{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.32.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\__init__.py\n"
     ]
    }
   ],
   "source": [
    "from datetime import timedelta, datetime\n",
    "import json\n",
    "import pprint\n",
    "from copy import deepcopy\n",
    "import pytz\n",
    "\n",
    "import cufflinks\n",
    "cufflinks.go_offline()\n",
    "import numpy as np\n",
    "from numpy.polynomial import Polynomial\n",
    "import pandas as pd\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import sys\n",
    "\n",
    "import os\n",
    " \n",
    "# Path to the folder containing the alternate version of PyRecoy\n",
    "\n",
    "alternate_pyrecoy_path = 'C:\\\\Users\\\\z004yn8c\\\\OneDrive - Siemens AG\\\\Documents\\\\GitHub\\\\Mooi-Kickstart\\\\pyrecoy\\\\pyrecoy'\n",
    " \n",
    "# Add the path to sys.path\n",
    "\n",
    "if alternate_pyrecoy_path not in sys.path:\n",
    "\n",
    "    sys.path.insert(0, alternate_pyrecoy_path)\n",
    " \n",
    "# Now import PyRecoy\n",
    "\n",
    "import pyrecoy2\n",
    "\n",
    "# Check the version or path to confirm\n",
    "\n",
    "print(pyrecoy2.__file__)\n",
    "\n",
    "\n",
    "from pyrecoy2.assets import Heatpump, Eboiler, GasBoiler, HotWaterStorage\n",
    "from pyrecoy2.colors import *\n",
    "from pyrecoy2.converters import *\n",
    "from pyrecoy2.financial import calculate_eb_ode, get_tax_tables, get_tax_rate, get_grid_tariffs_electricity\n",
    "from pyrecoy2.framework import TimeFramework\n",
    "from pyrecoy2.casestudy import CaseStudy\n",
    "from pyrecoy2.plotting import ebitda_bar_chart, npv_bar_chart\n",
    "from pyrecoy2.reports import CaseReport, ComparisonReport, BusinessCaseReport, SingleFigureComparison\n",
    "from pyrecoy2.sensitivity import SensitivityAnalysis\n",
    "from pyrecoy2.prices import get_tennet_data, get_afrr_capacity_fees_nl\n",
    "from pyrecoy2.forecasts import Mipf, Forecast\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "#### Development backlog"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "* aFRR (can improve the optimisation case)\n",
    "* Report\n",
    "* COP seems to have fix values, rather than COP curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "#### Meeting Notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "##### Meeting 25-11-2020"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "* aFRR can help optimisation case\n",
    "* SDE++ should be included\n",
    "* Tsource sensitivity really gives interesting insights\n",
    "* Sensitivities should be verified (especially CO2, Tsink, Tsource, time period)\n",
    "* AP TNO: Update/verify COP curve\n",
    "* AP TNO: Update CAPEX\n",
    "* AP Mark: \n",
    "    * Create graphs on COP curve with different Tsource, Tsink\n",
    "    * Generate table and output in .csv (send it to Andrew)\n",
    "* Investigate opportunity to lower the COP and negative electricity prices\n",
    "    * Technically feasible, but not really needed/possible to do it in this project\n",
    "* Could be interesting to run this model on a usecase with higher delta T\n",
    "* Conclusion: Finalize this model, but not add too much extra complexity, next steps is to go towards industrial partners with the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "# Kickstart: Heatpump Optimisation Framework\n",
    "\n",
    "***\n",
    "© Shahla Huseynova  \n",
    "July 2024\n",
    "##### Cases\n",
    "In this model, 4 cases are compared:\n",
    "* **Baseline** : All heat is supplied by steam turbine\n",
    "* **Heatpump case** : All heat is supplied by heatpump\n",
    "* **Hybrid case** : Steam boiler and heatpump run in hybrid mode, and are optimised on costs in real-time\n",
    "* **Heat pump & storage cases**: Heat pump only case is compared with different setup of storage +heat pump cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "#### Loading config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "class Config():\n",
    "    start = '2023-01-01'\n",
    "    end = '2023-12-31'\n",
    "    # year = '2019'\n",
    "    # startdate = year + '-01-01'\n",
    "    # enddate = str(year) + '-12-31'\n",
    "    # start = datetime.strptime(startdate, \"%Y-%m-%d\").astimezone(pytz.timezone('Europe/Amsterdam'))\n",
    "    # end = datetime.strptime(enddate, \"%Y-%m-%d\").astimezone(pytz.timezone('Europe/Amsterdam'))\n",
    "    # start = start.astimezone(pytz.UTC)\n",
    "    # end = end.astimezone(pytz.UTC)\n",
    "    \n",
    "    hp_vdg_e_power = 1.5 # MW\n",
    "    # hp_ndg_e_power = 7.7 # MW\n",
    "    hp_min_load = 0\n",
    "    hp_lifetime = 25\n",
    "    hp_capex = 200_000 # EUR/MWth\n",
    "    hp_opex = 0.01 # in % of CAPEX\n",
    "    hp_devex = 0.005 # in % of CAPEX\n",
    "    \n",
    "    gb_power = 5 # MW\n",
    "    gb_efficiency = 0.9\n",
    "    \n",
    "    storage_power = 1\n",
    "    # storage_volume = 1000 *3/50\n",
    "    # storage_cap_per_volume = 50 * 1e-3\n",
    "    rated_capacity = 3\n",
    "    storage_lifetime = 25\n",
    "    storage_temperature = 144\n",
    "    # storage_min_level = storage_volume * storage_cap_per_volume * 0.05\n",
    "    storage_min_level = rated_capacity * 0.00\n",
    "    # storage_min_level = rated_capacity\n",
    "    storage_capex_per_MW = 60_000\n",
    "    storage_capex_per_MWh = 0\n",
    "    storage_opex_perc_of_capex = 0.02\n",
    "    # storage_initial_level = 5\n",
    "    threshold = 20\n",
    "    \n",
    "    tax_bracket_g = 4 \n",
    "    tax_bracket_e = 4\n",
    "    \n",
    "    include_transport_costs = False\n",
    "    grid_operator = 'Liander'\n",
    "    connection_type = 'TS/MS'\n",
    "    \n",
    "    discount_rate = 0.1\n",
    "    project_duration = 12\n",
    "\n",
    "    forecast = 'ForeNeg'\n",
    "    gas_price_multiplier = 1\n",
    "    e_price_multiplier = 1\n",
    "    e_price_volatility_multiplier = 1\n",
    "    co2_price_multiplier = 1\n",
    "    tsource_delta = 0\n",
    "    tsink_delta = 0\n",
    "    energy_tax_multiplier = 1\n",
    "    \n",
    "    # Review the SDE implementation\n",
    "    sde_base_amount = 81\n",
    "    longterm_gas_price = 24.00\n",
    "    longterm_co2_price = 37.90\n",
    "    sde_switch_price_correction = 40\n",
    "    \n",
    "    day_ahead_buying_perc = 0.3\n",
    "    \n",
    "    afrr_capacity_fee = 25_000\n",
    "\n",
    "c = Config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "class Store():\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Model set-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the file paths\n",
    "excel_file_path = r'C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\Kickstart\\V2\\Demand_Data_Smurfit_Preprocessed3.xlsx'\n",
    "output_directory = r'C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\Kickstart\\V2\\data'\n",
    "output_file_name = 'smurfit_demand_preprocessed3.csv'\n",
    "csv_file_path = os.path.join(output_directory, output_file_name)\n",
    "\n",
    "df = pd.read_excel(excel_file_path)\n",
    "# df = df.fillna(0) \n",
    "\n",
    "# # Save the DataFrame to CSV\n",
    "df.to_csv(csv_file_path, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def load_demand_data(c, s):\n",
    "    demand = pd.read_csv('data/smurfit_demand_preprocessed3.csv', delimiter=',', decimal='.')\n",
    "    dt_index = pd.date_range(\n",
    "        start=s.time_fw.start,\n",
    "        end=s.time_fw.start + timedelta(days=365), \n",
    "        freq='1T',\n",
    "        tz='Europe/Amsterdam')\n",
    "    dt_index = dt_index[:len(demand)]\n",
    "    demand.index = dt_index\n",
    "    demand['Total demand'] = demand['MW (VDG)']\n",
    "    \n",
    "    demand = demand[c.start:c.end]\n",
    "    return demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = Store()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testetss\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:41: UserWarning:\n",
      "\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",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:62: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:115: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_18312\\2484316097.py:3: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def setup_model(c):\n",
    "    s = Store()\n",
    "    \n",
    "    s.time_fw = TimeFramework(start=c.start, end=c.end)\n",
    "    # s.minute_ix = s.time_fw.dt_index(freq='1T')\n",
    "    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",
    "    s.hpcase = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump only', data=mipf)\n",
    "    #s.hpcase_sde = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + SDE', data=mipf)\n",
    "    #s.optcase1 = CaseStudy(time_fw=s.time_fw, freq='1T', name='Optimisation', data=mipf)\n",
    "    s.storage_case_PCM = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage_PCM', data=mipf)\n",
    "    # s.storage_case_TCM = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage_TCM', data=mipf)\n",
    "    # s.storage_case_battery = CaseStudy(time_fw=s.time_fw, freq='1T', name='Heatpump + Storage_battery', data=mipf)\n",
    "    s.cases = list(CaseStudy.instances.values())\n",
    "    s.optcases = [s.hpcase, s.storage_case_PCM]\n",
    "    s.sde_cases = []\n",
    "    \n",
    "    s.demand = load_demand_data(c, s)\n",
    "    return s\n",
    "\n",
    "s = setup_model(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s.demand.resample('15T').mean()[['Tsource (VDG)', 'Tsink (VDG)']].iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Tsource (VDG)</th>\n",
       "      <th>Tsink (VDG)</th>\n",
       "      <th>MW (VDG)</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>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:01:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:02:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:03:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-01 00:04:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:55:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:56:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:57:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:58:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:59:00+01:00</th>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>525600 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Tsource (VDG)  Tsink (VDG)  MW (VDG)  Total demand\n",
       "2023-01-01 00:00:00+01:00             70          144         1             1\n",
       "2023-01-01 00:01:00+01:00             70          144         1             1\n",
       "2023-01-01 00:02:00+01:00             70          144         1             1\n",
       "2023-01-01 00:03:00+01:00             70          144         1             1\n",
       "2023-01-01 00:04:00+01:00             70          144         1             1\n",
       "...                                  ...          ...       ...           ...\n",
       "2023-12-31 23:55:00+01:00             70          144         1             1\n",
