diff --git a/EnergySystem.py b/EnergySystem.py index 33d2ba7..408b9ad 100644 --- a/EnergySystem.py +++ b/EnergySystem.py @@ -35,6 +35,8 @@ class EnergySystem: # 如果ESS也不足以满足需求，再从电网获取 - 当ESS中的存储电量也不足以补充时，再从电网购买剩余所需电量。 def simulate(self, data, time_interval): total_benefit = 0 + total_netto_benefit = 0 + total_gen = 0 for index, row in data.iterrows(): time = row['time'] sunlight_intensity = row['sunlight'] @@ -109,9 +111,11 @@ class EnergySystem: # 工厂需求量-总能量 # unmet_demand = max(0, factory_demand * time_interval - total_energy) # benefit = (total_energy - unmet_demand) * electricity_price + total_gen += saved_energy benefit = (saved_energy) * electricity_price cost = net_grid * electricity_price # print(f"time:{time} benefit: {benefit}, cost: {cost}") + total_netto_benefit += benefit total_benefit += benefit - cost # # spring week_start = self.season_start @@ -137,4 +141,4 @@ class EnergySystem: # self.winter_week_gen.append(generated_pv_power) # self.winter_week_soc.append(self.ess.storage / self.ess.capacity) - return total_benefit \ No newline at end of file + return (total_benefit, total_netto_benefit, total_gen) \ No newline at end of file diff --git a/config.json b/config.json index 63a7d89..cdedf6a 100644 --- a/config.json +++ b/config.json @@ -17,12 +17,12 @@ "pv_capacities":{ "begin": 0, "end": 50000, - "groups": 5 + "groups": 11 }, "ess_capacities":{ "begin": 0, "end": 100000, - "groups": 10 + "groups": 11 }, "time_interval":{ "numerator": 15, @@ -31,7 +31,8 @@ "annotated": { "unmet_prob": false, "benefit": false, - "cost": false + "cost": false, + "roi": false }, "figure_size":{ "height": 9, @@ -40,6 +41,7 @@ "plot_title":{ "unmet_prob": "Coverage Rate of Factory Electrical Demands", "cost": "Costs of Microgrid system [m-EUR]", - "benefit": "Financial Profit Based on Py & Ess Configuration (k-EUR / year)" + "benefit": "Financial Profit Based on Py & Ess Configuration (k-EUR / year)", + "roi": "ROI" } } \ No newline at end of file diff --git a/electricity_price_data.csv b/electricity_price_data.csv index 05e405d..8d02a9d 100644 --- a/electricity_price_data.csv +++ b/electricity_price_data.csv @@ -1,35041 +1,35041 @@ -Time,ElectricityPrice -2023-01-01 00:00:00,0.3 -2023-01-01 00:15:00,0.3 -2023-01-01 00:30:00,0.3 -2023-01-01 00:45:00,0.3 -2023-01-01 01:00:00,0.3 -2023-01-01 01:15:00,0.3 -2023-01-01 01:30:00,0.3 -2023-01-01 01:45:00,0.3 -2023-01-01 02:00:00,0.3 -2023-01-01 02:15:00,0.3 -2023-01-01 02:30:00,0.3 -2023-01-01 02:45:00,0.3 -2023-01-01 03:00:00,0.3 -2023-01-01 03:15:00,0.3 -2023-01-01 03:30:00,0.3 -2023-01-01 03:45:00,0.3 -2023-01-01 04:00:00,0.3 -2023-01-01 04:15:00,0.3 -2023-01-01 04:30:00,0.3 -2023-01-01 04:45:00,0.3 -2023-01-01 05:00:00,0.3 -2023-01-01 05:15:00,0.3 -2023-01-01 05:30:00,0.3 -2023-01-01 05:45:00,0.3 -2023-01-01 06:00:00,0.3 -2023-01-01 06:15:00,0.3 -2023-01-01 06:30:00,0.3 -2023-01-01 06:45:00,0.3 -2023-01-01 07:00:00,0.3 -2023-01-01 07:15:00,0.3 -2023-01-01 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0.0.5\")\n", "\n", "with open('config.json', 'r') as f:\n", " js_data = json.load(f)\n", @@ -61,6 +61,7 @@ " \n", "\n", "time_interval = js_data[\"time_interval\"][\"numerator\"] / js_data[\"time_interval\"][\"denominator\"]\n", + "print(time_interval)\n", "\n", "pv_loss = js_data[\"pv\"][\"loss\"]\n", "pv_cost_per_kW = js_data[\"pv\"][\"cost_per_kW\"]\n", @@ -85,10 +86,13 @@ "annot_unmet = js_data[\"annotated\"][\"unmet_prob\"]\n", "annot_benefit = js_data[\"annotated\"][\"benefit\"]\n", "annot_cost = js_data[\"annotated\"][\"cost\"]\n", + "annot_roi = js_data[\"annotated\"][\"roi\"]\n", "\n", "title_unmet = js_data[\"plot_title\"][\"unmet_prob\"]\n", "title_cost = js_data[\"plot_title\"][\"cost\"]\n", "title_benefit = js_data[\"plot_title\"][\"benefit\"]\n", + "title_roi = js_data[\"plot_title\"][\"roi\"]\n", + "\n", "\n", "figure_size = (js_data[\"figure_size\"][\"length\"], js_data[\"figure_size\"][\"height\"])\n", "\n", @@ -181,6 +185,59 @@ " plt.savefig(filename)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def draw_roi(costs, results, filename, title_roi, days=365, annot_roi=False, figure_size=(10, 10)):\n", + " costs = costs.astype(float)\n", + " costs = costs / 365 \n", + " costs = costs * days\n", + "\n", + " df = results\n", + " df = costs / df\n", + " if 0 in df.index and 0 in df.columns:\n", + " df.loc[0,0] = 100\n", + " df[df > 80] = 100\n", + " print(df)\n", + "\n", + " df = df.astype(float)\n", + " df.index = df.index / 