multi_energy_complementarity/test_scenario_storage_optimization.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""测试场景储能配置优化模块"""

import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))

from multi_scenario import MultiScenarioAnalyzer
from storage_optimization import SystemParameters
import numpy as np

def test_scenario_storage_optimization():
    """测试聚类场景的储能配置优化"""
    print("=== 场景储能配置优化测试 ===")
    
    # 生成模拟数据
    np.random.seed(42)
    
    solar_output = []
    wind_output = []
    load_demand = []
    
    for day in range(30):  # 30天数据用于测试
        # 光伏：白天有出力，夜间为0
        daily_solar = [0.0] * 6 + list(np.random.uniform(2, 8, 12)) + [0.0] * 6
        solar_output.extend(daily_solar)
        
        # 风电：相对随机
        daily_wind = np.random.exponential(3.0, 24).tolist()
        wind_output.extend(daily_wind)
        
        # 负荷：日内变化，夜间低，白天高
        daily_load = [4.0, 5.0, 6.0, 7.0, 9.0, 11.0, 13.0, 15.0, 17.0, 19.0, 21.0, 19.0,
                     17.0, 15.0, 13.0, 11.0, 9.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 3.0]
        load_demand.extend(daily_load)
    
    try:
        # 1. 执行聚类分析
        print("1. 执行多场景聚类分析...")
        analyzer = MultiScenarioAnalyzer(n_clusters=4, random_state=42)
        result = analyzer.fit_predict(solar_output, wind_output, load_demand)
        
        print(f"   识别出 {result.n_scenarios} 个场景")
        
        # 2. 对每个场景进行储能配置优化
        print("\n2. 对每个场景进行储能配置优化...")
        
        # 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
        )
        
        # 使用 analyzer 的优化功能
        optimization_results = analyzer.optimize_storage_for_scenarios(
            result, solar_output, wind_output, load_demand, system_params, safety_factor=1.2
        )
        
        # 3. 显示优化结果
        print("\n3. 储能配置优化结果...")
        analyzer.print_storage_optimization_summary(optimization_results)
        
        # 4. 测试结果验证
        assert 'summary' in optimization_results, "缺少汇总结果"
        assert optimization_results['summary']['n_scenarios'] == result.n_scenarios, "场景数不匹配"
        assert optimization_results['summary']['recommended_capacity'] > 0, "推荐容量应该大于0"
        
        print("\n✅ 场景储能配置优化测试通过")
        return True, optimization_results
        
    except Exception as e:
        print(f"❌ 场景储能配置优化测试失败: {str(e)}")
        import traceback
        traceback.print_exc()
        return False, None

def test_storage_optimization_with_different_scenarios():
    """测试不同场景数量的储能优化"""
    print("\n=== 不同场景数量的储能优化测试 ===")
    
    # 生成测试数据
    np.random.seed(123)
    
    solar_output = []
    wind_output = []
    load_demand = []
    
    for day in range(60):  # 60天数据
        # 光伏：模拟夏季和冬季差异
        if day < 30:  # 夏季
            daily_solar = [0.0] * 6 + list(np.random.uniform(3, 10, 12)) + [0.0] * 6
        else:  # 冬季
            daily_solar = [0.0] * 6 + list(np.random.uniform(1, 6, 12)) + [0.0] * 6
        solar_output.extend(daily_solar)
        
        # 风电：模拟季节性变化
        daily_wind = np.random.exponential(2.5, 24).tolist()
        wind_output.extend(daily_wind)
        
        # 负荷：模拟夏冬负荷差异
        if day < 30:  # 夏季负荷较高（空调）
            daily_load = [6.0, 7.0, 8.0, 9.0, 11.0, 13.0, 15.0, 17.0, 19.0, 21.0, 23.0, 21.0,
                         19.0, 17.0, 15.0, 13.0, 11.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 5.0]
        else:  # 冬季负荷相对较低
            daily_load = [4.0, 5.0, 6.0, 7.0, 9.0, 11.0, 13.0, 15.0, 17.0, 19.0, 21.0, 19.0,
                         17.0, 15.0, 13.0, 11.0, 9.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 3.0]
        load_demand.extend(daily_load)
    
    analyzer = MultiScenarioAnalyzer(random_state=123)
    