       "2023-12-31 23:56:00+01:00             70          144         1             1\n",
       "2023-12-31 23:57:00+01:00             70          144         1             1\n",
       "2023-12-31 23:58:00+01:00             70          144         1             1\n",
       "2023-12-31 23:59:00+01:00             70          144         1             1\n",
       "\n",
       "[525600 rows x 4 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.demand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Load in data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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"
   ]
  },
  {
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     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\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\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\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\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\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\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\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\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\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\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\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",
    "    \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",
    "\n",
    "    s.demand['Tsource (VDG)'] += c.tsource_delta\n",
    "    s.demand['Tsink (VDG)'] += 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)"
   ]
  },
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       "                            DAM     POS     NEG  ForeNeg  ForePos  \\\n",
       "2023-01-01 00:00:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
       "2023-01-01 00:01:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
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       "2023-01-01 00:04:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
       "...                         ...     ...     ...      ...      ...   \n",
       "2023-12-31 23:55:00+01:00  3.17   -6.81   -6.81    -7.10   -11.16   \n",
       "2023-12-31 23:56:00+01:00  3.17   -6.81   -6.81    -5.21    -8.12   \n",
       "2023-12-31 23:57:00+01:00  3.17   -6.81   -6.81    -5.61    -7.59   \n",
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       "2023-12-31 23:59:00+01:00  3.17   -6.81   -6.81    -5.70    -7.27   \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",
       "2023-12-31 23:55:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:56:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:57:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:58:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:59:00+01:00                 NaN                 NaN   \n",
       "\n",
       "                           CO2 prices (€/MWh)  Tsource (VDG)  Tsink (VDG)  \\\n",
       "2023-01-01 00:00:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:01:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:02:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:03:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:04:00+01:00                1.01             70          144   \n",
       "...                                       ...            ...          ...   \n",
       "2023-12-31 23:55:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:56:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:57:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:58:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:59:00+01:00                 NaN             70          144   \n",
       "\n",
       "                           MW (VDG)  Total demand  \n",
       "2023-01-01 00:00:00+01:00         1             1  \n",
       "2023-01-01 00:01:00+01:00         1             1  \n",
       "2023-01-01 00:02:00+01:00         1             1  \n",
       "2023-01-01 00:03:00+01:00         1             1  \n",
       "2023-01-01 00:04:00+01:00         1             1  \n",
       "...                             ...           ...  \n",
       "2023-12-31 23:55:00+01:00         1             1  \n",
       "2023-12-31 23:56:00+01:00         1             1  \n",
       "2023-12-31 23:57:00+01:00         1             1  \n",
       "2023-12-31 23:58:00+01:00         1             1  \n",
       "2023-12-31 23:59:00+01:00         1             1  \n",
       "\n",
       "[525600 rows x 12 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hpcase.data"
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  },
  {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:56:00+01:00</th>\n",
       "      <td>3.17</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-5.21</td>\n",
       "      <td>-8.12</td>\n",
       "      <td>29.927</td>\n",
       "      <td>77.25</td>\n",
       "      <td>14.90</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:57:00+01:00</th>\n",
       "      <td>3.17</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-5.61</td>\n",
       "      <td>-7.59</td>\n",
       "      <td>29.927</td>\n",
       "      <td>77.25</td>\n",
       "      <td>14.90</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:58:00+01:00</th>\n",
       "      <td>3.17</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-5.74</td>\n",
       "      <td>-7.87</td>\n",
       "      <td>29.927</td>\n",
       "      <td>77.25</td>\n",
       "      <td>14.90</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31 23:59:00+01:00</th>\n",
       "      <td>3.17</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-6.81</td>\n",
       "      <td>-5.70</td>\n",
       "      <td>-7.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>525600 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            DAM     POS     NEG  ForeNeg  ForePos  \\\n",
       "2023-01-01 00:00:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
       "2023-01-01 00:01:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
       "2023-01-01 00:02:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
       "2023-01-01 00:03:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
       "2023-01-01 00:04:00+01:00 -3.61 -209.40 -209.40      NaN      NaN   \n",
       "...                         ...     ...     ...      ...      ...   \n",
       "2023-12-31 23:55:00+01:00  3.17   -6.81   -6.81    -7.10   -11.16   \n",
       "2023-12-31 23:56:00+01:00  3.17   -6.81   -6.81    -5.21    -8.12   \n",
       "2023-12-31 23:57:00+01:00  3.17   -6.81   -6.81    -5.61    -7.59   \n",
       "2023-12-31 23:58:00+01:00  3.17   -6.81   -6.81    -5.74    -7.87   \n",
       "2023-12-31 23:59:00+01:00  3.17   -6.81   -6.81    -5.70    -7.27   \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",
       "2023-12-31 23:55:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:56:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:57:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:58:00+01:00              29.927               77.25   \n",
       "2023-12-31 23:59:00+01:00                 NaN                 NaN   \n",
       "\n",
       "                           CO2 prices (€/MWh)  Tsource (VDG)  Tsink (VDG)  \\\n",
       "2023-01-01 00:00:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:01:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:02:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:03:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:04:00+01:00                1.01             70          144   \n",
       "...                                       ...            ...          ...   \n",
       "2023-12-31 23:55:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:56:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:57:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:58:00+01:00               14.90             70          144   \n",
       "2023-12-31 23:59:00+01:00                 NaN             70          144   \n",
       "\n",
       "                           MW (VDG)  Total demand  \n",
       "2023-01-01 00:00:00+01:00         1             1  \n",
       "2023-01-01 00:01:00+01:00         1             1  \n",
       "2023-01-01 00:02:00+01:00         1             1  \n",
       "2023-01-01 00:03:00+01:00         1             1  \n",
       "2023-01-01 00:04:00+01:00         1             1  \n",
       "...                             ...           ...  \n",
       "2023-12-31 23:55:00+01:00         1             1  \n",
       "2023-12-31 23:56:00+01:00         1             1  \n",
       "2023-12-31 23:57:00+01:00         1             1  \n",
       "2023-12-31 23:58:00+01:00         1             1  \n",
       "2023-12-31 23:59:00+01:00         1             1  \n",
       "\n",
       "[525600 rows x 12 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "## Assets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### COP curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def cop_curve_new(Tsink, Tsource):\n",
    "    Tsink += 273\n",
    "    Tsource += 273\n",
    "    Tlift = Tsink - Tsource\n",
    "\n",
    "    c0 = 0.0005426*Tlift**2 - 0.1178*Tlift + 6.962\n",
    "    c1 = 6.7058 \n",
    "    c2 = -1.79\n",
    "    \n",
    "    return Polynomial([c0, c1, c2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$x \\mapsto \\text{19.2496754} + \\text{6.7058}\\,x - \\text{1.79}\\,x^{2}$"
      ],
      "text/plain": [
       "Polynomial([19.2496754,  6.7058   , -1.79     ], domain=[-1,  1], window=[-1,  1], symbol='x')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sourceT = 63 + 273\n",
    "sinkT = 140 + 273\n",
    "cop_curve_new(sourceT, sinkT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Create and assign assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def create_and_assign_assets(c, s):\n",
    "    heatpump_vdg = Heatpump(\n",
    "        name='Heatpump VDG',\n",
    "        max_th_power=c.hp_vdg_e_power,\n",
    "        min_th_power=c.hp_vdg_e_power * c.hp_min_load,\n",
    "        cop_curve=2.5\n",
    "    )\n",
    "\n",
    "\n",
    "    capex_vdg = c.hp_capex*(heatpump_vdg.max_th_power) \n",
    "    heatpump_vdg.set_financials(capex=capex_vdg, opex=c.hp_opex*capex_vdg, devex=c.hp_devex*capex_vdg, 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=None,\n",
    "        # volume=None, \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.storage_case_PCM.add_asset(heatpump_vdg)\n",
    "    s.storage_case_PCM.add_asset(waterstorage)\n",
    "    return s\n",
    "\n",
    "s = create_and_assign_assets(c, s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HotWaterStorage(name=HotWaterStorage, rated_power=1, capacity=3, temperature=144, min_storagelevel=0.0)"
      ]
     },
     "execution_count": 34,
     "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": 37,
   "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)']).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": 38,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def hponly(case):\n",
    "    hp_vdg = case.assets['Heatpump VDG']\n",
    "    demand_vdg = case.data['MW (VDG)'].to_list()\n",
