{ "cells": [ { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "source": [ "from notepad import WaterStorage, Heatpump\n", "\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Tsource (VDG)Tsink (VDG)MW (VDG)Tsource (NDG)Tsink (NDG)MW (NDG)
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" ], "text/plain": [ " Tsource (VDG) Tsink (VDG) MW (VDG) Tsource (NDG) \\\n", "2018-11-01 00:00:00 64.964783 142.003109 0.0 19.897433 \n", "2018-11-01 00:15:00 54.578777 138.960493 0.0 17.950905 \n", "2018-11-01 00:30:00 65.166672 139.885329 0.0 33.500757 \n", "2018-11-01 00:45:00 65.358078 139.731901 0.0 42.203876 \n", "2018-11-01 01:00:00 64.947536 139.577871 0.0 18.702675 \n", "\n", " Tsink (NDG) MW (NDG) \n", "2018-11-01 00:00:00 147.731814 0.0 \n", "2018-11-01 00:15:00 148.138964 0.0 \n", "2018-11-01 00:30:00 147.585426 0.0 \n", "2018-11-01 00:45:00 147.547612 0.0 \n", "2018-11-01 01:00:00 148.260335 0.0 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df=df.drop(['Unnamed: 7', 'Unnamed: 8', 'Unnamed: 9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12'], axis=1)\n", "df[:5]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "15" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "waterstorage = WaterStorage(\n", " name='MyStorage',\n", " max_power=10,\n", " min_power=-10,\n", " roundtrip_eff=0.90,\n", " energy_density = 50 * 10e-3,\n", " volume = 500,\n", " lifetime = 25,\n", " temperature = 368, #K\n", " min_storagelevel = 5,\n", " max_storagelevel = 23\n", " \n", ")\n", "waterstorage.set_freq('15T')\n", "waterstorage.set_storagelevel(15)\n", "waterstorage.storagelevel" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "def hp_mass_flow (hp_capacity, Tsink, Tref, Cp):\n", " return hp_capacity /(Cp*(Tsink - Tref)) \n", "\n", "def process_mass_flow (demand, Tsink, Tref, Cp):\n", " return demand /(Cp*(Tsink - Tref)) \n", "\n", "def COP_calculation(Tsink, Tsource):\n", " return Tsink / (Tsink - Tsource)\n", "\n", "from numpy.polynomial import Polynomial\n", "\n", "def cop_curve(Tsink, Tsource):\n", " c0 = Tsink / (Tsink - Tsource) \n", " return Polynomial([c0])\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'name': 'Heatpump',\n", " 'max_th_power': 20,\n", " 'min_th_power': 5,\n", " 'cop_curve': }" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# heatpump = Heatpump(\"heatpump1\", 50, cop_curve, 10)\n", "# heatpump.set_heat_output(50, Tsource=333, Tsink=413)\n", "cop_curve(140, 13)\n", "\n", "heatpump = Heatpump(\n", " name='Heatpump',\n", " max_th_power=20,\n", " min_th_power=5,\n", " cop_curve=cop_curve\n", ")\n", "\n", "heatpump.__dict__\n", "\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "341.450974129671" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Tsink = 413 #K\n", "Tsource = 333 #K\n", "Tref = 273 #K\n", "hp_capacity = 31 #MW\n", "demand = 25 #MW\n", "Cp = 4190 #J/kgK\n", "MW_to_J_per_s = 1000_000\n", "hp_capacity *= MW_to_J_per_s\n", "demand *= MW_to_J_per_s\n", "\n", "charge_mass_flow = hp_mass_flow (hp_capacity, Tsink, Tref, Cp) - process_mass_flow (demand, Tsink, Tref, Cp)\n", "charged_heat = (charge_mass_flow * Cp * (Tsink - Tref)) / MW_to_J_per_s\n", "charged_heat\n", "\n", "efficiency = 0.9\n", "Tstorage = 95 + 273\n", "discharged_heat = charged_heat * efficiency #MW\n", "discharged_heat *= MW_to_J_per_s \n", "discharge_mass_flow = discharged_heat /(Cp*(Tstorage - Tref))\n", "discharge_mass_flow\n", "process_mass_flow\n", "\n", "def Tsource_calculation(Tstorage, discharge_mass_flow, Tsource, process_mass_flow):\n", " return (Tstorage * discharge_mass_flow + Tsource * process_mass_flow)/ (discharge_mass_flow + process_mass_flow)\n", " \n", "\n", "Tsource_calculation(Tstorage, discharge_mass_flow, Tsource, process_mass_flow (demand, Tsink, Tref, Cp))\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def test_heatpump_and_waterstorage_system(Tsink, Tsource, process_demand_MW, e_price, waterstorage_level):\n", " \"\"\"\n", " 1. Follow a certain logic based on given price:\n", " - If price is low --> Heatpump at full power, and charge the heatbuffer\n", " - If price is high --> Discharge the heat buffer, and increase Tsource, which will increase COP\n", " 2. Above logic should adhere to a couple of constraints:\n", " - Storage levels\n", " - Capacity of the heat pump \n", " - Process demand\n", " - ....