1000\n", + " df.index = df.index.map(int)\n", + " df.columns = df.columns / 1000\n", + " df.columns = df.columns.map(int)\n", + " min_value = df.min().min()\n", + " max_value = df.max().max()\n", + " print(max_value)\n", + " max_scale = max(abs(min_value), abs(max_value))\n", + "\n", + " df[df.columns[-1] + 1] = df.iloc[:, -1] \n", + " new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns)\n", + " for i in df.columns:\n", + " new_Data[i] = df[i].iloc[-1]\n", + " df = pd.concat([df, new_Data])\n", + "\n", + " X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))\n", + "\n", + " def fmt(x,pos):\n", + " return '{:.0f}'.format(x)\n", + "\n", + " cmap = sns.color_palette(\"Greys\", as_cmap=True)\n", + " plt.figure(figsize=figure_size)\n", + " ax = sns.heatmap(df, fmt=\".1f\", cmap=cmap, vmin=0, vmax=100, annot=annot_benefit)\n", + " CS = ax.contour(X, Y, df, colors='black', alpha=0.5)\n", + " ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt))\n", + " plt.title(title_roi)\n", + " plt.gca().invert_yaxis()\n", + " plt.xlim(0, df.shape[1] - 1)\n", + " plt.ylim(0, df.shape[0] - 1)\n", + " plt.xlabel('ESS Capacity (MWh)')\n", + " plt.ylabel('PV Capacity (MW)')\n", + " plt.savefig(filename)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -253,8 +310,8 @@ " ax = sns.heatmap(df, fmt=\".00%\", cmap=cmap, vmin=0, vmax=1, annot=annot_unmet)\n", "\n", " cbar = ax.collections[0].colorbar\n", - " cbar.set_ticks([0, 0.25, 0.5, 0.75, 1])\n", - " cbar.set_ticklabels(['0%', '25%', '50%', '75%', '100%'])\n", + " cbar.set_ticks([0, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 1])\n", + " cbar.set_ticklabels(['0%', '10%', '20%', '30%', '40%', '50%', '60%', '70%', '80%', '90%', '100%'])\n", " cbar.ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'{x:.0%}'))\n", " X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))\n", "\n", @@ -289,7 +346,7 @@ "metadata": {}, "outputs": [], "source": [ - "def generate_data(pv_capacity, pv_cost_per_kW, pv_lifetime, pv_loss, ess_capacity, ess_cost_per_kW, ess_lifetime, ess_loss, grid_capacity, grid_loss, sell_price,time_interval, data, days):\n", + "def generate_data(pv_capacity, pv_cost_per_kW, pv_lifetime, pv_loss, ess_capacity, ess_cost_per_kW, ess_lifetime, ess_loss, grid_capacity, grid_loss, sell_price, time_interval, data, days):\n", " pv = pv_config(capacity=pv_capacity, \n", " cost_per_kW=pv_cost_per_kW,\n", " lifetime=pv_lifetime, \n", @@ -306,11 +363,11 @@ " energySystem = EnergySystem(pv_type=pv, \n", " ess_type=ess, \n", " grid_type= grid)\n", - " benefit = energySystem.simulate(data, time_interval)\n", + " (benefit, netto_benefit, gen_energy) = energySystem.simulate(data, time_interval)\n", " results = cal_profit(energySystem, benefit, days)\n", " overload_cnt = energySystem.overload_cnt\n", " costs = energySystem.ess.capacity * energySystem.ess.cost_per_kW + energySystem.pv.capacity * energySystem.pv.cost_per_kW\n", - " return (results, overload_cnt, costs)\n" + " return (results, overload_cnt, costs, netto_benefit, gen_energy, energySystem.generated)\n" ] }, { @@ -322,26 +379,38 @@ "months_results = []\n", "months_costs = []\n", "months_overload = []\n", + "months_nettos = []\n", + "months_gen_energy = []\n", + "months_gen_energy2 = []\n", "for index, month_data in enumerate(months_data):\n", " results = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n", " costs = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n", " overload_cnt = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n", + " nettos = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n", + " gen_energies = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n", + " gen_energies2 = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n", " for pv_capacity in pv_capacities:\n", " for ess_capacity in ess_capacities:\n", - " (result, overload, cost) = generate_data(pv_capacity=pv_capacity,pv_cost_per_kW=pv_cost_per_kW, pv_lifetime=pv_lifetime, pv_loss=pv_loss, ess_capacity=ess_capacity, ess_cost_per_kW=ess_cost_per_kW, ess_lifetime=ess_lifetime, ess_loss=ess_loss, grid_capacity=grid_capacity, grid_loss=grid_loss, sell_price=sell_price, time_interval=time_interval, data=month_data, days=months_days[index])\n", + " (result, overload, cost, netto, gen_energy, gen_energy2) = generate_data(pv_capacity=pv_capacity,pv_cost_per_kW=pv_cost_per_kW, pv_lifetime=pv_lifetime, pv_loss=pv_loss, ess_capacity=ess_capacity, ess_cost_per_kW=ess_cost_per_kW, ess_lifetime=ess_lifetime