    "    Tsink_vdg = case.data['Tsink (VDG)'].to_list()\n",
    "    Tsource_vdg = case.data['Tsource (VDG)'].to_list()\n",
    "\n",
    "    hp_vdg_input = [0] * len(case.data)\n",
    "    hp_vdg_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 - 0.66,\n",
    "                Tsink=Tsink_vdg[m],\n",
    "                Tsource=Tsource_vdg[m]\n",
    "            )\n",
    "\n",
    "    case.data['hp_output_MW'] = np.array(hp_vdg_output)\n",
    "    case.data['hp_input_MW'] = np.array(hp_vdg_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": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hp_storage_opt_enginge(c, ws, hp_vdg, pos, neg, dam, demand_vdg, tsource_vdg, tsink_vdg):\n",
    "    \n",
    "    from_storage_vdg_MW = 0\n",
    "    to_storage_vdg_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 - 0.66, 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 = ws.charging_power_limit\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",
    "            \n",
    "\n",
    "    elif pos > dam + c.threshold:\n",
    "        # take from storage\n",
    "        if demand_vdg != 0:\n",
    "            from_storage_vdg_MW = ws.discharging_power_limit\n",
    "            e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(demand_vdg, tsink_vdg, tsource_vdg)\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",
    "    else:\n",
    "        e_load_vdg, th_load_vdg = hp_vdg.set_heat_output(\n",
    "            heat_output=demand_vdg - 0.66,\n",
    "            Tsink=tsink_vdg,\n",
    "            Tsource=tsource_vdg\n",
    "        )\n",
    "\n",
    "    return e_load_vdg, th_load_vdg, tsource_vdg, to_storage_vdg_MW, from_storage_vdg_MW, ws, hp_vdg\n",
    "# Neg-take price, Pos-feed price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def hponly_with_storage(case):\n",
    "    hp_vdg = case.assets['Heatpump VDG']\n",
    "    ws = case.assets['HotWaterStorage']\n",
    "    demands_vdg = case.data['MW (VDG)'].to_list()\n",
    "    Tsink_vdg = case.data['Tsink (VDG)'].to_list()\n",
    "    Tsource_vdg = case.data['Tsource (VDG)'].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_vdg_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",
    "\n",
    "    new_tsources_vdg = case.data['Tsource (VDG)'].to_list()\n",
    "\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",
    "        demand_vdg = demands_vdg[m]\n",
    "        \n",
    "        e_load_vdg, th_load_vdg, tsource_vdg, to_storage_vdg_MW, from_storage_vdg_MW, ws, hp_vdg = hp_storage_opt_enginge(\n",
    "            c, ws, hp_vdg, pos_prices[m], neg_prices[m], dam_prices[m], demand_vdg, tsource_vdg, tsink_vdg\n",
    "        )\n",
    "            \n",
    "        hp_vdg_input[m] = e_load_vdg\n",
    "        hp_vdg_output[m] = th_load_vdg\n",
    "            \n",
    "        storage_levels[m] = ws.chargelevel\n",
    "        new_tsources_vdg[m] = tsource_vdg\n",
    "        to_storage_vdg_list[m] = to_storage_vdg_MW\n",
    "        from_storage_vdg_list[m] = from_storage_vdg_MW\n",
    "        \n",
    "    case.data['hp_output_MW'] = np.array(hp_vdg_output)\n",
    "    case.data['hp_input_MW'] = np.array(hp_vdg_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['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']\n",
    "    case.data['from_storage_MW'] = case.data['from_storage_vdg_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": 41,
   "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": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.storage_case_PCM = hponly_with_storage(s.storage_case_PCM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>...</th>\n",
       "      <th>from_storage_vdg_MW</th>\n",
       "      <th>to_storage_MW</th>\n",
       "      <th>from_storage_MW</th>\n",
       "      <th>storage_level_MWh</th>\n",
       "      <th>hp_output_MWh</th>\n",
       "      <th>hp_input_MWh</th>\n",
       "      <th>to_storage_vdg_MWh</th>\n",
       "      <th>from_storage_vdg_MWh</th>\n",
       "      <th>to_storage_MWh</th>\n",
       "      <th>from_storage_MWh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-11-21 17:55:00+01:00</th>\n",
       "      <td>145.80</td>\n",
       "      <td>92.74</td>\n",
       "      <td>92.74</td>\n",
       "      <td>144.20</td>\n",
       "      <td>144.20</td>\n",
       "      <td>45.735</td>\n",
       "      <td>76.35</td>\n",
       "      <td>14.72</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.014</td>\n",
       "      <td>-0.006</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>2023-06-09 22:35:00+02:00</th>\n",
       "      <td>97.48</td>\n",
       "      <td>75.66</td>\n",
       "      <td>75.66</td>\n",
       "      <td>81.45</td>\n",
       "      <td>78.85</td>\n",
       "      <td>26.914</td>\n",
       "      <td>82.19</td>\n",
       "      <td>15.85</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.014</td>\n",
       "      <td>-0.006</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              DAM    POS    NEG  ForeNeg  ForePos  \\\n",
       "2023-11-21 17:55:00+01:00  145.80  92.74  92.74   144.20   144.20   \n",
       "2023-06-09 22:35:00+02:00   97.48  75.66  75.66    81.45    78.85   \n",
       "\n",
       "                           Gas prices (€/MWh)  CO2 prices (€/ton)  \\\n",
       "2023-11-21 17:55:00+01:00              45.735               76.35   \n",
       "2023-06-09 22:35:00+02:00              26.914               82.19   \n",
       "\n",
       "                           CO2 prices (€/MWh)  Tsource (VDG)  Tsink (VDG)  \\\n",
       "2023-11-21 17:55:00+01:00               14.72             70          144   \n",
       "2023-06-09 22:35:00+02:00               15.85             70          144   \n",
       "\n",
       "                           ...  from_storage_vdg_MW  to_storage_MW  \\\n",
       "2023-11-21 17:55:00+01:00  ...                  0.0           -0.0   \n",
       "2023-06-09 22:35:00+02:00  ...                  0.0           -0.0   \n",
       "\n",
       "                           from_storage_MW  storage_level_MWh  hp_output_MWh  \\\n",
       "2023-11-21 17:55:00+01:00              0.0                3.0          0.014   \n",
       "2023-06-09 22:35:00+02:00              0.0                3.0          0.014   \n",
       "\n",
       "                           hp_input_MWh  to_storage_vdg_MWh  \\\n",
       "2023-11-21 17:55:00+01:00        -0.006                -0.0   \n",
       "2023-06-09 22:35:00+02:00        -0.006                -0.0   \n",
       "\n",
       "                           from_storage_vdg_MWh  to_storage_MWh  \\\n",
       "2023-11-21 17:55:00+01:00                   0.0            -0.0   \n",
       "2023-06-09 22:35:00+02:00                   0.0            -0.0   \n",
       "\n",
       "                           from_storage_MWh  \n",
       "2023-11-21 17:55:00+01:00               0.0  \n",
       "2023-06-09 22:35:00+02:00               0.0  \n",
       "\n",
       "[2 rows x 27 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</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>hp_output_MW</th>\n",
       "      <th>to_storage_MW</th>\n",
       "      <th>Total demand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>525600.000000</td>\n",
       "      <td>525600.000000</td>\n",
       "      <td>525600.000000</td>\n",
       "      <td>525600.000000</td>\n",
       "      <td>525600.000000</td>\n",
       "      <td>525600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>95.817313</td>\n",
       "      <td>93.568163</td>\n",
       "      <td>103.438997</td>\n",
       "      <td>0.695674</td>\n",
       "      <td>0.144249</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>49.046697</td>\n",
       "      <td>194.977533</td>\n",
       "      <td>195.696551</td>\n",
       "      <td>0.271801</td>\n",
       "      <td>0.226532</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-500.000000</td>\n",
       "      <td>-1549.470000</td>\n",
       "      <td>-1549.470000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>76.935000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>57.967500</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>99.215000</td>\n",
       "      <td>84.130000</td>\n",
       "      <td>88.570000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>122.400000</td>\n",
       "      <td>114.020000</td>\n",
       "      <td>119.592500</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>463.770000</td>\n",
       "      <td>2037.740000</td>\n",
       "      <td>2037.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 DAM            POS            NEG   hp_output_MW  \\\n",
       "count  525600.000000  525600.000000  525600.000000  525600.000000   \n",
       "mean       95.817313      93.568163     103.438997       0.695674   \n",
       "std        49.046697     194.977533     195.696551       0.271801   \n",
       "min      -500.000000   -1549.470000   -1549.470000       0.340000   \n",
       "25%        76.935000      47.000000      57.967500       0.340000   \n",
       "50%        99.215000      84.130000      88.570000       0.840000   \n",
       "75%       122.400000     114.020000     119.592500       0.840000   \n",
       "max       463.770000    2037.740000    2037.740000       1.000000   \n",
       "\n",
       "       to_storage_MW  Total demand  \n",
       "count  525600.000000      525600.0  \n",
       "mean        0.144249           1.0  \n",
       "std         0.226532           0.0  \n",
       "min         0.000000           1.0  \n",
       "25%         0.000000           1.0  \n",
       "50%         0.000000           1.0  \n",
       "75%         0.500000           1.0  \n",
       "max         0.500000           1.0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data[['DAM', 'POS', 'NEG', 'hp_output_MW', 'to_storage_MW', 'Total demand']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>hp_output_MW</th>\n",
       "      <th>to_storage_MW</th>\n",
       "      <th>from_storage_MW</th>\n",
       "      <th>Total demand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-05-25 19:10:00+02:00</th>\n",
       "      <td>107.11</td>\n",
       "      <td>55.51</td>\n",
       "      <td>55.51</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-29 06:32:00+02:00</th>\n",
       "      <td>121.06</td>\n",
       "      <td>191.89</td>\n",
       "      <td>191.89</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-27 09:18:00+02:00</th>\n",
       "      <td>94.92</td>\n",
       "      <td>66.79</td>\n",
       "      <td>66.79</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-02 17:41:00+01:00</th>\n",
       "      <td>141.39</td>\n",
       "      <td>1852.49</td>\n",
       "      <td>1852.49</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-07 11:29:00+02:00</th>\n",
       "      <td>-4.75</td>\n",
       "      <td>-107.08</td>\n",
       "      <td>-107.08</td>\n",
       "      <td>0.84</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-29 14:01:00+02:00</th>\n",
