
|
" case.data['gb_input_MW'] = np.array(gb_input)\n",
|
|
"\n",
|
|
" for col in case.data.columns:\n",
|
|
" if col.endswith('MW'):\n",
|
|
" case.data[col + 'h'] = case.data[col] / 60\n",
|
|
"\n",
|
|
" case.data = case.data.round(5)\n",
|
|
" return case"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"aFRR\n",
|
|
"* Bid in a volume (X MW) --> Strategy is to only bid in on aFRR down\n",
|
|
"* Remaining demand is filed in by gasboiler\n",
|
|
"* Bid price at switch price?\n",
|
|
"* Assume direct response to 0% for now?\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def calc_afrr_capacity(case):\n",
|
|
" hp_vdg = case.assets['Heatpump VDG']\n",
|
|
" hp_ndg = case.assets['Heatpump NDG']\n",
|
|
" \n",
|
|
" capacity = 0\n",
|
|
" for hp in [hp_vdg, hp_ndg]:\n",
|
|
" max_th_output = hp.max_th_power\n",
|
|
" cop = hp.get_cop(max_th_output, Tsink=135, Tsource=60)\n",
|
|
" e_power = max_th_output / cop\n",
|
|
" capacity += e_power\n",
|
|
" return capacity"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def aFRR_optimisation(case, s):\n",
|
|
" s.afrr_case.afrr_capacity = calc_afrr_capacity(s.afrr_case)\n",
|
|
" gb = case.assets['Gasboiler']\n",
|
|
" hp_vdg = case.assets['Heatpump VDG']\n",
|
|
" hp_ndg = case.assets['Heatpump NDG']\n",
|
|
" demand_vdg = case.data['MW (VDG)'].to_list()\n",
|
|
" demand_ndg = case.data['MW (NDG)'].to_list()\n",
|
|
" Tsink_vdg = case.data['Tsink (VDG)'].to_list()\n",
|
|
" Tsink_ndg = case.data['Tsink (NDG)'].to_list()\n",
|
|
" Tsource_vdg = case.data['Tsource (VDG)'].to_list()\n",
|
|
" Tsource_ndg = case.data['Tsource (NDG)'].to_list()\n",
|
|
" afrr_up = case.data['Hoogste_prijs_opregelen'].fillna(-999).to_list()\n",
|
|
" gas_prices = case.data['Gas prices (€/MWh)'].to_list()\n",
|
|
" co2_prices = case.data['CO2 prices (€/MWh)'].to_list()\n",
|
|
" eb_ode_g = s.eb_ode_g\n",
|
|
" eb_ode_e = s.eb_ode_e\n",
|
|
" \n",
|
|
" gb_input = [0] * len(case.data)\n",
|
|
" gb_output = [0] * len(case.data)\n",
|
|
"\n",
|
|
" minutes = range(len(case.data))\n",
|
|
" hp_output = [0] * len(case.data)\n",
|
|
" hp_input = [0] * len(case.data)\n",
|
|
"\n",
|
|
" for m in tqdm(minutes):\n",
|
|
" dem_vgd = demand_vdg[m]\n",
|
|
" if dem_vgd != 0:\n",
|
|
" max_load = min(hp_vdg.max_th_power, dem_vgd)\n",
|
|
" min_load = hp_vdg.min_th_power\n",
|
|
" Tsink = Tsink_vdg[m]\n",
|
|
" Tsource = Tsource_vdg[m]\n",
|
|
" cop_max_load = hp_vdg.get_cop(heat_output=max_load, Tsink=Tsink, Tsource=Tsource)\n",
|
|
" cop_min_load = hp_vdg.get_cop(heat_output=min_load, Tsink=Tsink_vdg[m], Tsource=Tsource_vdg[m])\n",
|
|
" \n",
|
|
" cost_full_load = cost_function(\n",
|
|
" th_load=max_load,\n",
|
|
" cop=cop_max_load,\n",
|
|
" electricity_cost=afrr_up[m] + eb_ode_e - c.sde_switch_price_correction,\n",
|
|
" alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n",
|
|
" demand=dem_vgd\n",
|
|
" )\n",
|
|
" \n",
|
|
" cost_min_load = cost_function(\n",
|
|
" th_load=min_load,\n",
|
|
" cop=cop_min_load,\n",
|
|
" electricity_cost=afrr_up[m] + eb_ode_e,\n",
|
|
" alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n",
|
|
" demand=dem_vgd\n",
|
|
" )\n",
|
|
" \n",
|
|
" if cost_full_load < cost_min_load:\n",
|
|
" hp_vdg_input, hp_vdg_output = hp_vdg.set_heat_output(max_load, Tsink, Tsource)\n",
|
|
" else:\n",
|
|
" hp_vdg_input, hp_vdg_output = hp_vdg.set_heat_output(min_load, Tsink, Tsource)\n",
|
|
" else:\n",
|
|
" hp_vdg_input, hp_vdg_output = (0, 0)\n",
|
|
"\n",
|
|
" dem_ngd = demand_ndg[m]\n",
|
|
" if dem_ngd != 0:\n",
|
|
" max_load = min(hp_ndg.max_th_power, dem_ngd)\n",
|
|
" min_load = hp_ndg.min_th_power\n",
|
|
" Tsink = Tsink_ndg[m]\n",
|
|
" Tsource = Tsource_ndg[m]\n",
|
|
" cop_max_load = hp_ndg.get_cop(heat_output=max_load, Tsink=Tsink, Tsource=Tsource)\n",
|
|
" cop_min_load = hp_ndg.get_cop(heat_output=min_load, Tsink=Tsink, Tsource=Tsource)\n",
|
|
" \n",
|
|
" cost_full_load = cost_function(\n",
|
|
" th_load=max_load,\n",
|
|
" cop=cop_max_load,\n",
|
|
" electricity_cost=afrr_up[m] + eb_ode_e,\n",
|
|
" alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n",
|
|
" demand=dem_ngd\n",
|
|
" )\n",
|
|
" \n",
|
|
" cost_min_load = cost_function(\n",
|
|
" th_load=min_load,\n",
|
|
" cop=cop_min_load,\n",
|
|
" electricity_cost=afrr_up[m] + eb_ode_e,\n",
|
|
" alt_heat_price=gas_prices[m] + co2_prices[m] + eb_ode_g/case.assets['Gasboiler'].efficiency,\n",
|
|
" demand=dem_vgd\n",
|
|
" )\n",
|
|
" \n",
|
|
" if cost_full_load <= cost_min_load:\n",
|
|
" hp_ndg_input, hp_ndg_output = hp_ndg.set_heat_output(max_load, Tsink=Tsink, Tsource=Tsource)\n",
|
|
" else:\n",
|
|
" hp_ndg_input, hp_ndg_output = hp_ndg.set_heat_output(min_load, Tsink=Tsink, Tsource=Tsource)\n",
|
|
" else:\n",
|
|
" hp_ndg_input, hp_ndg_output = (0, 0)\n",
|
|
"\n",
|
|
" hp_out = hp_vdg_output + hp_ndg_output\n",
|
|
" hp_output[m] = hp_out\n",
|
|
" hp_input[m] = hp_vdg_input + hp_ndg_input\n",
|
|
" remaining_demand = max(dem_vgd+dem_ngd-hp_out, 0)\n",
|
|
" gb_output[m], gb_input[m] = gb.set_heat_output(remaining_demand)\n",
|
|
"\n",
|
|
" case.data['hp_output_MW'] = np.array(hp_output)\n",
|
|
" case.data['hp_input_MW'] = np.array(hp_input)\n",
|
|
" case.data['gb_output_MW'] = np.array(gb_output)\n",
|
|
" case.data['gb_input_MW'] = np.array(gb_input)\n",
|
|
"\n",
|
|
" for col in case.data.columns:\n",
|
|
" if col.endswith('MW'):\n",
|
|
" case.data[col + 'h'] = case.data[col] / 60\n",
|
|
"\n",
|
|
" case.data = case.data.round(5)\n",
|
|
" return case"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"### Run optimisation"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def run_optimisation(c, s):\n",
|
|
" s.baseline = baseline_sim(s.baseline)\n",
|
|
" s.hpcase = hponly(s.hpcase)\n",
|
|
" s.storage_case_PCM = hponly_with_storage(s.storage_case_PCM)\n",
|
|
"# s.hpcase_sde.assign_algorithm(hponly)\n",
|
|
"# s.hpcase_sde.run()\n",
|
|
" \n",
|
|
"# s.optcase1.assign_algorithm(hybrid_imb_optimisation)\n",
|
|
"# s.optcase1.run(decimals=2, s=s)\n",
|
|
" \n",
|
|
"# s.afrr_case.assign_algorithm(aFRR_optimisation)\n",
|
|
"# s.afrr_case.run(s=s)\n",
|
|
" \n",
|
|
" for case in [s.hpcase, s.storage_case_PCM]: # [s.hpcase, s.hpcase_sde, s.optcase1, s.afrr_case]:\n",
|
|
" case.mean_cop = case.data['hp_output_MW'].sum() / case.data['hp_input_MW'].abs().sum()\n",
|
|
" \n",
|
|
" return s\n",
|
|
"\n",
|
|
"s = run_optimisation(c, s)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"s.hpcase.data['hp_output_MW'].sum() /s.hpcase.data['hp_input_MW'].abs().sum() "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"s.storage_case_PCM.data['hp_output_MW'].sum() /s.storage_case_PCM.data['hp_input_MW'].abs().sum() "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"s.hpcase.data.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"vis_data = s.storage_case_PCM.data[[\n",
|
|
" 'storage_level_MWh', \n",
|
|
" #'to_storage_ndg_MW', \n",
|
|
" #'to_storage_vdg_MW', \n",
|
|
" 'to_storage_MW', \n",
|
|
" #'from_storage_ndg_MW', \n",
|
|
" #'from_storage_vdg_MW',\n",
|
|
" 'from_storage_MW'\n",
|
|
"]]\n",
|
|
"\n",
|
|
"cops = pd.concat([s.hpcase.data['cop'], s.storage_case_PCM.data['cop']], axis=1)\n",
|
|
