""" 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()