重构了目录结构

main.py

@@ -13,6 +13,10 @@ import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 from datetime import datetime
+import sys
+import os
+sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))
+
 from storage_optimization import optimize_storage_capacity, SystemParameters
 from excel_reader import read_excel_data, create_excel_template, analyze_excel_data
 

scripts/example_usage.py

@@ -0,0 +1,533 @@
+"""
+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 sys
+import os
+sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'src'))
+
+import numpy as np
+import matplotlib.pyplot as plt
+from storage_optimization import optimize_storage_capacity, SystemParameters
+
+# 配置matplotlib支持中文显示
+plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
+plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题
+
+
+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': result,
+        'solar_output': solar_output,
+        'wind_output': wind_output,
+        'thermal_output': thermal_output,
+        'load_demand': load_demand
+    }
+
+
+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': result,
+        'solar_output': solar_output,
+        'wind_output': wind_output,
+        'thermal_output': thermal_output,
+        'load_demand': load_demand
+    }
+
+
+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': result,
+        'solar_output': solar_output,
+        'wind_output': wind_output,
+        'thermal_output': thermal_output,
+        'load_demand': load_demand
+    }
+
+
+def plot_results(result, title, solar_output, wind_output, thermal_output, load_demand):
+    """绘制结果图表"""
+    hours = list(range(24))
+    
+    fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(3, 2, figsize=(16, 12))
+    fig.suptitle(title, fontsize=16)
+    
+    # 发电与负荷对比
+    ax1.plot(hours, load_demand, 'r-', linewidth=2, label='负荷需求')
+    ax1.plot(hours, thermal_output, 'b-', linewidth=2, label='火电出力')
+    ax1.plot(hours, wind_output, 'g-', linewidth=2, label='风电出力')
+    ax1.plot(hours, solar_output, 'orange', linewidth=2, label='光伏出力')
+    
+    # 计算总发电量
+    total_generation = [thermal_output[i] + wind_output[i] + solar_output[i] for i in range(24)]
+    ax1.plot(hours, total_generation, 'k--', linewidth=1.5, alpha=0.7, label='总发电量')
+    
+    ax1.set_title('发电与负荷曲线')
+    ax1.set_xlabel('时间 (小时)')
+    ax1.set_ylabel('功率 (MW)')
+    ax1.legend()
+    ax1.grid(True)
+    
+    # 储能状态
+    ax2.plot(hours, result['storage_profile'], 'b-', linewidth=2)
+    ax2.set_title('储能状态 (MWh)')
+    ax2.set_xlabel('时间 (小时)')
+    ax2.set_ylabel('储能容量 (MWh)')
+    ax2.grid(True)
+    
+    # 充放电功率
+    ax3.plot(hours, result['charge_profile'], 'g-', label='充电', linewidth=2)
+    ax3.plot(hours, [-p for p in result['discharge_profile']], 'r-', label='放电', linewidth=2)
+    ax3.set_title('储能充放电功率 (MW)')
+    ax3.set_xlabel('时间 (小时)')
+    ax3.set_ylabel('功率 (MW)')
+    ax3.legend()
+    ax3.grid(True)
+    
+    # 弃风弃光
+    ax4.plot(hours, result['curtailed_wind'], 'c-', label='弃风', linewidth=2)
+    ax4.plot(hours, result['curtailed_solar'], 'm-', label='弃光', linewidth=2)
+    ax4.set_title('弃风弃光量 (MW)')
+    ax4.set_xlabel('时间 (小时)')
+    ax4.set_ylabel('功率 (MW)')
+    ax4.legend()
+    ax4.grid(True)
+    
+    # 上网电量/购电量
+    ax5.plot(hours, result['grid_feed_in'], 'orange', linewidth=2)
+    ax5.axhline(y=0, color='black', linestyle='-', linewidth=0.5, alpha=0.5)
+    ax5.fill_between(hours, 0, result['grid_feed_in'], 
+                     where=[x >= 0 for x in result['grid_feed_in']], 
+                     alpha=0.3, color='green', label='上网')
+    ax5.fill_between(hours, 0, result['grid_feed_in'], 
+                     where=[x < 0 for x in result['grid_feed_in']], 
+                     alpha=0.3, color='red', label='购电')
+    
+    # 动态设置标题
+    total_grid = sum(result['grid_feed_in'])
+    if total_grid < 0:
+        ax5.set_title(f'购电量 (总计: {abs(total_grid):.1f} MWh)')
+    else:
+        ax5.set_title(f'上网电量 (总计: {total_grid:.1f} MWh)')
+    
+    ax5.set_xlabel('时间 (小时)')
+    ax5.set_ylabel('功率 (MW)')
+    ax5.legend()
+    ax5.grid(True)
+    
+    # 能量平衡分析
+    total_gen = sum(thermal_output) + sum(wind_output) + sum(solar_output)
+    total_load = sum(load_demand)
+    total_curtailed = sum(result['curtailed_wind']) + sum(result['curtailed_solar'])
+    total_grid = sum(result['grid_feed_in'])
+    total_charge = sum(result['charge_profile'])
+    total_discharge = sum(result['discharge_profile'])
+    
+    # 创建能量平衡柱状图
+    categories = ['总发电量', '总负荷', '弃风弃光', '上网电量', '储能充电', '储能放电']
+    values = [total_gen, total_load, total_curtailed, total_grid, total_charge, total_discharge]
+    colors = ['blue', 'red', 'orange', 'green', 'cyan', 'magenta']
+    
+    bars = ax6.bar(categories, values, color=colors, alpha=0.7)
+    ax6.set_title('能量平衡分析')
+    ax6.set_ylabel('能量 (MWh)')
+    ax6.grid(True, axis='y', alpha=0.3)
+    
+    # 在柱状图上添加数值标签
+    for bar, value in zip(bars, values):
+        height = bar.get_height()
+        ax6.text(bar.get_x() + bar.get_width()/2., height,
+                f'{value:.1f}', ha='center', va='bottom', fontsize=9)
+    
+    plt.tight_layout()
+    plt.show()
+
+
+def example_5_high_load_grid_purchase_scenario():
+    """示例5: 高负荷购电场景"""
+    print("\n=== 示例5: 高负荷购电场景 ===")
+    
+    # 高负荷场景数据 - 有充电和放电时段
+    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,
+                   7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0]
+    
+    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,
+                  2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 5.5, 5.0, 5.0]
+    
+    thermal_output = [8.0] * 24  # 火电基荷
+    
+    # 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）
+    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,
+                  40.0, 38.0, 35.0, 30.0, 25.0, 20.0, 15.0, 12.0, 11.0, 10.0, 10.0, 10.0]
+    
+    # 系统参数 - max_grid_ratio只限制上网电量比例，不限制购电
+    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=0.3,          # 上网电量比例限制为30%，但不限制购电
+        storage_efficiency=0.9,      # 储能效率90%
+        discharge_rate=2.0,          # 2C放电，满足高峰需求
+        charge_rate=1.0,             # 1C充电
+        max_storage_capacity=8.0      # 限制储能容量为8MWh，确保储能被充分利用
+    )
+    
+    result = optimize_storage_capacity(solar_output, wind_output, thermal_output, load_demand, params, tolerance=0.1)
+    
+    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} (负值表示购电，正值表示上网)")
+    print(f"能量平衡校验: {'通过' if result['energy_balance_check'] else '未通过'}")
+    
+    # 调试信息
+    total_gen = sum(solar_output) + sum(wind_output) + sum(thermal_output)
+    total_load = sum(load_demand)
+    total_charge = sum(result['charge_profile'])
+    total_discharge = sum(result['discharge_profile'])
+    
+    print(f"\n=== 调试信息 ===")
+    print(f"总发电量: {total_gen:.2f} MWh")
+    print(f"总负荷: {total_load:.2f} MWh")
+    print(f"负荷-发电差: {total_load - total_gen:.2f} MWh")
+    print(f"总充电量: {total_charge:.2f} MWh")
+    print(f"总放电量: {total_discharge:.2f} MWh")
+    print(f"储能净变化: {total_discharge - total_charge:.2f} MWh")
+    
+    # 计算购电量统计
+    total_grid_feed = sum(result['grid_feed_in'])
+    if total_grid_feed < 0:
+        print(f"总购电量: {abs(total_grid_feed):.2f} MWh")
+    
+    # 显示前几个小时的详细情况
+    print(f"\n前6小时详细情况:")
+    print(f"小时 | 发电 | 负荷 | 储能充电 | 储能放电 | 购电")
+    print("-" * 55)
+    for i in range(6):
+        gen = solar_output[i] + wind_output[i] + thermal_output[i]
+        charge = result['charge_profile'][i]
+        discharge = result['discharge_profile'][i]
+        grid = result['grid_feed_in'][i]
+        print(f"{i:2d}   | {gen:4.1f} | {load_demand[i]:4.1f} | {charge:7.2f} | {discharge:7.2f} | {grid:5.2f}")
+    
+    # 计算最大缺电功率
+    max_deficit = 0
+    for hour in range(24):
+        total_gen = solar_output[hour] + wind_output[hour] + thermal_output[hour]
+        deficit = max(0, load_demand[hour] - total_gen - result['discharge_profile'][hour])
+        max_deficit = max(max_deficit, deficit)
+    
+    if max_deficit > 0:
+        print(f"\n最大缺电功率: {max_deficit:.2f} MW")
+    
+    return {
+        'result': result,
+        'solar_output': solar_output,
+        'wind_output': wind_output,
+        'thermal_output': thermal_output,
+        'load_demand': load_demand
+    }
+
+
+def example_6_grid_ratio_limited_scenario():
+    """示例6: 上网电量比例限制场景"""
+    print("\n=== 示例6: 上网电量比例限制场景 ===")
+    
+    # 高可再生能源场景 - 有大量盈余电力
+    solar_output = [0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 5.0, 8.0, 12.0, 16.0, 20.0, 18.0,
+                   15.0, 12.0, 8.0, 5.0, 2.0, 0.0, 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 = [6.0] * 24  # 中等火电出力
+    
+    # 低负荷场景 - 有大量盈余电力
+    load_demand = [8.0, 7.0, 6.0, 6.0, 7.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 18.0,
+                  16.0, 14.0, 12.0, 10.0, 9.0, 8.0, 7.0, 6.0, 6.0, 7.0, 8.0, 8.0]
+    
+    # 系统参数 - 限制上网电量比例
+    params = SystemParameters(
+        max_curtailment_wind=0.15,   # 允许一定弃风
+        max_curtailment_solar=0.1,   # 允许一定弃光
+        max_grid_ratio=0.15,         # 限制上网电量比例为15%
+        storage_efficiency=0.9,      # 储能效率90%
+        discharge_rate=1.0,          # 1C放电
+        charge_rate=1.0,             # 1C充电
+        max_storage_capacity=100.0   # 足够大的储能容量
+    )
+    
+    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"上网电量比例限制: {params.max_grid_ratio:.1%}")
+    print(f"实际上网电量比例: {result['total_grid_feed_in_ratio']:.3f}")
+    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"能量平衡校验: {'通过' if result['energy_balance_check'] else '未通过'}")
+    
+    # 检查是否达到上网电量比例限制
+    if result['total_grid_feed_in_ratio'] >= params.max_grid_ratio - 0.01:
+        print("注意：已达到上网电量比例限制")
+    
+    return {
+        'result': result,
+        'solar_output': solar_output,
+        '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 ===")
+    
+    # 运行六个场景
+    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()
+    data6 = example_6_grid_ratio_limited_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', '上网电量比例限制场景']
+    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'],
+        data6['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'],
+        data6['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'],
+        data6['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'],
+        data6['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",
+        f"{data6['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} {'上网比例':<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"{abs(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) or (data6['result']['max_storage_limit'] and i == 5) 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, data6
+
+
+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, data6 = 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'])
+        plot_results(data6['result'], "上网电量比例限制场景储能运行情况",
+                    data6['solar_output'], data6['wind_output'],
+                    data6['thermal_output'], data6['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！")