"vis_data = pd.concat([vis_data, cops], axis=1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"vis_data.loc['2023-01-01', :].iplot(subplots=True, shape=(5,1), dimensions=(1000, 800))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"total_to_storage = s.storage_case_PCM.data['to_storage_MWh'].sum()\n",
|
|
"total_from_storage = s.storage_case_PCM.data['from_storage_MWh'].sum()\n",
|
|
"total_to_storage, total_from_storage"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"s.storage_case_PCM.data.columns"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"hp_output = s.storage_case_PCM.data['hp_output_MW'].round(2)\n",
|
|
"demand_and_to_storage = (s.storage_case_PCM.data['Total demand'] + s.storage_case_PCM.data['to_storage_MW']).round(2)\n",
|
|
"hp_output.equals(demand_and_to_storage)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"pd.concat([hp_output, demand_and_to_storage], axis=1).resample('H').sum().iplot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"## Financials"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def calculate_sde_subsidy(c, s, case):\n",
|
|
" hp_capacity = abs(case.assets['Heatpump VDG'].max_th_power) + abs(case.assets['Heatpump NDG'].max_th_power)\n",
|
|
" case.full_load_hours = abs(case.data['hp_output_MWh'].sum() / hp_capacity)\n",
|
|
"\n",
|
|
" subsidized_hours = min(case.full_load_hours, 8000)\n",
|
|
" subsidized_MWh = subsidized_hours * hp_capacity\n",
|
|
"\n",
|
|
" base_amount_per_MWh_th = round(c.sde_base_amount*0.173 + c.longterm_gas_price, 2)\n",
|
|
" base_subsidy_amount = round(base_amount_per_MWh_th * subsidized_MWh, 2)\n",
|
|
" long_term_gas_price_LHV = round(EURperHHV_to_EURperLHV(c.longterm_gas_price), 2)\n",
|
|
" base_amount_gas = (2/3)*long_term_gas_price_LHV*0.9\n",
|
|
" mean_ttf_price_LHV = round(EURperHHV_to_EURperLHV(case.data['Gas prices (€/MWh)'].mean()), 2)\n",
|
|
" correction_amount_gas = max(mean_ttf_price_LHV, base_amount_gas)\n",
|
|
" avoided_gas_consumption = subsidized_MWh/0.9\n",
|
|
" correction_gas = round(correction_amount_gas * avoided_gas_consumption, 2)\n",
|
|
"\n",
|
|
" base_amount_co2 = (2/3)*c.longterm_co2_price\n",
|
|
" avoided_co2_emission = avoided_gas_consumption * 0.226\n",
|
|
" mean_ets_price = round(EURperHHV_to_EURperLHV(case.data['CO2 prices (€/ton)'].mean()), 2)\n",
|
|
" correction_amount_co2 = max(base_amount_co2, mean_ets_price)\n",
|
|
" correction_co2 = round(correction_amount_co2 * avoided_co2_emission, 2)\n",
|
|
"\n",
|
|
" sde_subsidy_corrected = max(base_subsidy_amount - (correction_gas + correction_co2), 0)\n",
|
|
" sde_per_MWh_th = sde_subsidy_corrected / subsidized_MWh\n",
|
|
" sde_per_MWh_e = sde_subsidy_corrected / case.data['hp_input_MWh'].abs().sum()\n",
|
|
"\n",
|
|
" case.sde_results = {\n",
|
|
" 'base_amount': base_subsidy_amount,\n",
|
|
" 'correction_gas': correction_gas,\n",
|
|
" 'correction_co2': correction_co2,\n",
|
|
" 'corrected_amount': sde_subsidy_corrected,\n",
|
|
" 'sde_per_MWh_th': sde_per_MWh_th,\n",
|
|
" 'sde_per_MWh_e': sde_per_MWh_e\n",
|
|
" }\n",
|
|
" return case"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"s.storage_case_PCM.data.columns"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def collect_cashflows(c, s):\n",
|
|
" s.hpcase.generate_electr_market_results(nom_col='hp_input_MWh', real_col='hp_input_MWh')\n",
|
|
" s.storage_case_PCM.data['nomination_MWh'] = s.hpcase.data['hp_input_MWh']\n",
|
|
" s.storage_case_PCM.generate_electr_market_results(nom_col='nomination_MWh', real_col='hp_input_MWh')\n",
|
|
"\n",
|
|
"# s.hpcase_sde.generate_electr_market_results(nom_col='hp_input_MWh', real_col='hp_input_MWh')\n",
|
|
" \n",
|
|
"# s.optcase1.data['DA Nom'] = s.hpcase.data['hp_input_MWh'] * c.day_ahead_buying_perc\n",
|
|
"# s.optcase1.generate_electr_market_results(nom_col='DA Nom', real_col='hp_input_MWh')\n",
|
|
" \n",
|
|
"# s.afrr_case.data['POS'] = s.afrr_case.data['aFRR_up'].fillna(value=s.afrr_case.data['POS'])\n",
|
|
"# s.afrr_case.data['DA Nom'] = s.hpcase.data['hp_input_MWh']\n",
|
|
"# s.afrr_case.generate_electr_market_results(nom_col='DA Nom', real_col='hp_input_MWh')\n",
|
|
"# s.afrr_case.add_cashflow('aFRR capacity fee (€)', c.afrr_capacity_fee * s.afrr_case.afrr_capacity)\n",
|
|
" \n",
|
|
" for case in [s.baseline]: #, s.optcase1, s.afrr_case]:\n",
|
|
" case.add_gas_costs(gasvolumes_col='gb_input_MWh')\n",
|
|
" case.add_co2_costs(volume_cols='gb_input_MWh', fuel='gas')\n",
|
|
" case.add_eb_ode(commodity='gas', tax_bracket=c.tax_bracket_g)\n",
|
|
" \n",
|
|
" for case in [s.hpcase, s.storage_case_PCM]: #, s.hpcase_sde, s.optcase1, s.afrr_case]:\n",
|
|
" \n",
|
|
" case.add_eb_ode(commodity='electricity', tax_bracket=c.tax_bracket_e, cons_col='hp_input_MWh')\n",
|
|
" case.add_grid_costs(\n",
|
|
" power_MW_col='hp_input_MW',\n",
|
|
" grid_operator=c.grid_operator,\n",
|
|
" year=2020, \n",
|
|
" connection_type=c.connection_type\n",
|
|
" )\n",
|
|
" \n",
|
|
" for case in s.sde_cases:\n",
|
|
" case = calculate_sde_subsidy(c=c, s=s, case=case)\n",
|
|
" case.add_cashflow('SDE++ subsidy (€)', case.sde_results['corrected_amount'])\n",
|
|
" return s\n",
|
|
"\n",
|
|
"s = collect_cashflows(c, s)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def calculate_financials(c, s):\n",
|
|
" for case in s.cases:\n",
|
|
" case.calculate_ebitda(c.project_duration)\n",
|
|
"\n",
|
|
" for case in [s.hpcase, s.storage_case_PCM]: #, s.hpcase_sde, s.optcase1, s.afrr_case]:\n",
|
|
" case.calculate_business_case(\n",
|
|
" project_duration=c.project_duration, \n",
|
|
" discount_rate=c.discount_rate, \n",
|
|
" baseline=s.baseline\n",
|
|
" )\n",
|
|
" \n",
|
|
" return s\n",
|
|
"\n",
|
|
"s = calculate_financials(c, s)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"## Visualisations"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig_demands_over_time = s.demand['Total demand'].resample('H').mean().iplot(\n",
|
|
" title='Smurfit Kappa: Heat demand by Hour in MW', \n",
|
|
" yTitle='MW', \n",
|
|
" colors=recoygreen,\n",
|
|
" asFigure=True,\n",
|
|
" dimensions=(800, 400)\n",
|
|
")\n",
|
|
"fig_demands_over_time"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"demands_fig = s.demand[['Tsource (VDG)', 'Tsink (VDG)', 'Tsource (NDG)', 'Tsink (NDG)']].resample('H').mean().iplot(\n",
|
|
" kind='box',\n",
|
|
" title='Smurfit Kappa: Source and Sink temperatures',\n",
|
|
" color=recoygreen,\n",
|
|
" yTitle='Temperature in degrees C',\n",
|
|
" legend=False,\n",
|
|
" asFigure=True,\n",
|
|
" dimensions=(800, 400)\n",
|
|
")\n",
|
|
"demands_fig"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"s.storage_case_PCM.data.columns"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"temp_comparison = pd.concat([s.storage_case_PCM.data[['tsource_ndg', 'tsource_vdg']], s.storage_case_PCM.data[['Tsource (NDG)', 'Tsource (VDG)']]], axis=1)\n",
|
|
"temp_comparison.columns = ['Tsource (NDG) -after', 'Tsource (VDG) - after', 'Tsource (NDG) - before', 'Tsource (VDG) - before']\n",
|
|
"temp_comparison.resample('15T').mean().iplot()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"s.storage_case_PCM.data[['tsource_ndg', 'tsource_vdg']].idxmin()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# s.storage_case_PCM.data.loc['2019-01-04 07:45:00']"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"case = s.storage_case_PCM\n",
