修正没有利用--excel参数的bug。
This commit is contained in:
dmy
131
tests/test_multi_scenario.py
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131
tests/test_multi_scenario.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""测试多场景聚类模块"""
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import sys
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import os
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sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))
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from multi_scenario import MultiScenarioAnalyzer
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import numpy as np
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def test_multi_scenario_basic():
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"""测试基本功能"""
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print("测试多场景聚类基本功能...")
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# 生成简单的测试数据（24小时）
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hours = 24
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solar_output = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0,
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5.0, 4.0, 3.0, 2.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
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wind_output = [2.0, 3.0, 4.0, 3.0, 2.0, 1.0, 1.0, 2.0, 3.0, 4.0, 3.0, 2.0,
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2.0, 3.0, 4.0, 3.0, 2.0, 1.0, 1.0, 2.0, 3.0, 4.0, 3.0, 2.0]
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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,
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16.0, 14.0, 12.0, 10.0, 8.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 2.0]
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# 创建分析器
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analyzer = MultiScenarioAnalyzer(n_clusters=3, random_state=42)
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try:
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# 执行聚类分析
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result = analyzer.fit_predict(solar_output, wind_output, load_demand)
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print("✅ 基本聚类测试通过")
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print(f" - 识别场景数: {result.n_scenarios}")
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print(f" - 轮廓系数: {result.silhouette_score:.3f}")
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print(f" - 场景名称: {result.scenario_names}")
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return True
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except Exception as e:
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print(f"❌ 测试失败: {str(e)}")
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import traceback
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traceback.print_exc()
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return False
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def test_multi_scenario_yearly():
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"""测试年度数据聚类"""
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print("\n测试年度数据聚类...")
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# 生成模拟的8760小时数据
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np.random.seed(42)
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# 简单的模拟数据
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solar_output = []
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wind_output = []
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load_demand = []
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for day in range(365):
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# 光伏：白天有出力，夜间为0
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daily_solar = [0.0] * 6 + list(np.random.uniform(1, 6, 12)) + [0.0] * 6
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solar_output.extend(daily_solar)
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# 风电：相对随机
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daily_wind = np.random.exponential(2.5, 24).tolist()
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wind_output.extend(daily_wind)
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# 负荷：日内变化，夜间低，白天高
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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,
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16.0, 14.0, 12.0, 10.0, 8.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 2.0]
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load_demand.extend(daily_load)
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try:
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analyzer = MultiScenarioAnalyzer(n_clusters=5, random_state=42)
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result = analyzer.fit_predict(solar_output, wind_output, load_demand, find_optimal_k=True)
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print("✅ 年度数据聚类测试通过")
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print(f" - 最优聚类数: {result.n_scenarios}")
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print(f" - 轮廓系数: {result.silhouette_score:.3f}")
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# 输出前3个场景的统计
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for i in range(min(3, result.n_scenarios)):
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stats = result.scenario_stats[f'scenario_{i}']
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print(f" - 场景{i+1}: 频率{stats['frequency']:.1%}, "
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f"光伏{stats['solar_mean']:.1f}MW, "
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f"风电{stats['wind_mean']:.1f}MW, "
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f"负荷{stats['load_mean']:.1f}MW")
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# 测试图表生成
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print(" - 测试图表生成...")
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analyzer.plot_scenario_analysis(result, solar_output, wind_output, load_demand,
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save_path="test_scenario_analysis.png")
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print(" ✅ 图表生成成功")
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return True
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except Exception as e:
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print(f"❌ 年度数据测试失败: {str(e)}")
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import traceback
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traceback.print_exc()
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return False
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def test_data_validation():
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"""测试数据验证"""
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print("\n测试数据验证...")
