210 lines
7.2 KiB
Python
210 lines
7.2 KiB
Python
import matplotlib.pyplot as plt
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import matplotlib.ticker as ticker
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from matplotlib.ticker import FuncFormatter
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import numpy as np
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import pandas as pd
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import os
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import seaborn as sns
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import json
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from matplotlib.colors import LinearSegmentedColormap
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def read_data(file_name: str):
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with open(file_name, 'r') as f:
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data = json.load(f)
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for key, value in data.items():
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for subkey, subvalue in value.items():
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data[key][subkey] = float(subvalue)
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df = pd.DataFrame.from_dict(data, orient='index')
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df = df.T
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df.index = pd.to_numeric(df.index)
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df.columns = pd.to_numeric(df.columns)
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return df
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def draw_results(results, filename, title_benefit, annot_benefit=False, figure_size=(10, 10)):
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df=results
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df = df.astype(float)
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df.index = df.index / 1000
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df.index = df.index.map(int)
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df.columns = df.columns / 1000
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df.columns = df.columns.map(int)
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min_value = df.min().min()
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max_value = df.max().max()
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max_scale = max(abs(min_value/1000), abs(max_value/1000))
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df[df.columns[-1] + 1] = df.iloc[:, -1]
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new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns)
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for i in df.columns:
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new_Data[i] = df[i].iloc[-1]
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df = pd.concat([df, new_Data])
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X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))
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def fmt(x,pos):
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return '{:.0f}'.format(x/1000)
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cmap = sns.color_palette("coolwarm", as_cmap=True)
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plt.figure(figsize=figure_size)
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ax = sns.heatmap(df/1000, fmt=".1f", cmap=cmap, vmin=-max_scale, vmax=max_scale, annot=annot_benefit)
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CS = ax.contour(X, Y, df, colors='black', alpha=0.5)
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ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt))
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plt.title(title_benefit)
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plt.gca().invert_yaxis()
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plt.xlim(0, df.shape[1] - 1)
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plt.ylim(0, df.shape[0] - 1)
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plt.xlabel('ESS Capacity (MWh)')
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plt.ylabel('PV Capacity (MW)')
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plt.savefig(filename)
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def draw_cost(costs, filename, title_cost, annot_cost=False, figure_size=(10, 10)):
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df = costs
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df = df.astype(int)
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df.index = df.index / 1000
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df.index = df.index.map(int)
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df.columns = df.columns / 1000
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df.columns = df.columns.map(int)
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df[df.columns[-1] + 1] = df.iloc[:, -1]
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new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns)
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for i in df.columns:
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new_Data[i] = df[i].iloc[-1]
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df = pd.concat([df, new_Data])
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X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))
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def fmt(x, pos):
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return '{:.0f}'.format(x / 1000000)
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plt.figure(figsize=figure_size)
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ax = sns.heatmap(df/1000000, fmt=".1f", cmap='viridis', annot=annot_cost)
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CS = ax.contour(X, Y, df, colors='black', alpha=0.5)
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ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt))
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plt.title(title_cost)
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plt.gca().invert_yaxis()
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plt.xlim(0, df.shape[1] - 1)
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plt.ylim(0, df.shape[0] - 1)
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plt.xlabel('ESS Capacity (MWh)')
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plt.ylabel('PV Capacity (MW)')
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plt.savefig(filename)
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def draw_overload(overload_cnt, filename, title_unmet, annot_unmet=False, figure_size=(10, 10)):
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df = overload_cnt
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df = (4 * 24 * 365 - df) / (4 * 24 * 365)
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df = df.astype(float)
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df.index = df.index / 1000
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df.index = df.index.map(int)
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df.columns = df.columns / 1000
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df.columns = df.columns.map(int)
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min_value = df.min().min()
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max_value = df.max().max()