scripts/solar_optimization_examples.py

@@ -0,0 +1,541 @@
+"""
+光伏优化模块场景示例
+
+该文件展示了光伏优化模块在不同场景下的应用，包括：
+1. 典型日场景 - 基础优化示例
+2. 高负荷场景 - 夏季高峰用电场景
+3. 低负荷场景 - 春秋季低负荷场景
+4. 风光互补场景 - 风电和光伏协同优化
+5. 储能受限场景 - 储能容量受限情况下的优化
+
+作者: iFlow CLI
+创建日期: 2025-12-26
+"""
+
+import sys
+import os
+sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'src'))
+
+import numpy as np
+import matplotlib.pyplot as plt
+from typing import List, Dict
+from solar_optimization import optimize_solar_output, plot_optimization_results, export_optimization_results
+from storage_optimization import SystemParameters
+
+
+def scenario_1_typical_day():
+    """
+    场景1：典型日场景
+    - 标准24小时负荷曲线
+    - 适中风光出力
+    - 常规系统参数
+    """
+    print("=" * 60)
+    print("场景1：典型日场景 - 基础优化示例")
+    print("=" * 60)
+    
+    # 典型日光伏出力（中午高峰）
+    solar_output = [0.0] * 6 + [0.5, 1.0, 2.0, 3.5, 5.0, 6.0, 5.5, 4.0, 2.5, 1.0, 0.5, 0.0] + [0.0] * 6
+    
+    # 典型日风电出力（夜间和早晨较高）
+    wind_output = [4.0, 5.0, 4.5, 3.5, 2.5, 2.0, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 2.0, 3.0, 4.0, 5.0, 4.5, 4.0, 3.5, 3.0, 2.5, 2.0, 1.5, 1.0]
+    
+    # 火电基础出力
+    thermal_output = [8.0] * 24
+    
+    # 典型日负荷曲线（早晚高峰）
+    load_demand = [2.0, 2.5, 3.0, 4.0, 6.0, 9.0, 12.0, 15.0, 18.0, 20.0, 19.0, 18.0,
+                   17.0, 16.0, 18.0, 19.0, 20.0, 18.0, 15.0, 12.0, 8.0, 5.0, 3.0, 2.0]
+    
+    # 标准系统参数
+    params = SystemParameters(
+        max_curtailment_wind=0.1,
+        max_curtailment_solar=0.1,
+        max_grid_ratio=0.15,
+        storage_efficiency=0.9,
+        discharge_rate=1.0,
+        charge_rate=1.0,
+        rated_thermal_capacity=100.0,
+        rated_solar_capacity=50.0,
+        rated_wind_capacity=50.0,
+        available_thermal_energy=2000.0,
+        available_solar_energy=400.0,
+        available_wind_energy=600.0
+    )
+    
+    # 执行优化
+    result = optimize_solar_output(
+        solar_output, wind_output, thermal_output, load_demand, params
+    )
+    
+    # 输出结果
+    print_scenario_result("典型日场景", result)
+    
+    # 绘制结果
+    plot_optimization_results(result, show_window=False)
+    
+    # 导出结果
+    filename = export_optimization_results(result, "scenario_1_typical_day.xlsx")
+    
+    return result
+
+
+def scenario_2_high_load():
+    """
+    场景2：高负荷场景
+    - 夏季高温，空调负荷高
+    - 白天负荷特别高
+    - 光伏出力与负荷匹配度较低
+    """
+    print("=" * 60)
+    print("场景2：高负荷场景 - 夏季高峰用电")
+    print("=" * 60)
+    
+    # 夏季光伏出力（较强）
+    solar_output = [0.0] * 5 + [0.8, 1.5, 3.0, 4.5, 6.0, 7.5, 8.0, 7.0, 5.0, 3.0, 1.5, 0.5, 0.0, 0.0] + [0.0] * 5
+    
+    # 夏季风电出力（相对较低）
+    wind_output = [2.0, 2.5, 3.0, 2.5, 2.0, 1.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.5, 2.0, 2.5, 3.0, 2.5, 2.0, 1.8, 1.6, 1.4, 1.2, 1.0, 0.8]
+    
+    # 火电高峰出力
+    thermal_output = [12.0] * 24
+    
+    # 夏季高负荷曲线（空调导致白天负荷极高）
+    load_demand = [3.0, 3.5, 4.0, 5.0, 8.0, 12.0, 18.0, 25.0, 30.0, 32.0, 31.0, 30.0,
+                   29.0, 28.0, 30.0, 31.0, 32.0, 28.0, 22.0, 18.0, 12.0, 8.0, 5.0, 3.0]
+    
+    # 高负荷场景参数（更宽松的弃风弃光限制）
+    params = SystemParameters(
+        max_curtailment_wind=0.15,
+        max_curtailment_solar=0.15,
+        max_grid_ratio=0.25,
+        storage_efficiency=0.85,
+        discharge_rate=1.2,
+        charge_rate=1.2,
+        rated_thermal_capacity=150.0,
+        rated_solar_capacity=80.0,
+        rated_wind_capacity=40.0,
+        available_thermal_energy=3000.0,
+        available_solar_energy=600.0,
+        available_wind_energy=400.0
+    )
+    
+    # 执行优化
+    result = optimize_solar_output(
+        solar_output, wind_output, thermal_output, load_demand, params
+    )
+    
+    # 输出结果
+    print_scenario_result("高负荷场景", result)
+    
+    # 绘制结果
+    plot_optimization_results(result, show_window=False)
+    
+    # 导出结果
+    filename = export_optimization_results(result, "scenario_2_high_load.xlsx")
+    
+    return result
+
+
+def scenario_3_low_load():
+    """
+    场景3：低负荷场景
+    - 春秋季，负荷较低
+    - 光伏出力相对较高
+    - 容易出现电力盈余
+    """
+    print("=" * 60)
+    print("场景3：低负荷场景 - 春秋季低负荷")
+    print("=" * 60)
+    
+    # 春秋季光伏出力（适中）
+    solar_output = [0.0] * 6 + [1.0, 2.0, 3.5, 5.0, 6.5, 7.0, 6.5, 5.0, 3.5, 2.0, 1.0, 0.0] + [0.0] * 6
+    
+    # 春秋季风电出力（较好）
+    wind_output = [5.0, 6.0, 5.5, 4.5, 3.5, 3.0, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 3.0, 4.0, 5.0, 6.0, 5.5, 5.0, 4.5, 4.0, 3.5, 3.0, 2.5, 2.0]
+    
+    # 火电基础出力（较低）
+    thermal_output = [5.0] * 24
+    
+    # 春秋季低负荷曲线
+    load_demand = [2.0, 2.2, 2.5, 3.0, 4.0, 6.0, 8.0, 10.0, 12.0, 13.0, 12.5, 12.0,
+                   11.5, 11.0, 12.0, 12.5, 13.0, 11.0, 9.0, 7.0, 5.0, 3.5, 2.5, 2.0]
+    
+    # 低负荷场景参数（更严格的弃风弃光限制）
+    params = SystemParameters(
+        max_curtailment_wind=0.05,
+        max_curtailment_solar=0.05,
+        max_grid_ratio=0.1,
+        storage_efficiency=0.92,
+        discharge_rate=0.8,
+        charge_rate=0.8,
+        rated_thermal_capacity=80.0,
+        rated_solar_capacity=60.0,
+        rated_wind_capacity=60.0,
+        available_thermal_energy=1500.0,
+        available_solar_energy=500.0,
+        available_wind_energy=700.0
+    )
+    
+    # 执行优化
+    result = optimize_solar_output(
+        solar_output, wind_output, thermal_output, load_demand, params
+    )
+    
+    # 输出结果
+    print_scenario_result("低负荷场景", result)
+    
+    # 绘制结果
+    plot_optimization_results(result, show_window=False)
+    
+    # 导出结果
+    filename = export_optimization_results(result, "scenario_3_low_load.xlsx")
+    
+    return result
+
+
+def scenario_4_wind_solar_complement():
+    """
+    场景4：风光互补场景
+    - 风电和光伏出力时间互补性强
+    - 夜间风电高，白天光伏高
+    - 系统整体平衡性较好
+    """
+    print("=" * 60)
+    print("场景4：风光互补场景 - 风电和光伏协同优化")
+    print("=" * 60)
+    
+    # 光伏出力（标准日间模式）
+    solar_output = [0.0] * 6 + [0.5, 1.5, 3.0, 4.5, 6.0, 7.0, 6.0, 4.5, 3.0, 1.5, 0.5, 0.0] + [0.0] * 6
+    
+    # 风电出力（与光伏互补，夜间和早晚较高）
+    wind_output = [8.0, 9.0, 8.5, 7.0, 5.0, 3.0, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 3.0, 5.0, 7.0, 8.0, 8.5, 8.0, 7.5, 7.0, 6.5, 6.0, 5.5, 5.0]
+    
+    # 火电出力（作为补充）
+    thermal_output = [6.0] * 24
+    
+    # 负荷曲线（相对平稳）
+    load_demand = [4.0, 4.5, 5.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 17.0, 16.5, 16.0,
+                   15.5, 15.0, 16.0, 16.5, 17.0, 15.0, 13.0, 11.0, 9.0, 7.0, 5.0, 4.0]
+    
+    # 风光互补场景参数
+    params = SystemParameters(
+        max_curtailment_wind=0.08,
+        max_curtailment_solar=0.08,
+        max_grid_ratio=0.12,
+        storage_efficiency=0.9,
+        discharge_rate=1.0,
+        charge_rate=1.0,
+        rated_thermal_capacity=100.0,
+        rated_solar_capacity=70.0,
+        rated_wind_capacity=70.0,
+        available_thermal_energy=1800.0,
+        available_solar_energy=450.0,
+        available_wind_energy=800.0
+    )
+    
+    # 执行优化
+    result = optimize_solar_output(
+        solar_output, wind_output, thermal_output, load_demand, params
+    )
+    
+    # 输出结果
+    print_scenario_result("风光互补场景", result)
+    
+    # 绘制结果
+    plot_optimization_results(result, show_window=False)
+    
+    # 导出结果
+    filename = export_optimization_results(result, "scenario_4_wind_solar_complement.xlsx")
+    
+    return result
+
+
+def scenario_5_storage_limited():
+    """
+    场景5：储能受限场景
+    - 储能容量受限
+    - 需要更精确的光伏出力调节
+    - 对电网交换更敏感
+    """
+    print("=" * 60)
+    print("场景5：储能受限场景 - 储能容量受限情况下的优化")
+    print("=" * 60)
+    
+    # 标准光伏出力
+    solar_output = [0.0] * 6 + [1.0, 2.0, 3.0, 4.5, 6.0, 7.0, 6.0, 4.5, 3.0, 2.0, 1.0, 0.0] + [0.0] * 6
+    
+    # 标准风电出力
+    wind_output = [3.0, 4.0, 3.5, 3.0, 2.5, 2.0, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 2.0, 3.0, 3.5, 4.0, 3.5, 3.0, 2.8, 2.6, 2.4, 2.2, 2.0, 1.8]
+    
+    # 火电出力
+    thermal_output = [7.0] * 24
+    
+    # 标准负荷曲线
+    load_demand = [3.0, 3.5, 4.0, 5.0, 7.0, 10.0, 13.0, 16.0, 18.0, 19.0, 18.5, 18.0,
+                   17.5, 17.0, 18.0, 18.5, 19.0, 17.0, 14.0, 11.0, 8.0, 6.0, 4.0, 3.0]
+    
+    # 储能受限场景参数（储能容量限制为50MWh）
+    params = SystemParameters(
+        max_curtailment_wind=0.12,
+        max_curtailment_solar=0.12,
+        max_grid_ratio=0.2,
+        storage_efficiency=0.88,
+        discharge_rate=1.5,
+        charge_rate=1.5,
+        max_storage_capacity=50.0,  # 储能容量受限
+        rated_thermal_capacity=100.0,
+        rated_solar_capacity=60.0,
+        rated_wind_capacity=50.0,
+        available_thermal_energy=2000.0,
+        available_solar_energy=480.0,
+        available_wind_energy=550.0
+    )
+    
+    # 执行优化
+    result = optimize_solar_output(
+        solar_output, wind_output, thermal_output, load_demand, params
+    )
+    
+    # 输出结果
+    print_scenario_result("储能受限场景", result)
+    
+    # 绘制结果
+    plot_optimization_results(result, show_window=False)
+    
+    # 导出结果
+    filename = export_optimization_results(result, "scenario_5_storage_limited.xlsx")
+    
+    return result
+
+
+def print_scenario_result(scenario_name: str, result):
+    """
+    打印场景优化结果
+    
+    Args:
+        scenario_name: 场景名称
+        result: 优化结果
+    """
+    print(f"\n=== {scenario_name}优化结果 ===")
+    print(f"最优光伏系数: {result.optimal_solar_coefficient:.3f}")
+    print(f"最小电网交换电量: {result.min_grid_exchange:.2f} MWh")
+    print(f"  - 购电量: {result.grid_purchase:.2f} MWh")
+    print(f"  - 上网电量: {result.grid_feed_in:.2f} MWh")
+    print(f"所需储能容量: {result.storage_result['required_storage_capacity']:.2f} MWh")
+    print(f"优化后弃风率: {result.storage_result['total_curtailment_wind_ratio']:.3f}")
+    print(f"优化后弃光率: {result.storage_result['total_curtailment_solar_ratio']:.3f}")
+    print(f"优化后上网电量比例: {result.storage_result['total_grid_feed_in_ratio']:.3f}")
+    
+    # 分析优化效果
+    if result.optimal_solar_coefficient > 1.0:
+        print(f"分析：建议将光伏出力提高 {(result.optimal_solar_coefficient - 1.0) * 100:.1f}% 以减少电网依赖")
+    elif result.optimal_solar_coefficient < 1.0:
+        print(f"分析：建议将光伏出力降低 {(1.0 - result.optimal_solar_coefficient) * 100:.1f}% 以避免电力过剩")
+    else:
+        print("分析：当前光伏出力已经是最优配置")
+
+
+def compare_scenarios(results: List[Dict]):
+    """
+    对比不同场景的优化结果
+    
+    Args:
+        results: 各场景优化结果列表
+    """
+    print("\n" + "=" * 80)
+    print("场景对比分析")
+    print("=" * 80)
+    
+    scenario_names = [
+        "典型日场景",
+        "高负荷场景", 
+        "低负荷场景",
+        "风光互补场景",
+        "储能受限场景"
+    ]
+    
+    # 创建对比表格
+    print(f"{'场景名称':<12} {'最优系数':<8} {'电网交换(MWh)':<12} {'购电量(MWh)':<10} {'上网电量(MWh)':<12} {'储能容量(MWh)':<12}")
+    print("-" * 80)
+    
+    for i, (name, result) in enumerate(zip(scenario_names, results)):
+        print(f"{name:<12} {result.optimal_solar_coefficient:<8.3f} "
+              f"{result.min_grid_exchange:<12.2f} {result.grid_purchase:<10.2f} "
+              f"{result.grid_feed_in:<12.2f} {result.storage_result['required_storage_capacity']:<12.2f}")
+    
+    # 分析趋势
+    print("\n=== 趋势分析 ===")
+    
+    # 找出最优和最差场景
+    min_exchange_result = min(results, key=lambda x: x.min_grid_exchange)
+    max_exchange_result = max(results, key=lambda x: x.min_grid_exchange)
+    
+    min_exchange_idx = results.index(min_exchange_result)
+    max_exchange_idx = results.index(max_exchange_result)
+    
+    print(f"电网交换最小场景：{scenario_names[min_exchange_idx]} ({min_exchange_result.min_grid_exchange:.2f} MWh)")
+    print(f"电网交换最大场景：{scenario_names[max_exchange_idx]} ({max_exchange_result.min_grid_exchange:.2f} MWh)")
+    
+    # 分析光伏系数趋势
+    avg_coefficient = sum(r.optimal_solar_coefficient for r in results) / len(results)
+    print(f"平均最优光伏系数：{avg_coefficient:.3f}")
+    
+    high_coefficient_scenarios = [name for name, result in zip(scenario_names, results) 
+                                 if result.optimal_solar_coefficient > avg_coefficient]
+    low_coefficient_scenarios = [name for name, result in zip(scenario_names, results) 
+                                if result.optimal_solar_coefficient < avg_coefficient]
+    
+    if high_coefficient_scenarios:
+        print(f"需要提高光伏出力的场景：{', '.join(high_coefficient_scenarios)}")
+    if low_coefficient_scenarios:
+        print(f"需要降低光伏出力的场景：{', '.join(low_coefficient_scenarios)}")
+
+
+def plot_scenario_comparison(results: List[Dict]):
+    """
+    绘制场景对比图表
+    
+    Args:
+        results: 各场景优化结果列表
+    """
+    scenario_names = [
+        "典型日",
+        "高负荷", 
+        "低负荷",
+        "风光互补",
+        "储能受限"
+    ]
+    
+    # 设置中文字体
+    plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
+    plt.rcParams['axes.unicode_minus'] = False
+    
+    # 创建图形
+    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(16, 12))
+    fig.suptitle('光伏优化场景对比分析', fontsize=16, fontweight='bold')
+    
+    # 1. 最优光伏系数对比
+    coefficients = [r.optimal_solar_coefficient for r in results]
+    bars1 = ax1.bar(scenario_names, coefficients, color='skyblue', alpha=0.7)
+    ax1.set_ylabel('最优光伏系数')
+    ax1.set_title('各场景最优光伏系数对比')
+    ax1.grid(True, alpha=0.3, axis='y')
+    ax1.axhline(y=1.0, color='red', linestyle='--', alpha=0.7, label='原始系数')
+    
+    # 添加数值标签
+    for bar, coeff in zip(bars1, coefficients):
+        height = bar.get_height()
+        ax1.text(bar.get_x() + bar.get_width()/2., height + 0.01,
+                f'{coeff:.3f}', ha='center', va='bottom', fontweight='bold')
+    
+    # 2. 电网交换电量对比
+    exchanges = [r.min_grid_exchange for r in results]
+    purchases = [r.grid_purchase for r in results]
+    feed_ins = [r.grid_feed_in for r in results]
+    
+    x = np.arange(len(scenario_names))
+    width = 0.25
+    
+    bars2 = ax2.bar(x - width, purchases, width, label='购电量', color='purple', alpha=0.7)
+    bars3 = ax2.bar(x, feed_ins, width, label='上网电量', color='brown', alpha=0.7)
+    bars4 = ax2.bar(x + width, exchanges, width, label='总交换电量', color='orange', alpha=0.7)
+    
+    ax2.set_ylabel('电量 (MWh)')
+    ax2.set_title('电网交换电量对比')
+    ax2.set_xticks(x)
+    ax2.set_xticklabels(scenario_names)
+    ax2.legend()
+    ax2.grid(True, alpha=0.3, axis='y')
+    
+    # 3. 储能容量需求对比
+    storage_capacities = [r.storage_result['required_storage_capacity'] for r in results]
+    bars5 = ax3.bar(scenario_names, storage_capacities, color='green', alpha=0.7)
+    ax3.set_ylabel('储能容量 (MWh)')
+    ax3.set_title('各场景储能容量需求对比')
+    ax3.grid(True, alpha=0.3, axis='y')
+    
+    # 添加数值标签
+    for bar, capacity in zip(bars5, storage_capacities):
+        height = bar.get_height()
+        ax3.text(bar.get_x() + bar.get_width()/2., height + height*0.01,
+                f'{capacity:.1f}', ha='center', va='bottom', fontweight='bold')
+    
+    # 4. 弃风弃光率对比
+    curtailment_winds = [r.storage_result['total_curtailment_wind_ratio'] for r in results]
+    curtailment_solars = [r.storage_result['total_curtailment_solar_ratio'] for r in results]
+    
+    bars6 = ax4.bar(x - width/2, curtailment_winds, width, label='弃风率', color='blue', alpha=0.7)
+    bars7 = ax4.bar(x + width/2, curtailment_solars, width, label='弃光率', color='orange', alpha=0.7)
+    
+    ax4.set_ylabel('弃风弃光率')
+    ax4.set_title('各场景弃风弃光率对比')
+    ax4.set_xticks(x)
+    ax4.set_xticklabels(scenario_names)
+    ax4.legend()
+    ax4.grid(True, alpha=0.3, axis='y')
+    
+    # 调整布局
+    plt.tight_layout()
+    
+    # 保存图片
+    plt.savefig('solar_optimization_scenario_comparison.png', dpi=300, bbox_inches='tight')
+    plt.close()
+    
+    print("场景对比图表已保存为 'solar_optimization_scenario_comparison.png'")
+
+
+def main():
+    """主函数，运行所有场景示例"""
+    print("光伏优化模块场景示例")
+    print("运行5个不同场景的优化分析...")
+    
+    # 运行所有场景
+    results = []
+    
+    try:
+        # 场景1：典型日场景
+        result1 = scenario_1_typical_day()
+        results.append(result1)
+        
+        # 场景2：高负荷场景
+        result2 = scenario_2_high_load()
+        results.append(result2)
+        
+        # 场景3：低负荷场景
+        result3 = scenario_3_low_load()
+        results.append(result3)
+        
+        # 场景4：风光互补场景
+        result4 = scenario_4_wind_solar_complement()
+        results.append(result4)
+        
+        # 场景5：储能受限场景
+        result5 = scenario_5_storage_limited()
+        results.append(result5)
+        
+        # 对比分析
+        compare_scenarios(results)
+        
+        # 绘制对比图表
+        plot_scenario_comparison(results)
+        
+        print("\n" + "=" * 80)
+        print("所有场景示例运行完成！")
+        print("=" * 80)
+        print("生成的文件：")
+        print("- scenario_1_typical_day.xlsx")
+        print("- scenario_2_high_load.xlsx") 
+        print("- scenario_3_low_load.xlsx")
+        print("- scenario_4_wind_solar_complement.xlsx")
+        print("- scenario_5_storage_limited.xlsx")
+        print("- solar_optimization_scenario_comparison.png")
+        
+    except Exception as e:
+        print(f"运行场景示例时出错：{str(e)}")
+        import traceback
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    main()

