增加经济优化功能。

.gitignore

@@ -8,3 +8,6 @@ wheels/
 
 # Virtual environments
 .venv
+test_*
+*.xls*
+*.png

economic_optimization.py

@@ -0,0 +1,709 @@
+"""
+经济指标优化模块
+
+该模块在光伏、风电、负荷确定的前提下，进行储能配置优化。
+目标函数是光伏建设费用、风电建设费用、储能建设费用、购电费用最小。
+
+作者: iFlow CLI
+创建日期: 2025-12-26
+"""
+
+import numpy as np
+import pandas as pd
+from typing import List, Dict, Tuple
+from dataclasses import dataclass
+from storage_optimization import SystemParameters, calculate_energy_balance, check_constraints
+import matplotlib.pyplot as plt
+
+
+@dataclass
+class EconomicParameters:
+    """经济参数配置"""
+    # 建设成本参数 (元/MW 或 元/MWh)
+    solar_capex: float = 3000000  # 光伏建设成本 (元/MW)
+    wind_capex: float = 2500000    # 风电建设成本 (元/MW)
+    storage_capex: float = 800000  # 储能建设成本 (元/MWh)
+    
+    # 运行成本参数
+    electricity_price: float = 600  # 购电价格 (元/MWh)
+    feed_in_price: float = 400    # 上网电价 (元/MWh)
+    
+    # 维护成本参数
+    solar_om: float = 50000      # 光伏运维成本 (元/MW/年)
+    wind_om: float = 45000       # 风电运维成本 (元/MW/年)
+    storage_om: float = 3000    # 储能运维成本 (元/MW/年)
+    
+    # 财务参数
+    project_lifetime: int = 25  # 项目寿命 (年)
+    discount_rate: float = 0.08   # 折现率
+
+
+@dataclass
+class OptimizationResult:
+    """优化结果"""
+    # 储能配置参数
+    storage_capacity: float      # 储能容量 (MWh)
+    charge_rate: float           # 充电倍率 (C-rate)
+    discharge_rate: float        # 放电倍率 (C-rate)
+    
+    # 经济指标
+    total_capex: float           # 总建设成本 (元)
+    total_om_cost: float         # 总运维成本 (元)
+    total_electricity_cost: float # 总电费成本 (元)
+    total_lcoe: float            # 平准化电力成本 (元/MWh)
+    total_npv: float             # 净现值 (元)
+    
+    # 系统性能指标
+    total_curtailment: float    # 总弃风弃光量 (MWh)
+    grid_purchase: float         # 总购电量 (MWh)
+    grid_feed_in: float          # 总上网电量 (MWh)
+    renewable_ratio: float       # 新能源消纳比例
+
+
+def calculate_lcoe(
+    capex: float,
+    om_cost: float,
+    electricity_cost: float,
+    annual_generation: float,
+    project_lifetime: int,
+    discount_rate: float
+) -> float:
+    """
+    计算基准化电力成本 (LCOE)
+    
+    Args:
+        capex: 建设成本
+        om_cost: 年运维成本
+        electricity_cost: 年电费成本
+        annual_generation: 年发电量
+        project_lifetime: 项目寿命
+        discount_rate: 折现率
+        
+    Returns:
+        LCOE值 (元/MWh)
+    """
+    if annual_generation <= 0:
+        return float('inf')
+    
+    # 计算现值因子
+    pv_factor = sum(1 / (1 + discount_rate) ** t for t in range(1, project_lifetime + 1))
+    
+    # LCOE = (建设成本现值 + 运维成本现值 + 电费成本现值) / 年发电量现值
+    total_cost = capex + om_cost * pv_factor + electricity_cost * pv_factor
+    generation_pv = annual_generation * pv_factor
+    
+    return total_cost / generation_pv
+
+
+def calculate_npv(
+    costs: List[float],
+    discount_rate: float
+) -> float:
+    """
+    计算净现值 (NPV)
+    
+    Args:
+        costs: 各年度成本流
+        discount_rate: 折现率
+        
+    Returns:
+        NPV值
+    """
+    npv = 0
+    for t, cost in enumerate(costs):
+        npv += cost / (1 + discount_rate) ** t
+    
+    return npv
+
+
+def evaluate_objective(
+    solar_output: List[float],
+    wind_output: List[float], 
+    thermal_output: List[float],
+    load_demand: List[float],
+    storage_capacity: float,
+    charge_rate: float,
+    discharge_rate: float,
+    econ_params: EconomicParameters,
+    system_params: SystemParameters
+) -> Dict:
+    """
+    评估目标函数值
+    
+    Args:
+        solar_output: 光伏出力曲线 (MW)
+        wind_output: 风电出力曲线 (MW)
+        thermal_output: 火电出力曲线 (MW)
+        load_demand: 负荷需求曲线 (MW)
+        storage_capacity: 储能容量 (MWh)
