71 lines
2.5 KiB
Python
71 lines
2.5 KiB
Python
import matplotlib.pyplot as plt
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import seaborn as sns
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import numpy as np
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import pandas as pd
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from EnergySystem import EnergySystem
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from config import pv_config, grid_config, ess_config
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if __name__ == '__main__':
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data = pd.read_csv('combined_data.csv')
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time_interval = 15 / 60
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pv_loss = 0.95
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pv_cost_per_kW = 200
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pv_base = 50000
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pv_lifetime = 25
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ess_loss = 0.95
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ess_cost_per_kW = 300
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ess_base = 50000
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ess_lifetime = 25
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grid_loss = 0.95
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sell_price = 0.4 #kWh
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grid_capacity = 5000 #kWh
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pv_step=10000
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ess_step=10000
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pv_capacities = np.linspace(50000, 150000, 11)
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ess_capacities = np.linspace(50000, 150000, 11)
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results = pd.DataFrame(index=pv_capacities, columns = ess_capacities)
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for pv_capacity in pv_capacities:
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print(f"pv_capacity:{pv_capacity}")
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for ess_capacity in ess_capacities:
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print(f"ess_capacity:{ess_capacity}")
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pv = pv_config(capacity=pv_capacity,
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cost_per_kW=pv_cost_per_kW,
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lifetime=pv_lifetime,
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loss=pv_loss)
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ess = ess_config(capacity=ess_capacity,
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cost_per_kW=ess_cost_per_kW,
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lifetime=ess_lifetime,
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loss=ess_loss,
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charge_power=ess_capacity,
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discharge_power=ess_capacity)
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grid = grid_config(capacity=grid_capacity,
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grid_loss=grid_loss,
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sell_price= sell_price)
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energySystem = EnergySystem(pv_type=pv,
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ess_type=ess,
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grid_type= grid)
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benefit = energySystem.simulate(data, time_interval)
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results.loc[pv_capacity,ess_capacity] = benefit
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results = results.astype(float)
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plt.figure(figsize=(10, 8)) # 设置图形大小
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sns.heatmap(results, annot=True, fmt=".1f", cmap='viridis')
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plt.title('Benefit Heatmap Based on PV and ESS Capacities')
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plt.xlabel('ESS Capacity (kWh)')
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plt.ylabel('PV Capacity (kW)')
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plt.show()
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# pv = pv_config(capacity=100000,cost_per_kW=200,lifetime=25,loss=0.95)
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# ess = ess_config(capacity=100000,cost_per_kW=300,lifetime=25,loss=0.95,charge_power=100000,discharge_power=100000)
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# grid = grid_config(price_schedule=price_schedule, capacity=5000, grid_loss=0.95, sell_price=0.4)
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# grid = grid_config(capacity=50000, grid_loss=0.95, sell_price=0.4)
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# print(benefit) |