完成基本功能。

main.py

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+"""
+多能互补系统储能容量优化可视化程序
+
+该程序绘制负荷曲线、发电曲线和储能出力曲线，直观展示系统运行状态。
+
+作者: iFlow CLI
+创建日期: 2025-12-25
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+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 plot_system_curves(solar_output, wind_output, thermal_output, load_demand, result):
+    """
+    绘制系统运行曲线
+    
+    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
+        result: 优化结果字典
+    """
+    hours = np.arange(len(solar_output))
+    data_length = len(solar_output)
+    
+    # 确定图表标题和采样率
+    if data_length == 8760:
+        title_suffix = " (全年8760小时)"
+        # 对于全年数据，我们采样显示（每6小时显示一个点）
+        sample_rate = 6
+        sampled_hours = hours[::sample_rate]
+        sampled_solar = solar_output[::sample_rate]
+        sampled_wind = wind_output[::sample_rate]
+        sampled_thermal = thermal_output[::sample_rate]
+        sampled_load = load_demand[::sample_rate]
+        sampled_storage = result['storage_profile'][::sample_rate]
+        sampled_charge = result['charge_profile'][::sample_rate]
+        sampled_discharge = result['discharge_profile'][::sample_rate]
+    else:
+        title_suffix = " (24小时)"
+        sampled_hours = hours
+        sampled_solar = solar_output
+        sampled_wind = wind_output
+        sampled_thermal = thermal_output
+        sampled_load = load_demand
+        sampled_storage = result['storage_profile']
+        sampled_charge = result['charge_profile']
+        sampled_discharge = result['discharge_profile']
+    
+    # 创建图形
+    fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(14, 12))
+    fig.suptitle('多能互补系统24小时运行曲线', fontsize=16, fontweight='bold')
+    
+    # === 第一个子图：发电和负荷曲线 ===
+    ax1.plot(sampled_hours, sampled_load, 'r-', linewidth=2, label='负荷需求')
+    ax1.plot(sampled_hours, sampled_thermal, 'b-', linewidth=2, label='火电出力')
+    ax1.plot(sampled_hours, sampled_wind, 'g-', linewidth=2, label='风电出力')
+    ax1.plot(sampled_hours, sampled_solar, 'orange', linewidth=2, label='光伏出力')
+    
+    # 计算总发电量
+    total_generation = [sampled_thermal[i] + sampled_wind[i] + sampled_solar[i] for i in range(len(sampled_thermal))]
+    ax1.plot(sampled_hours, total_generation, 'k--', linewidth=1.5, alpha=0.7, label='总发电量')
+    
+    ax1.set_xlabel('时间 (小时)')
+    ax1.set_ylabel('功率 (MW)')
+    ax1.set_title(f'发电与负荷曲线{title_suffix}')
+    ax1.legend(loc='upper right')
+    ax1.grid(True, alpha=0.3)
+    ax1.set_xlim(0, max(sampled_hours))
+    
+    # === 第二个子图：储能充放电曲线 ===
+    discharge_power = [-x for x in sampled_discharge]  # 放电显示为负值
+    
+    ax2.bar(sampled_hours, sampled_charge, color='green', alpha=0.7, label='充电功率')
+    ax2.bar(sampled_hours, discharge_power, color='red', alpha=0.7, label='放电功率')
+    
+    ax2.set_xlabel('时间 (小时)')
+    ax2.set_ylabel('功率 (MW)')
+    ax2.set_title(f'储能充放电功率{title_suffix}')
+    ax2.legend(loc='upper right')
+    ax2.grid(True, alpha=0.3)
+    ax2.set_xlim(0, max(sampled_hours))
+    ax2.axhline(y=0, color='black', linestyle='-', linewidth=0.5)
+    
+    # === 第三个子图：储能状态曲线 ===
+    ax3.plot(sampled_hours, sampled_storage, 'b-', linewidth=1, marker='o', markersize=2)
+    ax3.fill_between(sampled_hours, 0, sampled_storage, 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 (总容量: {result["required_storage_capacity"]:.2f} MWh){title_suffix}')
+    ax3.grid(True, alpha=0.3)
+    ax3.set_xlim(0, max(sampled_hours))
+    ax3.set_ylim(bottom=0)
+    
+    # 调整布局
+    plt.tight_layout()
+    
+    # 保存图片
+    plt.savefig('system_curves.png', dpi=300, bbox_inches='tight')
+    plt.close()  # 关闭图形，不显示窗口
+    
+    # 打印统计信息
+    print("\n=== 系统运行统计 ===")
+    print(f"所需储能总容量: {result['required_storage_capacity']:.2f} MWh")
+    print(f"最大储能状态: {max(result['storage_profile']):.2f} MWh")
+    print(f"最小储能状态: {min(result['storage_profile']):.2f} MWh")
+    print(f"总充电量: {sum(result['charge_profile']):.2f} MWh")
+    print(f"总放电量: {sum(result['discharge_profile']):.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}")
+
+
+def generate_yearly_data():
+    """生成8760小时的示例数据"""
+    # 基础日模式
+    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]
+    
+    # 添加季节性变化
+    import random
+    random.seed(42)
+    
+    yearly_solar = []
+    yearly_wind = []
+    yearly_thermal = []
+    yearly_load = []
+    
+    for day in range(365):
+        # 季节性因子（夏季光伏更强，冬季负荷更高）
+        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 * (random.random() - 0.5)
+            wind_variation = 1.0 + 0.3 * (random.random() - 0.5)
+            load_variation = 1.0 + 0.1 * (random.random() - 0.5)
+            
+            yearly_solar.append(daily_solar[hour] * season_factor * solar_variation)
+            yearly_wind.append(daily_wind[hour] * wind_variation)
+            yearly_thermal.append(daily_thermal[hour])
+            yearly_load.append(daily_load[hour] * (2.0 - season_factor) * load_variation)
+    
+    return yearly_solar, yearly_wind, yearly_thermal, yearly_load
+
+
+def main():
+    """主函数"""
+    import sys
+    
+    # 检查命令行参数
+    use_yearly_data = len(sys.argv) > 1 and sys.argv[1] == '--yearly'
+    
+    if use_yearly_data:
+        print("生成8760小时全年数据...")
+        solar_output, wind_output, thermal_output, load_demand = generate_yearly_data()
+        print(f"数据长度: {len(solar_output)} 小时")
+    else:
+        print("使用24小时示例数据...")
+        # 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
+    )
+    
+    # 绘制曲线
+    print("正在绘制系统运行曲线...")
+    plot_system_curves(solar_output, wind_output, thermal_output, load_demand, result)
+    
+    print("\n曲线图已保存为 'system_curves.png'")
+
+
+if __name__ == "__main__":
+    main()