|
|
"hp_vdg = case.assets['Heatpump VDG']\n",
|
|
"hp_ndg = case.assets['Heatpump NDG']\n",
|
|
"ws = case.assets['HotWaterStorage']\n",
|
|
"ws.storage_level = 47.083 + 25\n",
|
|
"pos = 35.650\n",
|
|
"neg = 43.850\n",
|
|
"dam = 68.400\n",
|
|
"demand_vdg = 0\n",
|
|
"demand_ndg = 0\n",
|
|
"tsource_vdg = 29.635\n",
|
|
"tsink_vdg = 134.840\n",
|
|
"tsource_ndg = 21.420\n",
|
|
"tsink_ndg = 118.755\n",
|
|
"\n",
|
|
"hp_storage_opt_enginge(c, ws, hp_vdg, hp_ndg, pos, neg, dam, demand_vdg, demand_ndg, tsource_vdg, tsink_vdg, tsource_ndg, tsink_ndg)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#e_load_vdg, th_load_vdg, e_load_ndg, th_load_ndg, tsource_vdg, tsource_ndg, to_storage_vdg_MW, from_storage_vdg_MW, to_storage_ndg_MW, from_storage_ndg_MW, ws, hp_vdg, hp_ndg"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"Tsource_VDG_before = s.hpcase.data['Tsource (VDG)'].round(2)\n",
|
|
"Tsource_VDG_after = s.storage_case_PCM.data['Tsource (VDG)'].round(2)\n",
|
|
"Tsource_VDG_before.equals(Tsource_VDG_after)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"Tsource_NDG_before = s.hpcase.data['Tsource (NDG)'].round(2)\n",
|
|
"Tsource_NDG_after = s.storage_case_PCM.data['Tsource (NDG)'].round(2)\n",
|
|
"Tsource_NDG_before.equals(Tsource_NDG_after)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"test_hp = Heatpump(\n",
|
|
" name='Heatpump for Testing',\n",
|
|
" max_th_power=1,\n",
|
|
" min_th_power=0.3,\n",
|
|
" cop_curve=cop_curve\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Tsrc_vdg, Tsnk_vdg, Tsrc_ndg, Tsnk_ndg = s.demand[['Tsource (VDG)', 'Tsink (VDG)', 'Tsource (NDG)', 'Tsink (NDG)']].mean().to_list()\n",
|
|
"# mean_gas_price = (s.optcase1.data['Gas prices (€/MWh)'].mean() \n",
|
|
"# + s.eb_ode_g \n",
|
|
"# + s.optcase1.data['CO2 prices (€/MWh)'].mean()\n",
|
|
"# )\n",
|
|
"\n",
|
|
"# max_gas_price = (s.optcase1.data['Gas prices (€/MWh)'].max() \n",
|
|
"# + s.eb_ode_g \n",
|
|
"# + s.optcase1.data['CO2 prices (€/MWh)'].max()\n",
|
|
"# )\n",
|
|
"\n",
|
|
"# min_gas_price = (s.optcase1.data['Gas prices (€/MWh)'].min() \n",
|
|
"# + s.eb_ode_g \n",
|
|
"# + s.optcase1.data['CO2 prices (€/MWh)'].min()\n",
|
|
"# )\n",
|
|
"\n",
|
|
"# Tsrc_vdg_min, Tsnk_vdg_min, Tsrc_ndg_min, Tsnk_ndg_min = s.demand[['Tsource (VDG)', 'Tsink (VDG)', 'Tsource (NDG)', 'Tsink (NDG)']].min().to_list()\n",
|
|
"# Tsrc_vdg_max, Tsnk_vdg_max, Tsrc_ndg_max, Tsnk_ndg_max = s.demand[['Tsource (VDG)', 'Tsink (VDG)', 'Tsource (NDG)', 'Tsink (NDG)']].max().to_list()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import plotly.graph_objs as go\n",
|
|
"def create_load_trace(gas_price, Tsnk, Tsrc, name):\n",
|
|
" loads = []\n",
|
|
" eprices = list(range(1000))\n",
|
|
" for eprice in eprices:\n",
|
|
" _, load = test_hp.set_opt_load(\n",
|
|
" electricity_cost=eprice + s.eb_ode_e,\n",
|
|
" alt_heat_price=gas_price / 0.9,\n",
|
|
" demand=1, \n",
|
|
" Tsink=Tsnk,\n",
|
|
" Tsource=Tsrc\n",
|
|
" )\n",
|
|
" loads.append(load)\n",
|
|
" trace = go.Scatter(x=eprices, y=loads, name=name)\n",
|
|
" return trace"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# import plotly.graph_objects as go\n",
|
|
"# import numpy as np\n",
|
|
"\n",
|
|
"# fig = go.Figure()\n",
|
|
"\n",
|
|
"# configs = {\n",
|
|
"# 'mean': [mean_gas_price, Tsnk_vdg, Tsrc_vdg],\n",
|
|
"# 'unfav_gas': [min_gas_price, Tsnk_vdg, Tsrc_vdg],\n",
|
|
"# 'fav_gas': [max_gas_price, Tsnk_vdg, Tsrc_vdg],\n",
|
|
"# 'unfav_all': [min_gas_price, Tsnk_vdg_max, Tsrc_vdg_min],\n",
|
|
"# 'fav_all': [max_gas_price, Tsnk_vdg_min, Tsrc_vdg_max],\n",
|
|
"# }\n",
|
|
"\n",
|
|
"# for name, config in configs.items():\n",
|
|
"# trace = create_load_trace(*config, name)\n",
|
|
"# fig.add_trace(trace)\n",
|
|
"\n",
|
|
"# fig.update_layout(title='Switch prices for different configurations')\n",
|
|
"# fig.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"date = s.baseline.data.index[0].strftime('%Y-%m-%d')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig_steamboiler = s.baseline.data[['Total demand', 'output_MW_th']].abs().loc[date, :].iplot(\n",
|
|
" subplots=True, \n",
|
|
" title=f'Steamboiler only case on {date}',\n",
|
|
" subplot_titles=['Total demand', 'Steam boiler output (MW)'],\n",
|
|
" legend=False,\n",
|
|
" dimensions=(800, 400),\n",
|
|
" colors=[recoydarkblue, recoygreen], \n",
|
|
" asFigure=True,\n",
|
|
" shape = (2, 1)\n",
|
|
")\n",
|
|
"fig_steamboiler"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig_heatpump = s.hpcase.data[['Total demand', 'hp_output_MW']].abs().loc[date, :].iplot(\n",
|
|
" subplots=True, \n",
|
|
" title=f'Heatpump only case on {date}', \n",
|
|
" subplot_titles=['Total demand', 'Heatpump output (MW)'],\n",
|
|
" legend=False,\n",
|
|
" dimensions=(800, 400),\n",
|
|
" colors=[recoydarkblue, recoygreen], \n",
|
|
" asFigure=True,\n",
|
|
" shape = (2, 1)\n",
|
|
")\n",
|
|
"fig_heatpump"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# fig_optcase = s.optcase1.data[['hp_output_MW', 'gb_output_MW', 'NEG']].loc[date, :].iplot(\n",
|
|
"# subplots=True, \n",
|
|
"# title=f'Hybrid case on {date}',\n",
|
|
"# subplot_titles=['Heatpump output (MW)', 'Steam boiler output (MW)', 'Imbalance price (€/MWh)'],\n",
|
|
"# legend=False,\n",
|
|
"# dimensions=(800, 600),\n",
|
|
"# colors=[recoydarkblue, recoygreen, recoyred],\n",
|
|
"# asFigure=True,\n",
|
|
"# shape=(3,1)\n",
|
|
"# )\n",
|
|
"# fig_optcase"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# date = '2019-09-04'\n",
|
|
"\n",
|
|
"# fig_optcase2 = s.optcase1.data[['hp_output_MW', 'gb_output_MW', 'NEG']].loc[date, :].iplot(\n",
|
|
"# subplots=True, \n",
|
|
"# title=f'Hybrid case on {date}',\n",
|
|
"# subplot_titles=['Heatpump output (MW)', 'Steam boiler output (MW)', 'Imbalance price (€/MWh)'],\n",
|
|
"# legend=False,\n",
|
|
"# dimensions=(800, 600),\n",
|
|
"# colors=[recoydarkblue, recoygreen, recoyred],\n",
|
|
"# asFigure=True,\n",
|
|
"# shape=(3,1)\n",
|
|
"# )\n",
|
|
"# fig_optcase2"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"report = ComparisonReport(\n",
|
|
" cases=s.optcases, \n",
|
|
" kind='electr_market_results',\n",
|
|
")\n",
|
|
"report.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"casereport = ComparisonReport(cases = s.cases, kind='ebitda_calc', baseline=s.baseline, comparison='relative')\n",
|
|
"casereport.show(presentation_format=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"BusinessCaseReport(s.hpcase).show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"#price chart\n",
|
|
"from copy import deepcopy\n",
|
|
"\n",
|
|
"data = deepcopy(s.baseline.data)\n",
|
|
"data = data[data.columns[:2]]\n",
|
|
"data[data.columns[1]] = MWh_gas_to_tonnes_CO2(data[data.columns[1]])\n",
|
|
"data = data.rename(columns={\"CO2 prices (€/ton)\": \"CO2 prices (€/MWh)\"})\n",
|
|
"\n",
|
|
"data.resample('D').mean().iplot(dimensions=(800, 300), title='Gasprices vs. CO2 prices', colors=recoycolors)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# #price chart\n",
|
|
"# from copy import deepcopy\n",
|
|
"\n",
|
|