scripts/solar_scenarios_demo.py

@@ -0,0 +1,419 @@
+"""
+光伏优化模块场景演示
+
+该文件展示了光伏优化模块在不同场景下的应用，包括：
+1. 典型日场景 - 基础优化示例
+2. 高负荷场景 - 夏季高峰用电场景  
+3. 低负荷场景 - 春秋季低负荷场景
+4. 风光互补场景 - 风电和光伏协同优化
+5. 储能受限场景 - 储能容量受限情况下的优化
+
+作者: iFlow CLI
+创建日期: 2025-12-26
+"""
+
+import sys
+import os
+sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'src'))
+
+import numpy as np
+import matplotlib.pyplot as plt
+from solar_optimization import optimize_solar_output, export_optimization_results
+from storage_optimization import SystemParameters
+
+
+def scenario_1_typical_day():
+    """场景1：典型日场景"""
+    print("=" * 60)
+    print("场景1：典型日场景 - 基础优化示例")
+    print("=" * 60)
+    
+    # 典型日数据（24小时）
+    solar_output = [0.0] * 6 + [0.5, 1.0, 2.0, 3.5, 5.0, 6.0, 5.5, 4.0, 2.5, 1.0, 0.5, 0.0] + [0.0] * 6
+    wind_output = [4.0, 5.0, 4.5, 3.5, 2.5, 2.0, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 2.0, 3.0, 4.0, 5.0, 4.5, 4.0, 3.5, 3.0, 2.5, 2.0, 1.5, 1.0]
+    thermal_output = [8.0] * 24
+    load_demand = [2.0, 2.5, 3.0, 4.0, 6.0, 9.0, 12.0, 15.0, 18.0, 20.0, 19.0, 18.0,
+                   17.0, 16.0, 18.0, 19.0, 20.0, 18.0, 15.0, 12.0, 8.0, 5.0, 3.0, 2.0]
+    
+    # 系统参数
+    params = SystemParameters(
+        max_curtailment_wind=0.1,
+        max_curtailment_solar=0.1,
+        max_grid_ratio=0.15,
+        storage_efficiency=0.9,
+        discharge_rate=1.0,
+        charge_rate=1.0,
+        rated_thermal_capacity=100.0,
+        rated_solar_capacity=50.0,
+        rated_wind_capacity=50.0,
+        available_thermal_energy=2000.0,
+        available_solar_energy=400.0,
+        available_wind_energy=600.0
+    )
+    
+    # 执行优化
+    result = optimize_solar_output(solar_output, wind_output, thermal_output, load_demand, params)
+    
+    # 输出结果
+    print_scenario_result("典型日场景", result)
+    
+    # 绘制光伏对比图
+    plot_solar_comparison(result, "典型日场景")
+    
+    # 导出结果
+    export_optimization_results(result, "scenario_1_typical_day.xlsx")
+    
+    return result
+
+
+def scenario_2_high_load():
+    """场景2：高负荷场景"""
+    print("=" * 60)
+    print("场景2：高负荷场景 - 夏季高峰用电")
+    print("=" * 60)
+    
+    # 夏季高负荷数据
+    solar_output = [0.0] * 5 + [0.8, 1.5, 3.0, 4.5, 6.0, 7.5, 8.0, 7.0, 5.0, 3.0, 1.5, 0.5, 0.0, 0.0] + [0.0] * 5
+    wind_output = [2.0, 2.5, 3.0, 2.5, 2.0, 1.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.5, 2.0, 2.5, 3.0, 2.5, 2.0, 1.8, 1.6, 1.4, 1.2, 1.0, 0.8]
+    thermal_output = [12.0] * 24
+    load_demand = [3.0, 3.5, 4.0, 5.0, 8.0, 12.0, 18.0, 25.0, 30.0, 32.0, 31.0, 30.0,
+                   29.0, 28.0, 30.0, 31.0, 32.0, 28.0, 22.0, 18.0, 12.0, 8.0, 5.0, 3.0]
+    
+    # 高负荷场景参数
+    params = SystemParameters(
+        max_curtailment_wind=0.15,
+        max_curtailment_solar=0.15,
+        max_grid_ratio=0.25,
+        storage_efficiency=0.85,
+        discharge_rate=1.2,
+        charge_rate=1.2,
+        rated_thermal_capacity=150.0,
+        rated_solar_capacity=80.0,
+        rated_wind_capacity=40.0,
+        available_thermal_energy=3000.0,
+        available_solar_energy=600.0,
+        available_wind_energy=400.0
+    )
+    
+    # 执行优化
+    result = optimize_solar_output(solar_output, wind_output, thermal_output, load_demand, params)
+    
+    # 输出结果
+    print_scenario_result("高负荷场景", result)
+    
+    # 绘制光伏对比图
+    plot_solar_comparison(result, "高负荷场景")
+    
+    # 导出结果
+    export_optimization_results(result, "scenario_2_high_load.xlsx")
+    
+    return result
+
+
+def scenario_3_low_load():
+    """场景3：低负荷场景"""
+    print("=" * 60)
+    print("场景3：低负荷场景 - 春秋季低负荷")
+    print("=" * 60)
+    
+    # 春秋季低负荷数据
+    solar_output = [0.0] * 6 + [1.0, 2.0, 3.5, 5.0, 6.5, 7.0, 6.5, 5.0, 3.5, 2.0, 1.0, 0.0] + [0.0] * 6
+    wind_output = [5.0, 6.0, 5.5, 4.5, 3.5, 3.0, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 3.0, 4.0, 5.0, 6.0, 5.5, 5.0, 4.5, 4.0, 3.5, 3.0, 2.5, 2.0]
+    thermal_output = [5.0] * 24
+    load_demand = [2.0, 2.2, 2.5, 3.0, 4.0, 6.0, 8.0, 10.0, 12.0, 13.0, 12.5, 12.0,
+                   11.5, 11.0, 12.0, 12.5, 13.0, 11.0, 9.0, 7.0, 5.0, 3.5, 2.5, 2.0]
+    
+    # 低负荷场景参数
+    params = SystemParameters(
+        max_curtailment_wind=0.05,
+        max_curtailment_solar=0.05,
+        max_grid_ratio=0.1,
+        storage_efficiency=0.92,
+        discharge_rate=0.8,
+        charge_rate=0.8,
+        rated_thermal_capacity=80.0,
+        rated_solar_capacity=60.0,
+        rated_wind_capacity=60.0,
+        available_thermal_energy=1500.0,
+        available_solar_energy=500.0,
+        available_wind_energy=700.0
+    )
+    
+    # 执行优化
+    result = optimize_solar_output(solar_output, wind_output, thermal_output, load_demand, params)
+    
+    # 输出结果
+    print_scenario_result("低负荷场景", result)
+    
+    # 绘制光伏对比图
+    plot_solar_comparison(result, "低负荷场景")
+    
+    # 导出结果
+    export_optimization_results(result, "scenario_3_low_load.xlsx")
+    
+    return result
+
+
+def scenario_4_wind_solar_complement():
+    """场景4：风光互补场景"""
+    print("=" * 60)
+    print("场景4：风光互补场景 - 风电和光伏协同优化")
+    print("=" * 60)
+    
+    # 风光互补数据
+    solar_output = [0.0] * 6 + [0.5, 1.5, 3.0, 4.5, 6.0, 7.0, 6.0, 4.5, 3.0, 1.5, 0.5, 0.0] + [0.0] * 6
+    wind_output = [8.0, 9.0, 8.5, 7.0, 5.0, 3.0, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 3.0, 5.0, 7.0, 8.0, 8.5, 8.0, 7.5, 7.0, 6.5, 6.0, 5.5, 5.0]
+    thermal_output = [6.0] * 24
+    load_demand = [4.0, 4.5, 5.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 17.0, 16.5, 16.0,
+                   15.5, 15.0, 16.0, 16.5, 17.0, 15.0, 13.0, 11.0, 9.0, 7.0, 5.0, 4.0]
+    
+    # 风光互补场景参数
+    params = SystemParameters(
+        max_curtailment_wind=0.08,
+        max_curtailment_solar=0.08,
+        max_grid_ratio=0.12,
+        storage_efficiency=0.9,
+        discharge_rate=1.0,
+        charge_rate=1.0,
+        rated_thermal_capacity=100.0,
+        rated_solar_capacity=70.0,
+        rated_wind_capacity=70.0,
+        available_thermal_energy=1800.0,
+        available_solar_energy=450.0,
+        available_wind_energy=800.0
+    )
+    
+    # 执行优化
+    result = optimize_solar_output(solar_output, wind_output, thermal_output, load_demand, params)
+    
+    # 输出结果
+    print_scenario_result("风光互补场景", result)
+    
+    # 绘制光伏对比图
+    plot_solar_comparison(result, "风光互补场景")
+    
+    # 导出结果
+    export_optimization_results(result, "scenario_4_wind_solar_complement.xlsx")
+    
+    return result
+
+
+def scenario_5_storage_limited():
+    """场景5：储能受限场景"""
+    print("=" * 60)
+    print("场景5：储能受限场景 - 储能容量受限情况下的优化")
+    print("=" * 60)
+    
+    # 储能受限数据
+    solar_output = [0.0] * 6 + [1.0, 2.0, 3.0, 4.5, 6.0, 7.0, 6.0, 4.5, 3.0, 2.0, 1.0, 0.0] + [0.0] * 6
+    wind_output = [3.0, 4.0, 3.5, 3.0, 2.5, 2.0, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 2.0, 3.0, 3.5, 4.0, 3.5, 3.0, 2.8, 2.6, 2.4, 2.2, 2.0, 1.8]
+    thermal_output = [7.0] * 24
+    load_demand = [3.0, 3.5, 4.0, 5.0, 7.0, 10.0, 13.0, 16.0, 18.0, 19.0, 18.5, 18.0,
+                   17.5, 17.0, 18.0, 18.5, 19.0, 17.0, 14.0, 11.0, 8.0, 6.0, 4.0, 3.0]
+    
+    # 储能受限场景参数
+    params = SystemParameters(
+        max_curtailment_wind=0.12,
+        max_curtailment_solar=0.12,
+        max_grid_ratio=0.2,
+        storage_efficiency=0.88,
+        discharge_rate=1.5,
+        charge_rate=1.5,
+        max_storage_capacity=50.0,  # 储能容量受限
+        rated_thermal_capacity=100.0,
+        rated_solar_capacity=60.0,
+        rated_wind_capacity=50.0,
+        available_thermal_energy=2000.0,
+        available_solar_energy=480.0,
+        available_wind_energy=550.0
+    )
+    
+    # 执行优化
+    result = optimize_solar_output(solar_output, wind_output, thermal_output, load_demand, params)
+    
+    # 输出结果
+    print_scenario_result("储能受限场景", result)
+    
+    # 绘制光伏对比图
+    plot_solar_comparison(result, "储能受限场景")
+    
+    # 导出结果
+    export_optimization_results(result, "scenario_5_storage_limited.xlsx")
+    
+    return result
+
+
+def print_scenario_result(scenario_name: str, result):
+    """打印场景优化结果"""
+    print(f"\n=== {scenario_name}优化结果 ===")
+    print(f"最优光伏系数: {result.optimal_solar_coefficient:.3f}")
+    print(f"最小电网交换电量: {result.min_grid_exchange:.2f} MWh")
+    print(f"  - 购电量: {result.grid_purchase:.2f} MWh")
+    print(f"  - 上网电量: {result.grid_feed_in:.2f} MWh")
+    print(f"所需储能容量: {result.storage_result['required_storage_capacity']:.2f} MWh")
+    print(f"优化后弃风率: {result.storage_result['total_curtailment_wind_ratio']:.3f}")
+    print(f"优化后弃光率: {result.storage_result['total_curtailment_solar_ratio']:.3f}")
+    print(f"优化后上网电量比例: {result.storage_result['total_grid_feed_in_ratio']:.3f}")
+    
+    # 分析优化效果
+    if result.optimal_solar_coefficient > 1.0:
+        print(f"分析：建议将光伏出力提高 {(result.optimal_solar_coefficient - 1.0) * 100:.1f}% 以减少电网依赖")
+    elif result.optimal_solar_coefficient < 1.0:
+        print(f"分析：建议将光伏出力降低 {(1.0 - result.optimal_solar_coefficient) * 100:.1f}% 以避免电力过剩")
+    else:
+        print("分析：当前光伏出力已经是最优配置")
+
+
+def plot_solar_comparison(result, scenario_name, show_window=True):
+    """
+    绘制光伏出力对比图
+    
+    Args:
+        result: 光伏优化结果
+        scenario_name: 场景名称
+        show_window: 是否显示图形窗口
+    """
+    # 设置中文字体
+    plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
+    plt.rcParams['axes.unicode_minus'] = False
+    
+    hours = list(range(len(result.original_solar_output)))
+    
+    # 创建图形
+    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8))
+    fig.suptitle(f'{scenario_name} - 光伏优化结果 (系数: {result.optimal_solar_coefficient:.3f})', 
+                 fontsize=14, fontweight='bold')
+    
+    # === 第一个子图：光伏出力对比 ===
+    ax1.plot(hours, result.original_solar_output, 'b-', linewidth=2, 
+             label='原始光伏出力', alpha=0.7)
+    ax1.plot(hours, result.optimized_solar_output, 'r-', linewidth=2, 
+             label=f'优化后光伏出力')
+    
+    ax1.set_xlabel('时间 (小时)')
+    ax1.set_ylabel('功率 (MW)')
+    ax1.set_title('光伏出力曲线对比')
+    ax1.legend(loc='upper right')
+    ax1.grid(True, alpha=0.3)
+    ax1.set_xlim(0, max(hours))
+    
+    # === 第二个子图：电网交换电量组成 ===
+    categories = ['购电量', '上网电量']
+    values = [result.grid_purchase, result.grid_feed_in]
+    colors = ['purple', 'brown']
+    
+    bars = ax2.bar(categories, values, color=colors, alpha=0.7)
+    ax2.set_ylabel('电量 (MWh)')
+    ax2.set_title(f'电网交换电量组成 (总计: {result.min_grid_exchange:.2f} MWh)')
+    ax2.grid(True, alpha=0.3, axis='y')
+    
+    # 在柱状图上添加数值标签
+    for bar, value in zip(bars, values):
+        height = bar.get_height()
+        ax2.text(bar.get_x() + bar.get_width()/2., height + height*0.01,
+                f'{value:.2f}', ha='center', va='bottom', fontweight='bold')
+    
+    # 调整布局
+    plt.tight_layout()
+    
+    # 根据参数决定是否显示图形窗口
+    if show_window:
+        try:
+            plt.show()
+        except Exception as e:
+            print(f"无法显示图形窗口：{str(e)}")
+    else:
+        plt.close()
+
+
+def compare_scenarios(results):
+    """对比不同场景的优化结果"""
+    print("\n" + "=" * 80)
+    print("场景对比分析")
+    print("=" * 80)
+    
+    scenario_names = [
+        "典型日场景",
+        "高负荷场景", 
+        "低负荷场景",
+        "风光互补场景",
+        "储能受限场景"
+    ]
+    
+    # 创建对比表格
+    print(f"{'场景名称':<12} {'最优系数':<8} {'电网交换(MWh)':<12} {'购电量(MWh)':<10} {'上网电量(MWh)':<12} {'储能容量(MWh)':<12}")
+    print("-" * 80)
+    
+    for i, (name, result) in enumerate(zip(scenario_names, results)):
+        print(f"{name:<12} {result.optimal_solar_coefficient:<8.3f} "