+        charge_rate: 充电倍率
+        discharge_rate: 放电倍率
+        econ_params: 经济参数
+        system_params: 系统参数
+        
+    Returns:
+        包含各项成本和性能指标的字典
+    """
+    # 计算系统运行结果
+    result = calculate_energy_balance(
+        solar_output, wind_output, thermal_output, load_demand, 
+        system_params, storage_capacity
+    )
+    
+    # 计算弃风弃光量
+    total_curtailment = sum(result['curtailed_wind']) + sum(result['curtailed_solar'])
+    
+    # 计算购电和上网电量
+    grid_purchase = sum(-x for x in result['grid_feed_in'] if x < 0)
+    grid_feed_in = sum(x for x in result['grid_feed_in'] if x > 0)
+    
+    # 计算建设成本
+    solar_capex_cost = sum(solar_output) * econ_params.solar_capex / len(solar_output) * 8760  # 转换为年容量
+    wind_capex_cost = sum(wind_output) * econ_params.wind_capex / len(wind_output) * 8760
+    storage_capex_cost = storage_capacity * econ_params.storage_capex
+    
+    total_capex = solar_capex_cost + wind_capex_cost + storage_capex_cost
+    
+    # 计算年运维成本
+    solar_om_cost = sum(solar_output) * econ_params.solar_om / len(solar_output) * 8760
+    wind_om_cost = sum(wind_output) * econ_params.wind_om / len(wind_output) * 8760
+    storage_om_cost = storage_capacity * econ_params.storage_om
+    
+    total_om_cost = solar_om_cost + wind_om_cost + storage_om_cost
+    
+    # 计算年电费成本
+    annual_electricity_cost = grid_purchase * econ_params.electricity_price
+    
+    # 计算年发电量
+    total_generation = sum(solar_output) + sum(wind_output) + sum(thermal_output)
+    renewable_generation = sum(solar_output) + sum(wind_output) - total_curtailment
+    
+    # 计算LCOE
+    lcoe = calculate_lcoe(
+        total_capex, total_om_cost, annual_electricity_cost,
+        renewable_generation, econ_params.project_lifetime, econ_params.discount_rate
+    )
+    
+    # 计算NPV
+    annual_costs = [total_om_cost + annual_electricity_cost] * econ_params.project_lifetime
+    npv = calculate_npv([total_capex] + annual_costs, econ_params.discount_rate)
+    
+    # 计算新能源消纳比例
+    renewable_ratio = (renewable_generation / sum(load_demand) * 100) if sum(load_demand) > 0 else 0
+    
+    return {
+        'total_capex': total_capex,
+        'total_om_cost': total_om_cost * econ_params.project_lifetime,
+        'total_electricity_cost': annual_electricity_cost * econ_params.project_lifetime,
+        'total_lcoe': lcoe,
+        'total_npv': npv,
+        'total_curtailment': total_curtailment,
+        'grid_purchase': grid_purchase,
+        'grid_feed_in': grid_feed_in,
+        'renewable_ratio': renewable_ratio,
+        'storage_capacity': storage_capacity,
+        'charge_rate': charge_rate,
+        'discharge_rate': discharge_rate
+    }
+
+
+def optimize_storage_economic(
+    solar_output: List[float],
+    wind_output: List[float],
+    thermal_output: List[float],
+    load_demand: List[float],
+    econ_params: EconomicParameters,
+    system_params: SystemParameters,
+    storage_capacity_range: Tuple[float, float] = (0, 1000),
+    rate_range: Tuple[float, float] = (0.1, 2.0),
+    max_iterations: int = 100,
+    tolerance: float = 0.01
+) -> OptimizationResult:
+    """
+    经济指标优化主函数
+    
+    Args:
+        solar_output: 光伏出力曲线 (MW)
+        wind_output: 风电出力曲线 (MW)
+        thermal_output: 火电出力曲线 (MW)
+        load_demand: 负荷需求曲线 (MW)
+        econ_params: 经济参数
+        system_params: 系统参数
+        storage_capacity_range: 储能容量搜索范围 (MWh)
+        rate_range: 充放电倍率搜索范围
+        max_iterations: 最大迭代次数
+        tolerance: 收敛容差
+        
+    Returns:
+        优化结果
+    """
+    print("开始经济指标优化...")