       "      <td>98.60</td>\n",
       "      <td>86.81</td>\n",
       "      <td>86.81</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-21 01:48:00+01:00</th>\n",
       "      <td>108.00</td>\n",
       "      <td>-9.69</td>\n",
       "      <td>-9.69</td>\n",
       "      <td>0.84</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-24 20:10:00+01:00</th>\n",
       "      <td>96.70</td>\n",
       "      <td>78.90</td>\n",
       "      <td>78.90</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-23 14:29:00+02:00</th>\n",
       "      <td>-20.00</td>\n",
       "      <td>-28.95</td>\n",
       "      <td>-28.95</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-21 02:43:00+01:00</th>\n",
       "      <td>96.12</td>\n",
       "      <td>104.09</td>\n",
       "      <td>104.09</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-05 23:32:00+02:00</th>\n",
       "      <td>139.13</td>\n",
       "      <td>112.28</td>\n",
       "      <td>112.28</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-31 00:13:00+02:00</th>\n",
       "      <td>65.13</td>\n",
       "      <td>423.84</td>\n",
       "      <td>423.84</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-20 06:31:00+02:00</th>\n",
       "      <td>73.02</td>\n",
       "      <td>88.69</td>\n",
       "      <td>88.69</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-25 21:13:00+02:00</th>\n",
       "      <td>118.13</td>\n",
       "      <td>75.20</td>\n",
       "      <td>75.20</td>\n",
       "      <td>0.84</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-18 17:53:00+01:00</th>\n",
       "      <td>160.00</td>\n",
       "      <td>120.84</td>\n",
       "      <td>120.84</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-09 23:59:00+01:00</th>\n",
       "      <td>46.18</td>\n",
       "      <td>-40.53</td>\n",
       "      <td>-40.53</td>\n",
       "      <td>0.84</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-08 20:41:00+01:00</th>\n",
       "      <td>153.77</td>\n",
       "      <td>193.90</td>\n",
       "      <td>193.90</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-11 12:27:00+02:00</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-17.39</td>\n",
       "      <td>-17.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-17 08:41:00+02:00</th>\n",
       "      <td>199.29</td>\n",
       "      <td>649.29</td>\n",
       "      <td>649.29</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-01-09 22:11:00+01:00</th>\n",
       "      <td>132.00</td>\n",
       "      <td>180.50</td>\n",
       "      <td>180.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              DAM      POS      NEG  hp_output_MW  \\\n",
       "2023-05-25 19:10:00+02:00  107.11    55.51    55.51          0.84   \n",
       "2023-06-29 06:32:00+02:00  121.06   191.89   191.89          1.00   \n",
       "2023-08-27 09:18:00+02:00   94.92    66.79    66.79          0.84   \n",
       "2023-11-02 17:41:00+01:00  141.39  1852.49  1852.49          1.00   \n",
       "2023-08-07 11:29:00+02:00   -4.75  -107.08  -107.08          0.84   \n",
       "2023-06-29 14:01:00+02:00   98.60    86.81    86.81          0.34   \n",
       "2023-02-21 01:48:00+01:00  108.00    -9.69    -9.69          0.84   \n",
       "2023-03-24 20:10:00+01:00   96.70    78.90    78.90          0.34   \n",
       "2023-05-23 14:29:00+02:00  -20.00   -28.95   -28.95          0.34   \n",
       "2023-11-21 02:43:00+01:00   96.12   104.09   104.09          0.34   \n",
       "2023-04-05 23:32:00+02:00  139.13   112.28   112.28          0.84   \n",
       "2023-03-31 00:13:00+02:00   65.13   423.84   423.84          1.00   \n",
       "2023-10-20 06:31:00+02:00   73.02    88.69    88.69          0.34   \n",
       "2023-07-25 21:13:00+02:00  118.13    75.20    75.20          0.84   \n",
       "2023-01-18 17:53:00+01:00  160.00   120.84   120.84          0.84   \n",
       "2023-12-09 23:59:00+01:00   46.18   -40.53   -40.53          0.84   \n",
       "2023-03-08 20:41:00+01:00  153.77   193.90   193.90          1.00   \n",
       "2023-10-11 12:27:00+02:00    0.00   -17.39   -17.39          0.34   \n",
       "2023-04-17 08:41:00+02:00  199.29   649.29   649.29          1.00   \n",
       "2023-01-09 22:11:00+01:00  132.00   180.50   180.50          1.00   \n",
       "\n",
       "                           to_storage_MW  from_storage_MW  Total demand  \n",
       "2023-05-25 19:10:00+02:00            0.5              0.0             1  \n",
       "2023-06-29 06:32:00+02:00            0.0              1.0             1  \n",
       "2023-08-27 09:18:00+02:00            0.5              0.0             1  \n",
       "2023-11-02 17:41:00+01:00            0.0              1.0             1  \n",
       "2023-08-07 11:29:00+02:00           -0.0              0.0             1  \n",
       "2023-06-29 14:01:00+02:00            0.0              0.0             1  \n",
       "2023-02-21 01:48:00+01:00           -0.0              0.0             1  \n",
       "2023-03-24 20:10:00+01:00            0.0              0.0             1  \n",
       "2023-05-23 14:29:00+02:00            0.0              0.0             1  \n",
       "2023-11-21 02:43:00+01:00            0.0              0.0             1  \n",
       "2023-04-05 23:32:00+02:00            0.5              0.0             1  \n",
       "2023-03-31 00:13:00+02:00            0.0              1.0             1  \n",
       "2023-10-20 06:31:00+02:00            0.0              0.0             1  \n",
       "2023-07-25 21:13:00+02:00           -0.0              0.0             1  \n",
       "2023-01-18 17:53:00+01:00            0.5              0.0             1  \n",
       "2023-12-09 23:59:00+01:00           -0.0              0.0             1  \n",
       "2023-03-08 20:41:00+01:00            0.0              1.0             1  \n",
       "2023-10-11 12:27:00+02:00            0.0              0.0             1  \n",
       "2023-04-17 08:41:00+02:00            0.0              1.0             1  \n",
       "2023-01-09 22:11:00+01:00            0.0              1.0             1  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n",
       "       'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'Tsource (VDG)',\n",
       "       'Tsink (VDG)', 'MW (VDG)', 'Total demand'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hpcase.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n",
       "       'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'Tsource (VDG)',\n",
       "       'Tsink (VDG)', 'MW (VDG)', 'Total demand', 'hp_output_MW',\n",
       "       'hp_input_MW', 'cop', 'tsource_vdg', 'to_storage_vdg_MW',\n",
       "       'from_storage_vdg_MW', 'to_storage_MW', 'from_storage_MW',\n",
       "       'storage_level_MWh', 'hp_output_MWh', 'hp_input_MWh',\n",
       "       'to_storage_vdg_MWh', 'from_storage_vdg_MWh', 'to_storage_MWh',\n",
       "       'from_storage_MWh'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": 49,
   "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>Total demand</th>\n",
       "      <th>hp_input_MW</th>\n",
       "      <th>hp_output_MW</th>\n",
       "      <th>cop</th>\n",
       "      <th>to_storage_MW</th>\n",
       "      <th>from_storage_MW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-06-20 17:15:00+02:00</th>\n",
       "      <td>118.63</td>\n",
       "      <td>475.44</td>\n",
       "      <td>475.44</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-06-19 16:57:00+02:00</th>\n",
       "      <td>98.16</td>\n",
       "      <td>93.95</td>\n",
       "      <td>93.95</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-14 01:50:00+02:00</th>\n",
       "      <td>96.36</td>\n",
       "      <td>67.33</td>\n",
       "      <td>67.33</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-15 22:49:00+01:00</th>\n",
       "      <td>116.70</td>\n",
       "      <td>74.13</td>\n",
       "      <td>74.13</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-01 13:06:00+02:00</th>\n",
       "      <td>54.90</td>\n",
       "      <td>90.10</td>\n",
       "      <td>90.10</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-22 11:45:00+01:00</th>\n",
       "      <td>123.96</td>\n",
       "      <td>87.50</td>\n",
       "      <td>87.50</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-03 11:04:00+02:00</th>\n",
       "      <td>68.14</td>\n",
       "      <td>-50.70</td>\n",
       "      <td>-50.70</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-07 07:05:00+02:00</th>\n",
       "      <td>109.20</td>\n",
       "      <td>86.49</td>\n",
       "      <td>173.56</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-04-01 01:07:00+02:00</th>\n",
       "      <td>63.90</td>\n",
       "      <td>125.74</td>\n",
       "      <td>125.74</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-05 01:30:00+01:00</th>\n",
       "      <td>108.40</td>\n",
       "      <td>141.20</td>\n",
       "      <td>141.20</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-28 01:00:00+02:00</th>\n",
       "      <td>84.00</td>\n",
       "      <td>55.34</td>\n",
       "      <td>55.34</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-26 23:27:00+01:00</th>\n",
       "      <td>132.98</td>\n",
       "      <td>141.91</td>\n",
       "      <td>141.91</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>0.34</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-25 08:15:00+01:00</th>\n",
       "      <td>0.08</td>\n",
       "      <td>-40.00</td>\n",
       "      <td>-40.00</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-25 13:29:00+02:00</th>\n",
       "      <td>13.00</td>\n",
       "      <td>88.52</td>\n",
       "      <td>88.52</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-31 17:47:00+02:00</th>\n",
       "      <td>51.87</td>\n",
       "      <td>333.47</td>\n",
       "      <td>333.47</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-13 02:52:00+02:00</th>\n",
       "      <td>78.80</td>\n",
       "      <td>56.13</td>\n",
       "      <td>56.13</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-12 05:26:00+01:00</th>\n",
       "      <td>91.98</td>\n",
       "      <td>44.45</td>\n",
       "      <td>44.45</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-07-31 02:46:00+02:00</th>\n",
       "      <td>44.09</td>\n",
       "      <td>65.56</td>\n",
       "      <td>65.56</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.400</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-02-28 03:05:00+01:00</th>\n",
       "      <td>124.59</td>\n",
       "      <td>91.64</td>\n",
       "      <td>91.64</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-01 13:54:00+02:00</th>\n",
       "      <td>61.96</td>\n",
       "      <td>-259.73</td>\n",
       "      <td>-259.73</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.336</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              DAM     POS     NEG  Total demand  hp_input_MW  \\\n",