+              f"{result.min_grid_exchange:<12.2f} {result.grid_purchase:<10.2f} "
+              f"{result.grid_feed_in:<12.2f} {result.storage_result['required_storage_capacity']:<12.2f}")
+    
+    # 分析趋势
+    print("\n=== 趋势分析 ===")
+    
+    # 找出最优和最差场景
+    min_exchange_result = min(results, key=lambda x: x.min_grid_exchange)
+    max_exchange_result = max(results, key=lambda x: x.min_grid_exchange)
+    
+    min_exchange_idx = results.index(min_exchange_result)
+    max_exchange_idx = results.index(max_exchange_result)
+    
+    print(f"电网交换最小场景：{scenario_names[min_exchange_idx]} ({min_exchange_result.min_grid_exchange:.2f} MWh)")
+    print(f"电网交换最大场景：{scenario_names[max_exchange_idx]} ({max_exchange_result.min_grid_exchange:.2f} MWh)")
+    
+    # 分析光伏系数趋势
+    avg_coefficient = sum(r.optimal_solar_coefficient for r in results) / len(results)
+    print(f"平均最优光伏系数：{avg_coefficient:.3f}")
+
+
+def main():
+    """主函数，运行所有场景示例"""
+    print("光伏优化模块场景演示")
+    print("运行5个不同场景的优化分析...")
+    
+    # 运行所有场景
+    results = []
+    
+    try:
+        # 场景1：典型日场景
+        result1 = scenario_1_typical_day()
+        results.append(result1)
+        
+        # 场景2：高负荷场景
+        result2 = scenario_2_high_load()
+        results.append(result2)
+        
+        # 场景3：低负荷场景
+        result3 = scenario_3_low_load()
+        results.append(result3)
+        
+        # 场景4：风光互补场景
+        result4 = scenario_4_wind_solar_complement()
+        results.append(result4)
+        
+        # 场景5：储能受限场景
+        result5 = scenario_5_storage_limited()
+        results.append(result5)
+        
+        # 对比分析
+        compare_scenarios(results)
+        
+        print("\n" + "=" * 80)
+        print("所有场景演示完成！")
+        print("=" * 80)
+        print("生成的文件：")
+        print("- scenario_1_typical_day.xlsx")
+        print("- scenario_2_high_load.xlsx") 
+        print("- scenario_3_low_load.xlsx")
+        print("- scenario_4_wind_solar_complement.xlsx")
+        print("- scenario_5_storage_limited.xlsx")
+        
+    except Exception as e:
+        print(f"运行场景演示时出错：{str(e)}")
+        import traceback
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    main()

src/advanced_visualization.py

@@ -0,0 +1,270 @@
+"""
+高级可视化程序 - 多能互补系统储能容量优化
+
+该程序提供更丰富的可视化功能，包括多种图表类型和交互式选项。
+
+作者: iFlow CLI
+创建日期: 2025-12-25
+"""
+
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+import numpy as np
+from datetime import datetime, timedelta
+from storage_optimization import optimize_storage_capacity, SystemParameters
+
+# 设置中文字体
+plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
+plt.rcParams['axes.unicode_minus'] = False
+
+
+def create_comprehensive_plot(solar_output, wind_output, thermal_output, load_demand, result, params):
+    """
+    创建综合可视化图表
+    
+    Args:
+        solar_output: 24小时光伏出力曲线 (MW)
+        wind_output: 24小时风电出力曲线 (MW)
+        thermal_output: 24小时火电出力曲线 (MW)
+        load_demand: 24小时负荷曲线 (MW)
+        result: 优化结果字典
+        params: 系统参数
+    """
+    hours = np.arange(24)
+    
+    # 创建大型图形
+    fig = plt.figure(figsize=(16, 12))
+    fig.suptitle('多能互补系统储能容量优化分析', fontsize=18, fontweight='bold')
+    
+    # 创建网格布局
+    gs = fig.add_gridspec(3, 3, hspace=0.3, wspace=0.3)
+    
+    # === 主要图表：发电和负荷 ===
+    ax1 = fig.add_subplot(gs[0, :])
+    
+    # 绘制各发电类型
+    ax1.fill_between(hours, 0, thermal_output, alpha=0.7, color='blue', label='火电')
+    ax1.fill_between(hours, thermal_output, 
+                     [thermal_output[i] + wind_output[i] for i in range(24)], 
+                     alpha=0.7, color='green', label='风电')
+    ax1.fill_between(hours, [thermal_output[i] + wind_output[i] for i in range(24)],
+                     [thermal_output[i] + wind_output[i] + solar_output[i] for i in range(24)],
+                     alpha=0.7, color='orange', label='光伏')
+    
+    # 绘制负荷曲线
+    ax1.plot(hours, load_demand, 'r-', linewidth=3, label='负荷需求')
+    
+    ax1.set_xlabel('时间 (小时)')
+    ax1.set_ylabel('功率 (MW)')
+    ax1.set_title('24小时发电与负荷平衡')
+    ax1.legend(loc='upper right')
+    ax1.grid(True, alpha=0.3)
+    ax1.set_xlim(0, 23)
+    
+    # === 储能充放电功率 ===
+    ax2 = fig.add_subplot(gs[1, 0])
+    charge_power = result['charge_profile']
+    discharge_power = [-x for x in result['discharge_profile']]
+    
+    ax2.bar(hours, charge_power, color='green', alpha=0.7, label='充电')
+    ax2.bar(hours, discharge_power, color='red', alpha=0.7, label='放电')
+    ax2.set_xlabel('时间 (小时)')
+    ax2.set_ylabel('功率 (MW)')
+    ax2.set_title('储能充放电功率')
+    ax2.legend()
+    ax2.grid(True, alpha=0.3)
+    ax2.axhline(y=0, color='black', linestyle='-', linewidth=0.5)
+    
+    # === 储能状态 ===
+    ax3 = fig.add_subplot(gs[1, 1])
+    storage_soc = result['storage_profile']
+    
+    ax3.plot(hours, storage_soc, 'b-', linewidth=2, marker='o')
+    ax3.fill_between(hours, 0, storage_soc, alpha=0.3, color='blue')
+    ax3.set_xlabel('时间 (小时)')
+    ax3.set_ylabel('储能容量 (MWh)')
+    ax3.set_title(f'储能状态 (容量: {result["required_storage_capacity"]:.1f} MWh)')
+    ax3.grid(True, alpha=0.3)
+    ax3.set_ylim(bottom=0)
+    
+    # === 弃风弃光 ===
+    ax4 = fig.add_subplot(gs[1, 2])
+    curtailed_wind = result['curtailed_wind']
+    curtailed_solar = result['curtailed_solar']
+    
+    ax4.bar(hours, curtailed_wind, color='lightblue', alpha=0.7, label='弃风')
+    ax4.bar(hours, curtailed_solar, color='yellow', alpha=0.7, label='弃光')
+    ax4.set_xlabel('时间 (小时)')
+    ax4.set_ylabel('功率 (MW)')
+    ax4.set_title('弃风弃光功率')
+    ax4.legend()
+    ax4.grid(True, alpha=0.3)
+    
+    # === 能量饼图 ===
+    ax5 = fig.add_subplot(gs[2, 0])
+    
+    # 计算总能量
+    total_gen = sum(thermal_output) + sum(wind_output) + sum(solar_output)
+    total_load = sum(load_demand)
+    total_curtailed = sum(curtailed_wind) + sum(curtailed_solar)
+    total_grid = sum(result['grid_feed_in'])
+    
+    # 处理上网电量为负的情况（购电）
+    if total_grid >= 0:
+        # 有上网电量
+        energy_data = [total_load, total_curtailed, total_grid]
+        energy_labels = [f'负荷\n({total_load:.1f} MWh)', 
+                         f'弃风弃光\n({total_curtailed:.1f} MWh)',
+                         f'上网电量\n({total_grid:.1f} MWh)']
+        colors = ['red', 'orange', 'green']
+        ax5.pie(energy_data, labels=energy_labels, colors=colors, autopct='%1.1f%%', startangle=90)
+        ax5.set_title('能量分配')
+    else:
+        # 从电网购电
+        grid_purchase = -total_grid  # 转为正值
+        energy_data = [total_load, total_curtailed, grid_purchase]
+        energy_labels = [f'负荷\n({total_load:.1f} MWh)', 
+                         f'弃风弃光\n({total_curtailed:.1f} MWh)',
+                         f'购电量\n({grid_purchase:.1f} MWh)']
+        colors = ['red', 'orange', 'blue']  # 购电用蓝色
+        ax5.pie(energy_data, labels=energy_labels, colors=colors, autopct='%1.1f%%', startangle=90)
+        ax5.set_title('能量分配（含购电）')
+    
+    # === 发电构成饼图 ===
+    ax6 = fig.add_subplot(gs[2, 1])
+    
+    gen_data = [sum(thermal_output), sum(wind_output), sum(solar_output)]
+    gen_labels = [f'火电\n({gen_data[0]:.1f} MWh)', 
+                  f'风电\n({gen_data[1]:.1f} MWh)',
+                  f'光伏\n({gen_data[2]:.1f} MWh)']
+    gen_colors = ['blue', 'green', 'orange']
+    
+    ax6.pie(gen_data, labels=gen_labels, colors=gen_colors, autopct='%1.1f%%', startangle=90)
+    ax6.set_title('发电构成')
+    
+    # === 关键指标文本 ===
+    ax7 = fig.add_subplot(gs[2, 2])
+    ax7.axis('off')
+    
+    # 显示关键指标
+    metrics_text = f"""
+    关键指标
+    ─────────────
+    所需储能容量: {result['required_storage_capacity']:.1f} MWh
+    储能效率: {params.storage_efficiency:.1%}
+    
+    弃风率: {result['total_curtailment_wind_ratio']:.1%}
+    弃光率: {result['total_curtailment_solar_ratio']:.1%}
+    上网电量比例: {result['total_grid_feed_in_ratio']:.1%}
+    
+    能量平衡: {'通过' if result['energy_balance_check'] else '未通过'}
+    
+    最大储能状态: {max(storage_soc):.1f} MWh
+    最小储能状态: {min(storage_soc):.1f} MWh
+    """
+    
+    ax7.text(0.1, 0.5, metrics_text, fontsize=11, verticalalignment='center',
+             fontfamily='SimHei', bbox=dict(boxstyle='round', facecolor='lightgray', alpha=0.8))
+    
+    # 保存图片
+    plt.savefig('comprehensive_analysis.png', dpi=300, bbox_inches='tight')
+    plt.close()
+    
+    print("综合分析图表已保存为 'comprehensive_analysis.png'")
+
+
+def create_time_series_plot(solar_output, wind_output, thermal_output, load_demand, result):
+    """
+    创建时间序列图表，模拟真实的时间轴
+    """
+    # 创建时间轴
+    base_time = datetime(2025, 1, 1, 0, 0, 0)
+    times = [base_time + timedelta(hours=i) for i in range(24)]
+    
+    fig, ax = plt.subplots(figsize=(14, 8))
+    
+    # 绘制发电和负荷
+    ax.plot(times, load_demand, 'r-', linewidth=3, label='负荷需求')
+    ax.plot(times, thermal_output, 'b-', linewidth=2, label='火电出力')
+    ax.plot(times, wind_output, 'g-', linewidth=2, label='风电出力')
+    ax.plot(times, solar_output, 'orange', linewidth=2, label='光伏出力')
+    
+    # 计算总发电量
+    total_generation = [thermal_output[i] + wind_output[i] + solar_output[i] for i in range(24)]
+    ax.plot(times, total_generation, 'k--', linewidth=1.5, alpha=0.7, label='总发电量')
+    
+    # 设置时间轴格式
+    ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
+    ax.xaxis.set_major_locator(mdates.HourLocator(interval=2))
+    
+    ax.set_xlabel('时间')
+    ax.set_ylabel('功率 (MW)')
+    ax.set_title('多能互补系统24小时发电曲线 (时间序列)')
+    ax.legend(loc='upper right')
+    ax.grid(True, alpha=0.3)
+    
+    # 旋转时间标签
+    plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)
+    
+    plt.tight_layout()
+    plt.savefig('time_series_curves.png', dpi=300, bbox_inches='tight')
+    plt.close()
+    
+    print("时间序列图表已保存为 'time_series_curves.png'")
+
+
+def main():
+    """主函数"""
+    # 示例数据
+    solar_output = [0.0] * 6 + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0] + [0.0] * 6
+    wind_output = [2.0, 3.0, 4.0, 3.0, 2.0, 1.0] * 4
+    thermal_output = [5.0] * 24
+    load_demand = [3.0, 4.0, 5.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 18.0, 
+                   16.0, 14.0, 12.0, 10.0, 8.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 2.0]
+    
+    # 系统参数
+    params = SystemParameters(
+        max_curtailment_wind=0.1,
+        max_curtailment_solar=0.1,
+        max_grid_ratio=0.2,
+        storage_efficiency=0.9,
+        discharge_rate=1.0,
+        charge_rate=1.0
+    )
+    
+    # 计算最优储能容量
+    print("正在计算最优储能容量...")
+    result = optimize_storage_capacity(
+        solar_output, wind_output, thermal_output, load_demand, params
+    )
+    
+    print("\n=== 优化结果 ===")
+    print(f"所需储能总容量: {result['required_storage_capacity']:.2f} MWh")
+    print(f"弃风率: {result['total_curtailment_wind_ratio']:.3f}")
+    print(f"弃光率: {result['total_curtailment_solar_ratio']:.3f}")
+    print(f"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'}")
+    
+    # 创建各种图表
+    print("\n正在生成可视化图表...")
+    
+    # 1. 基础曲线图（已在main.py中实现）
+    print("1. 基础系统运行曲线图")
+    
+    # 2. 综合分析图
+    print("2. 综合分析图表")
+    create_comprehensive_plot(solar_output, wind_output, thermal_output, load_demand, result, params)
+    
+    # 3. 时间序列图
+    print("3. 时间序列图表")
+    create_time_series_plot(solar_output, wind_output, thermal_output, load_demand, result)
+    
+    print("\n=== 所有图表生成完成 ===")
+    print("生成的文件:")
+    print("- system_curves.png: 基础系统运行曲线")
+    print("- comprehensive_analysis.png: 综合分析图表")
+    print("- time_series_curves.png: 时间序列图表")
+
+
+if __name__ == "__main__":
+    main()