"# s.optcase1.data['DAM'].resample('D').mean().iplot(dimensions=(800, 300), title='Electricity Prices', colors=recoycolors)\n",
|
|
"\n",
|
|
"# _source_output = s.optcase1.data[['hp_output_MWh', 'gb_output_MWh']].resample('M').sum()\n",
|
|
"# _total_output = _source_output.sum(axis=1)\n",
|
|
"# _data = _source_output.divide(_total_output, axis=0).rename(\n",
|
|
"# columns={'hp_output_MWh':'Heat pump', 'gb_output_MWh':'Gasboiler'}\n",
|
|
"# ) * 100\n",
|
|
"\n",
|
|
"# production_fig = _data.iplot(\n",
|
|
"# kind='bar',\n",
|
|
"# barmode='stack',\n",
|
|
"# colors=[recoydarkblue, recoygreen],\n",
|
|
"# title='Hybrid case: Heat production per Month by Source in % share',\n",
|
|
"# yTitle='Share of production in %',\n",
|
|
"# dimensions=(600, 400),\n",
|
|
"# asFigure=True\n",
|
|
"# )\n",
|
|
"\n",
|
|
"# production_fig = production_fig.update_layout(legend_traceorder=\"reversed\")\n",
|
|
"# production_fig"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"report = ComparisonReport(s.cases, kind='capex').report\n",
|
|
"report = report[report.index.str.contains('CAPEX')].T\n",
|
|
"capex_fig = report.iplot(\n",
|
|
" kind='bar', \n",
|
|
" barmode='relative',\n",
|
|
" colors=recoycolors,\n",
|
|
" title='CAPEX by Casestudy',\n",
|
|
" yTitle='CAPEX in €',\n",
|
|
" dimensions=(600, 400),\n",
|
|
" asFigure=True,\n",
|
|
")\n",
|
|
"\n",
|
|
"capex_fig = capex_fig.update_layout(legend_traceorder=\"reversed\")\n",
|
|
"capex_fig"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"cashflow_report = ComparisonReport(cases = s.cases, kind='cashflows')\n",
|
|
"\n",
|
|
"fig = cashflow_report.report.T.iplot(\n",
|
|
" kind='bar',\n",
|
|
" barmode='relative',\n",
|
|
" colors=recoycolors,\n",
|
|
" title='OPEX breakdown',\n",
|
|
" asFigure=True,\n",
|
|
" yTitle='€',\n",
|
|
" dimensions=(800, 600)\n",
|
|
")\n",
|
|
"\n",
|
|
"ebitda_report = ComparisonReport(cases=s.cases, kind='ebitda_calc')\n",
|
|
"scat = go.Scatter(\n",
|
|
" mode='markers',\n",
|
|
" y=ebitda_report.report.loc['EBITDA (€)', :].values, \n",
|
|
" x=ebitda_report.report.columns, \n",
|
|
" line=go.scatter.Line(color=recoydarkgrey),\n",
|
|
" marker=dict(\n",
|
|
" color=recoydarkgrey,\n",
|
|
" size=20,\n",
|
|
" line=dict(\n",
|
|
" color=recoydarkgrey,\n",
|
|
" width=3\n",
|
|
" ),\n",
|
|
" symbol='line-ew'\n",
|
|
" ),\n",
|
|
" name='EBITDA (€)'\n",
|
|
")\n",
|
|
"fig.add_trace(scat)\n",
|
|
"fig"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# ebitda_report.show(comparison='relative')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"_series = SingleFigureComparison(s.cases, 'ebitda', label='EBITDA').report\n",
|
|
"ebitda_graph = _series.iplot(\n",
|
|
" kind='bar',\n",
|
|
" title='Yearly EBITDA by Casestudy in €',\n",
|
|
" colors=recoygreen,\n",
|
|
" dimensions=(600, 400), \n",
|
|
" yTitle='EBITDA in €',\n",
|
|
" asFigure=True,\n",
|
|
" yrange=[_series.min() * 1.2, max(_series.max() * 2, 0)]\n",
|
|
")\n",
|
|
"\n",
|
|
"ebitda_graph.update_traces(\n",
|
|
" text=_series.values/1000_000, \n",
|
|
" textposition='outside', \n",
|
|
" texttemplate=\"%{text:.1f}M €\", \n",
|
|
")\n",
|
|
"\n",
|
|
"ebitda_graph"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"_series = SingleFigureComparison(s.optcases, 'npv', label='NPV').report\n",
|
|
"\n",
|
|
"npv_graph = _series.iplot(\n",
|
|
" kind='bar',\n",
|
|
" title='NPV by Casestudy in €',\n",
|
|
" colors=recoygreen,\n",
|
|
" dimensions=(600, 400), \n",
|
|
" yTitle='NPV in €',\n",
|
|
" asFigure=True,\n",
|
|
" yrange=[0, _series.max() * 1.1]\n",
|
|
")\n",
|
|
"\n",
|
|
"npv_graph.update_traces(\n",
|
|
" text=_series.values/1000_000, \n",
|
|
" textposition='outside', \n",
|
|
" texttemplate=\"%{text:.1f}M €\", \n",
|
|
")\n",
|
|
"\n",
|
|
"npv_graph"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"irr_report = (SingleFigureComparison(s.optcases, 'irr', label='IRR').report * 100)\n",
|
|
"irr_fig = irr_report.iplot(\n",
|
|
" kind='bar',\n",
|
|
" title='IRR by Casestudy in %',\n",
|
|
" colors=recoygreen,\n",
|
|
" dimensions=(600, 400),\n",
|
|
" yTitle='IRR in %',\n",
|
|
" asFigure=True,\n",
|
|
" yrange=[0, irr_report.max() * 1.2]\n",
|
|
")\n",
|
|
"\n",
|
|
"irr_fig.update_traces(\n",
|
|
" text=irr_report.values, \n",
|
|
" textposition='outside', \n",
|
|
" texttemplate=\"%{text:.0f} %\", \n",
|
|
")\n",
|
|
"\n",
|
|
"irr_fig"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"_series = (SingleFigureComparison(s.optcases, 'spp', label='Simple Payback Time').report)\n",
|
|
"spt_fig = _series.iplot(\n",
|
|
" kind='bar',\n",
|
|
" title='Simple Payback Time by Casestudy in Years',\n",
|
|
" colors=recoygreen,\n",
|
|
" dimensions=(600, 400),\n",
|
|
" yTitle='Years',\n",
|
|
" asFigure=True,\n",
|
|
" yrange=[0, _series.max() * 1.2]\n",
|
|
")\n",
|
|
"\n",
|
|
"spt_fig.update_traces(\n",
|
|
" text=_series.values, \n",
|
|
" textposition='outside', \n",
|
|
" texttemplate=\"%{text:.1f} years\", \n",
|
|
")\n",
|
|
"\n",
|
|
"spt_fig "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"mean_cops = (SingleFigureComparison(s.optcases, 'mean_cop', label='COP').report)\n",
|
|
"cop_fig = mean_cops.iplot(\n",
|
|
" kind='bar',\n",
|
|
" title='Mean COP by Casestudy',\n",
|
|
" colors=recoygreen,\n",
|
|
" dimensions=(600, 400),\n",
|
|
" yTitle='COP',\n",
|
|
" asFigure=True,\n",
|
|
" yrange=[0, mean_cops.max() * 1.2]\n",
|
|
")\n",
|
|
"\n",
|
|
"cop_fig.update_traces(\n",
|
|
" text=mean_cops.values, \n",
|
|
" textposition='outside', \n",
|
|
" texttemplate=\"%{text:.1f}\", \n",
|
|
")\n",
|
|
"\n",
|
|
"cop_fig "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"## Sensitivity analysis"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"We have agreed that for the sensitivity analysis we will vary the following key assumptions in the flex-model:\n",
|
|
"1. Market prices of gas and electricity. We will use the actual prices for 2019, the actual prices for 2020.\n",
|
|
"2. aFRR prices +/- 30%\n",
|
|
"3. CAPEX\n",
|
|
"4. CO2 price\n",
|
|
"5. Tsource +/- 10 degrees Celsius\n",
|
|
"6. Tsink\n",
|
|
" * Roam off the peak / lower pressure\n",
|
|
" * Stabalize / running average per hour/ 2 hours\n",
|
|
"7. __A scenario with a constraint on grid capacity and a scenario without grid capacity as a constraint__\n",
|
|
"8. Energy Tax and ODE +/- 30%"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def setup():\n",
|
|
" _c = Config()\n",
|
|
" s = setup_model(c=_c)\n",
|
|
" return s\n",
|
|
"\n",
|
|
"def routine(c, s):\n",
|
|
" s = load_data(c=c, s=s)\n",
|
|
" s = create_and_assign_assets(c=c, s=s)\n",
|
|
" #s = preprocessing(c=c, s=s)\n",
|
|
" s = run_optimisation(c=c, s=s)\n",
|
|
" #s = postprocessing(c=c, s=s)\n",
|
|
" s = collect_cashflows(c=c, s=s)\n",
|
|
" s = calculate_financials(c=c, s=s)\n",
|
|
" return s"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"%time _s = setup()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"%time result = routine(c, _s)\n",
|
|
"npv = result.hpcase.npv\n",
|
|
"npv"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# sensitivity:Storage temperature\n",
|
|
"values = range(80, 120, 10)\n",
|
|
"param = 'storage_temperature'\n",
|
|
