重构了目录结构
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dmy
362
src/solar_optimization.py
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362
src/solar_optimization.py
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"""
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光伏出力优化模块
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该模块通过调整光伏出力曲线的系数，在给定的系统参数条件下
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最小化与电网交换的电量，提高系统的自平衡能力。
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作者: iFlow CLI
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创建日期: 2025-12-26
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"""
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import numpy as np
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from typing import List, Dict, Tuple, Optional
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from dataclasses import dataclass
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from storage_optimization import SystemParameters, optimize_storage_capacity, calculate_energy_balance
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@dataclass
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class SolarOptimizationResult:
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"""光伏优化结果类"""
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optimal_solar_coefficient: float # 最优光伏系数
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original_solar_output: List[float] # 原始光伏出力曲线
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optimized_solar_output: List[float] # 优化后光伏出力曲线
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min_grid_exchange: float # 最小电网交换电量
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grid_purchase: float # 购电量
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grid_feed_in: float # 上网电量
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storage_result: Dict # 储能优化结果
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optimization_history: List[Dict] # 优化历史记录
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def calculate_grid_exchange_metric(
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solar_output: List[float],
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wind_output: List[float],
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thermal_output: List[float],
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load_demand: List[float],
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params: SystemParameters
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) -> Dict[str, float]:
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"""
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计算电网交换电量指标
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Args:
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solar_output: 光伏出力曲线 (MW)
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wind_output: 风电出力曲线 (MW)
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thermal_output: 火电出力曲线 (MW)
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load_demand: 负荷曲线 (MW)
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params: 系统参数配置
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Returns:
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包含电网交换指标的字典
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"""
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# 计算最优储能容量
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storage_result = optimize_storage_capacity(
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solar_output, wind_output, thermal_output, load_demand, params
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)
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# 计算电网交换电量
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grid_feed_in = storage_result['grid_feed_in']
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# 分离购电和上网电量
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total_purchase = sum(-x for x in grid_feed_in if x < 0) # 购电量（正值）
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total_feed_in = sum(x for x in grid_feed_in if x > 0) # 上网电量（正值）
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# 计算总交换电量（购电 + 上网）
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total_exchange = total_purchase + total_feed_in
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return {
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'total_exchange': total_exchange,
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'grid_purchase': total_purchase,
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'grid_feed_in': total_feed_in,
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'storage_capacity': storage_result['required_storage_capacity'],
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'storage_result': storage_result
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}
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def optimize_solar_output(
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original_solar_output: List[float],
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wind_output: List[float],
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thermal_output: List[float],
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load_demand: List[float],
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params: SystemParameters,
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coefficient_range: Tuple[float, float] = (0.1, 2.0),
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tolerance: float = 0.01,
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max_iterations: int = 50
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) -> SolarOptimizationResult:
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"""
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优化光伏出力系数以最小化电网交换电量
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Args:
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original_solar_output: 原始光伏出力曲线 (MW)
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wind_output: 风电出力曲线 (MW)
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thermal_output: 火电出力曲线 (MW)
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load_demand: 负荷曲线 (MW)
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params: 系统参数配置
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coefficient_range: 光伏系数搜索范围 (最小值, 最大值)
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tolerance: 收敛容差
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max_iterations: 最大迭代次数
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Returns:
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光伏优化结果
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"""
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print("开始光伏出力优化...")