, ess_loss=ess_loss, grid_capacity=grid_capacity, grid_loss=grid_loss, sell_price=sell_price, time_interval=time_interval, data=month_data, days=months_days[index])\n", " results.loc[pv_capacity,ess_capacity] = result\n", " overload_cnt.loc[pv_capacity,ess_capacity] = overload\n", " costs.loc[pv_capacity,ess_capacity] = cost\n", + " nettos.loc[pv_capacity,ess_capacity] = netto\n", + " gen_energies.loc[pv_capacity, ess_capacity] = gen_energy\n", + " gen_energies2.loc[pv_capacity, ess_capacity] = gen_energy2\n", " months_results.append(results)\n", " months_costs.append(costs)\n", " months_overload.append(overload_cnt)\n", + " months_nettos.append(nettos)\n", + " months_gen_energy.append(gen_energies)\n", + " months_gen_energy2.append(gen_energies2)\n", " draw_results(results=results, \n", - " filename=f'plots/pv-{pv_capacity}-ess-{ess_capacity}-{index}-benefit.png',\n", + " filename=f'plots/pv-{pv_capacity}-ess-{ess_capacity}-month-{index+1}-benefit.png',\n", " title_benefit=title_benefit,\n", " annot_benefit=annot_benefit,\n", " figure_size=figure_size)\n", " draw_overload(overload_cnt=overload_cnt, \n", - " filename=f'plots/pv-{pv_capacity}-ess-{ess_capacity}-{index}-unmet.png',\n", + " filename=f'plots/pv-{pv_capacity}-ess-{ess_capacity}-month-{index+1}-unmet.png',\n", " title_unmet=title_unmet,\n", " annot_unmet=annot_unmet,\n", " figure_size=figure_size,\n", @@ -351,6 +420,11 @@ "annual_result = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n", "annual_costs = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n", "annual_overload = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n", + "annual_nettos = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n", + "annual_gen = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n", + "annual_gen2 = pd.DataFrame(index=pv_capacities, columns= ess_capacities)\n", + "\n", + "\n", "\n", "# get the yearly results\n", "for pv_capacity in pv_capacities:\n", @@ -358,13 +432,22 @@ " results = 0\n", " costs = 0\n", " overload_cnt = 0\n", + " nettos = 0\n", + " gen = 0\n", + " gen2 = 0\n", " for index, month_data in enumerate(months_data):\n", " results += months_results[index].loc[pv_capacity,ess_capacity]\n", " costs += months_costs[index].loc[pv_capacity,ess_capacity]\n", " overload_cnt += months_overload[index].loc[pv_capacity, ess_capacity]\n", + " nettos += months_nettos[index].loc[pv_capacity, ess_capacity]\n", + " gen += months_gen_energy[index].loc[pv_capacity, ess_capacity]\n", + " gen2 += months_gen_energy[index].loc[pv_capacity, ess_capacity]\n", " annual_result.loc[pv_capacity, ess_capacity] = results\n", " annual_costs.loc[pv_capacity, ess_capacity] = costs\n", " annual_overload.loc[pv_capacity, ess_capacity] = overload_cnt\n", + " annual_nettos.loc[pv_capacity, ess_capacity] = nettos\n", + " annual_gen.loc[pv_capacity, ess_capacity] = gen\n", + " annual_gen2.loc[pv_capacity, ess_capacity] = gen2\n", "\n", "draw_cost(costs=annual_costs,\n", " filename='plots/annual_cost.png',\n", @@ -403,9 +486,27 @@ "if not os.path.isdir('data'):\n", " os.makedirs('data')\n", "\n", - "save_data(results, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-results')\n", - "save_data(costs, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-costs')\n", - "save_data(overload_cnt, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-overload_cnt')" + "save_data(annual_result, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-results')\n", + "save_data(annual_costs, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-costs')\n", + "save_data(annual_overload, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-overload_cnt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "draw_results(annual_result, 'plots/test.png', 'test', False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "draw_roi(annual_costs, annual_nettos, 'plots/annual_roi.png', title_roi, 365, annot_benefit, figure_size)\n" ] } ], diff --git a/main.py b/main.py index c106e2f..1764f13 100644 --- a/main.py +++ b/main.py @@ -1,12 +1,21 @@ #!