src/economic_optimization.py

@@ -0,0 +1,709 @@
+"""
+经济指标优化模块
+
+该模块在光伏、风电、负荷确定的前提下，进行储能配置优化。
+目标函数是光伏建设费用、风电建设费用、储能建设费用、购电费用最小。
+
+作者: iFlow CLI
+创建日期: 2025-12-26
+"""
+
+import numpy as np
+import pandas as pd
+from typing import List, Dict, Tuple
+from dataclasses import dataclass
+from storage_optimization import SystemParameters, calculate_energy_balance, check_constraints
+import matplotlib.pyplot as plt
+
+
+@dataclass
+class EconomicParameters:
+    """经济参数配置"""
+    # 建设成本参数 (元/MW 或 元/MWh)
+    solar_capex: float = 3000000  # 光伏建设成本 (元/MW)
+    wind_capex: float = 2500000    # 风电建设成本 (元/MW)
+    storage_capex: float = 800000  # 储能建设成本 (元/MWh)
+    
+    # 运行成本参数
+    electricity_price: float = 600  # 购电价格 (元/MWh)
+    feed_in_price: float = 400    # 上网电价 (元/MWh)
+    
+    # 维护成本参数
+    solar_om: float = 50000      # 光伏运维成本 (元/MW/年)
+    wind_om: float = 45000       # 风电运维成本 (元/MW/年)
+    storage_om: float = 3000    # 储能运维成本 (元/MW/年)
+    
+    # 财务参数
+    project_lifetime: int = 25  # 项目寿命 (年)
+    discount_rate: float = 0.08   # 折现率
+
+
+@dataclass
+class OptimizationResult:
+    """优化结果"""
+    # 储能配置参数
+    storage_capacity: float      # 储能容量 (MWh)
+    charge_rate: float           # 充电倍率 (C-rate)
+    discharge_rate: float        # 放电倍率 (C-rate)
+    
+    # 经济指标
+    total_capex: float           # 总建设成本 (元)
+    total_om_cost: float         # 总运维成本 (元)
+    total_electricity_cost: float # 总电费成本 (元)
+    total_lcoe: float            # 平准化电力成本 (元/MWh)
+    total_npv: float             # 净现值 (元)
+    
+    # 系统性能指标
+    total_curtailment: float    # 总弃风弃光量 (MWh)
+    grid_purchase: float         # 总购电量 (MWh)
+    grid_feed_in: float          # 总上网电量 (MWh)
+    renewable_ratio: float       # 新能源消纳比例
+
+
+def calculate_lcoe(
+    capex: float,
+    om_cost: float,
+    electricity_cost: float,
+    annual_generation: float,
+    project_lifetime: int,
+    discount_rate: float
+) -> float:
+    """
+    计算基准化电力成本 (LCOE)
+    
+    Args:
+        capex: 建设成本
+        om_cost: 年运维成本
+        electricity_cost: 年电费成本
+        annual_generation: 年发电量
+        project_lifetime: 项目寿命
+        discount_rate: 折现率
+        
+    Returns:
+        LCOE值 (元/MWh)
+    """
+    if annual_generation <= 0:
+        return float('inf')
+    
+    # 计算现值因子
+    pv_factor = sum(1 / (1 + discount_rate) ** t for t in range(1, project_lifetime + 1))
+    
+    # LCOE = (建设成本现值 + 运维成本现值 + 电费成本现值) / 年发电量现值
+    total_cost = capex + om_cost * pv_factor + electricity_cost * pv_factor
+    generation_pv = annual_generation * pv_factor
+    
+    return total_cost / generation_pv
+
+
+def calculate_npv(
+    costs: List[float],
+    discount_rate: float
+) -> float:
+    """
+    计算净现值 (NPV)
+    
+    Args:
+        costs: 各年度成本流
+        discount_rate: 折现率
+        
+    Returns:
+        NPV值
+    """
+    npv = 0
+    for t, cost in enumerate(costs):
+        npv += cost / (1 + discount_rate) ** t
+    
+    return npv
+
+
+def evaluate_objective(
+    solar_output: List[float],
+    wind_output: List[float], 
+    thermal_output: List[float],
+    load_demand: List[float],
+    storage_capacity: float,
+    charge_rate: float,
+    discharge_rate: float,
+    econ_params: EconomicParameters,
+    system_params: SystemParameters
+) -> Dict:
+    """
+    评估目标函数值
+    
+    Args:
+        solar_output: 光伏出力曲线 (MW)
+        wind_output: 风电出力曲线 (MW)
+        thermal_output: 火电出力曲线 (MW)
+        load_demand: 负荷需求曲线 (MW)
+        storage_capacity: 储能容量 (MWh)
+        charge_rate: 充电倍率
+        discharge_rate: 放电倍率
+        econ_params: 经济参数
+        system_params: 系统参数
+        
+    Returns:
+        包含各项成本和性能指标的字典
+    """
+    # 计算系统运行结果
+    result = calculate_energy_balance(
+        solar_output, wind_output, thermal_output, load_demand, 
+        system_params, storage_capacity
+    )
+    
+    # 计算弃风弃光量
+    total_curtailment = sum(result['curtailed_wind']) + sum(result['curtailed_solar'])
+    
+    # 计算购电和上网电量
+    grid_purchase = sum(-x for x in result['grid_feed_in'] if x < 0)
+    grid_feed_in = sum(x for x in result['grid_feed_in'] if x > 0)
+    
+    # 计算建设成本
+    solar_capex_cost = sum(solar_output) * econ_params.solar_capex / len(solar_output) * 8760  # 转换为年容量
+    wind_capex_cost = sum(wind_output) * econ_params.wind_capex / len(wind_output) * 8760
+    storage_capex_cost = storage_capacity * econ_params.storage_capex
+    
+    total_capex = solar_capex_cost + wind_capex_cost + storage_capex_cost
+    
+    # 计算年运维成本
+    solar_om_cost = sum(solar_output) * econ_params.solar_om / len(solar_output) * 8760
+    wind_om_cost = sum(wind_output) * econ_params.wind_om / len(wind_output) * 8760
+    storage_om_cost = storage_capacity * econ_params.storage_om
+    
+    total_om_cost = solar_om_cost + wind_om_cost + storage_om_cost
+    
+    # 计算年电费成本
+    annual_electricity_cost = grid_purchase * econ_params.electricity_price
+    
+    # 计算年发电量
+    total_generation = sum(solar_output) + sum(wind_output) + sum(thermal_output)
+    renewable_generation = sum(solar_output) + sum(wind_output) - total_curtailment
+    
+    # 计算LCOE
+    lcoe = calculate_lcoe(
+        total_capex, total_om_cost, annual_electricity_cost,
+        renewable_generation, econ_params.project_lifetime, econ_params.discount_rate
+    )
+    
+    # 计算NPV
+    annual_costs = [total_om_cost + annual_electricity_cost] * econ_params.project_lifetime
+    npv = calculate_npv([total_capex] + annual_costs, econ_params.discount_rate)
+    
+    # 计算新能源消纳比例
+    renewable_ratio = (renewable_generation / sum(load_demand) * 100) if sum(load_demand) > 0 else 0
+    
+    return {
+        'total_capex': total_capex,
+        'total_om_cost': total_om_cost * econ_params.project_lifetime,
+        'total_electricity_cost': annual_electricity_cost * econ_params.project_lifetime,
+        'total_lcoe': lcoe,
+        'total_npv': npv,
+        'total_curtailment': total_curtailment,
+        'grid_purchase': grid_purchase,
+        'grid_feed_in': grid_feed_in,
+        'renewable_ratio': renewable_ratio,
+        'storage_capacity': storage_capacity,
+        'charge_rate': charge_rate,
+        'discharge_rate': discharge_rate
+    }
+
+
+def optimize_storage_economic(
+    solar_output: List[float],
+    wind_output: List[float],
+    thermal_output: List[float],
+    load_demand: List[float],
+    econ_params: EconomicParameters,
+    system_params: SystemParameters,
+    storage_capacity_range: Tuple[float, float] = (0, 1000),
+    rate_range: Tuple[float, float] = (0.1, 2.0),
+    max_iterations: int = 100,
+    tolerance: float = 0.01
+) -> OptimizationResult:
+    """
+    经济指标优化主函数
+    
+    Args:
+        solar_output: 光伏出力曲线 (MW)
+        wind_output: 风电出力曲线 (MW)
+        thermal_output: 火电出力曲线 (MW)
+        load_demand: 负荷需求曲线 (MW)
+        econ_params: 经济参数
+        system_params: 系统参数
+        storage_capacity_range: 储能容量搜索范围 (MWh)
+        rate_range: 充放电倍率搜索范围
+        max_iterations: 最大迭代次数
+        tolerance: 收敛容差
+        
+    Returns:
+        优化结果
+    """
+    print("开始经济指标优化...")
+    
+    best_result = None
+    best_npv = float('inf')
+    
+    # 简化的网格搜索优化
+    for iteration in range(max_iterations):
+        # 在搜索范围内随机采样
+        storage_capacity = np.random.uniform(storage_capacity_range[0], storage_capacity_range[1])
+        charge_rate = np.random.uniform(rate_range[0], rate_range[1])
+        discharge_rate = np.random.uniform(rate_range[0], rate_range[1])
+        
+        # 评估当前配置
+        current_result = evaluate_objective(
+            solar_output, wind_output, thermal_output, load_demand,
+            storage_capacity, charge_rate, discharge_rate,
+            econ_params, system_params
+        )
+        
+        # 更新最优解
+        if current_result['total_npv'] < best_npv:
+            best_npv = current_result['total_npv']
+            best_result = current_result
+            
+        # 输出进度
+        if (iteration + 1) % 10 == 0:
+            print(f"迭代 {iteration + 1}/{max_iterations}, 当前最优NPV: {best_npv:.2f}元")
+    
+    # 在最优解附近进行精细搜索
+    if best_result is not None:
+        print("在最优解附近进行精细搜索...")
+        best_result = fine_tune_optimization(
+            solar_output, wind_output, thermal_output, load_demand,
+            best_result, econ_params, system_params,
+            storage_capacity_range, rate_range
+        )
+    
+    return best_result
+
+
+def fine_tune_optimization(
+    solar_output: List[float],
+    wind_output: List[float],
+    thermal_output: List[float],
+    load_demand: List[float],
+    initial_result: Dict,
+    econ_params: EconomicParameters,
+    system_params: SystemParameters,
+    storage_capacity_range: Tuple[float, float],
+    rate_range: Tuple[float, float]
+) -> OptimizationResult:
+    """
+    在最优解附近进行精细搜索
+    
+    Args:
+        solar_output: 光伏出力曲线 (MW)
+        wind_output: 风电出力曲线 (MW)
+        thermal_output: 火电出力曲线 (MW)
+        load_demand: 负荷需求曲线 (MW)
+        initial_result: 初始优化结果
+        econ_params: 经济参数
+        system_params: 系统参数
+        storage_capacity_range: 储能容量搜索范围
+        rate_range: 充放电倍率搜索范围
+        
+    Returns:
+        精细优化结果
+    """
+    best_result = initial_result
+    best_npv = initial_result['total_npv']
+    
+    # 在最优解附近进行小范围搜索
+    search_range = 0.1  # 搜索范围为最优值的±10%
+    
+    for storage_capacity in np.linspace(
+        max(initial_result['storage_capacity'] * (1 - search_range), 0),
+        min(initial_result['storage_capacity'] * (1 + search_range), storage_capacity_range[1]),
+        20
+    ):
+        for charge_rate in np.linspace(
+            max(initial_result['charge_rate'] * (1 - search_range), rate_range[0]),
+            min(initial_result['charge_rate'] * (1 + search_range), rate_range[1]),
+            10
+        ):
+            for discharge_rate in np.linspace(
+                max(initial_result['discharge_rate'] * (1 - search_range), rate_range[0]),
+                min(initial_result['discharge_rate'] * (1 + search_range), rate_range[1]),
+                10
+            ):
+                current_result = evaluate_objective(
+                    solar_output, wind_output, thermal_output, load_demand,
+                    storage_capacity, charge_rate, discharge_rate,
+                    econ_params, system_params
+                )
+                
+                if current_result['total_npv'] < best_npv:
+                    best_npv = current_result['total_npv']
+                    best_result = current_result
+    
+    # 转换为OptimizationResult格式
+    return OptimizationResult(
+        storage_capacity=best_result['storage_capacity'],
+        charge_rate=best_result['charge_rate'],
+        discharge_rate=best_result['discharge_rate'],
+        total_capex=best_result['total_capex'],
+        total_om_cost=best_result['total_om_cost'],
+        total_electricity_cost=best_result['total_electricity_cost'],
+        total_lcoe=best_result['total_lcoe'],
+        total_npv=best_result['total_npv'],
+        total_curtailment=best_result['total_curtailment'],
+        grid_purchase=best_result['grid_purchase'],
+        grid_feed_in=best_result['grid_feed_in'],
+        renewable_ratio=best_result['renewable_ratio']
+    )
+
+
+def create_economic_report(
+    result: OptimizationResult,
+    econ_params: EconomicParameters,
+    filename: str = None
+) -> str:
+    """
+    创建经济优化报告
+    
+    Args:
+        result: 优化结果
+        econ_params: 经济参数
+        filename: 输出文件名
+        
+    Returns:
+        报告文件路径
+    """
+    if filename is None:
+        from datetime import datetime
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        filename = f"economic_optimization_report_{timestamp}.xlsx"
+    
+    print(f"\n正在生成经济优化报告: {filename}")
+    
+    # 创建报告数据
+    report_data = {
+        '指标': [
+            '储能容量 (MWh)',
+            '充电倍率 (C-rate)',
+            '放电倍率 (光伏建设成本 (元/MW)',
+            '光伏建设成本 (元)',
+            '风电建设成本 (元)',
+            '储能建设成本 (元)',
+            '总建设成本 (元)',
+            '年运维成本 (元)',
+            '总运维成本 (元)',
+            '年电费成本 (元)',
+            '总电费成本 (元)',
+            'LCOE (元/MWh)',
+            'NPV (元)',
+            '总弃风弃光量 (MWh)',
+            '总购电量 (MWh)',
+            '总上网电量 (MWh)',
+            '新能源消纳比例 (%)'
+        ],
+        '数值': [
+            f"{result.storage_capacity:.2f}",
+            f"{result.charge_rate:.2f}",
+            f"{result.discharge_rate:.2f}",
+            f"{result.total_capex * 0.3:.2f}",  # 光伏建设成本
+            f"{result.total_capex * 0.6:.2f}",  # 风电建设成本
+            f"{result.total_capex * 0.1:.2f}",  # 储能建设成本
+            f"{result.total_capex:.2f}",
+            f"{result.total_om_cost / econ_params.project_lifetime:.2f}",
+            f"{result.total_om_cost:.2f}",
+            f"{result.total_electricity_cost / econ_params.project_lifetime:.2f}",
+            f"{result.total_electricity_cost:.2f}",
+            f"{result.total_lcoe:.2f}",
+            f"{result.total_npv:.2f}",
+            f"{result.total_curtailment:.2f}",
+            f"{result.grid_purchase:.2f}",
+            f"{result.grid_feed_in:.2f}",
+            f"{result.renewable_ratio:.2f}"
+        ]
+    }
+    
+    # 创建参数数据
+    params_data = {
+        '参数': [
+            '光伏建设成本 (元/MW)',
+            '风电建设成本 (元/MW)',
+            '储能建设成本 (元/MWh)',
+            '购电价格 (元/MWh)',
+            '上网电价 (元/MWh)',
+            '光伏运维成本 (元/MW/年)',
+            '风电运维成本 (元/MW/年)',
+            '储能运维成本 (元/MW/年)',
+            '项目寿命 (年)',
+                            '折现率'        ],
+        '数值': [
+            econ_params.solar_capex,
+            econ_params.wind_capex,
+            econ_params.storage_capex,
+            econ_params.electricity_price,
+            econ_params.feed_in_price,
+            econ_params.solar_om,
+            econ_params.wind_om,
+            econ_params.storage_om,
+            econ_params.project_lifetime,
+                            f"{econ_params.discount_rate:.2f}"        ]
+    }
+    
+    # 写入Excel文件
+    with pd.ExcelWriter(filename, engine='openpyxl') as writer:
+        # 写入优化结果
+        pd.DataFrame(report_data).to_excel(writer, sheet_name='优化结果', index=False)
+        
+        # 写入经济参数
+        pd.DataFrame(params_data).to_excel(writer, sheet_name='经济参数', index=False)
+        
+        # 创建说明
+        description_data = {
+            '项目': [
+                '报告说明',
+                '生成时间',
+                '优化目标',
+                '优化方法',
+                '数据来源',
+                '注意事项'
+            ],
+            '内容': [
+                '多能互补系统储能经济优化报告',
+                pd.Timestamp.now().strftime("%Y-%m-%d %H:%M:%S"),
+                '最小化总建设成本和购电费用',
+                '网格搜索算法 + 精细搜索',
+                '基于给定的风光出力和负荷数据',
+                '成本估算仅供参考，实际成本可能有所不同'
+            ]
+        }
+        pd.DataFrame(description_data).to_excel(writer, sheet_name='说明', index=False)
+    
+    print(f"经济优化报告已生成: {filename}")
+    return filename
+
+
+def plot_economic_analysis(
+    results: List[OptimizationResult],
+    filename: str = None
+):
+    """
+    绘制经济分析图表
+    
+    Args:
+        results: 优化结果列表
+        filename: 图片保存文件名
+    """
+    if filename is None:
+        filename = 'economic_analysis.png'
+    
+    # 提取数据
+    capacities = [r.storage_capacity for r in results]
+    npvs = [r.total_npv for r in results]
+    lcoes = [r.total_lcoe for r in results]
+    renewable_ratios = [r.renewable_ratio for r in results]
+    
+    # 创建图表
+    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 12))
+    fig.suptitle('储能配置经济分析', fontsize=16, fontweight='bold')
+    
+    # NPV vs 储能容量
+    ax1.scatter(capacities, npvs, alpha=0.6, c='blue')
+    ax1.set_xlabel('储能容量 (MWh)')
+    ax1.set_ylabel('NPV (元)')
+    ax1.set_title('NPV vs 储能容量')
+    ax1.grid(True, alpha=0.3)
+    
+    # LCOE vs 储能容量
+    ax2.scatter(capacities, lcoes, alpha=0.6, c='green')
+    ax2.set_xlabel('储能容量 (MWh)')
+    ax2.set_ylabel('LCOE (元/MWh)')
+    ax2.set_title('LCOE vs 储能容量')
+    ax2.grid(True, alpha=0.3)
+    
+    # 新能源消纳比例 vs 储能容量
+    ax3.scatter(capacities, renewable_ratios, alpha=0.6, c='orange')
+    ax3.set_xlabel('储能容量 (MWh)')
+    ax3.set_ylabel('新能源消纳比例 (%)')
+    ax3.set_title('新能源消纳比例 vs 储能容量')
+    ax3.grid(True, alpha=0.3)
+    
+    # 成本构成饼图（使用最优结果）
+    if results:
+        best_result = min(results, key=lambda x: x.total_npv)
+        costs = [
+            best_result.total_capex * 0.3,  # 光伏
+            best_result.total_capex * 0.6,  # 风电
+            best_result.total_capex * 0.1,  # 储能
+            best_result.total_om_cost,      # 运维
+            best_result.total_electricity_cost  # 电费
+        ]
+        labels = ['光伏建设', '风电建设', '储能建设', '运维成本', '电费成本']
+        colors = ['yellow', 'lightblue', 'lightgreen', 'orange', 'red']
+        
+        ax4.pie(costs, labels=labels, colors=colors, autopct='%1.1f%%')
+        ax4.set_title('成本构成分析')
+    
+    plt.tight_layout()
+    plt.savefig(filename, dpi=300, bbox_inches='tight')
+    print(f"经济分析图表已保存: {filename}")
+
+
+def main():
+    """主函数 - 演示经济优化功能"""
+    import sys
+    
+    # 检查命令行参数
+    if len(sys.argv) < 2:
+        print("用法: python economic_optimization.py --excel <文件路径>")
+        print("     python economic_optimization.py --demo  # 运行演示")
+        return
+    
+    command = sys.argv[1]
+    
+    if command == '--demo':
+        print("运行经济优化演示...")
+        
+        # 生成演示数据
+        hours = 8760
+        solar_output = [0.0] * 6 + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0] + [0.0] * 6
+        solar_output = solar_output * 365
+        
+        wind_output = [2.0, 3.0, 4.0, 3.0, 2.0, 1.0] * 4
+        wind_output = wind_output * 365
+        
+        thermal_output = [5.0] * 24
+        thermal_output = thermal_output * 365
+        
+        load_demand = [3.0, 4.0, 5.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 18.0,
+                       16.0, 14.0, 12.0, 10.0, 8.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 2.0] * 365
+        
+        # 经济参数
+        econ_params = EconomicParameters(
+            solar_capex=3000000,      # 300万/MW
+            wind_capex=2500000,       # 250万/MW
+            storage_capex=800000,     # 80万/MWh
+            electricity_price=600,     # 600元/MWh
+            feed_in_price=400,         # 400元/MWh
+            solar_om=50000,           # 5万/MW/年
+            wind_om=45000,            # 4.5万/MW/年
+            storage_om=3000,          # 3000/MW/年
+            project_lifetime=25,        # 25年
+            discount_rate=0.08        # 8%
+        )
+        
+        # 系统参数
+        system_params = SystemParameters(
+            max_curtailment_wind=0.1,
+            max_curtailment_solar=0.1,
+            max_grid_ratio=0.2,
+            storage_efficiency=0.9,
+            discharge_rate=1.0,
+            charge_rate=1.0,
+            max_storage_capacity=None,
+            rated_thermal_capacity=0,
+            rated_solar_capacity=0,
+            rated_wind_capacity=0,
+            available_thermal_energy=0,
+            available_solar_energy=0,
+            available_wind_energy=0
+        )
+        
+        # 运行优化
+        result = optimize_storage_economic(
+            solar_output, wind_output, thermal_output, load_demand,
+            econ_params, system_params,
+            storage_capacity_range=(0, 500),
+            rate_range=(0.1, 1.5),
+            max_iterations=50
+        )
+        
+        # 输出结果
+        print("\n=== 经济优化结果 ===")
+        print(f"最优储能容量: {result.storage_capacity:.2f} MWh")
+        print(f"最优充电倍率: {result.charge_rate:.2f}")
+        print(f"最优放电倍率: {result.discharge_rate:.2f}")
+        print(f"总建设成本: {result.total_capex:.2f} 元")
+        print(f"总运维成本: {result.total_om_cost:.2f} 元")
+        print(f"总电费成本: {result.total_electricity_cost:.2f} 元")
+        print(f"LCOE: {result.total_lcoe:.2f} 元/MWh")
+        print(f"NPV: {result.total_npv:.2f} 元")
+        print(f"新能源消纳比例: {result.renewable_ratio:.2f}%")
+        
+        # 生成报告
+        create_economic_report(result, econ_params)
+        
+        # 生成图表
+        plot_economic_analysis([result])
+        
+    elif command == '--excel':
+        if len(sys.argv) < 3:
+            print("错误：请指定Excel文件路径")
+            return
+        
+        excel_file = sys.argv[2]
+        print(f"从Excel文件读取数据: {excel_file}")
+        
+        try:
+            # 从Excel文件读取数据
+            from excel_reader import read_excel_data
+            
+            data = read_excel_data(excel_file, include_parameters=True)
+            
+            solar_output = data['solar_output']
+            wind_output = data['wind_output']
+            thermal_output = data['thermal_output']
+            load_demand = data['load_demand']
+            
+            # 获取系统参数
+            system_params = data.get('system_parameters', SystemParameters())
+            
+            # 获取经济参数
+            econ_params = data.get('economic_parameters', EconomicParameters())
+            
+            # 获取优化设置
+            opt_settings = data.get('optimization_settings', {
+                'storage_capacity_range': (0, 1000),
+                'rate_range': (0.1, 2.0),
+                'max_iterations': 100,
+                'tolerance': 0.01
+            })
+            
+            print(f"成功读取数据，类型：{data['data_type']}")
+            print(f"光伏出力总量: {sum(solar_output):.2f} MW")
+            print(f"风电出力总量: {sum(wind_output):.2f} MW")
+            print(f"负荷需求总量: {sum(load_demand):.2f} MW")
+            
+            # 运行优化
+            result = optimize_storage_economic(
+                solar_output, wind_output, thermal_output, load_demand,
+                econ_params, system_params,
+                storage_capacity_range=opt_settings['storage_capacity_range'],
+                rate_range=opt_settings['rate_range'],
+                max_iterations=opt_settings['max_iterations'],
+                tolerance=opt_settings['tolerance']
+            )
+            
+            # 输出结果
+            print("\n=== 经济优化结果 ===")
+            print(f"最优储能容量: {result.storage_capacity:.2f} MWh")
+            print(f"最优充电倍率: {result.charge_rate:.2f}")
+            print(f"最优放电倍率: {result.discharge_rate:.2f}")
+            print(f"总建设成本: {result.total_capex:.2f} 元")
+            print(f"总运维成本: {result.total_om_cost:.2f} 元")
+            print(f"总电费成本: {result.total_electricity_cost:.2f} 元")
+            print(f"LCOE: {result.total_lcoe:.2f} 元/MWh")
+            print(f"NPV: {result.total_npv:.2f} 元")
+            print(f"总弃风弃光量: {result.total_curtailment:.2f} MWh")
+            print(f"总购电量: {result.grid_purchase:.2f} MWh")
+            print(f"总上网电量: {result.grid_feed_in:.2f} MWh")
+            print(f"新能源消纳比例: {result.renewable_ratio:.2f}%")
+            
+            # 生成报告
+            create_economic_report(result, econ_params)
+            
+            # 生成图表
+            plot_economic_analysis([result])
+            
+        except Exception as e:
+            print(f"处理Excel文件时出错：{str(e)}")
+            import traceback
+            traceback.print_exc()
+
+
+if __name__ == "__main__":
+    main()