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# 初始化优化历史
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optimization_history = []
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# 使用黄金分割法进行一维优化
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phi = (1 + np.sqrt(5)) / 2 # 黄金比例
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resphi = 2 - phi
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a, b = coefficient_range
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c = b - resphi * (b - a)
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d = a + resphi * (b - a)
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# 计算初始点的目标函数值
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fc = calculate_grid_exchange_metric(
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[x * c for x in original_solar_output],
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wind_output, thermal_output, load_demand, params
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)
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fd = calculate_grid_exchange_metric(
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[x * d for x in original_solar_output],
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wind_output, thermal_output, load_demand, params
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)
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# 记录初始点
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optimization_history.append({
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'coefficient': c,
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'total_exchange': fc['total_exchange'],
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'grid_purchase': fc['grid_purchase'],
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'grid_feed_in': fc['grid_feed_in'],
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'storage_capacity': fc['storage_capacity']
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})
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optimization_history.append({
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'coefficient': d,
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'total_exchange': fd['total_exchange'],
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'grid_purchase': fd['grid_purchase'],
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'grid_feed_in': fd['grid_feed_in'],
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'storage_capacity': fd['storage_capacity']
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})
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# 黄金分割搜索
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for iteration in range(max_iterations):
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if abs(fc['total_exchange'] - fd['total_exchange']) < tolerance:
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break
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if fc['total_exchange'] < fd['total_exchange']:
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b = d
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d = c
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fd = fc
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c = b - resphi * (b - a)
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fc = calculate_grid_exchange_metric(
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[x * c for x in original_solar_output],
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wind_output, thermal_output, load_demand, params
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)
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optimization_history.append({
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'coefficient': c,
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'total_exchange': fc['total_exchange'],
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'grid_purchase': fc['grid_purchase'],
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'grid_feed_in': fc['grid_feed_in'],
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'storage_capacity': fc['storage_capacity']
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})
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else:
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a = c
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c = d
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fc = fd
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d = a + resphi * (b - a)
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fd = calculate_grid_exchange_metric(
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[x * d for x in original_solar_output],
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wind_output, thermal_output, load_demand, params
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)
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optimization_history.append({
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'coefficient': d,
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'total_exchange': fd['total_exchange'],
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'grid_purchase': fd['grid_purchase'],
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'grid_feed_in': fd['grid_feed_in'],
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'storage_capacity': fd['storage_capacity']
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})
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# 确定最优系数
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if fc['total_exchange'] < fd['total_exchange']:
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optimal_coefficient = c
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best_result = fc
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else:
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optimal_coefficient = d
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best_result = fd
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# 生成优化后的光伏出力曲线
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optimized_solar_output = [x * optimal_coefficient for x in original_solar_output]
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# 重新计算完整的最优储能配置
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final_storage_result = optimize_storage_capacity(
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optimized_solar_output, wind_output, thermal_output, load_demand, params
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)
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print(f"优化完成！最优光伏系数: {optimal_coefficient:.3f}")
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print(f"最小电网交换电量: {best_result['total_exchange']:.2f} MWh")
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return SolarOptimizationResult(
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optimal_solar_coefficient=optimal_coefficient,
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original_solar_output=original_solar_output,
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optimized_solar_output=optimized_solar_output,
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min_grid_exchange=best_result['total_exchange'],
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grid_purchase=best_result['grid_purchase'],
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grid_feed_in=best_result['grid_feed_in'],
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storage_result=final_storage_result,
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optimization_history=optimization_history
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)
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def export_optimization_results(result: SolarOptimizationResult, filename: str = None):
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"""
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导出光伏优化结果到Excel文件
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Args:
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result: 光伏优化结果
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filename: 输出文件名，如果为None则自动生成
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"""
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import pandas as pd
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from datetime import datetime
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if filename is None:
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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filename = f"solar_optimization_results_{timestamp}.xlsx"
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print(f"正在导出光伏优化结果到Excel文件: {filename}")