/usr/bin/env python # coding: utf-8 -# In[14]: +# In[ ]: import os import glob import shutil +import matplotlib.pyplot as plt +import matplotlib.ticker as ticker +from matplotlib.ticker import FuncFormatter +import numpy as np +import pandas as pd +import os +import seaborn as sns +import json +from matplotlib.colors import LinearSegmentedColormap def clear_folder_make_ess_pv(folder_path): if os.path.isdir(folder_path): @@ -19,7 +28,7 @@ folder_path = 'plots' clear_folder_make_ess_pv(folder_path) -# In[15]: +# In[ ]: import matplotlib.pyplot as plt @@ -30,18 +39,21 @@ from EnergySystem import EnergySystem from config import pv_config, grid_config, ess_config -# In[16]: +# In[ ]: import json -print("Version 0.0.2") +print("Version 0.0.5") with open('config.json', 'r') as f: js_data = json.load(f) -data = pd.read_csv('combined_data.csv') + + + time_interval = js_data["time_interval"]["numerator"] / js_data["time_interval"]["denominator"] +print(time_interval) pv_loss = js_data["pv"]["loss"] pv_cost_per_kW = js_data["pv"]["cost_per_kW"] @@ -66,22 +78,45 @@ ess_groups = js_data["ess_capacities"]["groups"] annot_unmet = js_data["annotated"]["unmet_prob"] annot_benefit = js_data["annotated"]["benefit"] annot_cost = js_data["annotated"]["cost"] +annot_roi = js_data["annotated"]["roi"] title_unmet = js_data["plot_title"]["unmet_prob"] title_cost = js_data["plot_title"]["cost"] title_benefit = js_data["plot_title"]["benefit"] +title_roi = js_data["plot_title"]["roi"] + figure_size = (js_data["figure_size"]["length"], js_data["figure_size"]["height"]) +data = pd.read_csv('combined_data.csv') + +granularity = js_data["time_interval"]["numerator"] + +months_days = [31,28,31,30,31,30,31,31,30,31,30,31] +def get_month_coe(num, granularity): + return 60 / granularity * 24 * months_days[num] + +months_index = [get_month_coe(num, granularity) for num in range(12)] +months_data = [] +for i in range(1,12): + months_index[i] += months_index[i-1] +for i in range(12): + start = 0 if i == 0 else months_index[i-1] + end = months_index[i] + months_data.append(data.iloc[int(start):int(end)]) + + + + pv_capacities = np.linspace(pv_begin, pv_end, pv_groups) ess_capacities = np.linspace(ess_begin, ess_end, ess_groups) -results = pd.DataFrame(index=pv_capacities, columns= ess_capacities) -affords = pd.DataFrame(index=pv_capacities, columns= ess_capacities) -costs = pd.DataFrame(index=pv_capacities, columns= ess_capacities) -overload_cnt = pd.DataFrame(index=pv_capacities, columns= ess_capacities) +# results = pd.DataFrame(index=pv_capacities, columns= ess_capacities) +# affords = pd.DataFrame(index=pv_capacities, columns= ess_capacities) +# costs = pd.DataFrame(index=pv_capacities, columns= ess_capacities) +# overload_cnt = pd.DataFrame(index=pv_capacities, columns= ess_capacities) -# In[17]: +# In[ ]: hour_demand = [] @@ -97,164 +132,309 @@ plt.savefig('plots/demand.png') plt.close() -# In[18]: +# In[ ]: -def cal_profit(es: EnergySystem, saved_money): - profit = saved_money - es.ess.get_cost_per_year() - es.pv.get_cost_per_year() +def draw_results(results, filename, title_benefit, annot_benefit=False, figure_size=(10, 10)): + df=results + df = df.astype(float) + df.index = df.index / 1000 + df.index = df.index.map(int) + df.columns = df.columns / 1000 + df.columns = df.columns.map(int) + min_value = df.min().min() + max_value = df.max().max() + max_scale = max(abs(min_value/1000), abs(max_value/1000)) + + df[df.columns[-1] + 1] = df.iloc[:, -1] + new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns) + for i in df.columns: + new_Data[i] = df[i].iloc[-1] + df = pd.concat([df, new_Data]) + + X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0])) + + def fmt(x,pos): + return '{:.0f}'.format(x/1000) + + cmap = sns.color_palette("coolwarm", as_cmap=True) + plt.figure(figsize=figure_size) + ax = sns.heatmap(df/1000, fmt=".1f", cmap=cmap, vmin=-max_scale, vmax=max_scale, annot=annot_benefit) + CS = ax.contour(X, Y, df, colors='black', alpha=0.5) + ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt)) + plt.title(title_benefit) + plt.gca().invert_yaxis() + plt.xlim(0, df.shape[1] - 1) + plt.ylim(0, df.shape[0] - 1) + plt.xlabel('ESS Capacity (MWh)') + plt.ylabel('PV Capacity (MW)') + plt.savefig(filename) + + +# In[ ]: + + +def draw_roi(costs, results, filename, title_roi, days=365, annot_roi=False, figure_size=(10, 10)): + costs = costs.astype(float) + costs = costs / 365 + costs = costs * days + + df = results + df = costs / df + if 0 in df.index and 0 in df.columns: + df.loc[0,0] = 100 + df[df > 80] = 100 + print(df) + + df = df.astype(float) + df.index = df.index / 1000 + df.index = df.index.map(int) + df.columns = df.columns / 1000 + df.columns = df.columns.map(int) + min_value = df.min().min() + max_value = df.max().max() + print(max_value) + max_scale = max(abs(min_value), abs(max_value)) + + df[df.columns[-1] + 