       "2023-06-20 17:15:00+02:00  118.63  475.44  475.44             1       -0.400   \n",
       "2023-06-19 16:57:00+02:00   98.16   93.95   93.95             1       -0.136   \n",
       "2023-07-14 01:50:00+02:00   96.36   67.33   67.33             1       -0.336   \n",
       "2023-02-15 22:49:00+01:00  116.70   74.13   74.13             1       -0.336   \n",
       "2023-04-01 13:06:00+02:00   54.90   90.10   90.10             1       -0.400   \n",
       "2023-02-22 11:45:00+01:00  123.96   87.50   87.50             1       -0.336   \n",
       "2023-04-03 11:04:00+02:00   68.14  -50.70  -50.70             1       -0.336   \n",
       "2023-07-07 07:05:00+02:00  109.20   86.49  173.56             1       -0.136   \n",
       "2023-04-01 01:07:00+02:00   63.90  125.74  125.74             1       -0.400   \n",
       "2023-03-05 01:30:00+01:00  108.40  141.20  141.20             1       -0.400   \n",
       "2023-05-28 01:00:00+02:00   84.00   55.34   55.34             1       -0.336   \n",
       "2023-02-26 23:27:00+01:00  132.98  141.91  141.91             1       -0.136   \n",
       "2023-12-25 08:15:00+01:00    0.08  -40.00  -40.00             1       -0.336   \n",
       "2023-05-25 13:29:00+02:00   13.00   88.52   88.52             1       -0.400   \n",
       "2023-05-31 17:47:00+02:00   51.87  333.47  333.47             1       -0.400   \n",
       "2023-08-13 02:52:00+02:00   78.80   56.13   56.13             1       -0.336   \n",
       "2023-03-12 05:26:00+01:00   91.98   44.45   44.45             1       -0.336   \n",
       "2023-07-31 02:46:00+02:00   44.09   65.56   65.56             1       -0.400   \n",
       "2023-02-28 03:05:00+01:00  124.59   91.64   91.64             1       -0.336   \n",
       "2023-08-01 13:54:00+02:00   61.96 -259.73 -259.73             1       -0.336   \n",
       "\n",
       "                           hp_output_MW  cop  to_storage_MW  from_storage_MW  \n",
       "2023-06-20 17:15:00+02:00          1.00  2.5            0.0              1.0  \n",
       "2023-06-19 16:57:00+02:00          0.34  2.5            0.0              0.0  \n",
       "2023-07-14 01:50:00+02:00          0.84  2.5           -0.0              0.0  \n",
       "2023-02-15 22:49:00+01:00          0.84  2.5            0.5              0.0  \n",
       "2023-04-01 13:06:00+02:00          1.00  2.5            0.0              1.0  \n",
       "2023-02-22 11:45:00+01:00          0.84  2.5            0.5              0.0  \n",
       "2023-04-03 11:04:00+02:00          0.84  2.5            0.5              0.0  \n",
       "2023-07-07 07:05:00+02:00          0.34  2.5            0.0              0.0  \n",
       "2023-04-01 01:07:00+02:00          1.00  2.5            0.0              1.0  \n",
       "2023-03-05 01:30:00+01:00          1.00  2.5            0.0              0.0  \n",
       "2023-05-28 01:00:00+02:00          0.84  2.5            0.5              0.0  \n",
       "2023-02-26 23:27:00+01:00          0.34  2.5            0.0              0.0  \n",
       "2023-12-25 08:15:00+01:00          0.84  2.5           -0.0              0.0  \n",
       "2023-05-25 13:29:00+02:00          1.00  2.5            0.0              0.0  \n",
       "2023-05-31 17:47:00+02:00          1.00  2.5            0.0              1.0  \n",
       "2023-08-13 02:52:00+02:00          0.84  2.5            0.5              0.0  \n",
       "2023-03-12 05:26:00+01:00          0.84  2.5            0.5              0.0  \n",
       "2023-07-31 02:46:00+02:00          1.00  2.5            0.0              1.0  \n",
       "2023-02-28 03:05:00+01:00          0.84  2.5            0.5              0.0  \n",
       "2023-08-01 13:54:00+02:00          0.84  2.5           -0.0              0.0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DAM                 95.817313\n",
       "POS                 93.568163\n",
       "NEG                103.438997\n",
       "Total demand         1.000000\n",
       "hp_input_MW         -0.278269\n",
       "hp_output_MW         0.695674\n",
       "cop                  2.500000\n",
       "to_storage_MW        0.144249\n",
       "from_storage_MW      0.144035\n",
       "dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selection.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "########################\n",
    "\n",
    "# This is where we left\n",
    "\n",
    "########################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": 53,
   "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",
    "    demand_vdg = case.data['MW (VDG)'].round(decimals).to_list()\n",
    "    Tsink_vdg = case.data['Tsink (VDG)'].round(decimals).to_list()\n",
    "    Tsource_vdg = case.data['Tsource (VDG)'].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",
    "\n",
    "        hp_out = hp_vdg_output\n",
    "        hp_output[m] = hp_out\n",
    "        hp_input[m] = hp_vdg_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": 55,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "def calc_afrr_capacity(case):\n",
    "    hp_vdg = case.assets['Heatpump VDG']\n",
    "    \n",
    "    capacity = 0\n",
    "    for hp in hp_vdg:\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": "markdown",
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "source": [
    "### Run optimisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.4999999999999987"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hpcase.data['hp_output_MW'].sum() /s.hpcase.data['hp_input_MW'].abs().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.499999999999999"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>Total demand</th>\n",
       "      <th>hp_output_MW</th>\n",
       "      <th>hp_input_MW</th>\n",
       "      <th>cop</th>\n",
       "      <th>hp_output_MWh</th>\n",
       "      <th>hp_input_MWh</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",
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       "      <td>63.998</td>\n",
       "      <td>5.23</td>\n",
       "      <td>1.01</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-0.002</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>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-0.002</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>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-0.002</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>70</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-0.136</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-0.002</td>\n",
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       "    <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",
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       "      <td>0.006</td>\n",
       "      <td>-0.002</td>\n",
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       "  </tbody>\n",
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      ],
      "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             70          144   \n",
       "2023-01-01 00:01:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:02:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:03:00+01:00                1.01             70          144   \n",
       "2023-01-01 00:04:00+01:00                1.01             70          144   \n",
       "\n",
       "                           MW (VDG)  Total demand  hp_output_MW  hp_input_MW  \\\n",
       "2023-01-01 00:00:00+01:00         1             1          0.34       -0.136   \n",
       "2023-01-01 00:01:00+01:00         1             1          0.34       -0.136   \n",
       "2023-01-01 00:02:00+01:00         1             1          0.34       -0.136   \n",
       "2023-01-01 00:03:00+01:00         1             1          0.34       -0.136   \n",
       "2023-01-01 00:04:00+01:00         1             1          0.34       -0.136   \n",
       "\n",
       "                           cop  hp_output_MWh  hp_input_MWh  \n",
       "2023-01-01 00:00:00+01:00  2.5          0.006        -0.002  \n",
       "2023-01-01 00:01:00+01:00  2.5          0.006        -0.002  \n",
       "2023-01-01 00:02:00+01:00  2.5          0.006        -0.002  \n",
       "2023-01-01 00:03:00+01:00  2.5          0.006        -0.002  \n",
       "2023-01-01 00:04:00+01:00  2.5          0.006        -0.002  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.hpcase.data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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",
    "    'NEG'\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": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
      "\n",
      "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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    "vis_data.loc['2023-10-02', :].iplot(subplots=True, shape=(6,1), dimensions=(1000, 800))"
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   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "storage_level_MWh       0.00\n",
       "to_storage_MW           0.00\n",
       "from_storage_MW         0.00\n",
       "NEG                 -1549.47\n",
       "cop                     2.50\n",
       "cop                     2.50\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vis_data.min()"
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   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1213.08, 1286.8299999999997)"
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     "execution_count": 64,
     "metadata": {},
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    "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": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n",
       "       'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'Tsource (VDG)',\n",
       "       'Tsink (VDG)', 'MW (VDG)', 'Total demand', 'hp_output_MW',\n",
       "       'hp_input_MW', 'cop', 'tsource_vdg', 'to_storage_vdg_MW',\n",
       "       'from_storage_vdg_MW', 'to_storage_MW', 'from_storage_MW',\n",
       "       'storage_level_MWh', 'hp_output_MWh', 'hp_input_MWh',\n",
       "       'to_storage_vdg_MWh', 'from_storage_vdg_MWh', 'to_storage_MWh',\n",
       "       'from_storage_MWh'],\n",
       "      dtype='object')"
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     "execution_count": 65,
     "metadata": {},
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   "execution_count": 66,
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       "False"
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     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
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   "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": 67,
   "metadata": {},
   "outputs": [
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      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_18312\\4223154146.py:1: FutureWarning:\n",
      "\n",
      "'H' is deprecated and will be removed in a future version, please use 'h' instead.\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n",
       "       'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'Tsource (VDG)',\n",
       "       'Tsink (VDG)', 'MW (VDG)', 'Total demand', 'hp_output_MW',\n",
