#!/usr/bin/env python # coding: utf-8 # In[14]: import os import glob import shutil def clear_folder_make_ess_pv(folder_path): if os.path.isdir(folder_path): shutil.rmtree(folder_path) os.makedirs(folder_path) os.makedirs(os.path.join(folder_path,'ess')) os.makedirs(os.path.join(folder_path,'pv')) folder_path = 'plots' clear_folder_make_ess_pv(folder_path) # In[15]: import matplotlib.pyplot as plt import seaborn as sns import numpy as np import pandas as pd from EnergySystem import EnergySystem from config import pv_config, grid_config, ess_config # In[16]: import json print("Version 0.0.2") 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"] pv_loss = js_data["pv"]["loss"] pv_cost_per_kW = js_data["pv"]["cost_per_kW"] pv_lifetime = js_data["pv"]["lifetime"] ess_loss = js_data["ess"]["loss"] ess_cost_per_kW = js_data["ess"]["cost_per_kW"] ess_lifetime = js_data["ess"]["lifetime"] grid_loss = js_data["grid"]["loss"] sell_price = js_data["grid"]["sell_price"] #kWh grid_capacity = js_data["grid"]["capacity"] #kWh pv_begin = js_data["pv_capacities"]["begin"] pv_end = js_data["pv_capacities"]["end"] pv_groups = js_data["pv_capacities"]["groups"] ess_begin = js_data["ess_capacities"]["begin"] ess_end = js_data["ess_capacities"]["end"] 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"] title_unmet = js_data["plot_title"]["unmet_prob"] title_cost = js_data["plot_title"]["cost"] title_benefit = js_data["plot_title"]["benefit"] figure_size = (js_data["figure_size"]["length"], js_data["figure_size"]["height"]) 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) # In[17]: hour_demand = [] for index, row in data.iterrows(): time = row['time'] demand = row['demand'] if time.endswith('00'): hour_demand.append(demand) plt.figure(figsize=(10,8)) plt.plot(hour_demand) plt.ylabel('Demand Power / kW') plt.savefig('plots/demand.png') plt.close() # In[18]: def cal_profit(es: EnergySystem, saved_money): profit = saved_money - es.ess.get_cost_per_year() - es.pv.get_cost_per_year() return profit # In[24]: for ess_capacity in ess_capacities: print(f"ess_capacity:{ess_capacity}") for pv_capacity in pv_capacities: print(f"pv_capacity:{ess_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 # 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 # 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() # 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() # 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]: def save_data(data, filename): data.to_csv(filename+'.csv') data.to_json(filename + '.json') # In[21]: 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') # In[22]: 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) # print(benefit) # 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')