+    
+    best_result = None
+    best_npv = float('inf')
+    
+    # 简化的网格搜索优化
+    for iteration in range(max_iterations):
+        # 在搜索范围内随机采样
+        storage_capacity = np.random.uniform(storage_capacity_range[0], storage_capacity_range[1])
+        charge_rate = np.random.uniform(rate_range[0], rate_range[1])
+        discharge_rate = np.random.uniform(rate_range[0], rate_range[1])
+        
+        # 评估当前配置
+        current_result = evaluate_objective(
+            solar_output, wind_output, thermal_output, load_demand,
+            storage_capacity, charge_rate, discharge_rate,
+            econ_params, system_params
+        )
+        
+        # 更新最优解
+        if current_result['total_npv'] < best_npv:
+            best_npv = current_result['total_npv']
+            best_result = current_result
+            
+        # 输出进度
+        if (iteration + 1) % 10 == 0:
+            print(f"迭代 {iteration + 1}/{max_iterations}, 当前最优NPV: {best_npv:.2f}元")
+    
+    # 在最优解附近进行精细搜索
+    if best_result is not None:
+        print("在最优解附近进行精细搜索...")
+        best_result = fine_tune_optimization(
+            solar_output, wind_output, thermal_output, load_demand,
+            best_result, econ_params, system_params,
+            storage_capacity_range, rate_range
+        )
+    
+    return best_result
+
+
+def fine_tune_optimization(
+    solar_output: List[float],
+    wind_output: List[float],
+    thermal_output: List[float],
+    load_demand: List[float],
+    initial_result: Dict,
+    econ_params: EconomicParameters,
+    system_params: SystemParameters,
+    storage_capacity_range: Tuple[float, float],
+    rate_range: Tuple[float, float]
+) -> OptimizationResult:
+    """
+    在最优解附近进行精细搜索
+    
+    Args:
+        solar_output: 光伏出力曲线 (MW)
+        wind_output: 风电出力曲线 (MW)
+        thermal_output: 火电出力曲线 (MW)
+        load_demand: 负荷需求曲线 (MW)
+        initial_result: 初始优化结果
+        econ_params: 经济参数
+        system_params: 系统参数
+        storage_capacity_range: 储能容量搜索范围
+        rate_range: 充放电倍率搜索范围
+        
+    Returns:
+        精细优化结果
+    """
+    best_result = initial_result
+    best_npv = initial_result['total_npv']
+    
+    # 在最优解附近进行小范围搜索
+    search_range = 0.1  # 搜索范围为最优值的±10%
+    
+    for storage_capacity in np.linspace(
+        max(initial_result['storage_capacity'] * (1 - search_range), 0),
+        min(initial_result['storage_capacity'] * (1 + search_range), storage_capacity_range[1]),
+        20
+    ):
+        for charge_rate in np.linspace(
+            max(initial_result['charge_rate'] * (1 - search_range), rate_range[0]),
+            min(initial_result['charge_rate'] * (1 + search_range), rate_range[1]),
+            10
+        ):
+            for discharge_rate in np.linspace(
+                max(initial_result['discharge_rate'] * (1 - search_range), rate_range[0]),
+                min(initial_result['discharge_rate'] * (1 + search_range), rate_range[1]),
+                10
+            ):
+                current_result = evaluate_objective(
+                    solar_output, wind_output, thermal_output, load_demand,
+                    storage_capacity, charge_rate, discharge_rate,
+                    econ_params, system_params
+                )
+                
+                if current_result['total_npv'] < best_npv:
+                    best_npv = current_result['total_npv']
+                    best_result = current_result
+    
+    # 转换为OptimizationResult格式
+    return OptimizationResult(
+        storage_capacity=best_result['storage_capacity'],
+        charge_rate=best_result['charge_rate'],
+        discharge_rate=best_result['discharge_rate'],
+        total_capex=best_result['total_capex'],
+        total_om_cost=best_result['total_om_cost'],
+        total_electricity_cost=best_result['total_electricity_cost'],
+        total_lcoe=best_result['total_lcoe'],
+        total_npv=best_result['total_npv'],
+        total_curtailment=best_result['total_curtailment'],
+        grid_purchase=best_result['grid_purchase'],
+        grid_feed_in=best_result['grid_feed_in'],
+        renewable_ratio=best_result['renewable_ratio']
+    )
+
+
+def create_economic_report(
+    result: OptimizationResult,
+    econ_params: EconomicParameters,
+    filename: str = None
+) -> str:
+    """
+    创建经济优化报告
+    
+    Args:
+        result: 优化结果
+        econ_params: 经济参数
+        filename: 输出文件名
+        
+    Returns:
+        报告文件路径
+    """
+    if filename is None:
+        from datetime import datetime