1] = df.iloc[:, -1] + new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns) + for i in df.columns: + new_Data[i] = df[i].iloc[-1] + df = pd.concat([df, new_Data]) + + X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0])) + + def fmt(x,pos): + return '{:.0f}'.format(x) + + cmap = sns.color_palette("Greys", as_cmap=True) + plt.figure(figsize=figure_size) + ax = sns.heatmap(df, fmt=".1f", cmap=cmap, vmin=0, vmax=100, annot=annot_benefit) + CS = ax.contour(X, Y, df, colors='black', alpha=0.5) + ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt)) + plt.title(title_roi) + plt.gca().invert_yaxis() + plt.xlim(0, df.shape[1] - 1) + plt.ylim(0, df.shape[0] - 1) + plt.xlabel('ESS Capacity (MWh)') + plt.ylabel('PV Capacity (MW)') + plt.savefig(filename) + + +# In[ ]: + + +def draw_cost(costs, filename, title_cost, annot_cost=False, figure_size=(10, 10)): + df = costs + df = df.astype(int) + df.index = df.index / 1000 + df.index = df.index.map(int) + df.columns = df.columns / 1000 + df.columns = df.columns.map(int) + + df[df.columns[-1] + 1] = df.iloc[:, -1] + new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns) + for i in df.columns: + new_Data[i] = df[i].iloc[-1] + df = pd.concat([df, new_Data]) + X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0])) + + def fmt(x, pos): + return '{:.0f}'.format(x / 1000000) + + plt.figure(figsize=figure_size) + ax = sns.heatmap(df/1000000, fmt=".1f", cmap='viridis', annot=annot_cost) + CS = ax.contour(X, Y, df, colors='black', alpha=0.5) + ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt)) + plt.title(title_cost) + plt.gca().invert_yaxis() + plt.xlim(0, df.shape[1] - 1) + plt.ylim(0, df.shape[0] - 1) + plt.xlabel('ESS Capacity (MWh)') + plt.ylabel('PV Capacity (MW)') + plt.savefig(filename) + + +# In[ ]: + + +def draw_overload(overload_cnt, filename, title_unmet, annot_unmet=False, figure_size=(10, 10), days=365, granularity=15): + df = overload_cnt + print(days, granularity) + coef = 60 / granularity * days * 24 + print(coef) + print(df) + df = ( coef - df) / coef + print(df) + + df = df.astype(float) + df.index = df.index / 1000 + df.index = df.index.map(int) + df.columns = df.columns / 1000 + df.columns = df.columns.map(int) + + + df[df.columns[-1] + 1] = df.iloc[:, -1] + new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns) + for i in df.columns: + new_Data[i] = df[i].iloc[-1] + # print(new_Data) + df = pd.concat([df, new_Data]) + + + plt.figure(figsize=figure_size) + cmap = LinearSegmentedColormap.from_list("", ["white", "blue"]) + ax = sns.heatmap(df, fmt=".00%", cmap=cmap, vmin=0, vmax=1, annot=annot_unmet) + + cbar = ax.collections[0].colorbar + cbar.set_ticks([0, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 1]) + cbar.set_ticklabels(['0%', '10%', '20%', '30%', '40%', '50%', '60%', '70%', '80%', '90%', '100%']) + cbar.ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'{x:.0%}')) + X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0])) + + def fmt(x, pos): + return '{:.0f}%'.format(x * 100) + CS = ax.contour(X, Y, df, colors='black', alpha=0.5) + + ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt)) + + plt.xlim(0, df.shape[1] - 1) + plt.ylim(0, df.shape[0] - 1) + plt.title(title_unmet) + plt.xlabel('ESS Capacity (MWh)') + plt.ylabel('PV Capacity (MW)') + plt.savefig(filename) + + +# In[ ]: + + +def cal_profit(es: EnergySystem, saved_money, days): + profit = saved_money - es.ess.get_cost_per_year() / 365 * days - es.pv.get_cost_per_year() / 365 * days return profit -# In[24]: +# In[ ]: -for ess_capacity in ess_capacities: - print(f"ess_capacity:{ess_capacity}") +def generate_data(pv_capacity, pv_cost_per_kW, pv_lifetime, pv_loss, ess_capacity, ess_cost_per_kW, ess_lifetime, ess_loss, grid_capacity, grid_loss, sell_price, time_interval, data, days): + pv = pv_config(capacity=pv_capacity, + cost_per_kW=pv_cost_per_kW, + lifetime=pv_lifetime, + loss=pv_loss) + ess = ess_config(capacity=ess_capacity, + cost_per_kW=ess_cost_per_kW, + lifetime=ess_lifetime, + loss=ess_loss, + charge_power=ess_capacity, + discharge_power=ess_capacity) + grid = grid_config(capacity=grid_capacity, + grid_loss=grid_loss, + sell_price= sell_price) + energySystem = EnergySystem(pv_type=pv, + ess_type=ess, + grid_type= grid) + (benefit, netto_benefit, gen_energy) = energySystem.simulate(data, time_interval) + results = cal_profit(energySystem, benefit, days) + overload_cnt = energySystem.overload_cnt + costs = energySystem.ess.capacity * energySystem.ess.cost_per_kW + energySystem.pv.capacity * energySystem.pv.cost_per_kW + return (results, overload_cnt, costs, netto_benefit, gen_energy, energySystem.generated) + + +# In[ ]: + + +months_results = [] +months_costs = [] +months_overload = [] +months_nettos = [] +months_gen_energy = [] +months_gen_energy2 = [] +for index, month_data in enumerate(months_data): + results = pd.DataFrame(index=pv_capacities, columns= ess_capacities) + costs = pd.DataFrame(index=pv_capacities, columns= ess_capacities) + overload_cnt = pd.DataFrame(index=pv_capacities, columns= ess_capacities) + nettos = pd.DataFrame(index=pv_capacities, columns= ess_capacities) + gen_energies = pd.DataFrame(index=pv_capacities, columns= ess_capacities) + gen_energies2 = pd.DataFrame(index=pv_capacities, columns= ess_capacities) for pv_capacity in pv_capacities: - print(f"pv_capacity:{pv_capacity}") - pv = pv_config(capacity=pv_capacity, - cost_per_kW=pv_cost_per_kW, - lifetime=pv_lifetime, - loss=pv_loss) - ess = ess_config(capacity=ess_capacity, - cost_per_kW=ess_cost_per_kW, - lifetime=ess_lifetime, - loss=ess_loss, - charge_power=ess_capacity, - discharge_power=ess_capacity) - grid = grid_config(capacity=grid_capacity, - grid_loss=grid_loss, - sell_price= sell_price) - energySystem = EnergySystem(pv_type=pv, - ess_type=ess, - grid_type= grid) - benefit = energySystem.simulate(data, time_interval) - results.loc[pv_capacity,ess_capacity] = cal_profit(energySystem, benefit) - affords.loc[pv_capacity,ess_capacity] = energySystem.afford - overload_cnt.loc[pv_capacity,ess_capacity] = energySystem.overload_cnt - costs.loc[pv_capacity,ess_capacity] = energySystem.ess.capacity * energySystem.ess.cost_per_kW + energySystem.pv.capacity * energySystem.pv.cost_per_kW - pv_generated = energySystem.day_generated - ess_generated = energySystem.hour_stored - ess_generated_2 = energySystem.hour_stored_2 - plt.figure(figsize=(10,8)); - plt.plot(ess_generated) - plt.xlabel('day #') - plt.ylabel('SoC %') - plt.title(f'14:00 ESS SoC \n PV cap:{pv_capacity}, ESS cap:{ess_capacity}') - plt.savefig(f'plots/ess/1400-{pv_capacity}-{ess_capacity}.png') - plt.close() - plt.figure(figsize=(10,8)); - plt.plot(ess_generated_2) - plt.xlabel('day #') - plt.ylabel('SoC%') - plt.title(f'08:00 ESS SoC \n PV cap:{pv_capacity}, ESS cap:{ess_capacity}') - plt.savefig(f'plots/ess/0800-{pv_capacity}-{ess_capacity}.png') - plt.close() - # print(energySystem.unmet) - # spring_week_start = energySystem.season_start - # spring_week_end = spring_week_start + energySystem.week_length - # summer_week_start = energySystem.season_start + 1 * energySystem.season_step - # summer_week_end = summer_week_start + energySystem.week_length - # autumn_week_start = energySystem.season_start + 2 * energySystem.season_step - # autumn_week_end = autumn_week_start + energySystem.week_length - # winter_week_start = energySystem.season_start + 3 * energySystem.season_step - # winter_week_end = winter_week_start+ energySystem.week_length + for ess_capacity in ess_capacities: + (result, overload, cost, netto, gen_energy, gen_energy2) = generate_data(pv_capacity=pv_capacity,pv_cost_per_kW=pv_cost_per_kW, pv_lifetime=pv_lifetime, pv_loss=pv_loss, ess_capacity=ess_capacity, ess_cost_per_kW=ess_cost_per_kW, ess_lifetime=ess_lifetime, ess_loss=ess_loss, grid_capacity=grid_capacity, grid_loss=grid_loss, sell_price=sell_price, time_interval=time_interval, data=month_data, days=months_days[index]) + results.loc[pv_capacity,ess_capacity] = result + overload_cnt.loc[pv_capacity,ess_capacity] = overload + costs.loc[pv_capacity,ess_capacity] = cost + nettos.loc[pv_capacity,ess_capacity] = netto + gen_energies.loc[pv_capacity, ess_capacity] = gen_energy + gen_energies2.loc[pv_capacity, ess_capacity] = gen_energy2 + months_results.append(results) + months_costs.append(costs) + months_overload.append(overload_cnt) + months_nettos.append(nettos) + months_gen_energy.append(gen_energies) + months_gen_energy2.append(gen_energies2) + draw_results(results=results, + filename=f'plots/pv-{pv_capacity}-ess-{ess_capacity}-month-{index+1}-benefit.png', + title_benefit=title_benefit, + annot_benefit=annot_benefit, + figure_size=figure_size) + draw_overload(overload_cnt=overload_cnt, + filename=f'plots/pv-{pv_capacity}-ess-{ess_capacity}-month-{index+1}-unmet.png', + title_unmet=title_unmet, + annot_unmet=annot_unmet, + figure_size=figure_size, + days=months_days[index], + granularity=granularity) - # spring_consume_data = [] - # summer_consume_data = [] - # autumn_consume_data = [] - # winter_consume_data = [] - # for index, row in data.iterrows(): - # if index in range(spring_week_start, spring_week_end): - # spring_consume_data.append(row['demand']) - # elif index in range(summer_week_start, summer_week_end): - # summer_consume_data.append(row['demand']) - # elif index in range(autumn_week_start, autumn_week_end): - # autumn_consume_data.append(row['demand']) - # elif index in range(winter_week_start, winter_week_end): - # winter_consume_data.append(row['demand']) - - # spring_week_time = list(range(spring_week_start, spring_week_end)) - # summer_week_time = list(range(summer_week_start, summer_week_end)) - # autumn_week_time = list(range(autumn_week_start, autumn_week_end)) - # winter_week_time = list(range(winter_week_start, winter_week_end)) - - # spring_pv_generated = energySystem.spring_week_gen - # summer_pv_generated = energySystem.summer_week_gen - # autumn_pv_generated = energySystem.autumn_week_gen - # winter_pv_generated = energySystem.winter_week_gen - - # spring_soc = energySystem.spring_week_soc - # summer_soc = energySystem.summer_week_soc - # autumn_soc = energySystem.autumn_week_soc - # winter_soc = energySystem.winter_week_soc +annual_result = pd.DataFrame(index=pv_capacities, columns= ess_capacities) +annual_costs = pd.DataFrame(index=pv_capacities, columns= ess_capacities) +annual_overload = pd.DataFrame(index=pv_capacities, columns= ess_capacities) +annual_nettos = pd.DataFrame(index=pv_capacities, columns= ess_capacities) +annual_gen = pd.DataFrame(index=pv_capacities, columns= ess_capacities) +annual_gen2 = pd.DataFrame(index=pv_capacities, columns= ess_capacities) - # fig, ax1 = plt.subplots() - # plt.plot(spring_week_time, spring_pv_generated, label = 'pv generation') - # plt.plot(spring_week_time, spring_consume_data, label = 'factory consume') - # plt.ylabel('Power / kW') - # plt.xlabel('15 min #') - # plt.title(f'ess: {energySystem.ess.capacity/1000 } MWh pv: {energySystem.pv.capacity/1000 } MW spring week generate condition') - # plt.legend() - # plt.savefig(f'plots/{energySystem.ess.capacity}-{energySystem.pv.capacity}-spring.png') - # plt.close() +# get the yearly results +for pv_capacity in pv_capacities: + for ess_capacity in ess_capacities: + results = 0 + costs = 0 + overload_cnt = 0 + nettos = 0 + gen = 0 + gen2 = 0 + for index, month_data in enumerate(months_data): + results += months_results[index].loc[pv_capacity,ess_capacity] + costs += months_costs[index].loc[pv_capacity,ess_capacity] + overload_cnt += months_overload[index].loc[pv_capacity, ess_capacity] + nettos += months_nettos[index].loc[pv_capacity, ess_capacity] + gen += months_gen_energy[index].loc[pv_capacity, ess_capacity] + gen2 += months_gen_energy[index].loc[pv_capacity, ess_capacity] + annual_result.loc[pv_capacity, ess_capacity] = results + annual_costs.loc[pv_capacity, ess_capacity] = costs + annual_overload.loc[pv_capacity, ess_capacity] = overload_cnt + annual_nettos.loc[pv_capacity, ess_capacity] = nettos + annual_gen.loc[pv_capacity, ess_capacity] = gen + annual_gen2.loc[pv_capacity, ess_capacity] = gen2 - # plt.plot(summer_week_time, summer_pv_generated, label = 'pv generation') - # plt.plot(summer_week_time, summer_consume_data, label = 'factory consume') - # plt.ylabel('Power / kW') - # plt.xlabel('15 min #') - # plt.title(f'ess: {energySystem.ess.capacity/1000 } MWh pv: {energySystem.pv.capacity/1000 } MW summer week generate condition') - # plt.legend() - # plt.savefig(f'plots/{energySystem.ess.capacity}-{energySystem.pv.capacity}-summer.png') - # plt.close() - - # plt.plot(autumn_week_time, autumn_pv_generated, label = 'pv generation') - # plt.plot(autumn_week_time, autumn_consume_data, label = 'factory consume') - # plt.ylabel('Power / kW') - # plt.xlabel('15 min #') - # plt.title(f'ess: {energySystem.ess.capacity/1000 } MWh pv: {energySystem.pv.capacity/1000 } MW autumn week generate condition') - # plt.legend() - # plt.savefig(f'plots/{energySystem.ess.capacity}-{energySystem.pv.capacity}-autumn.png') - # plt.close() - - # plt.plot(winter_week_time, winter_pv_generated, label = 'pv generation') - # plt.plot(winter_week_time, winter_consume_data, label = 'factory consume') - # plt.ylabel('Power / kW') - # plt.xlabel('15 min #') - # plt.title(f'ess: {energySystem.ess.capacity/1000 } MWh pv: {energySystem.pv.capacity/1000 } MW winter week generate