151 lines
7.0 KiB
Python
151 lines
7.0 KiB
Python
import os
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import pickle
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import matplotlib
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import seaborn as sns
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matplotlib.rc('text', usetex=True)
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pd.options.display.notebook_repr_html = False
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def plot_optimization_result(datasource, directory): # data source is dataframe
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sns.set_theme(style='whitegrid') # "white", "dark", "whitegrid", "darkgrid", "ticks"
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plt.rcParams["figure.figsize"] = (16, 9)
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fig, axs = plt.subplots(2, 2)
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plt.subplots_adjust(wspace=0.7, hspace=0.3)
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plt.autoscale(tight=True)
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T = np.array([i for i in range(24)])
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# plot step cost in ax[0]
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axs[0, 0].cla()
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axs[0, 0].set_ylabel('Costs')
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axs[0, 0].set_xlabel('Time(h)')
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axs[0, 0].bar(T, datasource['step_cost'])
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# axs[0,0].set_xticks([i for i in range(24)],[i for i in range(1,25)])
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# plot soc and price in ax[1]
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axs[0, 1].cla()
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axs[0, 1].set_ylabel('Price')
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axs[0, 1].set_xlabel('Time(h)')
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axs[0, 1].plot(T, datasource['price'], drawstyle='steps-mid', label='Price', color='pink')
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axs[0, 1] = axs[0, 1].twinx()
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axs[0, 1].set_ylabel('SOC')
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axs[0, 1].plot(T, datasource['soc'], drawstyle='steps-mid', label='SOC', color='grey')
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# axs[0,1].set_xticks([i for i in range(24)],[i for i in range(1,25)])
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axs[0, 1].legend(loc='upper right', fontsize=12, frameon=False, labelspacing=0.3)
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# plot accumulated generation and consumption in ax[2]
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axs[1, 0].cla()
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axs[1, 0].set_ylabel('Outputs of DGs and Battery')
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axs[1, 0].set_xlabel('Time(h)')
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battery_positive = np.array(datasource['battery_energy_change'])
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battery_negative = np.array(datasource['battery_energy_change'])
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battery_negative = np.minimum(battery_negative, 0) # discharge
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battery_positive = np.maximum(battery_positive, 0) # charge
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# deal with power exchange within the figure
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imported_from_grid = np.array(datasource['grid_import'])
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exported_2_grid = np.array(datasource['grid_export'])
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axs[1, 0].bar(T, datasource['gen1'], label='Gen1')
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axs[1, 0].bar(T, datasource['gen2'], label='Gen2', bottom=datasource['gen1'])
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axs[1, 0].bar(T, datasource['gen3'], label='Gen3', bottom=datasource['gen2'] + datasource['gen1'])
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axs[1, 0].bar(T, -battery_positive, color='blue', hatch='/', label='ESS charge')
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axs[1, 0].bar(T, -battery_negative, hatch='/', label='ESS discharge',
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bottom=datasource['gen3'] + datasource['gen2'] + datasource['gen1'])
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# import as generate
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axs[1, 0].bar(T, imported_from_grid, label='Grid import',
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bottom=-battery_negative + datasource['gen3'] + datasource['gen2'] + datasource['gen1'])
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# export as load
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axs[1, 0].bar(T, -exported_2_grid, label='Grid export', bottom=-battery_positive)
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axs[1, 0].plot(T, datasource['netload'], label='Netload', drawstyle='steps-mid', alpha=0.7)
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axs[1, 0].legend(loc='upper right', fontsize=12, frameon=False, labelspacing=0.3)
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# axs[1,0].set_xticks([i for i in range(24)],[i for i in range(1,25)])
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fig.savefig(f"{directory}/optimization_information.svg", format='svg', dpi=600, bbox_inches='tight')
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print('optimization results have been ploted and saved')
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def plot_evaluation_information(datasource, directory):
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sns.set_theme(style='whitegrid')
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with open(datasource, 'rb') as tf:
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test_data = pickle.load(tf)
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# plot unbalance, and reward of each step by bar figures
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plt.rcParams["figure.figsize"] = (16, 9)
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fig, axs = plt.subplots(2, 2)
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plt.subplots_adjust(wspace=0.7, hspace=0.3)