       "       'hp_input_MW', 'cop', 'tsource_vdg', 'to_storage_vdg_MW',\n",
       "       'from_storage_vdg_MW', 'to_storage_MW', 'from_storage_MW',\n",
       "       'storage_level_MWh', 'hp_output_MWh', 'hp_input_MWh',\n",
       "       'to_storage_vdg_MWh', 'from_storage_vdg_MWh', 'to_storage_MWh',\n",
       "       'from_storage_MWh'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n",
      "\n",
      "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n",
      "\n",
      "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "\n"
     ]
    }
   ],
   "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",
    "    \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=2023, \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": 71,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:540: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:541: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:542: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:540: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:541: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:542: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n"
     ]
    }
   ],
   "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": 73,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_18312\\1396106630.py:1: FutureWarning:\n",
      "\n",
      "'H' is deprecated and will be removed in a future version, please use 'h' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
      "\n",
      "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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          "width": 1.3
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         "mode": "lines",
         "name": "Total demand",
         "text": "",
         "type": "scatter",
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    "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",
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      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_18312\\1493830805.py:1: FutureWarning:\n",
      "\n",
      "'H' is deprecated and will be removed in a future version, please use 'h' instead.\n",
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        },
        "title": {
         "font": {
          "color": "#4D5663"
         },
         "text": "Smurfit Kappa: Source and Sink temperatures"
        },
        "width": 800,
        "xaxis": {
         "autorange": true,
         "gridcolor": "#E1E5ED",
         "range": [
          -0.5,
          1.5
         ],
         "showgrid": true,
         "tickfont": {
          "color": "#4D5663"
         },
         "title": {
          "font": {
           "color": "#4D5663"
          },
          "text": ""
         },
         "type": "category",
         "zerolinecolor": "#E1E5ED"
        },
        "yaxis": {
         "autorange": true,
         "gridcolor": "#E1E5ED",
         "range": [
          65.88888888888889,
          148.11111111111111
         ],
         "showgrid": true,
         "tickfont": {
          "color": "#4D5663"
         },
         "title": {
          "font": {
           "color": "#4D5663"
          },
          "text": "Temperature in degrees C"
         },
         "type": "linear",
         "zerolinecolor": "#E1E5ED"
        }
       }
      },
      "image/png": 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",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "demands_fig = s.demand[['Tsource (VDG)', 'Tsink (VDG)']].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": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DAM', 'POS', 'NEG', 'ForeNeg', 'ForePos', 'Gas prices (€/MWh)',\n",
       "       'CO2 prices (€/ton)', 'CO2 prices (€/MWh)', 'Tsource (VDG)',\n",
       "       'Tsink (VDG)', 'MW (VDG)', 'Total demand', 'hp_output_MW',\n",
       "       'hp_input_MW', 'cop', 'tsource_vdg', 'to_storage_vdg_MW',\n",
       "       'from_storage_vdg_MW', 'to_storage_MW', 'from_storage_MW',\n",
       "       'storage_level_MWh', 'hp_output_MWh', 'hp_input_MWh',\n",
       "       'to_storage_vdg_MWh', 'from_storage_vdg_MWh', 'to_storage_MWh',\n",
       "       'from_storage_MWh', 'nomination_MWh', 'Prod. vol.', 'Cons. vol.',\n",
       "       'Imb. vol.', 'Day-Ahead Result', 'POS Result', 'NEG Result',\n",
       "       'Imbalance Result', 'Combined Result'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2023-01-01 00:00:00+0100', tz='Europe/Amsterdam')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.storage_case_PCM.data['tsource_vdg'].idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# s.storage_case_PCM.data.loc['2019-01-04 07:45:00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0,\n",
       " 0,\n",
       " 29.635,\n",
       " 0,\n",
       " 0,\n",
       " HotWaterStorage(name=HotWaterStorage, rated_power=1, capacity=3, temperature=144, min_storagelevel=0.0),\n",
       " Heatpump(name='Heatpump VDG', max_thermal_power=1.5, cop_curve=2.5, min_th_power=0.0))"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "case = s.storage_case_PCM\n",
    "hp_vdg = case.assets['Heatpump VDG']\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",
    "tsource_vdg = 29.635\n",
    "tsink_vdg = 134.840\n",
    "\n",
    "hp_storage_opt_enginge(c, ws, hp_vdg, pos, neg, dam, demand_vdg, tsource_vdg, tsink_vdg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 82,
   "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=2.5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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": 84,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [],
   "source": [
    "date = s.baseline.data.index[0].strftime('%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
      "\n",
      "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n",
      "\n"
     ]
    },
    {
     "data": {
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       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 86,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
      "\n",
      "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
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       "data": [
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         "mode": "lines",
         "name": "Total demand",
         "text": "",
         "type": "scatter",
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   "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": 87,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\reports.py:46: FutureWarning:\n",
      "\n",
      "DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Heatpump only</th>\n",
       "      <th>Heatpump + Storage_PCM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total net volume (MWh)</th>\n",
       "      <td>-1,051</td>\n",
       "      <td>-2,515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total exported to grid (MWh)</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total consumed from grid (MWh)</th>\n",
       "      <td>1,051</td>\n",
       "      <td>2,515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total nominated volume (MWh)</th>\n",
       "      <td>-1,051</td>\n",
       "      <td>-1,051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Absolute imbalance volume (MWh)</th>\n",
       "      <td>-</td>\n",
       "      <td>1,464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean selling price (€/MWh)</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean buying price (€/MWh)</th>\n",
       "      <td>96</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total day-ahead result (€)</th>\n",
       "      <td>-100,723</td>\n",
       "      <td>-100,723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total POS result (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total NEG result (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>-170,525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total imbalance result (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>-170,525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total combined result (€)</th>\n",
       "      <td>-100,723</td>\n",
       "      <td>-271,248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Heatpump only Heatpump + Storage_PCM\n",
       "Total net volume (MWh)                 -1,051                 -2,515\n",
       "Total exported to grid (MWh)                -                      -\n",
       "Total consumed from grid (MWh)          1,051                  2,515\n",
       "Total nominated volume (MWh)           -1,051                 -1,051\n",
       "Absolute imbalance volume (MWh)             -                  1,464\n",
       "Mean selling price (€/MWh)                  -                      -\n",
       "Mean buying price (€/MWh)                  96                    108\n",
       "Total day-ahead result (€)           -100,723               -100,723\n",
       "Total POS result (€)                        -                      -\n",
       "Total NEG result (€)                        -               -170,525\n",
       "Total imbalance result (€)                  -               -170,525\n",
       "Total combined result (€)            -100,723               -271,248"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report = ComparisonReport(\n",
    "    cases=s.optcases, \n",
    "    kind='electr_market_results',\n",
    ")\n",
    "report.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\reports.py:46: FutureWarning:\n",
      "\n",
      "DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Heatpump only</th>\n",
       "      <th>Heatpump + Storage_PCM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gasboiler OPEX (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gas consumption costs (€)</th>\n",
       "      <td>393,966</td>\n",
       "      <td>393,966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO2 emission costs (€)</th>\n",
       "      <td>140,949</td>\n",
       "      <td>140,949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gas taxes (€)</th>\n",
       "      <td>33,689</td>\n",
       "      <td>33,689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heatpump VDG OPEX (€)</th>\n",
       "      <td>-3,000</td>\n",
       "      <td>-3,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Result on electricity market (€)</th>\n",
       "      <td>-100,723</td>\n",
       "      <td>-271,248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electricity taxes (€)</th>\n",
       "      <td>-999</td>\n",
       "      <td>-2,390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grid transport costs (€)</th>\n",
       "      <td>-11,391</td>\n",
       "      <td>-33,504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HotWaterStorage OPEX (€)</th>\n",
       "      <td>-</td>\n",
       "      <td>-1,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBITDA (€)</th>\n",
       "      <td>452,492</td>\n",
       "      <td>257,263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depreciation (€)</th>\n",