"kpis = ['npv','irr','spp', 'mean_cop']\n",
|
|
"\n",
|
|
"sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n",
|
|
"output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='volume(m3)',\n",
|
|
" yTitle='NPV in €',\n",
|
|
" yrange=[output.min().min()*1.1, 0],\n",
|
|
" title='Sensitivity: Storage volume',\n",
|
|
" colors=recoycolors,\n",
|
|
" dimensions=(600, 400),\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Sensitivity: Water storage volume"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"(_s.demand['MW (VDG)'] + _s.demand['MW (NDG)']).to_list()[:3]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"%time _s = setup()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"values = range(100, 500, 100)\n",
|
|
"param = 'storage_volume'\n",
|
|
"kpis = ['npv','irr','spp']\n",
|
|
"\n",
|
|
"sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"[case.name for case in s.cases][1:]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n",
|
|
"output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='volume(m3)',\n",
|
|
" yTitle='NPV in €',\n",
|
|
" yrange=[output.min().min()*1.1, 0],\n",
|
|
" title='Sensitivity: Storage volume',\n",
|
|
" colors=recoycolors,\n",
|
|
" dimensions=(600, 400),\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('irr', case_names=[case.name for case in s.cases][1:])\n",
|
|
"output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='volume(m3)',\n",
|
|
" yTitle='irr in %',\n",
|
|
" yrange=[output.min().min()*1.1, 0],\n",
|
|
" title='Sensitivity: IRR',\n",
|
|
" colors=recoycolors,\n",
|
|
" dimensions=(600, 400),\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n",
|
|
"output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='volume(m3)',\n",
|
|
" yTitle='years',\n",
|
|
" yrange=[output.min().min()*1.2, 0],\n",
|
|
" title='Sensitivity: payback time',\n",
|
|
" colors=recoycolors,\n",
|
|
" dimensions=(600, 400),\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('mean_cop', case_names=[case.name for case in s.cases][1:])\n",
|
|
"output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='volume(m3)',\n",
|
|
" yTitle='COP',\n",
|
|
" yrange=[output.min().min()*1.1, 0],\n",
|
|
" title='Sensitivity: COP',\n",
|
|
" colors=recoycolors,\n",
|
|
" dimensions=(600, 400),\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Sensitivity: CAPEX per MW storage"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"values = range(4_000, 10_000, 2_000)\n",
|
|
"param = 'storage_capex_per_MW'\n",
|
|
"kpis = ['npv', 'irr', 'spp', 'mean_cop']\n",
|
|
"\n",
|
|
"sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n",
|
|
"output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='CAPEX',\n",
|
|
" yTitle='NPV in Euro',\n",
|
|
" yrange=[output.min().min()*1.1, 0],\n",
|
|
" title='Sensitivity: NPV in euro',\n",
|
|
" colors=recoycolors,\n",
|
|
" dimensions=(600, 400),\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n",
|
|
"output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='CAPEX/MW',\n",
|
|
" yTitle='years',\n",
|
|
" yrange=[output.min().min()*1.2, 0],\n",
|
|
" title='Sensitivity: payback time',\n",
|
|
" colors=recoycolors,\n",
|
|
" dimensions=(600, 400),\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Sensitivity: CAPEX per MWh storage"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"values = range(1_000, 5_000, 1000)\n",
|
|
"param = 'storage_capex_per_MWh'\n",
|
|
"kpis = ['npv', 'spp', 'mean_cop']\n",
|
|
"\n",
|
|
"sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n",
|
|
"output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='CAPEX/MWh',\n",
|
|
" yTitle='NPV',\n",
|
|
" yrange=[output.min().min()*1.2, 0],\n",
|
|
" title='Sensitivity: NPV in Euro',\n",
|
|
" colors=recoycolors,\n",
|
|
" dimensions=(600, 400),\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n",
|
|
"output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='CAPEX/MWh',\n",
|
|
" yTitle='years',\n",
|
|
" yrange=[output.min().min()*1.2, 0],\n",
|
|
" title='Sensitivity: Payback time',\n",
|
|
" colors=recoycolors,\n",
|
|
" dimensions=(600, 400),\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Sensitivity: threshold price"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"values = range(10, 60, 10)\n",
|
|
"param = 'threshold'\n",
|
|
"kpis = ['npv', 'spp', 'mean_cop']\n",
|
|
"\n",
|
|
"sens = SensitivityAnalysis(c, _s, routine, param, values, kpis)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('npv', case_names=[case.name for case in s.cases][1:])\n",
|
|
"output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='threshold',\n",
|
|
" yTitle='NPV',\n",
|
|
" yrange=[output.min().min()*1.2, 0],\n",
|
|
" title='Sensitivity: NPV in Euro',\n",
|
|
" colors=recoycolors,\n",
|
|
" dimensions=(600, 400),\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('spp', case_names=[case.name for case in s.cases][1:])\n",
|
|
"output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='threshold',\n",
|
|
" yTitle='years',\n",
|
|
" yrange=[output.min().min()*1.2, 0],\n",
|
|
" title='Sensitivity: SPP in years',\n",
|
|
" colors=recoycolors,\n",
|
|
" dimensions=(600, 400),\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('mean_cop', case_names=[case.name for case in s.cases][1:])\n",
|
|
"output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='threshold',\n",
|
|
" yTitle='COP',\n",
|
|
" yrange=[output.min().min()*1.2, 0],\n",
|
|
" title='Sensitivity: SOP',\n",
|
|
" colors=recoycolors,\n",
|
|
" dimensions=(600, 400),\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"### Sensitivity: Heat Pump CAPEX"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"_s = setup()\n",
|
|
"configs = {}\n",
|
|
"values = [0.7, 1, 1.3, 2]\n",
|
|
"for value in values:\n",
|
|
" _c = Config()\n",
|
|
" _c.hp_capex *= value\n",
|
|
" configs[value*100] = _c"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"%%time\n",
|
|
"sens = SensitivityAnalysis(_s, routine, configs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
|
|
"\n",
|
|
"sens_capex = output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='CAPEX factor (%)',\n",
|
|
" yTitle='Payback Period in years',\n",
|
|
" yrange=[0, output.max().max()*1.1],\n",
|
|
" title='Sensitivity: Heat Pump CAPEX (€)',\n",
|
|
" colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
|
|
" dimensions=(600, 400),\n",
|
|
" asFigure=True\n",
|
|
")\n",
|
|
"sens_capex"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"### Sensitivity: CO2 prices"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"_s = setup()\n",
|
|
"configs = {}\n",
|
|
"mean = 24.86\n",
|
|
"co2_prices = [10, 25, 50, 100]\n",
|
|
"\n",
|
|
"for price in co2_prices:\n",
|
|
" _c = Config()\n",
|
|
" multiplier = price / mean\n",
|
|
" _c.co2_price_multiplier = multiplier\n",
|
|
" configs[price] = _c"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"%%time\n",
|
|
"sens = SensitivityAnalysis(_s, routine, configs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('ebitda', case_names=['Baseline', 'Heatpump + SDE', 'Heatpump only', 'Optimisation', 'Optimisation + aFRR'])\n",
|
|
"output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='Mean CO2 price in €',\n",
|
|