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# 测试数据长度不一致
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try:
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analyzer = MultiScenarioAnalyzer(n_clusters=2)
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analyzer.fit_predict([1, 2, 3], [1, 2], [1, 2, 3]) # 长度不一致
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print("❌ 应该检测到数据长度不一致")
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return False
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except ValueError as e:
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print("✅ 正确检测到数据长度不一致")
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return True
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if __name__ == "__main__":
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print("=== 多场景聚类模块测试 ===\n")
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success = True
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success &= test_multi_scenario_basic()
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success &= test_multi_scenario_yearly()
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success &= test_data_validation()
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print(f"\n{'='*50}")
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if success:
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print("🎉 所有测试通过！多场景聚类模块工作正常。")
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else:
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print("💥 部分测试失败，请检查代码。")
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print(f"{'='*50}")
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232
tests/test_scenario_storage_optimization.py
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232
tests/test_scenario_storage_optimization.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""测试场景储能配置优化模块"""
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import sys
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import os
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sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))
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from multi_scenario import MultiScenarioAnalyzer
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from storage_optimization import SystemParameters
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import numpy as np
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def test_scenario_storage_optimization():
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"""测试聚类场景的储能配置优化"""
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print("=== 场景储能配置优化测试 ===")
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# 生成模拟数据
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np.random.seed(42)
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solar_output = []
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wind_output = []
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load_demand = []
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for day in range(30): # 30天数据用于测试
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# 光伏：白天有出力，夜间为0
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daily_solar = [0.0] * 6 + list(np.random.uniform(2, 8, 12)) + [0.0] * 6
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solar_output.extend(daily_solar)
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# 风电：相对随机
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daily_wind = np.random.exponential(3.0, 24).tolist()
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wind_output.extend(daily_wind)
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# 负荷：日内变化，夜间低，白天高
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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,
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17.0, 15.0, 13.0, 11.0, 9.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 3.0]
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load_demand.extend(daily_load)
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try:
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# 1. 执行聚类分析
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print("1. 执行多场景聚类分析...")
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analyzer = MultiScenarioAnalyzer(n_clusters=4, random_state=42)
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result = analyzer.fit_predict(solar_output, wind_output, load_demand)
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print(f" 识别出 {result.n_scenarios} 个场景")
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# 2. 对每个场景进行储能配置优化
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print("\n2. 对每个场景进行储能配置优化...")
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# 系统参数
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system_params = SystemParameters(
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max_curtailment_wind=0.1,
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max_curtailment_solar=0.1,
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max_grid_ratio=0.2,
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storage_efficiency=0.9,
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discharge_rate=1.0,
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charge_rate=1.0
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)
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# 使用 analyzer 的优化功能
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optimization_results = analyzer.optimize_storage_for_scenarios(
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result, solar_output, wind_output, load_demand, system_params, safety_factor=1.2
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)
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# 3. 显示优化结果
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print("\n3. 储能配置优化结果...")
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analyzer.print_storage_optimization_summary(optimization_results)
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# 4. 测试结果验证
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assert 'summary' in optimization_results, "缺少汇总结果"
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assert optimization_results['summary']['n_scenarios'] == result.n_scenarios, "场景数不匹配"
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assert optimization_results['summary']['recommended_capacity'] > 0, "推荐容量应该大于0"
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print("\n✅ 场景储能配置优化测试通过")
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return True, optimization_results
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except Exception as e:
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print(f"❌ 场景储能配置优化测试失败: {str(e)}")
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import traceback
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traceback.print_exc()
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return False, None
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def test_storage_optimization_with_different_scenarios():
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"""测试不同场景数量的储能优化"""
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print("\n=== 不同场景数量的储能优化测试 ===")
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# 生成测试数据
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np.random.seed(123)
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solar_output = []
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wind_output = []
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load_demand = []
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for day in range(60): # 60天数据
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# 光伏：模拟夏季和冬季差异
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if day < 30: # 夏季
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daily_solar = [0.0] * 6 + list(np.random.uniform(3, 10, 12)) + [0.0] * 6
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else: # 冬季
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daily_solar = [0.0] * 6 + list(np.random.uniform(1, 6, 12)) + [0.0] * 6
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solar_output.extend(daily_solar)
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# 风电：模拟季节性变化
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daily_wind = np.random.exponential(2.5, 24).tolist()
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wind_output.extend(daily_wind)
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# 负荷：模拟夏冬负荷差异
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if day < 30: # 夏季负荷较高（空调）
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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,
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19.0, 17.0, 15.0, 13.0, 11.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 5.0]
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else: # 冬季负荷相对较低
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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,
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17.0, 15.0, 13.0, 11.0, 9.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 3.0]
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load_demand.extend(daily_load)
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analyzer = MultiScenarioAnalyzer(random_state=123)
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# 测试不同聚类数
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for n_clusters in [3, 5, 7]:
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print(f"\n测试 {n_clusters} 个场景的储能优化...")