src/excel_reader.py

@@ -0,0 +1,840 @@
+"""
+Excel数据读取模块
+
+该模块提供从Excel文件中读取8760小时负荷和发电曲线数据的功能。
+
+作者: iFlow CLI
+创建日期: 2025-12-25
+"""
+
+import pandas as pd
+import numpy as np
+from typing import Dict, List, Optional, Tuple, Any
+import os
+from storage_optimization import SystemParameters
+
+
+def validate_excel_data(df: pd.DataFrame, data_type: str = "8760") -> bool:
+    """
+    验证Excel数据格式是否正确
+    
+    Args:
+        df: pandas DataFrame对象
+        data_type: 数据类型，"24"或"8760"
+        
+    Returns:
+        bool: 验证是否通过
+    """
+    expected_length = 8760 if data_type == "8760" else 24
+    
+    # 检查行数
+    if len(df) != expected_length:
+        print(f"错误：数据行数应为{expected_length}，实际为{len(df)}")
+        return False
+    
+    # 检查必需的列
+    required_columns = ['光伏出力(MW)', '风电出力(MW)', '火电出力(MW)', '负荷需求(MW)']
+    missing_columns = [col for col in required_columns if col not in df.columns]
+    
+    if missing_columns:
+        print(f"错误：缺少必需的列：{missing_columns}")
+        return False
+    
+    # 检查数据类型和非负值
+    for col in required_columns:
+        if not pd.api.types.is_numeric_dtype(df[col]):
+            print(f"错误：列'{col}'必须为数值类型")
+            return False
+        
+        if (df[col] < 0).any():
+            print(f"错误：列'{col}'包含负值")
+            return False
+    
+    return True
+
+
+def read_system_parameters(file_path: str) -> SystemParameters:
+    """
+    从Excel文件读取系统参数
+    
+    Args:
+        file_path: Excel文件路径
+        
+    Returns:
+        SystemParameters对象
+        
+    Raises:
+        FileNotFoundError: 文件不存在
+        ValueError: 参数格式错误
+    """
+    # 检查文件是否存在
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"文件不存在：{file_path}")
+    
+    try:
+        # 读取参数工作表
+        df_params = pd.read_excel(file_path, sheet_name='参数')
+        
+        # 验证参数工作表格式
+        required_columns = ['参数名称', '参数值', '参数说明']
+        missing_columns = [col for col in required_columns if col not in df_params.columns]
+        
+        if missing_columns:
+            raise ValueError(f"参数工作表缺少必需的列：{missing_columns}")
+        
+        # 提取参数值
+        params_dict = {}
+        for _, row in df_params.iterrows():
+            param_name = row['参数名称']
+            param_value = row['参数值']
+            
+            # 跳过空行
+            if pd.isna(param_name) or pd.isna(param_value):
+                continue
+                
+            # 转换参数值
+            try:
+                if isinstance(param_value, str):
+                    # 尝试转换为浮点数
+                    param_value = float(param_value)
+                params_dict[param_name] = param_value
+            except (ValueError, TypeError):
+                raise ValueError(f"参数 '{param_name}' 的值 '{param_value}' 不是有效的数值")
+        
+        # 读取各参数值，如果找不到则使用默认值
+        get_param_value = lambda param_name: df_params.loc[df_params['参数名称'] == param_name, '参数值'].iloc[0] if param_name in df_params['参数名称'].values else None
+        
+        max_storage_capacity = get_param_value('最大储能容量')
+        # 处理空值或字符串"空"
+        if pd.isna(max_storage_capacity) or max_storage_capacity == '空':
+            max_storage_capacity = None
+        
+        try:
+            # 获取各参数值，区分None、NaN、0和有效值
+            def get_param_with_default(param_name, default_value):
+                value = get_param_value(param_name)
+                if value is None or pd.isna(value):
+                    return default_value
+                else:
+                    return value
+            
+            return SystemParameters(
+                max_curtailment_wind=get_param_with_default('最大弃风率', 0.1),
+                max_curtailment_solar=get_param_with_default('最大弃光率', 0.1),
+                max_grid_ratio=get_param_with_default('最大上网电量比例', 0.2),
+                storage_efficiency=get_param_with_default('储能效率', 0.9),
+                discharge_rate=get_param_with_default('放电倍率', 1.0),
+                charge_rate=get_param_with_default('充电倍率', 1.0),
+                max_storage_capacity=max_storage_capacity,
+                rated_thermal_capacity=get_param_with_default('额定火电装机容量', 100.0),
+                rated_solar_capacity=get_param_with_default('额定光伏装机容量', 100.0),
+                rated_wind_capacity=get_param_with_default('额定风电装机容量', 100.0),
+                available_thermal_energy=get_param_with_default('火电可用发电量', 2400.0),
+                available_solar_energy=get_param_with_default('光伏可用发电量', 600.0),
+                available_wind_energy=get_param_with_default('风电可用发电量', 1200.0)
+            )
+        except (KeyError, IndexError, Exception) as e:
+            print(f"读取参数失败：{str(e)}，使用默认参数")
+            return SystemParameters(
+                max_curtailment_wind=0.1,
+                max_curtailment_solar=0.1,
+                max_grid_ratio=0.2,
+                storage_efficiency=0.9,
+                discharge_rate=1.0,
+                charge_rate=1.0,
+                rated_thermal_capacity=100.0,
+                rated_solar_capacity=100.0,
+                rated_wind_capacity=100.0,
+                available_thermal_energy=2400.0,
+                available_solar_energy=600.0,
+                available_wind_energy=1200.0
+            )
+        
+    except Exception as e:
+        print(f"读取参数工作表失败，使用默认参数：{str(e)}")
+        # 如果参数工作表不存在或读取失败，返回默认参数
+        return SystemParameters()
+
+
+def read_excel_data(file_path: str, sheet_name: str = 0, include_parameters: bool = True) -> Dict[str, List[float]]:
+    """
+    从Excel文件读取8760小时数据
+    
+    Args:
+        file_path: Excel文件路径
+        sheet_name: 工作表名称或索引，默认为第一个工作表
+        include_parameters: 是否同时读取系统参数
+        
+    Returns:
+        包含所有数据的字典
+        
+    Raises:
+        FileNotFoundError: 文件不存在
+        ValueError: 数据格式错误
+    """
+    # 检查文件是否存在
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"文件不存在：{file_path}")
+    
+    try:
+        # 读取Excel文件
+        df = pd.read_excel(file_path, sheet_name=sheet_name)
+        
+        # 自动检测数据类型
+        data_type = "8760" if len(df) >= 8760 else "24"
+        
+        # 验证数据格式
+        if not validate_excel_data(df, data_type):
+            raise ValueError("Excel数据格式验证失败")
+        
+        # 提取数据并转换为列表
+        solar_output = df['光伏出力(MW)'].tolist()
+        wind_output = df['风电出力(MW)'].tolist()
+        thermal_output = df['火电出力(MW)'].tolist()
+        load_demand = df['负荷需求(MW)'].tolist()
+        
+        # 如果是24小时数据，扩展到8760小时（重复365天）
+        if data_type == "24" and len(df) == 24:
+            print("检测到24小时数据，自动扩展到8760小时（重复365天）")
+            solar_output = solar_output * 365
+            wind_output = wind_output * 365
+            thermal_output = thermal_output * 365
+            load_demand = load_demand * 365
+        
+        # 构建返回结果
+        result = {
+            'solar_output': solar_output,
+            'wind_output': wind_output,
+            'thermal_output': thermal_output,
+            'load_demand': load_demand,
+            'data_type': data_type,
+            'original_length': len(df)
+        }
+        
+        # 如果需要读取参数
+        if include_parameters:
+            try:
+                result['system_parameters'] = read_system_parameters(file_path)
+                print("成功读取系统参数")
+            except Exception as e:
+                print(f"读取系统参数失败，使用默认参数：{str(e)}")
+                result['system_parameters'] = SystemParameters()
+            
+            try:
+                result['economic_parameters'] = read_economic_parameters(file_path)
+                print("成功读取经济参数")
+            except Exception as e:
+                print(f"读取经济参数失败，使用默认参数：{str(e)}")
+                from economic_optimization import EconomicParameters
+                result['economic_parameters'] = EconomicParameters()
+            
+            try:
+                result['optimization_settings'] = get_optimization_settings(file_path)
+                print("成功读取优化设置")
+            except Exception as e:
+                print(f"读取优化设置失败，使用默认设置：{str(e)}")
+                result['optimization_settings'] = {
+                    'storage_capacity_range': (0, 1000),
+                    'rate_range': (0.1, 2.0),
+                    'max_iterations': 100,
+                    'tolerance': 0.01
+                }
+        
+        return result
+        
+    except Exception as e:
+        raise ValueError(f"读取Excel文件失败：{str(e)}")
+
+
+def create_excel_template(file_path: str, data_type: str = "8760"):
+    """
+    创建Excel数据模板文件
+    
+    Args:
+        file_path: 保存路径
+        data_type: 数据类型，"24"或"8760"
+    """
+    # 生成示例数据
+    if data_type == "24":
+        hours = 24
+        # 24小时典型日数据
+        solar = [0.0] * 6 + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0] + [0.0] * 6
+        wind = [2.0, 3.0, 4.0, 3.0, 2.0, 1.0] * 4
+        thermal = [5.0] * 24
+        load = [3.0, 4.0, 5.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 18.0, 
+                16.0, 14.0, 12.0, 10.0, 8.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 2.0]
+        description = "24小时典型日数据模板"
+    else:
+        hours = 8760
+        # 生成8760小时的模拟数据（基于日模式加季节变化）
+        daily_solar = [0.0] * 6 + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0] + [0.0] * 6
+        daily_wind = [2.0, 3.0, 4.0, 3.0, 2.0, 1.0] * 4
+        daily_thermal = [5.0] * 24
+        daily_load = [3.0, 4.0, 5.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 18.0, 
+                     16.0, 14.0, 12.0, 10.0, 8.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 2.0]
+        
+        solar = []
+        wind = []
+        thermal = []
+        load = []
+        
+        np.random.seed(42)  # 确保可重复性
+        
+        for day in range(365):
+            # 季节性因子
+            season_factor = 1.0 + 0.3 * np.sin(2 * np.pi * day / 365)
+            
+            for hour in range(24):
+                # 添加随机变化
+                solar_variation = 1.0 + 0.2 * (np.random.random() - 0.5)
+                wind_variation = 1.0 + 0.3 * (np.random.random() - 0.5)
+                load_variation = 1.0 + 0.1 * (np.random.random() - 0.5)
+                
+                solar.append(daily_solar[hour] * season_factor * solar_variation)
+                wind.append(daily_wind[hour] * wind_variation)
+                thermal.append(daily_thermal[hour])
+                load.append(daily_load[hour] * (2.0 - season_factor) * load_variation)
+        
+        description = "8760小时全年数据模板"
+    
+    # 创建DataFrame
+    df = pd.DataFrame({
+        '小时': range(1, hours + 1),
+        '光伏出力(MW)': solar,
+        '风电出力(MW)': wind,
+        '火电出力(MW)': thermal,
+        '负荷需求(MW)': load
+    })
+    
+    # 保存到Excel
+    with pd.ExcelWriter(file_path, engine='openpyxl') as writer:
+        df.to_excel(writer, sheet_name='数据', index=False)
+        
+        # 添加参数工作表
+        parameters_df = pd.DataFrame({
+            '参数名称': [
+                '最大弃风率',
+                '最大弃光率', 
+                '最大上网电量比例',
+                '储能效率',
+                '放电倍率',
+                '充电倍率',
+                '最大储能容量',
+                '额定火电装机容量',
+                '额定光伏装机容量',
+                '额定风电装机容量',
+                '火电可用发电量',
+                '光伏可用发电量',
+                '风电可用发电量'
+            ],
+            '参数值': [
+                0.1,    # 最大弃风率
+                0.1,    # 最大弃光率
+                0.2,    # 最大上网电量比例
+                0.9,    # 储能效率
+                1.0,    # 放电倍率
+                1.0,    # 充电倍率
+                '',     # 最大储能容量（空表示无限制）
+                0.0,    # 额定火电装机容量（可以为0）
+                100.0,  # 额定光伏装机容量
+                100.0,  # 额定风电装机容量
+                2400.0, # 火电可用发电量
+                600.0,  # 光伏可用发电量
+                1200.0  # 风电可用发电量
+            ],
+            '参数说明': [
+                '允许的最大弃风率（0.0-1.0）',
+                '允许的最大弃光率（0.0-1.0）',
+                '允许的最大上网电量比例（0.0-∞，只限制上网电量）',
+                '储能充放电效率（0.0-1.0）',
+                '储能放电倍率（C-rate，>0）',
+                '储能充电倍率（C-rate，>0）',
+                '储能容量上限（MWh，空表示无限制）',
+                '额定火电装机容量（MW，可以为0）',
+                '额定光伏装机容量（MW）',
+                '额定风电装机容量（MW）',
+                '火电可用发电量（MWh）',
+                '光伏可用发电量（MWh）',
+                '风电可用发电量（MWh）'
+            ],
+            '取值范围': [
+                '0.0-1.0',
+                '0.0-1.0', 
+                '≥0.0',
+                '0.0-1.0',
+                '>0',
+                '>0',
+                '>0或空',
+                '≥0',
+                '>0',
+                '>0',
+                '≥0',
+                '≥0',
+                '≥0'
+            ],
+            '默认值': [
+                '0.1',
+                '0.1',
+                '0.2', 
+                '0.9',
+                '1.0',
+                '1.0',
+                '无限制',
+                '0.0',
+                '100.0',
+                '100.0',
+                '2400.0',
+                '600.0',
+                '1200.0'
+            ]
+        })
+        parameters_df.to_excel(writer, sheet_name='参数', index=False)
+        
+        # 添加经济参数工作表
+        economic_params_df = pd.DataFrame({
+            '参数名称': [
+                '光伏建设成本',
+                '风电建设成本',
+                '储能建设成本',
+                '购电价格',
+                '上网电价',
+                '光伏运维成本',
+                '风电运维成本',
+                '储能运维成本',
+                '项目寿命',
+                '折现率',
+                '储能容量搜索范围-最小值',
+                '储能容量搜索范围-最大值',
+                '充放电倍率搜索范围-最小值',
+                '充放电倍率搜索范围-最大值',
+                '最大迭代次数',
+                '收敛容差'
+            ],
+            '参数值': [
+                3000000,    # 光伏建设成本 (元/MW)
+                2500000,     # 风电建设成本 (元/MW)
+                800000,      # 储能建设成本 (元/MWh)
+                600,         # 购电价格 (元/MWh)
+                400,         # 上网电价 (元/MWh)
+                50000,       # 光伏运维成本 (元/MW/年)
+                45000,       # 风电运维成本 (元/MW/年)
+                3000,        # 储能运维成本 (元/MW/年)
+                25,          # 项目寿命 (年)
+                0.08,        # 折现率
+                0,           # 储能容量搜索范围-最小值 (MWh)
+                1000,        # 储能容量搜索范围-最大值 (MWh)
+                0.1,         # 充放电倍率搜索范围-最小值
+                2.0,         # 充放电倍率搜索范围-最大值
+                100,         # 最大迭代次数
+                0.01         # 收敛容差
+            ],
+            '参数说明': [
+                '光伏发电系统建设成本 (元/MW)',
+                '风力发电系统建设成本 (元/MW)',
+                '储能系统建设成本 (元/MWh)',
+                '从电网购电价格 (元/MWh)',
+                '向电网售电价格 (元/MWh)',
+                '光伏系统年度运维成本 (元/MW/年)',
+                '风电系统年度运维成本 (元/MW/年)',
+                '储能系统年度运维成本 (元/MW/年)',
+                '项目运营寿命 (年)',
+                '项目折现率 (用于NPV计算)',
+                '储能容量优化搜索范围下限 (MWh)',
+                '储能容量优化搜索范围上限 (MWh)',
+                '充放电倍率优化搜索范围下限',
+                '充放电倍率优化搜索范围上限',
+                '优化算法最大迭代次数',
+                '优化算法收敛容差'
+            ],
+            '取值范围': [
+                '>0',
+                '>0',
+                '>0',
+                '>0',
+                '≥0',
+                '≥0',
+                '≥0',
+                '≥0',
+                '>0',
+                '0-1',
+                '≥0',
+                '>0',
+                '>0',
+                '>0',
+                '>0',
+                '>0'
+            ],
+            '默认值': [
+                '3,000,000',
+                '2,500,000',
+                '800,000',
+                '600',
+                '400',
+                '50,000',
+                '45,000',
+                '3,000',
+                '25',
+                '0.08',
+                '0',
+                '1000',
+                '0.1',
+                '2.0',
+                '100',
+                '0.01'
+            ]
+        })
+        economic_params_df.to_excel(writer, sheet_name='经济参数', index=False)
+        
+        # 添加说明工作表
+        description_df = pd.DataFrame({
+            '项目': ['数据说明', '数据类型', '时间范围', '单位', '注意事项', '参数说明', '经济优化说明'],
+            '内容': [
+                description,
+                f'{data_type}小时电力数据',
+                f'1-{hours}小时',
+                'MW (兆瓦)',
+                '所有数值必须为非负数',
+                '系统参数请在"参数"工作表中修改',
+                '经济优化参数请在"经济参数"工作表中修改'
+            ]
+        })
+        description_df.to_excel(writer, sheet_name='说明', index=False)
+    
+    print(f"Excel模板已创建：{file_path}")
+
+
+def analyze_excel_data(file_path: str) -> Dict[str, float]:
+    """
+    分析Excel数据的基本统计信息
+    
+    Args:
+        file_path: Excel文件路径
+        
+    Returns:
+        包含统计信息的字典
+    """
+    try:
+        data = read_excel_data(file_path)
+        
+        solar = data['solar_output']
+        wind = data['wind_output']
+        thermal = data['thermal_output']
+        load = data['load_demand']
+        
+        return {
+            'data_length': len(solar),
+            'total_solar': sum(solar),
+            'total_wind': sum(wind),
+            'total_thermal': sum(thermal),
+            'total_generation': sum(solar) + sum(wind) + sum(thermal),
+            'total_load': sum(load),
+            'max_solar': max(solar),
+            'max_wind': max(wind),
+            'max_thermal': max(thermal),
+            'max_load': max(load),
+            'avg_solar': np.mean(solar),
+            'avg_wind': np.mean(wind),
+            'avg_thermal': np.mean(thermal),
+            'avg_load': np.mean(load)
+        }
+    except Exception as e:
+        print(f"分析数据失败：{str(e)}")
+        return {}
+
+
+def read_economic_parameters(file_path: str):
+    """
+    从Excel文件读取经济参数
+    
+    Args:
+        file_path: Excel文件路径
+        
+    Returns:
+        EconomicParameters对象
+        
+    Raises:
+        FileNotFoundError: 文件不存在
+        ValueError: 参数格式错误
+    """
+    from economic_optimization import EconomicParameters
+    
+    # 检查文件是否存在
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"文件不存在：{file_path}")
+    
+    try:
+        # 读取经济参数工作表
+        df_params = pd.read_excel(file_path, sheet_name='经济参数')
+        
+        # 验证经济参数工作表格式
+        required_columns = ['参数名称', '参数值', '参数说明']
+        missing_columns = [col for col in required_columns if col not in df_params.columns]
+        
+        if missing_columns:
+            raise ValueError(f"经济参数工作表缺少必需的列：{missing_columns}")
+        
+        # 提取参数值
+        params_dict = {}
+        for _, row in df_params.iterrows():
+            param_name = row['参数名称']
+            param_value = row['参数值']
+            
+            # 跳过空行
+            if pd.isna(param_name) or pd.isna(param_value):
+                continue
+                
+            # 转换参数值
+            try:
+                if isinstance(param_value, str):
+                    # 尝试转换为浮点数
+                    param_value = float(param_value)
+                params_dict[param_name] = param_value
+            except (ValueError, TypeError):
+                raise ValueError(f"经济参数 '{param_name}' 的值 '{param_value}' 不是有效的数值")
+        
+        # 读取各参数值，如果找不到则使用默认值
+        get_param_value = lambda param_name: df_params.loc[df_params['参数名称'] == param_name, '参数值'].iloc[0] if param_name in df_params['参数名称'].values else None
+        
+        try:
+            # 获取各参数值，区分None、NaN、0和有效值
+            def get_param_with_default(param_name, default_value):
+                value = get_param_value(param_name)
+                if value is None or pd.isna(value):
+                    return default_value
+                else:
+                    return value
+            
+            return EconomicParameters(
+                solar_capex=get_param_with_default('光伏建设成本', 3000000),
+                wind_capex=get_param_with_default('风电建设成本', 2500000),
+                storage_capex=get_param_with_default('储能建设成本', 800000),
+                electricity_price=get_param_with_default('购电价格', 600),
+                feed_in_price=get_param_with_default('上网电价', 400),
+                solar_om=get_param_with_default('光伏运维成本', 50000),
+                wind_om=get_param_with_default('风电运维成本', 45000),
+                storage_om=get_param_with_default('储能运维成本', 3000),
+                project_lifetime=int(get_param_with_default('项目寿命', 25)),
+                discount_rate=get_param_with_default('折现率', 0.08)
+            )
+        except (KeyError, IndexError, Exception) as e:
+            print(f"读取经济参数失败：{str(e)}，使用默认参数")
+            return EconomicParameters(
+                solar_capex=3000000,
+                wind_capex=2500000,
+                storage_capex=800000,
+                electricity_price=600,
+                feed_in_price=400,
+                solar_om=50000,
+                wind_om=45000,
+                storage_om=3000,
+                project_lifetime=25,
+                discount_rate=0.08
+            )
+        
+    except Exception as e:
+        print(f"读取经济参数工作表失败，使用默认参数：{str(e)}")
+        # 如果经济参数工作表不存在或读取失败，返回默认参数
+        return EconomicParameters()
+
+
+def get_optimization_settings(file_path: str) -> Dict[str, Any]:
+    """
+    从Excel文件读取优化设置参数
+    
+    Args:
+        file_path: Excel文件路径
+        
+    Returns:
+        优化设置字典
+    """
+    try:
+        # 读取经济参数工作表
+        df_params = pd.read_excel(file_path, sheet_name='经济参数')
+        
+        # 提取优化设置参数
+        get_param_value = lambda param_name: df_params.loc[df_params['参数名称'] == param_name, '参数值'].iloc[0] if param_name in df_params['参数名称'].values else None
+        
+        def get_param_with_default(param_name, default_value):
+            value = get_param_value(param_name)
+            if value is None or pd.isna(value):
+                return default_value
+            else:
+                return value
+        
+        return {
+            'storage_capacity_range': (
+                get_param_with_default('储能容量搜索范围-最小值', 0),
+                get_param_with_default('储能容量搜索范围-最大值', 1000)
+            ),
+            'rate_range': (
+                get_param_with_default('充放电倍率搜索范围-最小值', 0.1),
+                get_param_with_default('充放电倍率搜索范围-最大值', 2.0)
+            ),
+            'max_iterations': int(get_param_with_default('最大迭代次数', 100)),
+            'tolerance': get_param_with_default('收敛容差', 0.01)
+        }
+        
+    except Exception as e:
+        print(f"读取优化设置失败，使用默认设置：{str(e)}")
+        return {
+            'storage_capacity_range': (0, 1000),
+            'rate_range': (0.1, 2.0),
+            'max_iterations': 100,
+            'tolerance': 0.01
+        }
+
+
+def validate_system_parameters(params: SystemParameters) -> Dict[str, Any]:
+    """
+    验证系统参数的有效性
+    
+    Args:
+        params: SystemParameters对象
+        
+    Returns:
+        验证结果字典
+    """
+    validation_result = {
+        'valid': True,
+        'errors': [],
+        'warnings': []
+    }
+    
+    # 检查弃风率
+    if not (0.0 <= params.max_curtailment_wind <= 1.0):
+        validation_result['valid'] = False
+        validation_result['errors'].append(f"弃风率必须在0.0-1.0之间，当前值：{params.max_curtailment_wind}")
+    
+    # 检查弃光率
+    if not (0.0 <= params.max_curtailment_solar <= 1.0):
+        validation_result['valid'] = False
+        validation_result['errors'].append(f"弃光率必须在0.0-1.0之间，当前值：{params.max_curtailment_solar}")
+    
+    # 检查上网电量比例
+    if not (0.0 <= params.max_grid_ratio):
+        validation_result['valid'] = False
+        validation_result['errors'].append(f"上网电量比例必须为非负值，当前值：{params.max_grid_ratio}")
+    
+    # 检查储能效率
+    if not (0.0 < params.storage_efficiency <= 1.0):
+        validation_result['valid'] = False
+        validation_result['errors'].append(f"储能效率必须在0.0-1.0之间，当前值：{params.storage_efficiency}")
+    
+    # 检查放电倍率
+    if params.discharge_rate <= 0:
+        validation_result['valid'] = False
+        validation_result['errors'].append(f"放电倍率必须大于0，当前值：{params.discharge_rate}")
+    
+    # 检查充电倍率
+    if params.charge_rate <= 0:
+        validation_result['valid'] = False
+        validation_result['errors'].append(f"充电倍率必须大于0，当前值：{params.charge_rate}")
+    
+    # 检查储能容量上限
+    if params.max_storage_capacity is not None and params.max_storage_capacity <= 0:
+        validation_result['valid'] = False
+        validation_result['errors'].append(f"储能容量上限必须大于0，当前值：{params.max_storage_capacity}")
+    
+    # 添加警告信息
+    if params.storage_efficiency < 0.8:
+        validation_result['warnings'].append("储能效率较低，可能影响系统性能")
+    
+    if params.max_curtailment_wind > 0.3 or params.max_curtailment_solar > 0.3:
+        validation_result['warnings'].append("弃风弃光率较高，可能造成能源浪费")
+    
+    if params.max_grid_ratio > 0.5:
+        validation_result['warnings'].append("上网电量比例较高，可能影响电网稳定性")
+    
+    return validation_result
+
+
+def main():
+    """主函数，演示Excel数据读取功能"""
+    import sys
+    
+    # 检查命令行参数
+    if len(sys.argv) > 1 and sys.argv[1] == '--economic':
+        print("=== 创建经济优化Excel模板 ===")
+        
+        # 创建经济优化模板文件
+        economic_template_8760 = "economic_data_template_8760.xlsx"
+        economic_template_24 = "economic_data_template_24.xlsx"
+        
+        print("\n1. 创建经济优化Excel模板文件...")
+        create_excel_template(economic_template_8760, "8760")
+        create_excel_template(economic_template_24, "24")
+        
+        print(f"\n[OK] 经济优化Excel模板创建完成！")
+        print(f"[FILE] 8760小时模板: {economic_template_8760}")
+        print(f"[FILE] 24小时模板: {economic_template_24}")
+        print(f"\n[INFO] 模板包含以下工作表：")
+        print(f"   1. 数据 - 8760小时电力数据")
+        print(f"   2. 参数 - 系统运行参数")
+        print(f"   3. 经济参数 - 经济优化参数")
+        print(f"   4. 说明 - 使用说明")
+        print(f"\n[USAGE] 使用方法：")
+        print(f"   uv run python economic_optimization.py --excel {economic_template_8760}")
+        return
+    
+    print("=== Excel数据读取模块演示 ===")
+    
+    # 创建模板文件
+    template_8760 = "data_template_8760.xlsx"
+    template_24 = "data_template_24.xlsx"
+    
+    print("\n1. 创建Excel模板文件...")
+    create_excel_template(template_8760, "8760")
+    create_excel_template(template_24, "24")
+    
+    # 分析模板数据
+    print(f"\n2. 分析{template_8760}数据...")
+    stats = analyze_excel_data(template_8760)
+    if stats:
+        print("数据统计信息：")
+        for key, value in stats.items():
+            print(f"  {key}: {value:.2f}")
+    
+    print(f"\n3. 演示读取{template_24}数据...")
+    try:
+        data = read_excel_data(template_24)
+        print(f"成功读取数据，类型：{data['data_type']}")
+        print(f"光伏出力前10小时：{data['solar_output'][:10]}")
+        print(f"风电出力前10小时：{data['wind_output'][:10]}")
+        print(f"负荷需求前10小时：{data['load_demand'][:10]}")
+        
+        # 演示参数读取
+        if 'system_parameters' in data:
+            params = data['system_parameters']
+            print(f"\n系统参数：")
+            print(f"  最大弃风率: {params.max_curtailment_wind}")
+            print(f"  最大弃光率: {params.max_curtailment_solar}")
+            print(f"  最大上网电量比例: {params.max_grid_ratio}")
+            print(f"  储能效率: {params.storage_efficiency}")
+            print(f"  放电倍率: {params.discharge_rate}")
+            print(f"  充电倍率: {params.charge_rate}")
+            print(f"  最大储能容量: {params.max_storage_capacity}")
+            
+            # 验证参数
+            validation = validate_system_parameters(params)
+            if validation['valid']:
+                print("[OK] 参数验证通过")
+            else:
+                print("[ERROR] 参数验证失败:")
+                for error in validation['errors']:
+                    print(f"  - {error}")
+            
+            if validation['warnings']:
+                print("[WARNING] 参数警告:")
+                for warning in validation['warnings']:
+                    print(f"  - {warning}")
+        
+    except Exception as e:
+        print(f"读取失败：{str(e)}")
+    
+    print("\n=== 演示完成 ===")
+    print("模板文件已创建，您可以根据实际数据修改Excel文件。")
+    print("系统参数可以在Excel的'参数'工作表中直接修改。")
+
+
+if __name__ == "__main__":
+    main()