" yTitle='EBITDA in €',\n",
|
|
" yrange=[output.min().min() * 1.1, 0],\n",
|
|
" title='Sensitivity: CO2 prices > EBITDA',\n",
|
|
" colors=[recoypurple, recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
|
|
" dimensions=(600, 400),\n",
|
|
" asFigure=True\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
|
|
"sens_co2_spp = output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='Mean CO2 price in €',\n",
|
|
" yTitle='Payback Period in Years',\n",
|
|
" yrange=[0, output.max().max()*1.1],\n",
|
|
" title='Sensitivity: CO2 prices',\n",
|
|
" colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
|
|
" dimensions=(600, 400),\n",
|
|
" asFigure=True\n",
|
|
")\n",
|
|
"sens_co2_spp"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
|
|
"output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='Mean CO2 price in €',\n",
|
|
" yTitle='NPV in €',\n",
|
|
" yrange=[output.min().min() * 10, output.max().max()*1.1],\n",
|
|
" title='Sensitivity: CO2 prices > NPV',\n",
|
|
" colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
|
|
" dimensions=(600, 400)\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"### Sensitivity: Gas prices"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"_s = setup()\n",
|
|
"configs = {}\n",
|
|
"values = [0.7, 1, 1.3]\n",
|
|
"for value in values:\n",
|
|
" _c = Config()\n",
|
|
" _c.gas_price_multiplier = value\n",
|
|
" configs[value * 100] = _c"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"%%time\n",
|
|
"sens = SensitivityAnalysis(_s, routine, configs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
|
|
"output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='Gas price factor (%)',\n",
|
|
" yTitle='NPV in €',\n",
|
|
" yrange=[output.min().min()*1.5, output.max().max()*1.1],\n",
|
|
" title='Sensitivity: Gas prices',\n",
|
|
" colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
|
|
" dimensions=(600, 400)\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"### Sensitivity: Day Ahead buying amount"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"_s = setup()\n",
|
|
"configs = {}\n",
|
|
"values = [0, 0.5, 1]\n",
|
|
"for value in values:\n",
|
|
" _c = Config()\n",
|
|
" _c.day_ahead_buying_perc = value\n",
|
|
" configs[value * 100] = _c"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"sens = SensitivityAnalysis(_s, routine, configs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
|
|
"output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='Volume in %',\n",
|
|
" yTitle='NPV in €',\n",
|
|
" yrange=[0, output.max().max()*1.1],\n",
|
|
" title='Sensitivity: Volume bought on Day-Ahead market in %',\n",
|
|
" colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
|
|
" dimensions=(600, 400)\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"### Sensitivity: Electricity prices"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"_s = setup()\n",
|
|
"configs = {}\n",
|
|
"values = [0.7, 1, 1.3]\n",
|
|
"for value in values:\n",
|
|
" _c = Config()\n",
|
|
" _c.e_price_multiplier = value\n",
|
|
" configs[value * 100] = _c"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"sens = SensitivityAnalysis(_s, routine, configs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
|
|
"sens_eprices = output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='Electricity price factor in %',\n",
|
|
" yTitle='Payback Period in years',\n",
|
|
" yrange=[0, output.max().max()*1.1],\n",
|
|
" title='Sensitivity: Electricity prices',\n",
|
|
" colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
|
|
" dimensions=(600, 400),\n",
|
|
" asFigure=True\n",
|
|
")\n",
|
|
"sens_eprices"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"### Sensitivity: Electricity price volatility"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"_s = setup()\n",
|
|
"configs = {}\n",
|
|
"values = [0.7, 1, 1.3]\n",
|
|
"for value in values:\n",
|
|
" _c = Config()\n",
|
|
" _c.e_price_volatility_multiplier = value\n",
|
|
" configs[value] = _c"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"sens = SensitivityAnalysis(_s, routine, configs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
|
|
"sens_evol = output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='Volatility factor',\n",
|
|
" yTitle='Payback period in years',\n",
|
|
" yrange=[0, output.max().max()*1.1],\n",
|
|
" title='Sensitivity: E-price volatility',\n",
|
|
" colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
|
|
" dimensions=(600, 400),\n",
|
|
" asFigure=True\n",
|
|
")\n",
|
|
"sens_evol"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"### Sensitivity: aFRR capacity fee"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"_s = setup()\n",
|
|
"configs = {}\n",
|
|
"values = [10_000, 25_000, 50_000]\n",
|
|
"for value in values:\n",
|
|
" _c = Config()\n",
|
|
" _c.afrr_capacity_fee = value\n",
|
|
" configs[value] = _c"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"sens = SensitivityAnalysis(_s, routine, configs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
|
|
"sens_affr_fee = output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='aFRR capacity fee in €/MW',\n",
|
|
" yTitle='Payback Period in years',\n",
|
|
" yrange=[0, output.max().max()*1.1],\n",
|
|
" title='Sensitivity: aFRR capacity fee',\n",
|
|
" colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
|
|
" dimensions=(600, 400),\n",
|
|
" asFigure=True\n",
|
|
")\n",
|
|
"sens_affr_fee"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"### Sensitivity: Energy tax"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"_s = setup()\n",
|
|
"configs = {}\n",
|
|
"values = [0.7, 1, 1.3]\n",
|
|
"for value in values:\n",
|
|
" _c = Config()\n",
|
|
" _c.energy_tax_multiplier = value\n",
|
|
" configs[value] = _c"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"sens = SensitivityAnalysis(_s, routine, configs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
|
|
"output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='Factor',\n",
|
|
" yTitle='NPV in €',\n",
|
|
" yrange=[0, output.max().max()*1.1],\n",
|
|
" title='Sensitivity: Energy taxes',\n",
|
|
" colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
|
|
" dimensions=(600, 400)\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"### Sensitivity: Tsource"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"_s = setup()\n",
|
|
"configs = {}\n",
|
|
"values = [-25, -10, 0, 10, 25]\n",
|
|
"for value in values:\n",
|
|
" _c = Config()\n",
|
|
" _c.tsource_delta = value\n",
|
|
" configs[value] = _c"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"sens = SensitivityAnalysis(_s, routine, configs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
|
|
"output.loc[-25, 'Heatpump only'] = np.nan\n",
|
|
"sens_tsource = output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='Tsource delta',\n",
|
|
" yTitle='Payback Period in years',\n",
|
|
" yrange=[0, output.max().max()*1.1],\n",
|
|
" title='Sensitivity: Tsource',\n",
|
|
" colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
|
|
" dimensions=(600, 400),\n",
|
|
" asFigure=True\n",
|
|
")\n",
|
|
"sens_tsource"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('mean_cop', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