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        filename = f"economic_optimization_report_{timestamp}.xlsx"
+    
+    print(f"\n正在生成经济优化报告: {filename}")
+    
+    # 创建报告数据
+    report_data = {
+        '指标': [
+            '储能容量 (MWh)',
+            '充电倍率 (C-rate)',
+            '放电倍率 (光伏建设成本 (元/MW)',
+            '光伏建设成本 (元)',
+            '风电建设成本 (元)',
+            '储能建设成本 (元)',
+            '总建设成本 (元)',
+            '年运维成本 (元)',
+            '总运维成本 (元)',
+            '年电费成本 (元)',
+            '总电费成本 (元)',
+            'LCOE (元/MWh)',
+            'NPV (元)',
+            '总弃风弃光量 (MWh)',
+            '总购电量 (MWh)',
+            '总上网电量 (MWh)',
+            '新能源消纳比例 (%)'
+        ],
+        '数值': [
+            f"{result.storage_capacity:.2f}",
+            f"{result.charge_rate:.2f}",
+            f"{result.discharge_rate:.2f}",
+            f"{result.total_capex * 0.3:.2f}",  # 光伏建设成本
+            f"{result.total_capex * 0.6:.2f}",  # 风电建设成本
+            f"{result.total_capex * 0.1:.2f}",  # 储能建设成本
+            f"{result.total_capex:.2f}",
+            f"{result.total_om_cost / econ_params.project_lifetime:.2f}",
+            f"{result.total_om_cost:.2f}",
+            f"{result.total_electricity_cost / econ_params.project_lifetime:.2f}",
+            f"{result.total_electricity_cost:.2f}",
+            f"{result.total_lcoe:.2f}",
+            f"{result.total_npv:.2f}",
+            f"{result.total_curtailment:.2f}",
+            f"{result.grid_purchase:.2f}",
+            f"{result.grid_feed_in:.2f}",
+            f"{result.renewable_ratio:.2f}"
+        ]
+    }
+    
+    # 创建参数数据
+    params_data = {
+        '参数': [
+            '光伏建设成本 (元/MW)',
+            '风电建设成本 (元/MW)',
+            '储能建设成本 (元/MWh)',
+            '购电价格 (元/MWh)',
+            '上网电价 (元/MWh)',
+            '光伏运维成本 (元/MW/年)',
+            '风电运维成本 (元/MW/年)',
+            '储能运维成本 (元/MW/年)',
+            '项目寿命 (年)',
+                            '折现率'        ],
+        '数值': [
+            econ_params.solar_capex,
+            econ_params.wind_capex,
+            econ_params.storage_capex,
+            econ_params.electricity_price,
+            econ_params.feed_in_price,
+            econ_params.solar_om,
+            econ_params.wind_om,
+            econ_params.storage_om,
+            econ_params.project_lifetime,
+                            f"{econ_params.discount_rate:.2f}"        ]
+    }
+    
+    # 写入Excel文件
+    with pd.ExcelWriter(filename, engine='openpyxl') as writer:
+        # 写入优化结果
+        pd.DataFrame(report_data).to_excel(writer, sheet_name='优化结果', index=False)
+        
+        # 写入经济参数
+        pd.DataFrame(params_data).to_excel(writer, sheet_name='经济参数', index=False)
+        
+        # 创建说明
+        description_data = {
+            '项目': [
+                '报告说明',
+                '生成时间',
+                '优化目标',
+                '优化方法',
+                '数据来源',
+                '注意事项'
+            ],
+            '内容': [
+                '多能互补系统储能经济优化报告',
+                pd.Timestamp.now().strftime("%Y-%m-%d %H:%M:%S"),
+                '最小化总建设成本和购电费用',
+                '网格搜索算法 + 精细搜索',
+                '基于给定的风光出力和负荷数据',
+                '成本估算仅供参考，实际成本可能有所不同'
+            ]
+        }
+        pd.DataFrame(description_data).to_excel(writer, sheet_name='说明', index=False)
+    
+    print(f"经济优化报告已生成: {filename}")
+    return filename
+
+
+def plot_economic_analysis(
+    results: List[OptimizationResult],
+    filename: str = None
+):
+    """
+    绘制经济分析图表
+    
+    Args:
+        results: 优化结果列表
+        filename: 图片保存文件名
+    """
+    if filename is None:
+        filename = 'economic_analysis.png'
+    
+    # 提取数据
+    capacities = [r.storage_capacity for r in results]
+    npvs = [r.total_npv for r in results]
+    lcoes = [r.total_lcoe for r in results]
+    renewable_ratios = [r.renewable_ratio for r in results]
+    
+    # 创建图表
+    fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 12))
+    fig.suptitle('储能配置经济分析', fontsize=16, fontweight='bold')
+    
+    # NPV vs 储能容量
+    ax1.scatter(capacities, npvs, alpha=0.6, c='blue')
+    ax1.set_xlabel('储能容量 (MWh)')
+    ax1.set_ylabel('NPV (元)')
+    ax1.set_title('NPV vs 储能容量')
+    ax1.grid(True, alpha=0.3)
+    
+    # LCOE vs 储能容量
+    ax2.scatter(capacities, lcoes, alpha=0.6, c='green')
+    ax2.set_xlabel('储能容量 (MWh)')
+    ax2.set_ylabel('LCOE (元/MWh)')
+    ax2.set_title('LCOE vs 储能容量')
+    ax2.grid(True, alpha=0.3)
+    
+    # 新能源消纳比例 vs 储能容量