src/solar_optimization.py

@@ -0,0 +1,362 @@
+"""
+光伏出力优化模块
+
+该模块通过调整光伏出力曲线的系数，在给定的系统参数条件下
+最小化与电网交换的电量，提高系统的自平衡能力。
+
+作者: iFlow CLI
+创建日期: 2025-12-26
+"""
+
+import numpy as np
+from typing import List, Dict, Tuple, Optional
+from dataclasses import dataclass
+from storage_optimization import SystemParameters, optimize_storage_capacity, calculate_energy_balance
+
+
+@dataclass
+class SolarOptimizationResult:
+    """光伏优化结果类"""
+    optimal_solar_coefficient: float        # 最优光伏系数
+    original_solar_output: List[float]      # 原始光伏出力曲线
+    optimized_solar_output: List[float]     # 优化后光伏出力曲线
+    min_grid_exchange: float                # 最小电网交换电量
+    grid_purchase: float                    # 购电量
+    grid_feed_in: float                     # 上网电量
+    storage_result: Dict                    # 储能优化结果
+    optimization_history: List[Dict]        # 优化历史记录
+
+
+def calculate_grid_exchange_metric(
+    solar_output: List[float],
+    wind_output: List[float],
+    thermal_output: List[float],
+    load_demand: List[float],
+    params: SystemParameters
+) -> Dict[str, float]:
+    """
+    计算电网交换电量指标
+    
+    Args:
+        solar_output: 光伏出力曲线 (MW)
+        wind_output: 风电出力曲线 (MW)
+        thermal_output: 火电出力曲线 (MW)
+        load_demand: 负荷曲线 (MW)
+        params: 系统参数配置
+        
+    Returns:
+        包含电网交换指标的字典
+    """
+    # 计算最优储能容量
+    storage_result = optimize_storage_capacity(
+        solar_output, wind_output, thermal_output, load_demand, params
+    )
+    
+    # 计算电网交换电量
+    grid_feed_in = storage_result['grid_feed_in']
+    
+    # 分离购电和上网电量
+    total_purchase = sum(-x for x in grid_feed_in if x < 0)  # 购电量（正值）
+    total_feed_in = sum(x for x in grid_feed_in if x > 0)    # 上网电量（正值）
+    
+    # 计算总交换电量（购电 + 上网）
+    total_exchange = total_purchase + total_feed_in
+    
+    return {
+        'total_exchange': total_exchange,
+        'grid_purchase': total_purchase,
+        'grid_feed_in': total_feed_in,
+        'storage_capacity': storage_result['required_storage_capacity'],
+        'storage_result': storage_result
+    }
+
+
+def optimize_solar_output(
+    original_solar_output: List[float],
+    wind_output: List[float],
+    thermal_output: List[float],
+    load_demand: List[float],
+    params: SystemParameters,
+    coefficient_range: Tuple[float, float] = (0.1, 2.0),
+    tolerance: float = 0.01,
+    max_iterations: int = 50
+) -> SolarOptimizationResult:
+    """
+    优化光伏出力系数以最小化电网交换电量
+    
+    Args:
+        original_solar_output: 原始光伏出力曲线 (MW)
+        wind_output: 风电出力曲线 (MW)
+        thermal_output: 火电出力曲线 (MW)
+        load_demand: 负荷曲线 (MW)
+        params: 系统参数配置
+        coefficient_range: 光伏系数搜索范围 (最小值, 最大值)
+        tolerance: 收敛容差
+        max_iterations: 最大迭代次数
+        
+    Returns:
+        光伏优化结果
+    """
+    print("开始光伏出力优化...")
+    
+    # 初始化优化历史
+    optimization_history = []
+    
+    # 使用黄金分割法进行一维优化
+    phi = (1 + np.sqrt(5)) / 2  # 黄金比例
+    resphi = 2 - phi
+    
+    a, b = coefficient_range
+    c = b - resphi * (b - a)
+    d = a + resphi * (b - a)
+    
+    # 计算初始点的目标函数值
+    fc = calculate_grid_exchange_metric(
+        [x * c for x in original_solar_output],
+        wind_output, thermal_output, load_demand, params
+    )
+    
+    fd = calculate_grid_exchange_metric(
+        [x * d for x in original_solar_output],
+        wind_output, thermal_output, load_demand, params
+    )
+    
+    # 记录初始点
+    optimization_history.append({
+        'coefficient': c,
+        'total_exchange': fc['total_exchange'],
+        'grid_purchase': fc['grid_purchase'],
+        'grid_feed_in': fc['grid_feed_in'],
+        'storage_capacity': fc['storage_capacity']
+    })
+    
+    optimization_history.append({
+        'coefficient': d,
+        'total_exchange': fd['total_exchange'],
+        'grid_purchase': fd['grid_purchase'],
+        'grid_feed_in': fd['grid_feed_in'],
+        'storage_capacity': fd['storage_capacity']
+    })
+    
+    # 黄金分割搜索
+    for iteration in range(max_iterations):
+        if abs(fc['total_exchange'] - fd['total_exchange']) < tolerance:
+            break
+            
+        if fc['total_exchange'] < fd['total_exchange']:
+            b = d
+            d = c
+            fd = fc
+            c = b - resphi * (b - a)
+            
+            fc = calculate_grid_exchange_metric(
+                [x * c for x in original_solar_output],
+                wind_output, thermal_output, load_demand, params
+            )
+            
+            optimization_history.append({
+                'coefficient': c,
+                'total_exchange': fc['total_exchange'],
+                'grid_purchase': fc['grid_purchase'],
+                'grid_feed_in': fc['grid_feed_in'],
+                'storage_capacity': fc['storage_capacity']
+            })
+        else:
+            a = c
+            c = d
+            fc = fd
+            d = a + resphi * (b - a)
+            
+            fd = calculate_grid_exchange_metric(
+                [x * d for x in original_solar_output],
+                wind_output, thermal_output, load_demand, params
+            )
+            
+            optimization_history.append({
+                'coefficient': d,
+                'total_exchange': fd['total_exchange'],
+                'grid_purchase': fd['grid_purchase'],
+                'grid_feed_in': fd['grid_feed_in'],
+                'storage_capacity': fd['storage_capacity']
+            })
+    
+    # 确定最优系数
+    if fc['total_exchange'] < fd['total_exchange']:
+        optimal_coefficient = c
+        best_result = fc
+    else:
+        optimal_coefficient = d
+        best_result = fd
+    
+    # 生成优化后的光伏出力曲线
+    optimized_solar_output = [x * optimal_coefficient for x in original_solar_output]
+    
+    # 重新计算完整的最优储能配置
+    final_storage_result = optimize_storage_capacity(
+        optimized_solar_output, wind_output, thermal_output, load_demand, params
+    )
+    
+    print(f"优化完成！最优光伏系数: {optimal_coefficient:.3f}")
+    print(f"最小电网交换电量: {best_result['total_exchange']:.2f} MWh")
+    
+    return SolarOptimizationResult(
+        optimal_solar_coefficient=optimal_coefficient,
+        original_solar_output=original_solar_output,
+        optimized_solar_output=optimized_solar_output,
+        min_grid_exchange=best_result['total_exchange'],
+        grid_purchase=best_result['grid_purchase'],
+        grid_feed_in=best_result['grid_feed_in'],
+        storage_result=final_storage_result,
+        optimization_history=optimization_history
+    )
+
+
+
+
+
+def export_optimization_results(result: SolarOptimizationResult, filename: str = None):
+    """
+    导出光伏优化结果到Excel文件
+    
+    Args:
+        result: 光伏优化结果
+        filename: 输出文件名，如果为None则自动生成
+    """
+    import pandas as pd
+    from datetime import datetime
+    
+    if filename is None:
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        filename = f"solar_optimization_results_{timestamp}.xlsx"
+    
+    print(f"正在导出光伏优化结果到Excel文件: {filename}")
+    
+    hours = list(range(1, len(result.original_solar_output) + 1))
+    
+    # 创建主要数据DataFrame
+    data_df = pd.DataFrame({
+        '小时': hours,
+        '原始光伏出力(MW)': result.original_solar_output,
+        '优化后光伏出力(MW)': result.optimized_solar_output,
+        '出力变化(MW)': [result.optimized_solar_output[i] - result.original_solar_output[i] 
+                        for i in range(len(result.original_solar_output))],
+        '变化比例(%)': [(result.optimized_solar_output[i] / result.original_solar_output[i] - 1) * 100 
+                       if result.original_solar_output[i] > 0 else 0 
+                       for i in range(len(result.original_solar_output))]
+    })
+    
+    # 创建优化结果摘要DataFrame
+    summary_df = pd.DataFrame({
+        '指标': [
+            '最优光伏系数',
+            '最小电网交换电量',
+            '购电量',
+            '上网电量',
+            '所需储能容量',
+            '优化后弃风率',
+            '优化后弃光率',
+            '优化后上网电量比例'
+        ],
+        '数值': [
+            f"{result.optimal_solar_coefficient:.3f}",
+            f"{result.min_grid_exchange:.2f} MWh",
+            f"{result.grid_purchase:.2f} MWh",
+            f"{result.grid_feed_in:.2f} MWh",
+            f"{result.storage_result['required_storage_capacity']:.2f} MWh",
+            f"{result.storage_result['total_curtailment_wind_ratio']:.3f}",
+            f"{result.storage_result['total_curtailment_solar_ratio']:.3f}",
+            f"{result.storage_result['total_grid_feed_in_ratio']:.3f}"
+        ]
+    })
+    
+    # 创建优化历史DataFrame
+    history_df = pd.DataFrame(result.optimization_history)
+    history_df.columns = ['光伏系数', '电网交换电量(MWh)', '购电量(MWh)', '上网电量(MWh)', '储能容量(MWh)']
+    
+    # 写入Excel文件
+    with pd.ExcelWriter(filename, engine='openpyxl') as writer:
+        # 写入主要数据
+        data_df.to_excel(writer, sheet_name='出力曲线对比', index=False)
+        
+        # 写入优化结果摘要
+        summary_df.to_excel(writer, sheet_name='优化结果摘要', index=False)
+        
+        # 写入优化历史
+        history_df.to_excel(writer, sheet_name='优化历史', index=False)
+        
+        # 创建说明工作表
+        description_df = pd.DataFrame({
+            '项目': [
+                '文件说明',
+                '生成时间',
+                '优化目标',
+                '优化方法',
+                '数据长度',
+                '注意事项'
+            ],
+            '内容': [
+                '光伏出力优化结果 - 通过调整光伏系数最小化电网交换电量',
+                datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+                '最小化与电网交换的总电量（购电 + 上网）',
+                '黄金分割一维优化算法',
+                f"{len(result.original_solar_output)} 小时",
+                '优化结果在给定的系统参数约束下得出'
+            ]
+        })
+        description_df.to_excel(writer, sheet_name='说明', index=False)
+    
+    print(f"光伏优化结果已成功导出到: {filename}")
+    return filename
+
+
+def main():
+    """主函数，提供示例使用"""
+    # 示例数据
+    original_solar_output = [0.0] * 6 + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0] + [0.0] * 6
+    wind_output = [2.0, 3.0, 4.0, 3.0, 2.0, 1.0] * 4
+    thermal_output = [5.0] * 24
+    load_demand = [3.0, 4.0, 5.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 18.0,
+                   16.0, 14.0, 12.0, 10.0, 8.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 2.0]
+    
+    # 系统参数
+    params = SystemParameters(
+        max_curtailment_wind=0.1,
+        max_curtailment_solar=0.1,
+        max_grid_ratio=0.2,
+        storage_efficiency=0.9,
+        discharge_rate=1.0,
+        charge_rate=1.0,
+        rated_thermal_capacity=100.0,
+        rated_solar_capacity=100.0,
+        rated_wind_capacity=100.0,
+        available_thermal_energy=2400.0,
+        available_solar_energy=600.0,
+        available_wind_energy=1200.0
+    )
+    
+    # 执行光伏优化
+    result = optimize_solar_output(
+        original_solar_output, wind_output, thermal_output, load_demand, params
+    )
+    
+    # 打印结果
+    print("\n=== 光伏出力优化结果 ===")
+    print(f"最优光伏系数: {result.optimal_solar_coefficient:.3f}")
+    print(f"最小电网交换电量: {result.min_grid_exchange:.2f} MWh")
+    print(f"其中购电量: {result.grid_purchase:.2f} MWh")
+    print(f"其中上网电量: {result.grid_feed_in:.2f} MWh")
+    print(f"所需储能容量: {result.storage_result['required_storage_capacity']:.2f} MWh")
+    print(f"优化后弃风率: {result.storage_result['total_curtailment_wind_ratio']:.3f}")
+    print(f"优化后弃光率: {result.storage_result['total_curtailment_solar_ratio']:.3f}")
+    print(f"优化后上网电量比例: {result.storage_result['total_grid_feed_in_ratio']:.3f}")
+    
+    
+    
+    # 导出结果
+    export_optimization_results(result)
+    
+    return result
+
+
+if __name__ == "__main__":
+    main()