|
|
"output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='Tsource delta',\n",
|
|
" yTitle='COP',\n",
|
|
" yrange=[0, output.max().max()*1.1],\n",
|
|
" title='Sensitivity: Tsource > Mean COP',\n",
|
|
" colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
|
|
" dimensions=(600, 400)\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"### Sensitivity: Tsink"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"_s = setup()\n",
|
|
"configs = {}\n",
|
|
"values = [-25, -10, 0, 10, 25]\n",
|
|
"for value in values:\n",
|
|
" _c = Config()\n",
|
|
" _c.tsink_delta = value\n",
|
|
" configs[value] = _c"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"sens = SensitivityAnalysis(_s, routine, configs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('spp', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
|
|
"sens_tsink = output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='Tsink delta',\n",
|
|
" yTitle='Payback Period in Years',\n",
|
|
" yrange=[0, output.max().max()*1.1],\n",
|
|
" title='Sensitivity: Tsink',\n",
|
|
" colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
|
|
" dimensions=(600, 400),\n",
|
|
" asFigure=True\n",
|
|
")\n",
|
|
"sens_tsink"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"output = sens.single_kpi_overview('mean_cop', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
|
|
"sens_tsink_cop = output.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='Tsink delta',\n",
|
|
" yTitle='COP',\n",
|
|
" yrange=[0, output.max().max()*1.1],\n",
|
|
" title='Sensitivity: Tsink > Mean COP',\n",
|
|
" colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
|
|
" dimensions=(600, 400),\n",
|
|
" asFigure=True\n",
|
|
")\n",
|
|
"sens_tsink_cop"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"### Sensitivity: Time period"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def routine2(c, s):\n",
|
|
" s = setup_model(c=c)\n",
|
|
" s = load_data(c=c, s=s)\n",
|
|
" s = create_and_assign_assets(c=c, s=s)\n",
|
|
" s = run_optimisation(c=c, s=s)\n",
|
|
" s = collect_cashflows(c=c, s=s)\n",
|
|
" s = calculate_financials(c=c, s=s)\n",
|
|
" return s"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"configs = {}\n",
|
|
"start_values = ['2018-01-01', '2019-01-01', '2019-11-01']\n",
|
|
"\n",
|
|
"for value in start_values:\n",
|
|
" _c = Config()\n",
|
|
" _c.start = value\n",
|
|
" _c.end = (pd.to_datetime(value) + timedelta(days=364)).strftime('%Y-%m-%d')\n",
|
|
" configs[value] = _c"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"%%time\n",
|
|
"sens = SensitivityAnalysis(_s, routine2, configs)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"result = sens.single_kpi_overview('npv', case_names=['Heatpump only', 'Heatpump + SDE', 'Optimisation', 'Optimisation + aFRR'])\n",
|
|
"result.iplot(\n",
|
|
" mode='lines+markers',\n",
|
|
" symbol='circle-dot',\n",
|
|
" size=10,\n",
|
|
" xTitle='Start date',\n",
|
|
" yTitle='NPV in €',\n",
|
|
" yrange=[0, result.max().max()*1.1],\n",
|
|
" title='Sensitivity: Modelled time period',\n",
|
|
" colors=[recoygreen, recoyyellow, recoydarkblue, recoyred],\n",
|
|
" dimensions=(600, 400)\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"source": [
|
|
"## Report"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"tags": [
|
|
"exclude"
|
|
]
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"renderer = 'svg'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# __Presentation Flexible Heatpumps__\n",
|
|
"\n",
|
|
"ENCORE meeting: 17-12-2020\n",
|
|
"Mark Kremer\n",
|
|
"\n",
|
|
" \n",
|
|
" \n",
|
|
" \n",
|
|
"\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## __Central question:__\n",
|
|
"#### *Can Smurfit Kappi shorten the Payback Period of an investment in a Heatpump by operating it in a flexible manner?*"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### __Flexible operations on imbalance market__\n",
|
|
"\n",
|
|
"Benefiting from fluctuations in electricity market prices by ramping the asset up- and down (increasing and decreasing the electricity consumption)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig_example_1 = s.hpcase.data[['DAM']].iloc[:60*24].iplot(\n",
|
|
" title='Day-Ahead prices on arbitrary day in €/MWh',\n",
|
|
" yrange=[-100, 200],\n",
|
|
" colors=recoygreen, \n",
|
|
" yTitle='Price in €/MWh',\n",
|
|
" xTitle='Time of Day',\n",
|
|
" dimensions=(800, 400),\n",
|
|
" asFigure=True\n",
|
|
")\n",
|
|
"\n",
|
|
"fig_example_1.show(renderer=renderer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Imbalance prices are very volatile, with prices below -100 and above 200 €/MWh on a daily basis."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig_example_2 = s.hpcase.data[['DAM', 'POS']].rename(columns={'POS':'IMB'}).iloc[:60*24].iplot(\n",
|
|
" title='Imbalance Prices on arbitrary day in €/MWh',\n",
|
|
" colors=[recoygreen, recoydarkblue], \n",
|
|
" yTitle='Price in €/MWh',\n",
|
|
" xTitle='Time of Day',\n",
|
|
" dimensions=(800, 400),\n",
|
|
" asFigure=True\n",
|
|
")\n",
|
|
"\n",
|
|
"fig_example_2.show(renderer=renderer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"It is possible to benefit from these fluctiations, if you have __flexibility__\n",
|
|
"* Storage options\n",
|
|
"* Hybrid installations (e.g. with gas-powered assets)\n",
|
|
"\n",
|
|
"In this case we are looking at a __hybrid set-up of a Steamboiler and a Heatpump__"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### __Simulations & mathematical modelling__"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"* To answer the central question, we have build a simulation model\n",
|
|
"* The model simulates the operations of a hybrid set-up of a Heatpump and a Steamboiler over the timespan of 1 years (on a 1 minute basis)\n",
|
|
"* The goal of the model is to minimize the operating costs, in order to reach the shortest Payback Period\n",
|
|
"* We are taking into account all major investment and operating costs, including:\n",
|
|
" * Asset CAPEX\n",
|
|
" * Commodity costs for gas and electricity\n",
|
|
" * Energy taxes\n",
|
|
" * Grid transport costs\n",
|
|
" * SDE++ subsidies\n",
|
|
" * Maintenance costs\n",
|
|
" * CO2 allowances\n",
|
|
"* The output of the model is a Payback Period for an investment in an heatpump, in different scenario's"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### __Casestudies__\n",
|
|
"5 main scenario's"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"\n",
|
|
"1. Steamboiler only (baseline, baseload)\n",
|
|
"2. Heatpump only (stand-alone, baseload, without SDE++)\n",
|
|
"3. Heatpump + SDE\n",
|
|
"4. Heatpump + SDE + Steam boiler (hybrid set-up) on Imbalance market\n",
|
|
"5. Heatpump + SDE + Steam boiler (hybrid set-up) on aFRR (secondary reserve market)\n",
|
|
"\n",
|
|
"Besides that, we modelled 11 sensitivities:\n",
|
|
"* Heatpump CAPEX\n",
|
|
"* Gas & CO2 prices\n",
|
|
"* Electricity prices & volatility\n",
|
|
"* Energy taxes\n",
|
|
"* Bidding strategies\n",
|
|
"* Source temperatures (affecting COP)\n",
|
|
"* Sink temperatures (affecting COP)\n",
|
|
