471 lines
20 KiB
Python
471 lines
20 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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# 配置matplotlib支持中文显示
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plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
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plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
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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 {
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'result': result,
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'solar_output': solar_output,
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'wind_output': wind_output,
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'thermal_output': thermal_output,
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'load_demand': load_demand
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}
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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 {
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'result': result,
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'solar_output': solar_output,
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'wind_output': wind_output,
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'thermal_output': thermal_output,
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'load_demand': load_demand
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}
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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 {
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'result': result,
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'solar_output': solar_output,
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'wind_output': wind_output,
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'thermal_output': thermal_output,
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'load_demand': load_demand
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}
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def plot_results(result, title, solar_output, wind_output, thermal_output, load_demand):
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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), (ax5, ax6)) = plt.subplots(3, 2, figsize=(16, 12))
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fig.suptitle(title, fontsize=16)
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# 发电与负荷对比
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ax1.plot(hours, load_demand, 'r-', linewidth=2, label='负荷需求')
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ax1.plot(hours, thermal_output, 'b-', linewidth=2, label='火电出力')
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ax1.plot(hours, wind_output, 'g-', linewidth=2, label='风电出力')
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ax1.plot(hours, solar_output, 'orange', linewidth=2, label='光伏出力')
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# 计算总发电量
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total_generation = [thermal_output[i] + wind_output[i] + solar_output[i] for i in range(24)]
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ax1.plot(hours, total_generation, 'k--', linewidth=1.5, alpha=0.7, label='总发电量')
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ax1.set_title('发电与负荷曲线')
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ax1.set_xlabel('时间 (小时)')
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ax1.set_ylabel('功率 (MW)')
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ax1.legend()
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ax1.grid(True)
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# 储能状态
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ax2.plot(hours, result['storage_profile'], 'b-', linewidth=2)
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ax2.set_title('储能状态 (MWh)')
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ax2.set_xlabel('时间 (小时)')
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ax2.set_ylabel('储能容量 (MWh)')
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ax2.grid(True)
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# 充放电功率
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ax3.plot(hours, result['charge_profile'], 'g-', label='充电', linewidth=2)
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ax3.plot(hours, [-p for p in result['discharge_profile']], 'r-', label='放电', linewidth=2)
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ax3.set_title('储能充放电功率 (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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# 弃风弃光
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ax4.plot(hours, result['curtailed_wind'], 'c-', label='弃风', linewidth=2)
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ax4.plot(hours, result['curtailed_solar'], 'm-', label='弃光', linewidth=2)
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ax4.set_title('弃风弃光量 (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.legend()
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ax4.grid(True)
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# 上网电量/购电量
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ax5.plot(hours, result['grid_feed_in'], 'orange', linewidth=2)
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ax5.axhline(y=0, color='black', linestyle='-', linewidth=0.5, alpha=0.5)
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ax5.fill_between(hours, 0, result['grid_feed_in'],
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where=[x >= 0 for x in result['grid_feed_in']],
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alpha=0.3, color='green', label='上网')
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ax5.fill_between(hours, 0, result['grid_feed_in'],
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where=[x < 0 for x in result['grid_feed_in']],
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alpha=0.3, color='red', label='购电')
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# 动态设置标题
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total_grid = sum(result['grid_feed_in'])
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if total_grid < 0:
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ax5.set_title(f'购电量 (总计: {abs(total_grid):.1f} MWh)')
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else:
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ax5.set_title(f'上网电量 (总计: {total_grid:.1f} MWh)')
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ax5.set_xlabel('时间 (小时)')
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ax5.set_ylabel('功率 (MW)')
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ax5.legend()
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ax5.grid(True)
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# 能量平衡分析
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total_gen = sum(thermal_output) + sum(wind_output) + sum(solar_output)
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total_load = sum(load_demand)
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total_curtailed = sum(result['curtailed_wind']) + sum(result['curtailed_solar'])
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total_grid = sum(result['grid_feed_in'])
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total_charge = sum(result['charge_profile'])
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total_discharge = sum(result['discharge_profile'])
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# 创建能量平衡柱状图
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categories = ['总发电量', '总负荷', '弃风弃光', '上网电量', '储能充电', '储能放电']
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values = [total_gen, total_load, total_curtailed, total_grid, total_charge, total_discharge]
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colors = ['blue', 'red', 'orange', 'green', 'cyan', 'magenta']
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bars = ax6.bar(categories, values, color=colors, alpha=0.7)
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ax6.set_title('能量平衡分析')
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ax6.set_ylabel('能量 (MWh)')
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ax6.grid(True, axis='y', alpha=0.3)
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# 在柱状图上添加数值标签
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for bar, value in zip(bars, values):
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height = bar.get_height()
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ax6.text(bar.get_x() + bar.get_width()/2., height,
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f'{value:.1f}', ha='center', va='bottom', fontsize=9)
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plt.tight_layout()
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plt.show()
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def example_5_high_load_grid_purchase_scenario():
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"""示例5: 高负荷购电场景"""
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print("\n=== 示例5: 高负荷购电场景 ===")
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# 高负荷场景数据 - 有充电和放电时段
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solar_output = [0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 4.0, 6.0, 8.0, 10.0, 9.0, 8.0,
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7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0]
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wind_output = [5.0, 5.5, 6.0, 6.5, 6.0, 5.5, 5.0, 4.5, 4.0, 3.5, 3.0, 2.5,
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2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 5.5, 5.0, 5.0]
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thermal_output = [8.0] * 24 # 火电基荷
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# 负荷曲线：夜间低负荷（充电时段），白天高负荷（放电和购电时段）
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load_demand = [10.0, 9.0, 8.0, 7.0, 8.0, 12.0, 18.0, 25.0, 35.0, 42.0, 45.0, 43.0,
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40.0, 38.0, 35.0, 30.0, 25.0, 20.0, 15.0, 12.0, 11.0, 10.0, 10.0, 10.0]
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# 系统参数 - 允许从电网购电（负的上网电量）
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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=-0.3, # 负值表示允许从电网购电，最大购电比例30%
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storage_efficiency=0.9, # 储能效率90%
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discharge_rate=2.0, # 2C放电，满足高峰需求
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charge_rate=1.0, # 1C充电
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max_storage_capacity=8.0 # 限制储能容量为8MWh，确保储能被充分利用
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)
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result = optimize_storage_capacity(solar_output, wind_output, thermal_output, load_demand, params, tolerance=0.1)
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print(f"所需储能容量: {result['required_storage_capacity']:.2f} MWh")
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print(f"储能容量上限: {result['max_storage_limit']:.2f} MWh")
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print(f"是否达到容量上限: {'是' if result['capacity_limit_reached'] else '否'}")
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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} (负值表示购电)")
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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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# 调试信息
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total_gen = sum(solar_output) + sum(wind_output) + sum(thermal_output)
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total_load = sum(load_demand)
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total_charge = sum(result['charge_profile'])
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total_discharge = sum(result['discharge_profile'])
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print(f"\n=== 调试信息 ===")
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print(f"总发电量: {total_gen:.2f} MWh")
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print(f"总负荷: {total_load:.2f} MWh")
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print(f"负荷-发电差: {total_load - total_gen:.2f} MWh")
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print(f"总充电量: {total_charge:.2f} MWh")
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print(f"总放电量: {total_discharge:.2f} MWh")
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print(f"储能净变化: {total_discharge - total_charge:.2f} MWh")
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# 计算购电量统计
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total_grid_feed = sum(result['grid_feed_in'])
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if total_grid_feed < 0:
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print(f"总购电量: {abs(total_grid_feed):.2f} MWh")
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# 显示前几个小时的详细情况
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print(f"\n前6小时详细情况:")
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print(f"小时 | 发电 | 负荷 | 储能充电 | 储能放电 | 购电")
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print("-" * 55)
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for i in range(6):
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gen = solar_output[i] + wind_output[i] + thermal_output[i]
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charge = result['charge_profile'][i]
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discharge = result['discharge_profile'][i]
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grid = result['grid_feed_in'][i]
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print(f"{i:2d} | {gen:4.1f} | {load_demand[i]:4.1f} | {charge:7.2f} | {discharge:7.2f} | {grid:5.2f}")
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# 计算最大缺电功率
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max_deficit = 0
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for hour in range(24):
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total_gen = solar_output[hour] + wind_output[hour] + thermal_output[hour]
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deficit = max(0, load_demand[hour] - total_gen - result['discharge_profile'][hour])
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max_deficit = max(max_deficit, deficit)
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if max_deficit > 0:
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print(f"\n最大缺电功率: {max_deficit:.2f} MW")
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return {
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'result': result,
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'solar_output': solar_output,
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'wind_output': wind_output,
|
||
'thermal_output': thermal_output,
|
||
'load_demand': load_demand
|
||
}
|
||
|
||
|
||
def example_4_capacity_limited_scenario():
|
||
"""示例4: 储能容量限制场景"""
|
||
print("\n=== 示例4: 储能容量限制场景 ===")
|
||
|
||
# 使用基础场景的数据
|
||
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
|
||
|
||
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 - 设置储能容量上限为10 MWh
|
||
params = SystemParameters(
|
||
max_curtailment_wind=0.1,
|
||
max_curtailment_solar=0.05,
|
||
max_grid_ratio=0.15,
|
||
storage_efficiency=0.9,
|
||
discharge_rate=1.0,
|
||
charge_rate=1.0,
|
||
max_storage_capacity=10.0 # 限制储能容量上限为10 MWh
|
||
)
|
||
|
||
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"储能容量上限: {result['max_storage_limit']:.2f} MWh")
|
||
print(f"是否达到容量上限: {'是' if result['capacity_limit_reached'] else '否'}")
|
||
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': result,
|
||
'solar_output': solar_output,
|
||
'wind_output': wind_output,
|
||
'thermal_output': thermal_output,
|
||
'load_demand': load_demand
|
||
}
|
||
|
||
|
||
def compare_scenarios():
|
||
"""比较不同场景的结果"""
|
||
print("\n=== 场景比较 ===")
|
||
|
||
# 运行五个场景
|
||
data1 = example_1_basic_scenario()
|
||
data2 = example_2_high_renewable_scenario()
|
||
data3 = example_3_winter_scenario()
|
||
data4 = example_4_capacity_limited_scenario()
|
||
data5 = example_5_high_load_grid_purchase_scenario()
|
||
|
||
# 比较结果
|
||
scenarios = ['基础场景', '高可再生能源场景', '冬季场景', '容量限制场景', '高负荷购电场景']
|
||
storage_capacities = [
|
||
data1['result']['required_storage_capacity'],
|
||
data2['result']['required_storage_capacity'],
|
||
data3['result']['required_storage_capacity'],
|
||
data4['result']['required_storage_capacity'],
|
||
data5['result']['required_storage_capacity']
|
||
]
|
||
curtailment_wind = [
|
||
data1['result']['total_curtailment_wind_ratio'],
|
||
data2['result']['total_curtailment_wind_ratio'],
|
||
data3['result']['total_curtailment_wind_ratio'],
|
||
data4['result']['total_curtailment_wind_ratio'],
|
||
data5['result']['total_curtailment_wind_ratio']
|
||
]
|
||
curtailment_solar = [
|
||
data1['result']['total_curtailment_solar_ratio'],
|
||
data2['result']['total_curtailment_solar_ratio'],
|
||
data3['result']['total_curtailment_solar_ratio'],
|
||
data4['result']['total_curtailment_solar_ratio'],
|
||
data5['result']['total_curtailment_solar_ratio']
|
||
]
|
||
grid_feed_in = [
|
||
data1['result']['total_grid_feed_in_ratio'],
|
||
data2['result']['total_grid_feed_in_ratio'],
|
||
data3['result']['total_grid_feed_in_ratio'],
|
||
data4['result']['total_grid_feed_in_ratio'],
|
||
data5['result']['total_grid_feed_in_ratio']
|
||
]
|
||
capacity_limit = [
|
||
'无',
|
||
'无',
|
||
'无',
|
||
f"{data4['result']['max_storage_limit']:.1f}MWh",
|
||
f"{data5['result']['max_storage_limit']:.1f}MWh"
|
||
]
|
||
|
||
print("\n场景比较结果:")
|
||
print(f"{'场景':<15} {'储能容量(MWh)':<12} {'容量限制':<10} {'弃风率':<8} {'弃光率':<8} {'上网/购电':<8}")
|
||
print("-" * 80)
|
||
for i, scenario in enumerate(scenarios):
|
||
grid_text = f"{grid_feed_in[i]:.3f}" if grid_feed_in[i] >= 0 else f"{grid_feed_in[i]:.3f}↓"
|
||
limit_reached = "*" if (data4['result']['capacity_limit_reached'] and i == 3) or (data5['result']['capacity_limit_reached'] and i == 4) else ""
|
||
print(f"{scenario:<15} {storage_capacities[i]:<12.2f} {capacity_limit[i]:<10} {curtailment_wind[i]:<8.3f} "
|
||
f"{curtailment_solar[i]:<8.3f} {grid_text:<8} {limit_reached}")
|
||
|
||
return data1, data2, data3, data4, data5
|
||
|
||
|
||
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
|
||
data1, data2, data3, data4, data5 = compare_scenarios()
|
||
|
||
# 绘制图表（如果matplotlib可用）
|
||
try:
|
||
plot_results(data1['result'], "基础场景储能运行情况",
|
||
data1['solar_output'], data1['wind_output'],
|
||
data1['thermal_output'], data1['load_demand'])
|
||
plot_results(data2['result'], "高可再生能源场景储能运行情况",
|
||
data2['solar_output'], data2['wind_output'],
|
||
data2['thermal_output'], data2['load_demand'])
|
||
plot_results(data3['result'], "冬季场景储能运行情况",
|
||
data3['solar_output'], data3['wind_output'],
|
||
data3['thermal_output'], data3['load_demand'])
|
||
plot_results(data4['result'], "容量限制场景储能运行情况",
|
||
data4['solar_output'], data4['wind_output'],
|
||
data4['thermal_output'], data4['load_demand'])
|
||
plot_results(data5['result'], "高负荷购电场景储能运行情况",
|
||
data5['solar_output'], data5['wind_output'],
|
||
data5['thermal_output'], data5['load_demand'])
|
||
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！")
|