condition') - # plt.legend() - # plt.savefig(f'plots/{energySystem.ess.capacity}-{energySystem.pv.capacity}-winter.png') - # plt.close() - - # plt.figure(); - # plt.plot(pv_generated) - # plt.xlabel('day #') - # plt.ylabel('Electricity kWh') - # plt.title(f'PV generated pv cap:{pv_capacity}, ess cap:{ess_capacity}') - # plt.savefig(f'plots/pv/{pv_capacity}-{ess_capacity}.png') - # plt.close() +draw_cost(costs=annual_costs, + filename='plots/annual_cost.png', + title_cost=title_cost, + annot_cost=annot_cost, + figure_size=figure_size) +draw_results(results=annual_result, + filename='plots/annual_benefit.png', + title_benefit=title_benefit, + annot_benefit=annot_benefit, + figure_size=figure_size) +draw_overload(overload_cnt=annual_overload, + filename='plots/annual_unmet.png', + title_unmet=title_unmet, + annot_unmet=annot_unmet, + figure_size=figure_size) - # plt.show() - - - - -# results = results.astype(float) - - -# pv = pv_config(capacity=100000,cost_per_kW=200,lifetime=25,loss=0.95) -# ess = ess_config(capacity=100000,cost_per_kW=300,lifetime=25,loss=0.95,charge_power=100000,discharge_power=100000) -# grid = grid_config(price_schedule=price_schedule, capacity=5000, grid_loss=0.95, sell_price=0.4) -# grid = grid_config(capacity=50000, grid_loss=0.95, sell_price=0.4) - - - # print(benefit) - - -# In[20]: +# In[ ]: def save_data(data, filename): @@ -262,83 +442,25 @@ def save_data(data, filename): data.to_json(filename + '.json') -# In[21]: +# In[ ]: -import matplotlib.ticker as ticker - if not os.path.isdir('data'): os.makedirs('data') -save_data(results, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-results') -save_data(costs, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-costs') -save_data(overload_cnt, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-overload_cnt') -df=results -df = df.astype(float) -df.index = df.index / 1000 -df.columns = df.columns / 1000 -min_value = df.min().min() -max_value = df.max().max() -max_scale = max(abs(min_value/1000), abs(max_value/1000)) -plt.figure(figsize=figure_size) -cmap = sns.color_palette("coolwarm", as_cmap=True) -ax = sns.heatmap(df/1000, fmt=".1f", cmap=cmap, vmin=-max_scale, vmax=max_scale, annot=annot_benefit) -# ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%.1f')) -plt.title(title_benefit) -plt.gca().invert_yaxis() -plt.xlabel('ESS Capacity (MWh)') -plt.ylabel('PV Capacity (MW)') -plt.savefig('plots/benefit.png') +save_data(annual_result, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-results') +save_data(annual_costs, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-costs') +save_data(annual_overload, f'data/{pv_begin}-{pv_end}-{pv_groups}-{ess_begin}-{ess_end}-{ess_groups}-overload_cnt') -# In[22]: +# In[ ]: -df = costs -df = df.astype(int) -df.index = df.index / 1000 -df.columns = df.columns / 1000 - -plt.figure(figsize=figure_size) -sns.heatmap(df/1000000, fmt=".1f", cmap='viridis', annot=annot_cost) -plt.title(title_cost) -plt.gca().invert_yaxis() -plt.xlabel('ESS Capacity (MWh)') -plt.ylabel('PV Capacity (MW)') -plt.savefig('plots/costs.png') - - # pv = pv_config(capacity=100000,cost_per_kW=200,lifetime=25,loss=0.95) - # ess = ess_config(capacity=100000,cost_per_kW=300,lifetime=25,loss=0.95,charge_power=100000,discharge_power=100000) - # grid = grid_config(price_schedule=price_schedule, capacity=5000, grid_loss=0.95, sell_price=0.4) - # grid = grid_config(capacity=50000, grid_loss=0.95, sell_price=0.4) +draw_results(annual_result, 'plots/test.png', 'test', False) - # print(benefit) +# In[ ]: -# In[23]: - - -from matplotlib.colors import LinearSegmentedColormap -df = overload_cnt -df = df.astype(int) -df.index = df.index / 1000 -df.columns = df.columns / 1000 -min_value = df.min().min() -max_value = df.max().max() -max_scale = max(abs(min_value/1000), abs(max_value/1000)) - -plt.figure(figsize=figure_size) -cmap = LinearSegmentedColormap.from_list("", ["white", "blue"]) -ax = sns.heatmap(df/(4*24*365), fmt=".00%", cmap=cmap, vmin=0, vmax=1, annot=annot_unmet) -cbar = ax.collections[0].colorbar -cbar.set_ticks([0, 0.25, 0.5, 0.75, 1]) -cbar.set_ticklabels(['0%', '25%', '50%', '75%', '100%']) -cbar.ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'{x:.0%}')) - -plt.title(title_unmet) -plt.gca().invert_yaxis() -plt.xlabel('ESS Capacity (MWh)') -plt.ylabel('PV Capacity (MW)') -plt.savefig('plots/unmet.png') +draw_roi(annual_costs, annual_nettos, 'plots/annual_roi.png', title_roi, 365, annot_benefit, figure_size)