230 lines
8.8 KiB
Python
230 lines
8.8 KiB
Python
"""
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多能互补系统储能容量优化计算程序使用示例
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该文件展示了如何使用储能优化程序处理不同的实际场景。
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作者: iFlow CLI
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创建日期: 2025-12-25
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"""
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import numpy as np
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import matplotlib.pyplot as plt
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from storage_optimization import optimize_storage_capacity, SystemParameters
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def example_1_basic_scenario():
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"""示例1: 基础场景"""
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print("=== 示例1: 基础场景 ===")
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# 基础数据 - 夏日典型日
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solar_output = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 2.0, 4.0, 6.0, 8.0, 9.0,
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8.0, 6.0, 4.0, 2.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
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wind_output = [4.0, 4.5, 5.0, 5.5, 5.0, 4.5, 4.0, 3.5, 3.0, 2.5, 2.0, 1.5,
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1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 5.0, 4.5, 4.0]
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thermal_output = [8.0] * 24 # 火电基荷
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load_demand = [6.0, 5.5, 5.0, 5.0, 5.5, 7.0, 9.0, 12.0, 15.0, 18.0, 20.0, 19.0,
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18.0, 17.0, 16.0, 15.0, 14.0, 13.0, 12.0, 10.0, 8.0, 7.0, 6.0, 6.0]
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# 系统参数
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params = SystemParameters(
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max_curtailment_wind=0.1, # 最大弃风率10%
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max_curtailment_solar=0.05, # 最大弃光率5%
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max_grid_ratio=0.15, # 最大上网电量比例15%
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storage_efficiency=0.9, # 储能效率90%
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discharge_rate=1.0, # 1C放电
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charge_rate=1.0 # 1C充电
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)
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# 计算最优储能容量
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result = optimize_storage_capacity(solar_output, wind_output, thermal_output, load_demand, params)
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# 打印结果
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print(f"所需储能容量: {result['required_storage_capacity']:.2f} MWh")
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print(f"实际弃风率: {result['total_curtailment_wind_ratio']:.3f} (约束: {params.max_curtailment_wind})")
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print(f"实际弃光率: {result['total_curtailment_solar_ratio']:.3f} (约束: {params.max_curtailment_solar})")
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print(f"实际上网电量比例: {result['total_grid_feed_in_ratio']:.3f} (约束: {params.max_grid_ratio})")
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print(f"能量平衡校验: {'通过' if result['energy_balance_check'] else '未通过'}")
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return result
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def example_2_high_renewable_scenario():
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"""示例2: 高可再生能源渗透场景"""
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print("\n=== 示例2: 高可再生能源渗透场景 ===")
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# 高可再生能源数据
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solar_output = [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 3.0, 6.0, 10.0, 14.0, 18.0, 20.0,
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18.0, 14.0, 10.0, 6.0, 3.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
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wind_output = [8.0, 9.0, 10.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0,
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3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 10.0, 9.0, 8.0]
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thermal_output = [4.0] * 24 # 较低的火电基荷
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load_demand = [8.0, 7.5, 7.0, 7.0, 7.5, 9.0, 11.0, 14.0, 17.0, 20.0, 22.0, 21.0,
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20.0, 19.0, 18.0, 17.0, 16.0, 15.0, 14.0, 12.0, 10.0, 9.0, 8.0, 8.0]
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# 系统参数 - 较高的弃风弃光容忍度
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params = SystemParameters(
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max_curtailment_wind=0.2, # 最大弃风率20%
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max_curtailment_solar=0.15, # 最大弃光率15%
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max_grid_ratio=0.25, # 最大上网电量比例25%
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storage_efficiency=0.85, # 较低的储能效率
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discharge_rate=1.0,
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charge_rate=1.0
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)
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result = optimize_storage_capacity(solar_output, wind_output, thermal_output, load_demand, params)
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print(f"所需储能容量: {result['required_storage_capacity']:.2f} MWh")
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print(f"实际弃风率: {result['total_curtailment_wind_ratio']:.3f}")
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print(f"实际弃光率: {result['total_curtailment_solar_ratio']:.3f}")
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print(f"实际上网电量比例: {result['total_grid_feed_in_ratio']:.3f}")
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print(f"能量平衡校验: {'通过' if result['energy_balance_check'] else '未通过'}")
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return result
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def example_3_winter_scenario():
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"""示例3: 冬季场景"""
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print("\n=== 示例3: 冬季场景 ===")
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# 冬季数据 - 光照弱，风电强，负荷高
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solar_output = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.8, 1.5, 2.0, 2.5, 2.8,
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2.5, 2.0, 1.5, 0.8, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
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wind_output = [12.0, 13.0, 14.0, 15.0, 14.0, 13.0, 12.0, 11.0, 10.0, 9.0, 8.0, 7.0,
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7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 14.0, 13.0, 12.0]
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thermal_output = [12.0] * 24 # 高火电基荷
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load_demand = [12.0, 11.5, 11.0, 11.0, 11.5, 13.0, 15.0, 18.0, 21.0, 24.0, 26.0, 25.0,
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24.0, 23.0, 22.0, 21.0, 20.0, 19.0, 18.0, 16.0, 14.0, 13.0, 12.0, 12.0]
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# 系统参数 - 严格的弃风弃光控制
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params = SystemParameters(
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max_curtailment_wind=0.05, # 严格的弃风控制
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max_curtailment_solar=0.02, # 严格的弃光控制
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max_grid_ratio=0.1, # 低上网电量比例
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storage_efficiency=0.92, # 高储能效率
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discharge_rate=1.0,