    # 测试不同聚类数
    for n_clusters in [3, 5, 7]:
        print(f"\n测试 {n_clusters} 个场景的储能优化...")
        
        try:
            result = analyzer.fit_predict(solar_output, wind_output, load_demand, 
                                        n_clusters=n_clusters)
            
            print(f"   实际识别出 {result.n_scenarios} 个场景")
            
            # 执行储能优化
            optimization_results = analyzer.optimize_storage_for_scenarios(
                result, solar_output, wind_output, load_demand, 
                safety_factor=1.3
            )
            
            # 显示结果
            summary = optimization_results['summary']
            print(f"   加权平均储能需求: {summary['weighted_average_storage']:.2f} MWh")
            print(f"   推荐储能容量: {summary['recommended_capacity']:.2f} MWh")
            
        except Exception as e:
            print(f"   ❌ {n_clusters}场景测试失败: {str(e)}")
            return False
    
    print("\n✅ 不同场景数量的储能优化测试通过")
    return True

def test_edge_cases():
    """测试边界情况"""
    print("\n=== 边界情况测试 ===")
    
    # 测试短时间数据
    print("1. 测试短时间数据...")
    analyzer = MultiScenarioAnalyzer(n_clusters=2, random_state=42)
    
    # 只生成24小时数据
    solar_24h = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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, 0.0, 0.0, 0.0, 0.0, 0.0]
    wind_24h = [2.0, 3.0, 4.0, 3.0, 2.0, 1.0, 1.0, 2.0, 3.0, 4.0, 3.0, 2.0,
               2.0, 3.0, 4.0, 3.0, 2.0, 1.0, 1.0, 2.0, 3.0, 4.0, 3.0, 2.0]
    load_24h = [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]
    
    try:
        result = analyzer.fit_predict(solar_24h, wind_24h, load_24h)
        
        # 对于短时间数据，应该会被扩展到24小时
        optimization_results = analyzer.optimize_storage_for_scenarios(
            result, solar_24h, wind_24h, load_24h
        )
        
        print("   ✅ 短时间数据测试通过")
        
    except Exception as e:
        print(f"   ❌ 短时间数据测试失败: {str(e)}")
        return False
    
    # 测试极大储能需求情况
    print("2. 测试极大储能需求情况...")
    
    # 创建储能需求很大的场景
    solar_extreme = [10.0] * 24  # 始终高出力
    wind_extreme = [8.0] * 24    # 始终高出力
    load_extreme = [1.0] * 24    # 始终低负荷
    
    try:
        result_extreme = analyzer.fit_predict(solar_extreme, wind_extreme, load_extreme)
        optimization_results_extreme = analyzer.optimize_storage_for_scenarios(
            result_extreme, solar_extreme, wind_extreme, load_extreme
        )
        
        print(f"   极端场景推荐储能容量: {optimization_results_extreme['summary']['recommended_capacity']:.2f} MWh")
        print("   ✅ 极大储能需求情况测试通过")
        
    except Exception as e:
        print(f"   ❌ 极大储能需求情况测试失败: {str(e)}")
        return False
    
    print("\n✅ 所有边界情况测试通过")
    return True

if __name__ == "__main__":
    print("=== 场景储能配置优化模块测试 ===\n")
    
    success = True
    
    # 基础功能测试
    success1, optimization_results = test_scenario_storage_optimization()
    success &= success1
    
    # 不同场景数量测试
    success2 = test_storage_optimization_with_different_scenarios()
    success &= success2
    
    # 边界情况测试
    success3 = test_edge_cases()
    success &= success3
    
    print(f"\n{'='*60}")
    if success:
        print("🎉 所有场景储能配置优化测试通过！")
        print("   储能优化模块功能正常工作。")
    else:
        print("💥 部分测试失败，请检查代码。")
    print(f"{'='*60}")
    
    # 返回测试结果供其他模块使用
    if success:
        print(f"\n测试数据示例:")
        if optimization_results:
            summary = optimization_results['summary']
            print(f"   - 加权平均储能需求: {summary['weighted_average_storage']:.2f} MWh")
            print(f"   - 推荐储能容量: {summary['recommended_capacity']:.2f} MWh")
            print(f"   - 安全系数: {summary['safety_factor']}")
            print(f"   - 分析场景数: {summary['n_scenarios']}")