multi_energy_complementarity/scripts/solar_optimization_examples.py

"""
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()