src/storage_optimization.py

@@ -0,0 +1,571 @@
+"""
+多能互补系统储能容量优化计算程序
+
+该程序计算多能互补系统中所需的储能容量，确保系统在24小时内电能平衡，
+同时满足用户定义的弃风弃光率和上网电量比例约束。
+
+作者: iFlow CLI
+创建日期: 2025-12-25
+"""
+
+import numpy as np
+from typing import List, Dict, Tuple, Optional
+from dataclasses import dataclass
+
+
+@dataclass
+class SystemParameters:
+    """系统参数配置类"""
+    max_curtailment_wind: float = 0.1    # 最大允许弃风率 (0.0-1.0)
+    max_curtailment_solar: float = 0.1   # 最大允许弃光率 (0.0-1.0)
+    max_grid_ratio: float = 0.2          # 最大允许上网电量比例 (0.0-∞，只限制上网电量，不限制购电)
+    storage_efficiency: float = 0.9      # 储能充放电效率 (0.0-1.0)
+    discharge_rate: float = 1.0          # 储能放电倍率 (C-rate)
+    charge_rate: float = 1.0             # 储能充电倍率 (C-rate)
+    max_storage_capacity: Optional[float] = None  # 储能容量上限 (MWh)，None表示无限制
+    # 新增额定装机容量参数
+    rated_thermal_capacity: float = 100.0  # 额定火电装机容量 (MW)
+    rated_solar_capacity: float = 100.0    # 额定光伏装机容量 (MW)
+    rated_wind_capacity: float = 100.0     # 额定风电装机容量 (MW)
+    # 新增可用发电量参数
+    available_thermal_energy: float = 2400.0  # 火电可用发电量 (MWh)
+    available_solar_energy: float = 600.0    # 光伏可用发电量 (MWh)
+    available_wind_energy: float = 1200.0    # 风电可用发电量 (MWh)
+
+
+def validate_inputs(
+    solar_output: List[float],
+    wind_output: List[float],
+    thermal_output: List[float],
+    load_demand: List[float],
+    params: SystemParameters
+) -> None:
+    """
+    验证输入数据的有效性
+    
+    Args:
+        solar_output: 24小时光伏出力曲线 (MW)
+        wind_output: 24小时风电出力曲线 (MW)
+        thermal_output: 24小时火电出力曲线 (MW)
+        load_demand: 24小时负荷曲线 (MW)
+        params: 系统参数配置
+        
+    Raises:
+        ValueError: 当输入数据无效时抛出异常
+    """
+    # 检查数据长度（支持24小时或8760小时）
+    data_length = len(solar_output)
+    valid_lengths = [24, 8760]
+    
+    if data_length not in valid_lengths:
+        raise ValueError(f"输入数据长度必须为24小时或8760小时，当前长度为{data_length}")
+    
+    if len(wind_output) != data_length or len(thermal_output) != data_length or len(load_demand) != data_length:
+        raise ValueError("所有输入数据长度必须一致")
+    
+    # 检查数据类型和范围
+    for name, data in [
+        ("光伏出力", solar_output), ("风电出力", wind_output),
+        ("火电出力", thermal_output), ("负荷需求", load_demand)
+    ]:
+        if not all(isinstance(x, (int, float)) for x in data):
+            raise ValueError(f"{name}必须包含数值数据")
+        if any(x < 0 for x in data):
+            raise ValueError(f"{name}不能包含负值")
+    
+    # 检查参数范围
+    if not (0.0 <= params.max_curtailment_wind <= 1.0):
+        raise ValueError("弃风率必须在0.0-1.0之间")
+    if not (0.0 <= params.max_curtailment_solar <= 1.0):
+        raise ValueError("弃光率必须在0.0-1.0之间")
+    # max_grid_ratio只限制上网电量比例，必须为非负值
+    if not (0.0 <= params.max_grid_ratio):
+        raise ValueError("上网电量比例限制必须为非负值")
+    if not (0.0 < params.storage_efficiency <= 1.0):
+        raise ValueError("储能效率必须在0.0-1.0之间")
+    if params.discharge_rate <= 0 or params.charge_rate <= 0:
+        raise ValueError("充放电倍率必须大于0")
+    if params.max_storage_capacity is not None and params.max_storage_capacity <= 0:
+        raise ValueError("储能容量上限必须大于0")
+    # 验证新增的额定装机容量参数
+    if params.rated_thermal_capacity < 0:
+        raise ValueError("额定火电装机容量必须为非负值")
+    if params.rated_solar_capacity <= 0:
+        raise ValueError("额定光伏装机容量必须大于0")
+    if params.rated_wind_capacity <= 0:
+        raise ValueError("额定风电装机容量必须大于0")
+    # 验证新增的可用发电量参数
+    if params.available_thermal_energy < 0:
+        raise ValueError("火电可用发电量必须为非负值")
+    if params.available_solar_energy < 0:
+        raise ValueError("光伏可用发电量必须为非负值")
+    if params.available_wind_energy < 0:
+        raise ValueError("风电可用发电量必须为非负值")
+
+
+def calculate_energy_balance(
+    solar_output: List[float],
+    wind_output: List[float],
+    thermal_output: List[float],
+    load_demand: List[float],
+    params: SystemParameters,
+    storage_capacity: float
+) -> Dict[str, List[float]]:
+    """
+    计算给定储能容量下的系统电能平衡
+    
+    Args:
+        solar_output: 光伏出力曲线 (MW) - 支持24小时或8760小时
+        wind_output: 风电出力曲线 (MW) - 支持24小时或8760小时
+        thermal_output: 火电出力曲线 (MW) - 支持24小时或8760小时
+        load_demand: 负荷曲线 (MW) - 支持24小时或8760小时
+        params: 系统参数配置
+        storage_capacity: 储能容量 (MWh)
+        
+    Returns:
+        包含各种功率曲线的字典
+    """
+    # 转换为numpy数组便于计算
+    solar = np.array(solar_output)
+    wind = np.array(wind_output)
+    thermal = np.array(thermal_output)
+    load = np.array(load_demand)
+    
+    # 初始化输出数组
+    hours = len(solar_output)
+    storage_soc = np.zeros(hours)          # 储能状态 (MWh)
+    charge_power = np.zeros(hours)         # 充电功率 (MW)
+    discharge_power = np.zeros(hours)      # 放电功率 (MW)
+    curtailed_wind = np.zeros(hours)       # 弃风量 (MW)
+    curtailed_solar = np.zeros(hours)      # 弃光量 (MW)
+    grid_feed_in = np.zeros(hours)         # 上网电量 (MW)
+    
+    # 计算总发电潜力
+    total_potential_wind = np.sum(wind)
+    total_potential_solar = np.sum(solar)
+    
+# 判断是否只有一种可再生能源
+    has_wind = total_potential_wind > 0
+    has_solar = total_potential_solar > 0
+    single_renewable = (has_wind and not has_solar) or (has_solar and not has_wind)
+    
+    # 计算允许的最大弃风弃光量
+    if single_renewable:
+        # 只有一种可再生能源时，弃电量不受限制
+        max_curtailed_wind_total = float('inf')
+        max_curtailed_solar_total = float('inf')
+    elif params.max_grid_ratio == 0:
+        # 上网电量限制为0时，所有超额电力都必须被弃掉，不受弃风弃光限制
+        max_curtailed_wind_total = float('inf')
+        max_curtailed_solar_total = float('inf')
+    else:
+        # 有多种可再生能源且上网电量限制不为0时，应用弃风弃光限制
+        max_curtailed_wind_total = total_potential_wind * params.max_curtailment_wind
+        max_curtailed_solar_total = total_potential_solar * params.max_curtailment_solar
+    
+    # 初始化累计弃风弃光量
+    accumulated_curtailed_wind = 0.0
+    accumulated_curtailed_solar = 0.0
+    
+    # 计算总可用发电量上限（不考虑火电）
+    total_available_energy = params.available_solar_energy + params.available_wind_energy
+    max_total_grid_feed_in = total_available_energy * params.max_grid_ratio
+    
+    # 初始化累计上网电量
+    cumulative_grid_feed_in = 0.0
+    
+    # 逐小时计算
+    for hour in range(hours):
+        # 确保储能状态不为负
+        storage_soc[hour] = max(0, storage_soc[hour])
+        
+        # 可用发电量（未考虑弃风弃光）
+        available_generation = thermal[hour] + wind[hour] + solar[hour]
+        
+        # 需求电量（负荷）
+        demand = load[hour]
+        
+        # 计算功率平衡
+        power_surplus = available_generation - demand
+        
+        if power_surplus > 0:
+            # 有盈余电力，优先储能
+            max_charge = min(
+                storage_capacity - storage_soc[hour],  # 储能空间限制
+                storage_capacity * params.charge_rate,  # 充电功率限制
+                power_surplus  # 可用盈余电力
+            )
+            
+            # 实际充电功率
+            actual_charge = min(max_charge, power_surplus)
+            charge_power[hour] = actual_charge
+            
+            # 更新储能状态（考虑充电效率）
+            if hour < hours - 1:
+                storage_soc[hour + 1] = storage_soc[hour] + actual_charge * params.storage_efficiency
+            
+            # 剩余电力优先上网，超出上网电量比例限制时才弃风弃光
+            remaining_surplus = power_surplus - actual_charge
+            
+            # 计算当前允许的最大上网电量
+            # 基于总可用发电量和已累计上网电量
+            remaining_grid_quota = max_total_grid_feed_in - cumulative_grid_feed_in
+            
+            # 优先上网，但不超过剩余配额
+            grid_feed_allowed = min(remaining_surplus, max(0, remaining_grid_quota))
+            grid_feed_in[hour] = grid_feed_allowed
+            cumulative_grid_feed_in += grid_feed_allowed
+            
+            # 剩余电力考虑弃风弃光
+            remaining_surplus -= grid_feed_allowed
+            
+            # 计算弃风弃光（优先弃光，然后弃风）
+            if remaining_surplus > 0:
+                # 在单一可再生能源场景下，弃风弃光不受限制
+                if single_renewable:
+                    # 优先弃光
+                    if solar[hour] > 0:
+                        curtailed_solar[hour] = min(solar[hour], remaining_surplus)
+                        remaining_surplus -= curtailed_solar[hour]
+                        accumulated_curtailed_solar += curtailed_solar[hour]
+                    
+                    # 如果还有剩余，弃风
+                    if remaining_surplus > 0 and wind[hour] > 0:
+                        curtailed_wind[hour] = min(wind[hour], remaining_surplus)
+                        remaining_surplus -= curtailed_wind[hour]
+                        accumulated_curtailed_wind += curtailed_wind[hour]
+                else:
+                    # 混合可再生能源场景，弃风弃光受限制
+                    # 计算当前可弃光量
+                    if max_curtailed_solar_total == float('inf'):
+                        # 无限制弃光
+                        available_solar_curtail = solar[hour]
+                    else:
+                        # 受限制弃光
+                        available_solar_curtail = min(
+                            solar[hour],
+                            max_curtailed_solar_total - accumulated_curtailed_solar
+                        )
+                    
+                    if available_solar_curtail > 0:
+                        curtailed_solar[hour] = min(available_solar_curtail, remaining_surplus)
+                        remaining_surplus -= curtailed_solar[hour]
+                        accumulated_curtailed_solar += curtailed_solar[hour]
+                    
+                    # 如果还有剩余，弃风
+                    if remaining_surplus > 0:
+                        if max_curtailed_wind_total == float('inf'):
+                            # 无限制弃风
+                            available_wind_curtail = wind[hour]
+                        else:
+                            # 受限制弃风
+                            available_wind_curtail = min(
+                                wind[hour],
+                                max_curtailed_wind_total - accumulated_curtailed_wind
+                            )
+                        
+                        if available_wind_curtail > 0:
+                            curtailed_wind[hour] = min(available_wind_curtail, remaining_surplus)
+                            remaining_surplus -= curtailed_wind[hour]
+                            accumulated_curtailed_wind += curtailed_wind[hour]
+                
+                # 确保电力平衡：如果仍有剩余电力，强制弃掉（安全机制）
+                if remaining_surplus > 0:
+                    # 记录警告但不影响计算
+                    # 在实际系统中，这种情况不应该发生，但作为安全保护
+                    pass
+            
+        else:
+            # 电力不足，优先放电
+            power_deficit = -power_surplus
+            grid_feed_in[hour] = 0  # 初始化购电为0
+            
+            max_discharge = min(
+                storage_soc[hour],  # 储能状态限制
+                storage_capacity * params.discharge_rate,  # 放电功率限制
+                power_deficit  # 缺电功率
+            )
+            
+            # 实际放电功率
+            actual_discharge = min(max_discharge, power_deficit)
+            discharge_power[hour] = actual_discharge
+            
+            # 更新储能状态（考虑放电效率）
+            if hour < hours - 1:
+                storage_soc[hour + 1] = storage_soc[hour] - actual_discharge / params.storage_efficiency
+            
+            # 计算剩余缺电，需要从电网购电
+            remaining_deficit = power_deficit - actual_discharge
+            
+            # 如果还有缺电，从电网购电
+            if remaining_deficit > 0:
+                # 购电功率为负值，表示从电网输入
+                grid_feed_in[hour] = -remaining_deficit
+    
+    return {
+        'storage_profile': storage_soc.tolist(),
+        'charge_profile': charge_power.tolist(),
+        'discharge_profile': discharge_power.tolist(),
+        'curtailed_wind': curtailed_wind.tolist(),
+        'curtailed_solar': curtailed_solar.tolist(),
+        'grid_feed_in': grid_feed_in.tolist()
+    }
+
+
+def check_constraints(
+    solar_output: List[float],
+    wind_output: List[float],
+    thermal_output: List[float],
+    balance_result: Dict[str, List[float]],
+    params: SystemParameters
+) -> Dict[str, float]:
+    """
+    检查约束条件是否满足
+    
+    Args:
+        solar_output: 光伏出力曲线 (MW) - 支持24小时或8760小时
+        wind_output: 风电出力曲线 (MW) - 支持24小时或8760小时
+        thermal_output: 火电出力曲线 (MW) - 支持24小时或8760小时
+        balance_result: 电能平衡计算结果
+        params: 系统参数配置
+        
+    Returns:
+        包含各约束实际比例的字典
+    """
+    # 计算总量
+    total_wind_potential = sum(wind_output)
+    total_solar_potential = sum(solar_output)
+    total_thermal = sum(thermal_output)
+    
+    total_curtailed_wind = sum(balance_result['curtailed_wind'])
+    total_curtailed_solar = sum(balance_result['curtailed_solar'])
+    total_grid_feed_in = sum(balance_result['grid_feed_in'])
+    
+    # 实际发电量（考虑弃风弃光）
+    actual_wind_generation = total_wind_potential - total_curtailed_wind
+    actual_solar_generation = total_solar_potential - total_curtailed_solar
+    total_generation = total_thermal + actual_wind_generation + actual_solar_generation
+    
+    # 计算比例
+    actual_curtailment_wind_ratio = total_curtailed_wind / total_wind_potential if total_wind_potential > 0 else 0
+    actual_curtailment_solar_ratio = total_curtailed_solar / total_solar_potential if total_solar_potential > 0 else 0
+    actual_grid_feed_in_ratio = total_grid_feed_in / total_generation if total_generation > 0 else 0
+    
+    return {
+        'total_curtailment_wind_ratio': actual_curtailment_wind_ratio,
+        'total_curtailment_solar_ratio': actual_curtailment_solar_ratio,
+        'total_grid_feed_in_ratio': actual_grid_feed_in_ratio
+    }
+
+
+def optimize_storage_capacity(
+    solar_output: List[float],
+    wind_output: List[float],
+    thermal_output: List[float],
+    load_demand: List[float],
+    params: SystemParameters,
+    max_iterations: int = 100,
+    tolerance: float = 0.01
+) -> Dict:
+    """
+    优化储能容量，使用迭代方法寻找满足所有约束的最小储能容量
+    
+    Args:
+        solar_output: 光伏出力曲线 (MW) - 支持24小时或8760小时
+        wind_output: 风电出力曲线 (MW) - 支持24小时或8760小时
+        thermal_output: 火电出力曲线 (MW) - 支持24小时或8760小时
+        load_demand: 负荷曲线 (MW) - 支持24小时或8760小时
+        params: 系统参数配置
+        max_iterations: 最大迭代次数
+        tolerance: 收敛容差
+        
+    Returns:
+        包含优化结果的字典
+    """
+    # 验证输入
+    validate_inputs(solar_output, wind_output, thermal_output, load_demand, params)
+    
+    # 初始化搜索范围
+    lower_bound = 0.0
+    theoretical_max = max(sum(solar_output) + sum(wind_output) + sum(thermal_output), sum(load_demand))
+    
+    # 应用储能容量上限限制
+    if params.max_storage_capacity is not None:
+        upper_bound = min(theoretical_max, params.max_storage_capacity)
+    else:
+        upper_bound = theoretical_max
+    
+    # 二分搜索寻找最小储能容量
+    best_capacity = upper_bound
+    best_result = None
+    solution_found = False  # 标记是否找到可行解
+    
+    for iteration in range(max_iterations):
+        mid_capacity = (lower_bound + upper_bound) / 2
+        
+        # 计算当前容量下的平衡
+        balance_result = calculate_energy_balance(
+            solar_output, wind_output, thermal_output, load_demand, params, mid_capacity
+        )
+        
+        # 检查约束条件
+        constraint_results = check_constraints(solar_output, wind_output, thermal_output, balance_result, params)
+        
+        # 检查是否满足所有约束
+        # max_grid_ratio只限制上网电量比例，不约束购电
+        # 只有当grid_feed_in为正时（上网）才需要检查约束
+        total_grid_feed_in = sum(balance_result['grid_feed_in'])
+        if total_grid_feed_in > 0:
+            # 有上网电量，检查是否超过限制
+            grid_constraint_satisfied = constraint_results['total_grid_feed_in_ratio'] <= params.max_grid_ratio
+        else:
+            # 没有上网电量或为负值（购电），总是满足约束
+            grid_constraint_satisfied = True
+        
+        # 判断是否只有一种可再生能源
+        has_wind = sum(wind_output) > 0
+        has_solar = sum(solar_output) > 0
+        single_renewable = (has_wind and not has_solar) or (has_solar and not has_wind)
+        
+        # 特殊情况：当上网电量限制为0时，所有超额电力都必须被弃掉
+        # 此时应该允许无限制弃风弃光
+        grid_quota_zero = params.max_grid_ratio == 0
+        
+        if single_renewable or grid_quota_zero:
+            # 只有一种可再生能源时，或上网电量限制为0时，跳过弃风弃光约束检查
+            constraints_satisfied = grid_constraint_satisfied
+        else:
+            # 有多种可再生能源且上网电量限制不为0时，检查所有约束
+            constraints_satisfied = (
+                constraint_results['total_curtailment_wind_ratio'] <= params.max_curtailment_wind and
+                constraint_results['total_curtailment_solar_ratio'] <= params.max_curtailment_solar and
+                grid_constraint_satisfied
+            )
+        
+        # 检查储能日平衡（周期结束时储能状态应接近初始值）
+        storage_initial = balance_result['storage_profile'][0]
+        storage_final = balance_result['storage_profile'][-1]
+        daily_balance = abs(storage_final - storage_initial) < tolerance
+        
+        if constraints_satisfied and daily_balance:
+            # 满足条件，尝试减小容量
+            best_capacity = mid_capacity
+            best_result = {**balance_result, **constraint_results}
+            solution_found = True
+            upper_bound = mid_capacity
+        else:
+            # 不满足条件，增大容量
+            lower_bound = mid_capacity
+        
+        # 检查收敛
+        if upper_bound - lower_bound < tolerance:
+            break
+    
+    # 处理储能容量上限限制的情况
+    if not solution_found and params.max_storage_capacity is not None:
+        print(f"警告：在储能容量上限 {params.max_storage_capacity:.2f} MWh 内无法找到满足所有约束的解")
+        print("使用最大允许容量进行计算，但某些约束条件可能无法满足")
+        
+        # 使用最大允许容量计算结果
+        balance_result = calculate_energy_balance(
+            solar_output, wind_output, thermal_output, load_demand, params, params.max_storage_capacity
+        )
+        constraint_results = check_constraints(solar_output, wind_output, thermal_output, balance_result, params)
+        best_result = {**balance_result, **constraint_results}
+        best_capacity = params.max_storage_capacity
+    elif best_result is None:
+        # 如果没有找到可行解（且没有容量上限限制），使用最大容量
+        balance_result = calculate_energy_balance(
+            solar_output, wind_output, thermal_output, load_demand, params, upper_bound
+        )
+        constraint_results = check_constraints(solar_output, wind_output, thermal_output, balance_result, params)
+        best_result = {**balance_result, **constraint_results}
+        best_capacity = upper_bound
+    
+    # 添加能量平衡校验
+    total_generation = sum(thermal_output) + sum(wind_output) + sum(solar_output)
+    total_consumption = sum(load_demand)
+    total_curtailed = sum(best_result['curtailed_wind']) + sum(best_result['curtailed_solar'])
+    total_grid = sum(best_result['grid_feed_in'])
+    total_charge = sum(best_result['charge_profile'])
+    total_discharge = sum(best_result['discharge_profile'])
+    storage_net_change = best_result['storage_profile'][-1] - best_result['storage_profile'][0]
+    
+    # 能量平衡校验：发电量 + 放电量/效率 = 负荷 + 充电量*效率 + 弃风弃光 + 上网电量
+    # 考虑储能充放电效率的能量平衡
+    energy_from_storage = total_discharge / params.storage_efficiency  # 储能提供的有效能量
+    energy_to_storage = total_charge * params.storage_efficiency      # 储能消耗的电网能量
+    
+    # 能量平衡校验：应该接近0，但允许一定误差
+    # 当total_grid为负时（购电），应该加到左侧（供给侧）
+    # 当total_grid为正时（上网），应该加到右侧（需求侧）
+    if total_grid < 0:  # 购电情况
+        energy_balance_error = abs(
+            total_generation + energy_from_storage + abs(total_grid) - total_consumption - energy_to_storage - total_curtailed
+        )
+    else:  # 上网情况
+        energy_balance_error = abs(
+            total_generation + energy_from_storage - total_consumption - energy_to_storage - total_curtailed - total_grid
+        )
+    # 使用更大的容差，考虑储能效率损失和数值误差
+    # 允许误差为总发电量的15%或10MW，取较大者
+    # 储能效率损失可能达到总能量的10%以上
+    tolerance = max(10.0, total_generation * 0.15)
+    energy_balance_check = energy_balance_error < tolerance
+    
+    # 返回最终结果
+    return {
+        'required_storage_capacity': best_capacity,
+        'storage_profile': best_result['storage_profile'],
+        'charge_profile': best_result['charge_profile'],
+        'discharge_profile': best_result['discharge_profile'],
+        'curtailed_wind': best_result['curtailed_wind'],
+        'curtailed_solar': best_result['curtailed_solar'],
+        'grid_feed_in': best_result['grid_feed_in'],
+        'total_curtailment_wind_ratio': best_result['total_curtailment_wind_ratio'],
+        'total_curtailment_solar_ratio': best_result['total_curtailment_solar_ratio'],
+        'total_grid_feed_in_ratio': best_result['total_grid_feed_in_ratio'],
+        'energy_balance_check': energy_balance_check,
+        'capacity_limit_reached': params.max_storage_capacity is not None and best_capacity >= params.max_storage_capacity,
+        'theoretical_optimal_capacity': best_capacity if solution_found else None,
+        'max_storage_limit': params.max_storage_capacity
+    }
+
+
+def main():
+    """主函数，提供示例使用"""
+    # 示例数据
+    solar_output = [0.0] * 6 + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0] * 2
+    wind_output = [2.0, 3.0, 4.0, 3.0, 2.0, 1.0] * 4
+    thermal_output = [5.0] * 24
+    load_demand = [3.0, 4.0, 5.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 18.0, 
+                   16.0, 14.0, 12.0, 10.0, 8.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 2.0]
+    
+    # 系统参数
+    params = SystemParameters(
+        max_curtailment_wind=0.1,
+        max_curtailment_solar=0.1,
+        max_grid_ratio=0.2,
+        storage_efficiency=0.9,
+        discharge_rate=1.0,
+        charge_rate=1.0
+    )
+    
+    # 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("多能互补系统储能容量优化结果:")
+    print(f"所需储能总容量: {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
+
+
+if __name__ == "__main__":
+    main()