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charge_rate=1.0
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)
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result = optimize_storage_capacity(solar_output, wind_output, thermal_output, load_demand, params)
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print(f"所需储能容量: {result['required_storage_capacity']:.2f} MWh")
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print(f"实际弃风率: {result['total_curtailment_wind_ratio']:.3f}")
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print(f"实际弃光率: {result['total_curtailment_solar_ratio']:.3f}")
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print(f"实际上网电量比例: {result['total_grid_feed_in_ratio']:.3f}")
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print(f"能量平衡校验: {'通过' if result['energy_balance_check'] else '未通过'}")
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return result
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def plot_results(result, title):
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"""绘制结果图表"""
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hours = list(range(24))
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fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 10))
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fig.suptitle(title, fontsize=16)
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# 储能状态
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ax1.plot(hours, result['storage_profile'], 'b-', linewidth=2)
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ax1.set_title('储能状态 (MWh)')
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ax1.set_xlabel('时间 (小时)')
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ax1.set_ylabel('储能容量 (MWh)')
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ax1.grid(True)
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# 充放电功率
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ax2.plot(hours, result['charge_profile'], 'g-', label='充电', linewidth=2)
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ax2.plot(hours, [-p for p in result['discharge_profile']], 'r-', label='放电', linewidth=2)
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ax2.set_title('储能充放电功率 (MW)')
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ax2.set_xlabel('时间 (小时)')
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ax2.set_ylabel('功率 (MW)')
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ax2.legend()
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ax2.grid(True)
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# 弃风弃光
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ax3.plot(hours, result['curtailed_wind'], 'c-', label='弃风', linewidth=2)
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ax3.plot(hours, result['curtailed_solar'], 'm-', label='弃光', linewidth=2)
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ax3.set_title('弃风弃光量 (MW)')
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ax3.set_xlabel('时间 (小时)')
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ax3.set_ylabel('功率 (MW)')
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ax3.legend()
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ax3.grid(True)
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# 上网电量
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ax4.plot(hours, result['grid_feed_in'], 'orange', linewidth=2)
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ax4.set_title('上网电量 (MW)')
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ax4.set_xlabel('时间 (小时)')
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ax4.set_ylabel('功率 (MW)')
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ax4.grid(True)
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plt.tight_layout()
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plt.show()
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def compare_scenarios():
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"""比较不同场景的结果"""
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print("\n=== 场景比较 ===")
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# 运行三个场景
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result1 = example_1_basic_scenario()
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result2 = example_2_high_renewable_scenario()
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result3 = example_3_winter_scenario()
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# 比较结果
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scenarios = ['基础场景', '高可再生能源场景', '冬季场景']
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storage_capacities = [
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result1['required_storage_capacity'],
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result2['required_storage_capacity'],
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result3['required_storage_capacity']
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]
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curtailment_wind = [
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result1['total_curtailment_wind_ratio'],
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result2['total_curtailment_wind_ratio'],
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result3['total_curtailment_wind_ratio']
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]
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curtailment_solar = [
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result1['total_curtailment_solar_ratio'],
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result2['total_curtailment_solar_ratio'],
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result3['total_curtailment_solar_ratio']
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]
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grid_feed_in = [
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result1['total_grid_feed_in_ratio'],
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result2['total_grid_feed_in_ratio'],
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result3['total_grid_feed_in_ratio']
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]
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print("\n场景比较结果:")
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print(f"{'场景':<15} {'储能容量(MWh)':<12} {'弃风率':<8} {'弃光率':<8} {'上网比例':<8}")
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print("-" * 55)
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for i, scenario in enumerate(scenarios):
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print(f"{scenario:<15} {storage_capacities[i]:<12.2f} {curtailment_wind[i]:<8.3f} "
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f"{curtailment_solar[i]:<8.3f} {grid_feed_in[i]:<8.3f}")
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return result1, result2, result3
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if __name__ == "__main__":
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print("多能互补系统储能容量优化计算程序示例")
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print("=" * 50)
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# 运行示例
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result1, result2, result3 = compare_scenarios()
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# 绘制图表（如果matplotlib可用）
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try:
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plot_results(result1, "基础场景储能运行情况")
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plot_results(result2, "高可再生能源场景储能运行情况")
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plot_results(result3, "冬季场景储能运行情况")
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except ImportError:
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print("\n注意: matplotlib未安装，无法绘制图表")
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print("要安装matplotlib，请运行: pip install matplotlib")
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print("\n示例运行完成！")
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