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