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df[df.columns[-1] + 1] = df.iloc[:, -1]
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new_Data = pd.DataFrame(index=[df.index[-1] + 1], columns=df.columns)
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for i in df.columns:
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new_Data[i] = df[i].iloc[-1]
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# print(new_Data)
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df = pd.concat([df, new_Data])
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plt.figure(figsize=figure_size)
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cmap = LinearSegmentedColormap.from_list("", ["white", "blue"])
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ax = sns.heatmap(df, fmt=".00%", cmap=cmap, vmin=0, vmax=1, annot=annot_unmet)
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cbar = ax.collections[0].colorbar
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cbar.set_ticks([0, 0.25, 0.5, 0.75, 1])
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cbar.set_ticklabels(['0%', '25%', '50%', '75%', '100%'])
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cbar.ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: f'{x:.0%}'))
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X, Y = np.meshgrid(np.arange(df.shape[1]), np.arange(df.shape[0]))
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def fmt(x, pos):
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return '{:.0f}%'.format(x * 100)
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CS = ax.contour(X, Y, df, colors='black', alpha=0.5)
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ax.clabel(CS, inline=True, fontsize=10, fmt=FuncFormatter(fmt))
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plt.xlim(0, df.shape[1] - 1)
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plt.ylim(0, df.shape[0] - 1)
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plt.title(title_unmet)
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plt.xlabel('ESS Capacity (MWh)')
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plt.ylabel('PV Capacity (MW)')
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plt.savefig(filename)
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with open('config.json', 'r') as f:
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js_data = json.load(f)
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data = pd.read_csv('combined_data.csv')
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time_interval = js_data["time_interval"]["numerator"] / js_data["time_interval"]["denominator"]
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pv_loss = js_data["pv"]["loss"]
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pv_cost_per_kW = js_data["pv"]["cost_per_kW"]
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pv_lifetime = js_data["pv"]["lifetime"]
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ess_loss = js_data["ess"]["loss"]
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ess_cost_per_kW = js_data["ess"]["cost_per_kW"]
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ess_lifetime = js_data["ess"]["lifetime"]
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grid_loss = js_data["grid"]["loss"]
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sell_price = js_data["grid"]["sell_price"]
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grid_capacity = js_data["grid"]["capacity"]
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pv_begin = js_data["pv_capacities"]["begin"]
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pv_end = js_data["pv_capacities"]["end"]
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pv_groups = js_data["pv_capacities"]["groups"]
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ess_begin = js_data["ess_capacities"]["begin"]
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ess_end = js_data["ess_capacities"]["end"]
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ess_groups = js_data["ess_capacities"]["groups"]
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annot_unmet = js_data["annotated"]["unmet_prob"]
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annot_benefit = js_data["annotated"]["benefit"]
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annot_cost = js_data["annotated"]["cost"]
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title_unmet = js_data["plot_title"]["unmet_prob"]
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title_cost = js_data["plot_title"]["cost"]
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title_benefit = js_data["plot_title"]["benefit"]
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figure_size = (js_data["figure_size"]["length"], js_data["figure_size"]["height"])
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directory = 'data/'
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file_list = [f for f in os.listdir(directory) if os.path.isfile(os.path.join(directory, f))]
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split_files = [f.split('-') for f in file_list]
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costs_files = [f for f in split_files if f[-1].endswith('costs.json')]
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print(f'find costs files: {costs_files}')
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overload_files = [f for f in split_files if f[-1].endswith('overload_cnt.json')]
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print(f'find coverage/unmet files: {overload_files}')
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results_files = [f for f in split_files if f[-1].endswith('results.json')]
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print(f'find profit/benefit files: {results_files}')
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costs_dfs = [read_data(directory + '-'.join(f)) for f in costs_files]
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overload_dfs = [read_data(directory + '-'.join(f)) for f in overload_files]
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results_dfs = [read_data(directory + '-'.join(f)) for f in results_files]
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for costs_df, overload_df, results_df in zip(costs_dfs, overload_dfs, results_dfs):
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draw_cost(costs_df,
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f'plots/costs-ess-{int(costs_df.columns[0])}-{int(costs_df.columns[-1])}-pv-{int(costs_df.index[0])}-{int(costs_df.index[-1])}.png',
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title_cost=title_cost,
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annot_cost=annot_cost)
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draw_overload(overload_df,
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f'plots/overload-ess-{overload_df.columns[0]}-{overload_df.columns[-1]}-pv-{overload_df.index[0]}-{overload_df.index[-1]}.png',
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title_unmet=title_unmet,
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annot_unmet=False)
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draw_results(results_df,
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f'plots/results-ess-{results_df.columns[0]}-{results_df.columns[-1]}-pv-{results_df.index[0]}-{results_df.index[-1]}.png',
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title_benefit=title_benefit,
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annot_benefit=False)
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