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try:
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result = analyzer.fit_predict(solar_output, wind_output, load_demand,
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n_clusters=n_clusters)
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print(f" 实际识别出 {result.n_scenarios} 个场景")
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# 执行储能优化
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optimization_results = analyzer.optimize_storage_for_scenarios(
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result, solar_output, wind_output, load_demand,
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safety_factor=1.3
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)
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# 显示结果
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summary = optimization_results['summary']
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print(f" 加权平均储能需求: {summary['weighted_average_storage']:.2f} MWh")
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print(f" 推荐储能容量: {summary['recommended_capacity']:.2f} MWh")
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except Exception as e:
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print(f" ❌ {n_clusters}场景测试失败: {str(e)}")
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return False
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print("\n✅ 不同场景数量的储能优化测试通过")
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return True
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def test_edge_cases():
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"""测试边界情况"""
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print("\n=== 边界情况测试 ===")
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# 测试短时间数据
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print("1. 测试短时间数据...")
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analyzer = MultiScenarioAnalyzer(n_clusters=2, random_state=42)
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# 只生成24小时数据
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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,
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5.0, 4.0, 3.0, 2.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
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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,
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2.0, 3.0, 4.0, 3.0, 2.0, 1.0, 1.0, 2.0, 3.0, 4.0, 3.0, 2.0]
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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,
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16.0, 14.0, 12.0, 10.0, 8.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 2.0]
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try:
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result = analyzer.fit_predict(solar_24h, wind_24h, load_24h)
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# 对于短时间数据，应该会被扩展到24小时
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optimization_results = analyzer.optimize_storage_for_scenarios(
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result, solar_24h, wind_24h, load_24h
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)
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print(" ✅ 短时间数据测试通过")
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except Exception as e:
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print(f" ❌ 短时间数据测试失败: {str(e)}")
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return False
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# 测试极大储能需求情况
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print("2. 测试极大储能需求情况...")
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# 创建储能需求很大的场景
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solar_extreme = [10.0] * 24 # 始终高出力
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wind_extreme = [8.0] * 24 # 始终高出力
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load_extreme = [1.0] * 24 # 始终低负荷
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try:
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result_extreme = analyzer.fit_predict(solar_extreme, wind_extreme, load_extreme)
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optimization_results_extreme = analyzer.optimize_storage_for_scenarios(
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result_extreme, solar_extreme, wind_extreme, load_extreme
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)
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print(f" 极端场景推荐储能容量: {optimization_results_extreme['summary']['recommended_capacity']:.2f} MWh")
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print(" ✅ 极大储能需求情况测试通过")
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except Exception as e:
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print(f" ❌ 极大储能需求情况测试失败: {str(e)}")
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return False
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print("\n✅ 所有边界情况测试通过")
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return True
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if __name__ == "__main__":
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print("=== 场景储能配置优化模块测试 ===\n")
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success = True
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# 基础功能测试
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success1, optimization_results = test_scenario_storage_optimization()
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success &= success1
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# 不同场景数量测试
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success2 = test_storage_optimization_with_different_scenarios()
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success &= success2
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# 边界情况测试
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success3 = test_edge_cases()
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success &= success3
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print(f"\n{'='*60}")
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if success:
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print("🎉 所有场景储能配置优化测试通过！")
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print(" 储能优化模块功能正常工作。")
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else:
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print("💥 部分测试失败，请检查代码。")
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print(f"{'='*60}")
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# 返回测试结果供其他模块使用
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if success:
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print(f"\n测试数据示例:")
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if optimization_results:
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summary = optimization_results['summary']
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print(f" - 加权平均储能需求: {summary['weighted_average_storage']:.2f} MWh")
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print(f" - 推荐储能容量: {summary['recommended_capacity']:.2f} MWh")
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print(f" - 安全系数: {summary['safety_factor']}")
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print(f" - 分析场景数: {summary['n_scenarios']}")
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Reference in New Issue
Block a user