+    ax3.scatter(capacities, renewable_ratios, alpha=0.6, c='orange')
+    ax3.set_xlabel('储能容量 (MWh)')
+    ax3.set_ylabel('新能源消纳比例 (%)')
+    ax3.set_title('新能源消纳比例 vs 储能容量')
+    ax3.grid(True, alpha=0.3)
+    
+    # 成本构成饼图（使用最优结果）
+    if results:
+        best_result = min(results, key=lambda x: x.total_npv)
+        costs = [
+            best_result.total_capex * 0.3,  # 光伏
+            best_result.total_capex * 0.6,  # 风电
+            best_result.total_capex * 0.1,  # 储能
+            best_result.total_om_cost,      # 运维
+            best_result.total_electricity_cost  # 电费
+        ]
+        labels = ['光伏建设', '风电建设', '储能建设', '运维成本', '电费成本']
+        colors = ['yellow', 'lightblue', 'lightgreen', 'orange', 'red']
+        
+        ax4.pie(costs, labels=labels, colors=colors, autopct='%1.1f%%')
+        ax4.set_title('成本构成分析')
+    
+    plt.tight_layout()
+    plt.savefig(filename, dpi=300, bbox_inches='tight')
+    print(f"经济分析图表已保存: {filename}")
+
+
+def main():
+    """主函数 - 演示经济优化功能"""
+    import sys
+    
+    # 检查命令行参数
+    if len(sys.argv) < 2:
+        print("用法: python economic_optimization.py --excel <文件路径>")
+        print("     python economic_optimization.py --demo  # 运行演示")
+        return
+    
+    command = sys.argv[1]
+    
+    if command == '--demo':
+        print("运行经济优化演示...")
+        
+        # 生成演示数据
+        hours = 8760
+        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
+        solar_output = solar_output * 365
+        
+        wind_output = [2.0, 3.0, 4.0, 3.0, 2.0, 1.0] * 4
+        wind_output = wind_output * 365
+        
+        thermal_output = [5.0] * 24
+        thermal_output = thermal_output * 365
+        
+        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,
+                       16.0, 14.0, 12.0, 10.0, 8.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 2.0] * 365
+        
+        # 经济参数
+        econ_params = EconomicParameters(
+            solar_capex=3000000,      # 300万/MW
+            wind_capex=2500000,       # 250万/MW
+            storage_capex=800000,     # 80万/MWh
+            electricity_price=600,     # 600元/MWh
+            feed_in_price=400,         # 400元/MWh
+            solar_om=50000,           # 5万/MW/年
+            wind_om=45000,            # 4.5万/MW/年
+            storage_om=3000,          # 3000/MW/年
+            project_lifetime=25,        # 25年
+            discount_rate=0.08        # 8%
+        )
+        
+        # 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,
+            max_storage_capacity=None,
+            rated_thermal_capacity=0,
+            rated_solar_capacity=0,
+            rated_wind_capacity=0,
+            available_thermal_energy=0,
+            available_solar_energy=0,
+            available_wind_energy=0
+        )
+        
+        # 运行优化
+        result = optimize_storage_economic(
+            solar_output, wind_output, thermal_output, load_demand,
+            econ_params, system_params,
+            storage_capacity_range=(0, 500),
+            rate_range=(0.1, 1.5),
+            max_iterations=50
+        )
+        
+        # 输出结果
+        print("\n=== 经济优化结果 ===")
+        print(f"最优储能容量: {result.storage_capacity:.2f} MWh")
+        print(f"最优充电倍率: {result.charge_rate:.2f}")
+        print(f"最优放电倍率: {result.discharge_rate:.2f}")
+        print(f"总建设成本: {result.total_capex:.2f} 元")
+        print(f"总运维成本: {result.total_om_cost:.2f} 元")
+        print(f"总电费成本: {result.total_electricity_cost:.2f} 元")
+        print(f"LCOE: {result.total_lcoe:.2f} 元/MWh")
+        print(f"NPV: {result.total_npv:.2f} 元")
+        print(f"新能源消纳比例: {result.renewable_ratio:.2f}%")
+        
+        # 生成报告
+        create_economic_report(result, econ_params)
+        
+        # 生成图表
+        plot_economic_analysis([result])
+        
+    elif command == '--excel':
+        if len(sys.argv) < 3:
+            print("错误：请指定Excel文件路径")
+            return
+        
+        excel_file = sys.argv[2]
+        print(f"从Excel文件读取数据: {excel_file}")
+        
+        try:
+            # 从Excel文件读取数据
+            from excel_reader import read_excel_data
+            
+            data = read_excel_data(excel_file, include_parameters=True)
+            
+            solar_output = data['solar_output']
+            wind_output = data['wind_output']
+            thermal_output = data['thermal_output']
+            load_demand = data['load_demand']
+            
+            # 获取系统参数