\n", " 3. This function should contain: \n", " - Heat pump \n", " - Water storage\n", " - Interactions / logic between them\n", " 4. Output of the function:\n", " - Power of the heatpump \n", " - \"New\" water storage level\n", " \"\"\"\n", " waterstorage.storage_level = waterstorage_level\n", " \n", " if e_price < 50:\n", " hp_load = heatpump.max_th_power #bunu yoxla heat pump-a birbasa set load demek olmur. Ve funksiyada heatpump obyekti var ama o evvel initialize olunmayib\n", " energy_to_storage = hp_load - process_demand_MW\n", " waterstorage.charge(energy_to_storage)\n", " waterstorage.charged_energy = waterstorage.MW_to_MWh(energy_to_storage)\n", " waterstorage_level = waterstorage.storage_level\n", " new_cl = waterstorage.storage_level + waterstorage.charged_energy\n", " if e_price > 100:\n", " Tstorage = 320\n", " energy_from_storage = discharged_heat\n", " waterstorage_level = waterstorage.storage_level\n", " waterstorage.discharged_energy = waterstorage.MW_to_MWh(energy_from_storage)\n", " new_cl = waterstorage.storage_level - waterstorage.discharged_energy\n", " def Tsource_calculation(Tstorage, discharge_mass_flow, Tsource, process_mass_flow):\n", " return (\n", " (Tstorage * discharge_mass_flow + Tsource * process_mass_flow)\n", " / (discharge_mass_flow + process_mass_flow)\n", " )\n", " new_Tsource = Tsource_calculation(Tstorage, discharge_mass_flow, Tsource, process_mass_flow (demand, Tsink, Tref, Cp))\n", " new_COP = COP_calculation (Tsink, new_Tsource)\n", " hp_load = heatpump.set_heat_output(process_demand_MW, Tsink, Tsource) #bu da hemcinin set load assetin funksiyasidir, \n", " #heatpump da overwrite edilib. men evezinde yazdim ki set_heat_output\n", " #sen gor hansi funksiya sene lazimdir.\n", "\n", " return hp_load" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Shahla Huseynova\\python\\Encore\\notepad.py:318: UserWarning: Chosen heat output is out of range [5 - 20]. Heat output is being limited to the closest boundary.\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "-4.25531914893617" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eload = test_heatpump_and_waterstorage_system(\n", " Tsink = 150+273, \n", " Tsource = 60+273, \n", " process_demand_MW = 25, \n", " e_price = 130, \n", " waterstorage_level = 23\n", ")\n", "\n", "eload[0]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Shahla Huseynova\\python\\Encore\\notepad.py:318: UserWarning: Chosen heat output is out of range [5 - 20]. Heat output is being limited to the closest boundary.\n", " warnings.warn(\n" ] } ], "source": [ "for i in df.index:\n", " df.loc[i, 'e_load'] = test_heatpump_and_waterstorage_system(df.loc[i, 'Tsink (VDG)']+273, df.loc[i, 'Tsource (VDG)']+273, df.loc[i, 'MW (VDG)'], 130, 23)[0]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Tsource (VDG)Tsink (VDG)MW (VDG)Tsource (NDG)Tsink (NDG)MW (NDG)e_load
2018-11-01 00:00:0064.964783142.0031090.00000019.897433147.7318140.000000-0.928166
2018-11-01 00:15:0054.578777138.9604930.00000017.950905148.1389640.000000-1.024148
2018-11-01 00:30:0065.166672139.8853290.00000033.500757147.5854260.000000-0.904835
2018-11-01 00:45:0065.358078139.7319010.00000042.203876147.5476120.000000-0.900994
2018-11-01 01:00:0064.947536139.5778710.00000018.702675148.2603350.000000-0.904439
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2018-11-01 15:30:0065.016212157.22950820.71692664.996845167.1359016.798455-4.286702
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67 rows × 7 columns

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" ], "text/plain": [ " Tsource (VDG) Tsink (VDG) MW (VDG) Tsource (NDG) \\\n", "2018-11-01 00:00:00 64.964783 142.003109 0.000000 19.897433 \n", "2018-11-01 00:15:00 54.578777 138.960493 0.000000 17.950905 \n", "2018-11-01 00:30:00 65.166672 139.885329 0.000000 33.500757 \n", "2018-11-01 00:45:00 65.358078 139.731901 0.000000 42.203876 \n", "2018-11-01 01:00:00 64.947536 139.577871 0.000000 18.702675 \n", "... ... ... ... ... \n", "2018-11-01 15:30:00 65.016212 157.229508 20.716926 64.996845 \n", "2018-11-01 15:45:00 64.977669 157.649509 20.631462 64.929398 \n", "2018-11-01 16:00:00 64.946152 157.807772 20.608221 64.789982 \n", "2018-11-01 16:15:00 64.940338 157.965580 20.589894 64.489296 \n", "2018-11-01 16:30:00 64.940338 158.070484 20.799079 64.245148 \n", "\n", " Tsink (NDG) MW (NDG) e_load \n", "2018-11-01 00:00:00 147.731814 0.000000 -0.928166 \n", "2018-11-01 00:15:00 148.138964 0.000000 -1.024148 \n", "2018-11-01 00:30:00 147.585426 0.000000 -0.904835 \n", "2018-11-01 00:45:00 147.547612 0.000000 -0.900994 \n", "2018-11-01 01:00:00 148.260335 0.000000 -0.904439 \n", "... ... ... ... \n", "2018-11-01 15:30:00 167.135901 6.798455 -4.286702 \n", "2018-11-01 15:45:00 166.147289 6.785675 -4.303817 \n", "2018-11-01 16:00:00 164.705055 6.795423 -4.311047 \n", "2018-11-01 16:15:00 163.434915 6.804813 -4.317061 \n", "2018-11-01 16:30:00 163.312629 6.876395 -4.320878 \n", "\n", "[67 rows x 7 columns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "interpreter": { "hash": "dd1accba5c44bbc1a722925963d63420d7a225a16ee8ad40deae87a5c5fb7f29" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.5" } }, "nbformat": 4, "nbformat_minor": 4 }