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plt.autoscale(tight=True)
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# prepare data for evaluation the environment here
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eval_data = pd.DataFrame(test_data['system_info'])
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eval_data.columns = ['time_step', 'price', 'netload', 'action', 'real_action', 'soc', 'battery', 'gen1', 'gen2',
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'gen3', 'temperature', 'irradiance', 'unbalance', 'operation_cost']
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# plot unbalance in axs[0]
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axs[0, 0].cla()
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axs[0, 0].set_ylabel('Unbalance of Generation and Load')
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axs[0, 0].bar(eval_data['time_step'], eval_data['unbalance'], label='Exchange with Grid', width=0.4)
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axs[0, 0].bar(eval_data['time_step'] + 0.4, eval_data['netload'], label='Netload', width=0.4)
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axs[0, 0].legend(loc='upper right', fontsize=12, frameon=False, labelspacing=0.5)
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# axs[0,0].set_xticks([i for i in range(24)],[i for i in range(1,25)])
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# plot energy charge/discharge with price in ax[1]
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axs[0, 1].cla()
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axs[0, 1].set_ylabel('Price')
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axs[0, 1].set_xlabel('Time Steps')
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axs[0, 1].plot(eval_data['time_step'], eval_data['price'], drawstyle='steps-mid', label='Price', color='pink')
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axs[0, 1] = axs[0, 1].twinx()
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axs[0, 1].set_ylabel('SOC')
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# axs[0,1].set_xticks([i for i in range(24)], [i for i in range(1, 25)])
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axs[0, 1].plot(eval_data['time_step'], eval_data['soc'], drawstyle='steps-mid', label='SOC', color='grey')
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axs[0, 1].legend(loc='upper right', fontsize=12, frameon=False, labelspacing=0.3)
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# plot generation and netload in ax[2]
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axs[1, 0].cla()
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axs[1, 0].set_ylabel('Outputs of Units and Netload (kWh)')
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# axs[1,0].set_xticks([i for i in range(24)], [i for i in range(1, 25)])
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battery_positive = np.array(eval_data['battery'])
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battery_negative = np.array(eval_data['battery'])
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battery_positive = np.maximum(battery_positive, 0) # charge
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battery_negative = np.minimum(battery_negative, 0) # discharge
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# deal with power exchange within the figure
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imported_from_grid = np.minimum(np.array(eval_data['unbalance']), 0)
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exported_2_grid = np.maximum(np.array(eval_data['unbalance']), 0)
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x = eval_data['time_step']
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axs[1, 0].bar(x, eval_data['gen1'], label='Gen1')
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axs[1, 0].bar(x, eval_data['gen2'], label='Gen2', bottom=eval_data['gen1'])
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axs[1, 0].bar(x, eval_data['gen3'], label='Gen3', bottom=eval_data['gen1'] + eval_data['gen2'])
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axs[1, 0].bar(x, -battery_positive, color='blue', hatch='/', label='ESS charge')
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axs[1, 0].bar(x, -battery_negative, label='ESS discharge', hatch='/',
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bottom=eval_data['gen1'] + eval_data['gen2'] + eval_data['gen3'])
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axs[1, 0].bar(x, -imported_from_grid, label='Grid import',
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bottom=eval_data['gen1'] + eval_data['gen2'] + eval_data['gen3'] - battery_negative)
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axs[1, 0].bar(x, -exported_2_grid, label='Grid export', bottom=-battery_positive)
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axs[1, 0].plot(x, eval_data['netload'], drawstyle='steps-mid', label='Netload')
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axs[1, 0].legend(loc='upper right', fontsize=12, frameon=False, labelspacing=0.3)
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# plot reward in axs[3]
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axs[1, 1].cla()
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axs[1, 1].set_ylabel('Costs')
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axs[1, 1].bar(eval_data['time_step'], eval_data['operation_cost'])
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fig.savefig(f"{directory}/evaluation_information.svg", format='svg', dpi=600, bbox_inches='tight')
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print('evaluation figure have been ploted and saved')
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def make_dir(directory, feature_change):
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cwd = f'{directory}/DRL_{feature_change}_plots'
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os.makedirs(cwd, exist_ok=True)
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return cwd
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class PlotArgs:
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def __init__(self) -> None:
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self.cwd = None
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self.feature_change = None
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self.plot_on = None
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