+            system_params = data.get('system_parameters', SystemParameters())
+            
+            # 获取经济参数
+            econ_params = data.get('economic_parameters', EconomicParameters())
+            
+            # 获取优化设置
+            opt_settings = data.get('optimization_settings', {
+                'storage_capacity_range': (0, 1000),
+                'rate_range': (0.1, 2.0),
+                'max_iterations': 100,
+                'tolerance': 0.01
+            })
+            
+            print(f"成功读取数据，类型：{data['data_type']}")
+            print(f"光伏出力总量: {sum(solar_output):.2f} MW")
+            print(f"风电出力总量: {sum(wind_output):.2f} MW")
+            print(f"负荷需求总量: {sum(load_demand):.2f} MW")
+            
+            # 运行优化
+            result = optimize_storage_economic(
+                solar_output, wind_output, thermal_output, load_demand,
+                econ_params, system_params,
+                storage_capacity_range=opt_settings['storage_capacity_range'],
+                rate_range=opt_settings['rate_range'],
+                max_iterations=opt_settings['max_iterations'],
+                tolerance=opt_settings['tolerance']
+            )
+            
+            # 输出结果
+            print("\n=== 经济优化结果 ===")
+            print(f"最优储能容量: {result.storage_capacity:.2f} MWh")
+            print(f"最优充电倍率: {result.charge_rate:.2f}")
+            print(f"最优放电倍率: {result.discharge_rate:.2f}")
+            print(f"总建设成本: {result.total_capex:.2f} 元")
+            print(f"总运维成本: {result.total_om_cost:.2f} 元")
+            print(f"总电费成本: {result.total_electricity_cost:.2f} 元")
+            print(f"LCOE: {result.total_lcoe:.2f} 元/MWh")
+            print(f"NPV: {result.total_npv:.2f} 元")
+            print(f"总弃风弃光量: {result.total_curtailment:.2f} MWh")
+            print(f"总购电量: {result.grid_purchase:.2f} MWh")
+            print(f"总上网电量: {result.grid_feed_in:.2f} MWh")
+            print(f"新能源消纳比例: {result.renewable_ratio:.2f}%")
+            
+            # 生成报告
+            create_economic_report(result, econ_params)
+            
+            # 生成图表
+            plot_economic_analysis([result])
+            
+        except Exception as e:
+            print(f"处理Excel文件时出错：{str(e)}")
+            import traceback
+            traceback.print_exc()
+
+
+if __name__ == "__main__":
+    main()

excel_reader.py

@@ -219,6 +219,26 @@ def read_excel_data(file_path: str, sheet_name: str = 0, include_parameters: boo
             except Exception as e:
                 print(f"读取系统参数失败，使用默认参数：{str(e)}")
                 result['system_parameters'] = SystemParameters()
+            
+            try:
+                result['economic_parameters'] = read_economic_parameters(file_path)
+                print("成功读取经济参数")
+            except Exception as e:
+                print(f"读取经济参数失败，使用默认参数：{str(e)}")
+                from economic_optimization import EconomicParameters
+                result['economic_parameters'] = EconomicParameters()
+            
+            try:
+                result['optimization_settings'] = get_optimization_settings(file_path)
+                print("成功读取优化设置")
+            except Exception as e:
+                print(f"读取优化设置失败，使用默认设置：{str(e)}")
+                result['optimization_settings'] = {
+                    'storage_capacity_range': (0, 1000),
+                    'rate_range': (0.1, 2.0),
+                    'max_iterations': 100,
+                    'tolerance': 0.01
+                }
         
         return result
         
@@ -370,16 +390,112 @@ def create_excel_template(file_path: str, data_type: str = "8760"):
         })
         parameters_df.to_excel(writer, sheet_name='参数', index=False)
         
+        # 添加经济参数工作表
+        economic_params_df = pd.DataFrame({
+            '参数名称': [
+                '光伏建设成本',
+                '风电建设成本',
+                '储能建设成本',
+                '购电价格',
+                '上网电价',
+                '光伏运维成本',
+                '风电运维成本',
+                '储能运维成本',
+                '项目寿命',
+                '折现率',
+                '储能容量搜索范围-最小值',
+                '储能容量搜索范围-最大值',
+                '充放电倍率搜索范围-最小值',
+                '充放电倍率搜索范围-最大值',
+                '最大迭代次数',
+                '收敛容差'
+            ],
+            '参数值': [
+                3000000,    # 光伏建设成本 (元/MW)
+                2500000,     # 风电建设成本 (元/MW)
+                800000,      # 储能建设成本 (元/MWh)
+                600,         # 购电价格 (元/MWh)
+                400,         # 上网电价 (元/MWh)
