multi_energy_complementarity/example_usage.py

"""
多能互补系统储能容量优化计算程序使用示例

该文件展示了如何使用储能优化程序处理不同的实际场景。

作者: iFlow CLI
创建日期: 2025-12-25
"""

import numpy as np
import matplotlib.pyplot as plt
from storage_optimization import optimize_storage_capacity, SystemParameters

# 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
    ax5.plot(hours, result['grid_feed_in'], 'orange', linewidth=2)
    ax5.set_title('上网电量 (MW)')
    ax5.set_xlabel('时间 (小时)')
    ax5.set_ylabel('功率 (MW)')
    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 compare_scenarios():
    """比较不同场景的结果"""
    print("\n=== 场景比较 ===")
    
    # 运行三个场景
    data1 = example_1_basic_scenario()
    data2 = example_2_high_renewable_scenario()
    data3 = example_3_winter_scenario()
    
    # 比较结果
    scenarios = ['基础场景', '高可再生能源场景', '冬季场景']
    storage_capacities = [
        data1['result']['required_storage_capacity'],
        data2['result']['required_storage_capacity'],
        data3['result']['required_storage_capacity']
    ]
    curtailment_wind = [
        data1['result']['total_curtailment_wind_ratio'],
        data2['result']['total_curtailment_wind_ratio'],
        data3['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']
    ]
    grid_feed_in = [
        data1['result']['total_grid_feed_in_ratio'],
        data2['result']['total_grid_feed_in_ratio'],
        data3['result']['total_grid_feed_in_ratio']
    ]
    
    print("\n场景比较结果:")
    print(f"{'场景':<15} {'储能容量(MWh)':<12} {'弃风率':<8} {'弃光率':<8} {'上网比例':<8}")
    print("-" * 55)
    for i, scenario in enumerate(scenarios):
        print(f"{scenario:<15} {storage_capacities[i]:<12.2f} {curtailment_wind[i]:<8.3f} "
              f"{curtailment_solar[i]:<8.3f} {grid_feed_in[i]:<8.3f}")
    
    return data1, data2, data3


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 = 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'])
    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！")