       "      <td>-12,000</td>\n",
       "      <td>-14,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBITDA + depr (€)</th>\n",
       "      <td>440,492</td>\n",
       "      <td>242,863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Heatpump only Heatpump + Storage_PCM\n",
       "Gasboiler OPEX (€)                           -                      -\n",
       "Gas consumption costs (€)              393,966                393,966\n",
       "CO2 emission costs (€)                 140,949                140,949\n",
       "Gas taxes (€)                           33,689                 33,689\n",
       "Heatpump VDG OPEX (€)                   -3,000                 -3,000\n",
       "Result on electricity market (€)      -100,723               -271,248\n",
       "Electricity taxes (€)                     -999                 -2,390\n",
       "Grid transport costs (€)               -11,391                -33,504\n",
       "HotWaterStorage OPEX (€)                     -                 -1,200\n",
       "EBITDA (€)                             452,492                257,263\n",
       "Depreciation (€)                       -12,000                -14,400\n",
       "EBITDA + depr (€)                      440,492                242,863"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 89,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\styling.py:42: FutureWarning:\n",
      "\n",
      "DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year 0</th>\n",
       "      <th>Year 1</th>\n",
       "      <th>Year 2</th>\n",
       "      <th>Year 3</th>\n",
       "      <th>Year 4</th>\n",
       "      <th>Year 5</th>\n",
       "      <th>Year 6</th>\n",
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       "      <th>Year 9</th>\n",
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       "      <th>Year 12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CAPEX (€)</th>\n",
       "      <td>-301,500</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
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       "      <th>Regular Earnings (€)</th>\n",
       "      <td></td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
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       "    <tr>\n",
       "      <th>Irregular Cashflows (€)</th>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
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       "      <td>-</td>\n",
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       "      <td>-</td>\n",
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       "    <tr>\n",
       "      <th>EBITDA (€)</th>\n",
       "      <td></td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
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       "      <td>452,493</td>\n",
       "      <td>452,493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depreciations (€) -/-</th>\n",
       "      <td></td>\n",
       "      <td>-12,000</td>\n",
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       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "      <td>-12,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBIT (€)</th>\n",
       "      <td></td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "      <td>440,493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income tax (Vpb.) (€)</th>\n",
       "      <td></td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "      <td>-96,873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOPLAT (€)</th>\n",
       "      <td></td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "      <td>343,619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Depreciations (€) +/+</th>\n",
       "      <td></td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "      <td>12,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Free Cash Flow (€)</th>\n",
       "      <td>-301,500</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "      <td>355,619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Simple Payback Period</th>\n",
       "      <td>0.8478165594669402 years</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IRR (%)</th>\n",
       "      <td>118%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WACC (%)</th>\n",
       "      <td>10%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NPV of explicit period (€)</th>\n",
       "      <td>1,928,710</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discounted residual value (€)</th>\n",
       "      <td>50,184</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NPV (€)</th>\n",
       "      <td>1,928,710</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Year 0   Year 1   Year 2  \\\n",
       "CAPEX (€)                                      -301,500                     \n",
       "Regular Earnings (€)                                     452,493  452,493   \n",
       "Irregular Cashflows (€)                                        -        -   \n",
       "EBITDA (€)                                               452,493  452,493   \n",
       "Depreciations (€) -/-                                    -12,000  -12,000   \n",
       "EBIT (€)                                                 440,493  440,493   \n",
       "Income tax (Vpb.) (€)                                    -96,873  -96,873   \n",
       "NOPLAT (€)                                               343,619  343,619   \n",
       "Depreciations (€) +/+                                     12,000   12,000   \n",
       "Free Cash Flow (€)                             -301,500  355,619  355,619   \n",
       "Simple Payback Period          0.8478165594669402 years                     \n",
       "IRR (%)                                            118%                     \n",
       "WACC (%)                                            10%                     \n",
       "NPV of explicit period (€)                    1,928,710                     \n",
       "Discounted residual value (€)                    50,184                     \n",
       "NPV (€)                                       1,928,710                     \n",
       "\n",
       "                                Year 3   Year 4   Year 5   Year 6   Year 7  \\\n",
       "CAPEX (€)                                                                    \n",
       "Regular Earnings (€)           452,493  452,493  452,493  452,493  452,493   \n",
       "Irregular Cashflows (€)              -        -        -        -        -   \n",
       "EBITDA (€)                     452,493  452,493  452,493  452,493  452,493   \n",
       "Depreciations (€) -/-          -12,000  -12,000  -12,000  -12,000  -12,000   \n",
       "EBIT (€)                       440,493  440,493  440,493  440,493  440,493   \n",
       "Income tax (Vpb.) (€)          -96,873  -96,873  -96,873  -96,873  -96,873   \n",
       "NOPLAT (€)                     343,619  343,619  343,619  343,619  343,619   \n",
       "Depreciations (€) +/+           12,000   12,000   12,000   12,000   12,000   \n",
       "Free Cash Flow (€)             355,619  355,619  355,619  355,619  355,619   \n",
       "Simple Payback Period                                                        \n",
       "IRR (%)                                                                      \n",
       "WACC (%)                                                                     \n",
       "NPV of explicit period (€)                                                   \n",
       "Discounted residual value (€)                                                \n",
       "NPV (€)                                                                      \n",
       "\n",
       "                                Year 8   Year 9  Year 10  Year 11  Year 12  \n",
       "CAPEX (€)                                                                   \n",
       "Regular Earnings (€)           452,493  452,493  452,493  452,493  452,493  \n",
       "Irregular Cashflows (€)              -        -        -        -        -  \n",
       "EBITDA (€)                     452,493  452,493  452,493  452,493  452,493  \n",
       "Depreciations (€) -/-          -12,000  -12,000  -12,000  -12,000  -12,000  \n",
       "EBIT (€)                       440,493  440,493  440,493  440,493  440,493  \n",
       "Income tax (Vpb.) (€)          -96,873  -96,873  -96,873  -96,873  -96,873  \n",
       "NOPLAT (€)                     343,619  343,619  343,619  343,619  343,619  \n",
       "Depreciations (€) +/+           12,000   12,000   12,000   12,000   12,000  \n",
       "Free Cash Flow (€)             355,619  355,619  355,619  355,619  355,619  \n",
       "Simple Payback Period                                                       \n",
       "IRR (%)                                                                     \n",
       "WACC (%)                                                                    \n",
       "NPV of explicit period (€)                                                  \n",
       "Discounted residual value (€)                                               \n",
       "NPV (€)                                                                     "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BusinessCaseReport(s.hpcase).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\anaconda3\\Lib\\site-packages\\cufflinks\\plotlytools.py:117: FutureWarning:\n",
      "\n",
      "DatetimeIndex.format is deprecated and will be removed in a future version. Convert using index.astype(str) or index.map(formatter) instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
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          "dash": "solid",
          "shape": "linear",
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         "mode": "lines",
         "name": "Gas prices (€/MWh)",
         "text": "",
         "type": "scatter",
         "x": [
          "2023-01-01 00:00:00+01:00",
          "2023-01-02 00:00:00+01:00",
          "2023-01-03 00:00:00+01:00",
          "2023-01-04 00:00:00+01:00",
          "2023-01-05 00:00:00+01:00",
          "2023-01-06 00:00:00+01:00",
          "2023-01-07 00:00:00+01:00",
          "2023-01-08 00:00:00+01:00",
          "2023-01-09 00:00:00+01:00",
          "2023-01-10 00:00:00+01:00",
          "2023-01-11 00:00:00+01:00",
          "2023-01-12 00:00:00+01:00",
          "2023-01-13 00:00:00+01:00",
          "2023-01-14 00:00:00+01:00",
          "2023-01-15 00:00:00+01:00",
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    }
   ],
   "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": 91,
   "metadata": {
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   "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"
   ]
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   "execution_count": 92,
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   ],
   "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",
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  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "tags": [
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   },
   "outputs": [