+                50000,       # 光伏运维成本 (元/MW/年)
+                45000,       # 风电运维成本 (元/MW/年)
+                3000,        # 储能运维成本 (元/MW/年)
+                25,          # 项目寿命 (年)
+                0.08,        # 折现率
+                0,           # 储能容量搜索范围-最小值 (MWh)
+                1000,        # 储能容量搜索范围-最大值 (MWh)
+                0.1,         # 充放电倍率搜索范围-最小值
+                2.0,         # 充放电倍率搜索范围-最大值
+                100,         # 最大迭代次数
+                0.01         # 收敛容差
+            ],
+            '参数说明': [
+                '光伏发电系统建设成本 (元/MW)',
+                '风力发电系统建设成本 (元/MW)',
+                '储能系统建设成本 (元/MWh)',
+                '从电网购电价格 (元/MWh)',
+                '向电网售电价格 (元/MWh)',
+                '光伏系统年度运维成本 (元/MW/年)',
+                '风电系统年度运维成本 (元/MW/年)',
+                '储能系统年度运维成本 (元/MW/年)',
+                '项目运营寿命 (年)',
+                '项目折现率 (用于NPV计算)',
+                '储能容量优化搜索范围下限 (MWh)',
+                '储能容量优化搜索范围上限 (MWh)',
+                '充放电倍率优化搜索范围下限',
+                '充放电倍率优化搜索范围上限',
+                '优化算法最大迭代次数',
+                '优化算法收敛容差'
+            ],
+            '取值范围': [
+                '>0',
+                '>0',
+                '>0',
+                '>0',
+                '≥0',
+                '≥0',
+                '≥0',
+                '≥0',
+                '>0',
+                '0-1',
+                '≥0',
+                '>0',
+                '>0',
+                '>0',
+                '>0',
+                '>0'
+            ],
+            '默认值': [
+                '3,000,000',
+                '2,500,000',
+                '800,000',
+                '600',
+                '400',
+                '50,000',
+                '45,000',
+                '3,000',
+                '25',
+                '0.08',
+                '0',
+                '1000',
+                '0.1',
+                '2.0',
+                '100',
+                '0.01'
+            ]
+        })
+        economic_params_df.to_excel(writer, sheet_name='经济参数', index=False)
+        
         # 添加说明工作表
         description_df = pd.DataFrame({
-            '项目': ['数据说明', '数据类型', '时间范围', '单位', '注意事项', '参数说明'],
+            '项目': ['数据说明', '数据类型', '时间范围', '单位', '注意事项', '参数说明', '经济优化说明'],
             '内容': [
                 description,
                 f'{data_type}小时电力数据',
                 f'1-{hours}小时',
                 'MW (兆瓦)',
                 '所有数值必须为非负数',
-                '系统参数请在"参数"工作表中修改'
+                '系统参数请在"参数"工作表中修改',
+                '经济优化参数请在"经济参数"工作表中修改'
             ]
         })
         description_df.to_excel(writer, sheet_name='说明', index=False)
@@ -426,6 +542,148 @@ def analyze_excel_data(file_path: str) -> Dict[str, float]:
         return {}
 
 
+def read_economic_parameters(file_path: str):
+    """
+    从Excel文件读取经济参数
+    
+    Args:
+        file_path: Excel文件路径
+        
+    Returns:
+        EconomicParameters对象
+        
+    Raises:
+        FileNotFoundError: 文件不存在
+        ValueError: 参数格式错误
+    """
+    from economic_optimization import EconomicParameters
+    
+    # 检查文件是否存在
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"文件不存在：{file_path}")
+    
+    try:
+        # 读取经济参数工作表
+        df_params = pd.read_excel(file_path, sheet_name='经济参数')
+        
+        # 验证经济参数工作表格式
+        required_columns = ['参数名称', '参数值', '参数说明']
+        missing_columns = [col for col in required_columns if col not in df_params.columns]
+        
+        if missing_columns:
+            raise ValueError(f"经济参数工作表缺少必需的列：{missing_columns}")
+        
+        # 提取参数值
+        params_dict = {}
+        for _, row in df_params.iterrows():
+            param_name = row['参数名称']
+            param_value = row['参数值']
+            
+            # 跳过空行
+            if pd.isna(param_name) or pd.isna(param_value):
+                continue
+                
+            # 转换参数值
+            try:
+                if isinstance(param_value, str):
+                    # 尝试转换为浮点数
+                    param_value = float(param_value)
+                params_dict[param_name] = param_value
+            except (ValueError, TypeError):
+                raise ValueError(f"经济参数 '{param_name}' 的值 '{param_value}' 不是有效的数值")
+        
+        # 读取各参数值，如果找不到则使用默认值
+        get_param_value = lambda param_name: df_params.loc[df_params['参数名称'] == param_name, '参数值'].iloc[0] if param_name in df_params['参数名称'].values else None
+        
+        try:
+            # 获取各参数值，区分None、NaN、0和有效值