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hours = list(range(1, len(result.original_solar_output) + 1))
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# 创建主要数据DataFrame
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data_df = pd.DataFrame({
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'小时': hours,
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'原始光伏出力(MW)': result.original_solar_output,
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'优化后光伏出力(MW)': result.optimized_solar_output,
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'出力变化(MW)': [result.optimized_solar_output[i] - result.original_solar_output[i]
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for i in range(len(result.original_solar_output))],
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'变化比例(%)': [(result.optimized_solar_output[i] / result.original_solar_output[i] - 1) * 100
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if result.original_solar_output[i] > 0 else 0
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for i in range(len(result.original_solar_output))]
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})
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# 创建优化结果摘要DataFrame
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summary_df = pd.DataFrame({
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'指标': [
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'最优光伏系数',
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'最小电网交换电量',
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'购电量',
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'上网电量',
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'所需储能容量',
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'优化后弃风率',
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'优化后弃光率',
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'优化后上网电量比例'
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],
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'数值': [
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f"{result.optimal_solar_coefficient:.3f}",
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f"{result.min_grid_exchange:.2f} MWh",
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f"{result.grid_purchase:.2f} MWh",
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f"{result.grid_feed_in:.2f} MWh",
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f"{result.storage_result['required_storage_capacity']:.2f} MWh",
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f"{result.storage_result['total_curtailment_wind_ratio']:.3f}",
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f"{result.storage_result['total_curtailment_solar_ratio']:.3f}",
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f"{result.storage_result['total_grid_feed_in_ratio']:.3f}"
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]
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})
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# 创建优化历史DataFrame
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history_df = pd.DataFrame(result.optimization_history)
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history_df.columns = ['光伏系数', '电网交换电量(MWh)', '购电量(MWh)', '上网电量(MWh)', '储能容量(MWh)']
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# 写入Excel文件
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with pd.ExcelWriter(filename, engine='openpyxl') as writer:
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# 写入主要数据
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data_df.to_excel(writer, sheet_name='出力曲线对比', index=False)
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# 写入优化结果摘要
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summary_df.to_excel(writer, sheet_name='优化结果摘要', index=False)
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# 写入优化历史
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history_df.to_excel(writer, sheet_name='优化历史', index=False)
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# 创建说明工作表
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description_df = pd.DataFrame({
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'项目': [
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'文件说明',
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'生成时间',
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'优化目标',
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'优化方法',
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'数据长度',
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'注意事项'
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],
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'内容': [
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'光伏出力优化结果 - 通过调整光伏系数最小化电网交换电量',
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datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
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'最小化与电网交换的总电量（购电 + 上网）',
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'黄金分割一维优化算法',
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f"{len(result.original_solar_output)} 小时",
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'优化结果在给定的系统参数约束下得出'
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]
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})
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description_df.to_excel(writer, sheet_name='说明', index=False)
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print(f"光伏优化结果已成功导出到: {filename}")
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return filename
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def main():
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"""主函数，提供示例使用"""
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# 示例数据
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original_solar_output = [0.0] * 6 + [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] * 6
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wind_output = [2.0, 3.0, 4.0, 3.0, 2.0, 1.0] * 4
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thermal_output = [5.0] * 24
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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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# 系统参数
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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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rated_thermal_capacity=100.0,
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rated_solar_capacity=100.0,
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rated_wind_capacity=100.0,
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available_thermal_energy=2400.0,
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available_solar_energy=600.0,
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available_wind_energy=1200.0
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)
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# 执行光伏优化
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result = optimize_solar_output(
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original_solar_output, wind_output, thermal_output, load_demand, params
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)
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# 打印结果
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print("\n=== 光伏出力优化结果 ===")
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print(f"最优光伏系数: {result.optimal_solar_coefficient:.3f}")
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print(f"最小电网交换电量: {result.min_grid_exchange:.2f} MWh")
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print(f"其中购电量: {result.grid_purchase:.2f} MWh")
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print(f"其中上网电量: {result.grid_feed_in:.2f} MWh")
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print(f"所需储能容量: {result.storage_result['required_storage_capacity']:.2f} MWh")
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print(f"优化后弃风率: {result.storage_result['total_curtailment_wind_ratio']:.3f}")
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print(f"优化后弃光率: {result.storage_result['total_curtailment_solar_ratio']:.3f}")
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print(f"优化后上网电量比例: {result.storage_result['total_grid_feed_in_ratio']:.3f}")
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# 导出结果
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export_optimization_results(result)
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return result
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if __name__ == "__main__":
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main()
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