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       "                            \n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ebitda_report.show(comparison='relative')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
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",
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    "_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"
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  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
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    "_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",
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    "\n",
    "npv_graph.update_traces(\n",
    "    text=_series.values/1000_000, \n",
    "    textposition='outside', \n",
    "    texttemplate=\"%{text:.1f}M €\", \n",
    ")\n",
    "\n",
    "npv_graph"
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  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
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   "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": 98,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
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    "_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",
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    "    text=_series.values, \n",
    "    textposition='outside', \n",
    "    texttemplate=\"%{text:.1f} years\", \n",
    ")\n",
    "\n",
    "spt_fig "
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 102,
   "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": 103,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testetss\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:41: UserWarning:\n",
      "\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",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:62: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\forecasts.py:115: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\framework.py:54: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 15.4 s\n",
      "Wall time: 41.1 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\AppData\\Local\\Temp\\ipykernel_18312\\2484316097.py:3: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%time _s = setup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "tags": [
     "exclude"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\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\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\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\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\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\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\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\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\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\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\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\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n",
      "\n",
      "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:369: FutureWarning:\n",
      "\n",
      "'T' is deprecated and will be removed in a future version, please use 'min' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\casestudy.py:372: FutureWarning:\n",
      "\n",
      "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:540: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:541: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:542: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:540: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:541: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n",
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\financial.py:542: FutureWarning:\n",
      "\n",
      "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 3.58 s\n",
      "Wall time: 11.3 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1928709.8797698324"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time result = routine(c, _s)\n",
    "npv = result.hpcase.npv\n",
    "npv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0459737342bf41bd970b008382e28530",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\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\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\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\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\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\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\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\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\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\\z004yn8c\\OneDrive - Siemens AG\\Documents\\GitHub\\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"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'to_list'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_18312\\4147334808.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m80\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m120\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mparam\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'storage_temperature'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mkpis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'npv'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'irr'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'spp'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'mean_cop'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0msens\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSensitivityAnalysis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_s\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mroutine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkpis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\sensitivity.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, c, s, routine, param, values, output_kpis)\u001b[0m\n\u001b[0;32m     23\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mroutine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_kpis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfigs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_generate_configs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m         \u001b[0moutput_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prepare_output_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcases\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_kpis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkpis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_sensitivities\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mroutine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_kpis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\OneDrive - Siemens AG\\Documents\\GitHub\\Mooi-Kickstart\\pyrecoy\\pyrecoy\\pyrecoy2\\sensitivity.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, s, routine, output_kpis, output_dict)\u001b[0m\n\u001b[0;32m     44\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_sensitivities\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mroutine\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_kpis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfigs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m             \u001b[0m_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 47\u001b[1;33m             \u001b[0m_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mroutine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_s\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     48\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mkpi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0moutput_kpis\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     49\u001b[0m                 \u001b[1;32mfor\u001b[0m \u001b[0mcase\u001b[0m \u001b[1;32min\u001b[0m \u001b[0m_s\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcases\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m                     \u001b[0moutput_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkpi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkpi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_18312\\3648020848.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(c, s)\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mroutine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mload_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcreate_and_assign_assets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[1;31m#s = preprocessing(c=c, s=s)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m     \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrun_optimisation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m     \u001b[1;31m#s = postprocessing(c=c, s=s)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcollect_cashflows\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m     \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcalculate_financials\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_18312\\3066241253.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(c, s)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mrun_optimisation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbaseline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbaseline_sim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbaseline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhpcase\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhponly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhpcase\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstorage_case_PCM\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhponly_with_storage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstorage_case_PCM\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m#     s.hpcase_sde.assign_algorithm(hponly)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_18312\\3686723783.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(case)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mbaseline_sim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mgasboiler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0massets\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mdemand\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'MW (VDG)'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mminutes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0miter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mth_output\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcase\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   6295\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6296\u001b[0m             \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6297\u001b[0m         \u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6298\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6299\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'to_list'"
     ]
    }
   ],
   "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."
   ]
  }
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