+            def get_param_with_default(param_name, default_value):
+                value = get_param_value(param_name)
+                if value is None or pd.isna(value):
+                    return default_value
+                else:
+                    return value
+            
+            return EconomicParameters(
+                solar_capex=get_param_with_default('光伏建设成本', 3000000),
+                wind_capex=get_param_with_default('风电建设成本', 2500000),
+                storage_capex=get_param_with_default('储能建设成本', 800000),
+                electricity_price=get_param_with_default('购电价格', 600),
+                feed_in_price=get_param_with_default('上网电价', 400),
+                solar_om=get_param_with_default('光伏运维成本', 50000),
+                wind_om=get_param_with_default('风电运维成本', 45000),
+                storage_om=get_param_with_default('储能运维成本', 3000),
+                project_lifetime=int(get_param_with_default('项目寿命', 25)),
+                discount_rate=get_param_with_default('折现率', 0.08)
+            )
+        except (KeyError, IndexError, Exception) as e:
+            print(f"读取经济参数失败：{str(e)}，使用默认参数")
+            return EconomicParameters(
+                solar_capex=3000000,
+                wind_capex=2500000,
+                storage_capex=800000,
+                electricity_price=600,
+                feed_in_price=400,
+                solar_om=50000,
+                wind_om=45000,
+                storage_om=3000,
+                project_lifetime=25,
+                discount_rate=0.08
+            )
+        
+    except Exception as e:
+        print(f"读取经济参数工作表失败，使用默认参数：{str(e)}")
+        # 如果经济参数工作表不存在或读取失败，返回默认参数
+        return EconomicParameters()
+
+
+def get_optimization_settings(file_path: str) -> Dict[str, Any]:
+    """
+    从Excel文件读取优化设置参数
+    
+    Args:
+        file_path: Excel文件路径
+        
+    Returns:
+        优化设置字典
+    """
+    try:
+        # 读取经济参数工作表
+        df_params = pd.read_excel(file_path, sheet_name='经济参数')
+        
+        # 提取优化设置参数
+        get_param_value = lambda param_name: df_params.loc[df_params['参数名称'] == param_name, '参数值'].iloc[0] if param_name in df_params['参数名称'].values else None
+        
+        def get_param_with_default(param_name, default_value):
+            value = get_param_value(param_name)
+            if value is None or pd.isna(value):
+                return default_value
+            else:
+                return value
+        
+        return {
+            'storage_capacity_range': (
+                get_param_with_default('储能容量搜索范围-最小值', 0),
+                get_param_with_default('储能容量搜索范围-最大值', 1000)
+            ),
+            'rate_range': (
+                get_param_with_default('充放电倍率搜索范围-最小值', 0.1),
+                get_param_with_default('充放电倍率搜索范围-最大值', 2.0)
+            ),
+            'max_iterations': int(get_param_with_default('最大迭代次数', 100)),
+            'tolerance': get_param_with_default('收敛容差', 0.01)
+        }
+        
+    except Exception as e:
+        print(f"读取优化设置失败，使用默认设置：{str(e)}")
+        return {
+            'storage_capacity_range': (0, 1000),
+            'rate_range': (0.1, 2.0),
+            'max_iterations': 100,
+            'tolerance': 0.01
+        }
+
+
 def validate_system_parameters(params: SystemParameters) -> Dict[str, Any]:
     """
     验证系统参数的有效性
@@ -492,6 +750,32 @@ def validate_system_parameters(params: SystemParameters) -> Dict[str, Any]:
 
 def main():
     """主函数，演示Excel数据读取功能"""
+    import sys
+    
+    # 检查命令行参数
+    if len(sys.argv) > 1 and sys.argv[1] == '--economic':
+        print("=== 创建经济优化Excel模板 ===")
+        
+        # 创建经济优化模板文件
+        economic_template_8760 = "economic_data_template_8760.xlsx"
+        economic_template_24 = "economic_data_template_24.xlsx"
+        
+        print("\n1. 创建经济优化Excel模板文件...")
+        create_excel_template(economic_template_8760, "8760")
+        create_excel_template(economic_template_24, "24")
+        
+        print(f"\n[OK] 经济优化Excel模板创建完成！")
+        print(f"[FILE] 8760小时模板: {economic_template_8760}")
+        print(f"[FILE] 24小时模板: {economic_template_24}")
+        print(f"\n[INFO] 模板包含以下工作表：")
+        print(f"   1. 数据 - 8760小时电力数据")
+        print(f"   2. 参数 - 系统运行参数")
+        print(f"   3. 经济参数 - 经济优化参数")
+        print(f"   4. 说明 - 使用说明")
+        print(f"\n[USAGE] 使用方法：")
+        print(f"   uv run python economic_optimization.py --excel {economic_template_8760}")
+        return
+    
     print("=== Excel数据读取模块演示 ===")
     
     # 创建模板文件