"* Time period (2018, 2019, 2020)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### __Smurfit Kappa case__"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The model is based on the context of Smurfit Kappa (paper factory)\n",
|
|
"* Currently a Steamboiler is providing the 20-30 MW of average heat demand for drying processes\n",
|
|
"* We add a 31 MW heatpump (to make sure it can cover entire demand)\n",
|
|
"* The steam demand must be fulfilled at all times, by either the heatpump or the gasboiler\n",
|
|
"* The heatpump and steam boiler can both respond very quickly (within minutes) within a flexible range (30%-100% for heatpump)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig_demands_over_time.show(renderer=renderer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"* Source temperatures of around 65 degrees C\n",
|
|
"* Sink temperatures of 125-170 degrees C \n",
|
|
"* Average Temperature lift of about 85 degrees C"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"demands_fig.show(renderer=renderer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### __Heat pump__"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"COP roughly between 4 and 1.5, depending on load, Tsource and Tsink"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def cop_curve(Tsink, Tsource):\n",
|
|
" Tsink += 273\n",
|
|
" Tsource += 273\n",
|
|
"\n",
|
|
" c1 = 0.267 * Tsink / (Tsink - Tsource)\n",
|
|
" c2 = 0.333 * Tsink / (Tsink - Tsource)\n",
|
|
" \n",
|
|
" return Polynomial([c2, c1])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sourceT = 63\n",
|
|
"sinkT = 140\n",
|
|
"cop_curve(sourceT, sinkT)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig_cop_curve.show(renderer=renderer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### __Optimisation__"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"* At each moment in time, we calculate the cheapest option to produce the required heat. \n",
|
|
"* Taking into account the COP fluctuations, due to changing Tsource and Tsink\n",
|
|
"* Taking into account fluctuating market prices (electricity, gas, CO2)\n",
|
|
"* We are predicting real-time electricity prices using our forecasting models"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"__Some example days:__"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Steamboiler only is following demand pattern"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig_steamboiler.show(renderer=renderer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Similar pattern for heatpump only case"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig_heatpump.show(renderer=renderer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Hybrid set-up is responding to price fluctuactions, steam boiler taking over at high prices"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig_optcase.show(renderer=renderer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig_optcase2.show(renderer=renderer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### __Business case__"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"CAPEX of around 6 M€ (200.000 €/MW), which need to be earned back by savings in operating costs"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"capex_fig.show(renderer=renderer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"* Savings in EBITDA compared to the baseline are about 1.5 mln € without subsidy, and up to 4.5 mln € including subsidy\n",
|
|
"* The optimisation on aFRR allows for a 30-40% improvement in EBITDA"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"ebitda_graph.show(renderer=renderer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Resulting in a Payback Period of 5.4 years without subsidy, and 1.8 years with subsidy"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"spt_fig.show(renderer=renderer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"The added value of the optimisation is limited (in absolute terms), which is explained by the high COP of the heatpump"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"production_fig.show(renderer=renderer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"* The heatpump is filling in 95-98 % of the demand. \n",
|
|
"* Because of its high COP, it is almost always cheaper to run than the steam boiler\n",
|
|
"* Switch price is on average around 90€/MWh (excluding subsidies)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### __Sensitivities__"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"If CAPEX is 200%, subsidy is needed to keep a good Payback Time"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sens_capex.show(renderer=renderer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Subsidies are protecting the business case againsts low CO2 prices"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sens_co2_spp.show(renderer=renderer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"* The businesscase is quite sensitive to Tsource and Tsink differences, because they directly impact the COP\n",
|
|
"* The Smurtfit Kappa case, with a temperature lifte of about 85 degrees C on average, looks favorable. \n",
|
|
"* When the temperature lift is higher, the COP will decrease and the business case will degrade"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sens_tsource.show(renderer=renderer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sens_tsink.show(renderer=renderer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sens_tsink_cop.show(renderer=renderer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sens_eprices.show(renderer=renderer)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"#### __Conclusions__"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"* The business case for a Heat Pump seems favourable\n",
|
|
"* Flexible operation, using aFRR, can improve the operational results by 30-40%\n",
|
|
"* However, this only results in a marginal improvement of the business case\n",
|
|
"* SDE++ has a very favourable effect on the business case, but is not critical\n",
|
|
"* The business case is notably sensitive to the temperature lift required, and is therefore strongly dependent on the specific use case."
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
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|
|
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|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
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|
|
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|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.9"
|
|
},
|
|
"toc-autonumbering": false,
|
|
"toc-showmarkdowntxt": false,
|
|
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|
|
"application/vnd.jupyter.widget-state+json": {
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|
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|
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|
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"version_minor": 0
|
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|
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|
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|
|
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|
|
"nbformat_minor": 4
|
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|