重构了目录结构
This commit is contained in:
dmy
709
src/economic_optimization.py
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709
src/economic_optimization.py
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"""
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经济指标优化模块
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该模块在光伏、风电、负荷确定的前提下，进行储能配置优化。
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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。
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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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import pandas as pd
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from typing import List, Dict, Tuple
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from dataclasses import dataclass
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from storage_optimization import SystemParameters, calculate_energy_balance, check_constraints
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import matplotlib.pyplot as plt
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@dataclass
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class EconomicParameters:
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"""经济参数配置"""
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# 建设成本参数 (元/MW 或 元/MWh)
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solar_capex: float = 3000000 # 光伏建设成本 (元/MW)
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wind_capex: float = 2500000 # 风电建设成本 (元/MW)
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storage_capex: float = 800000 # 储能建设成本 (元/MWh)
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# 运行成本参数
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electricity_price: float = 600 # 购电价格 (元/MWh)
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feed_in_price: float = 400 # 上网电价 (元/MWh)
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# 维护成本参数
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solar_om: float = 50000 # 光伏运维成本 (元/MW/年)
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wind_om: float = 45000 # 风电运维成本 (元/MW/年)
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storage_om: float = 3000 # 储能运维成本 (元/MW/年)
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# 财务参数
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project_lifetime: int = 25 # 项目寿命 (年)
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discount_rate: float = 0.08 # 折现率
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@dataclass
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class OptimizationResult:
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"""优化结果"""
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# 储能配置参数
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storage_capacity: float # 储能容量 (MWh)
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charge_rate: float # 充电倍率 (C-rate)
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discharge_rate: float # 放电倍率 (C-rate)
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# 经济指标
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total_capex: float # 总建设成本 (元)
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total_om_cost: float # 总运维成本 (元)
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total_electricity_cost: float # 总电费成本 (元)
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total_lcoe: float # 平准化电力成本 (元/MWh)
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total_npv: float # 净现值 (元)
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# 系统性能指标
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total_curtailment: float # 总弃风弃光量 (MWh)
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grid_purchase: float # 总购电量 (MWh)
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grid_feed_in: float # 总上网电量 (MWh)
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renewable_ratio: float # 新能源消纳比例
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def calculate_lcoe(
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capex: float,
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om_cost: float,
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electricity_cost: float,
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annual_generation: float,
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project_lifetime: int,
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discount_rate: float
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) -> float:
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"""
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计算基准化电力成本 (LCOE)
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Args:
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capex: 建设成本
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om_cost: 年运维成本
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electricity_cost: 年电费成本
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annual_generation: 年发电量
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project_lifetime: 项目寿命
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discount_rate: 折现率
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Returns:
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LCOE值 (元/MWh)
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"""
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if annual_generation <= 0:
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return float('inf')
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# 计算现值因子
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pv_factor = sum(1 / (1 + discount_rate) ** t for t in range(1, project_lifetime + 1))
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# 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
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total_cost = capex + om_cost * pv_factor + electricity_cost * pv_factor
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generation_pv = annual_generation * pv_factor
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return total_cost / generation_pv
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def calculate_npv(
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costs: List[float],
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discount_rate: float
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) -> float:
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"""
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计算净现值 (NPV)
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Args:
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costs: 各年度成本流
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discount_rate: 折现率
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Returns:
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NPV值
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"""
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npv = 0
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for t, cost in enumerate(costs):
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npv += cost / (1 + discount_rate) ** t
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return npv
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def evaluate_objective(
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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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storage_capacity: float,
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charge_rate: float,
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discharge_rate: float,
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econ_params: EconomicParameters,
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system_params: SystemParameters
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) -> Dict:
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"""
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评估目标函数值
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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: 负荷需求曲线 (MW)
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storage_capacity: 储能容量 (MWh)
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charge_rate: 充电倍率
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discharge_rate: 放电倍率
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econ_params: 经济参数
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system_params: 系统参数
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Returns:
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包含各项成本和性能指标的字典
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"""
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# 计算系统运行结果
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result = calculate_energy_balance(
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solar_output, wind_output, thermal_output, load_demand,
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system_params, storage_capacity
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)
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# 计算弃风弃光量
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total_curtailment = sum(result['curtailed_wind']) + sum(result['curtailed_solar'])
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# 计算购电和上网电量
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grid_purchase = sum(-x for x in result['grid_feed_in'] if x < 0)
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grid_feed_in = sum(x for x in result['grid_feed_in'] if x > 0)
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# 计算建设成本
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solar_capex_cost = sum(solar_output) * econ_params.solar_capex / len(solar_output) * 8760 # 转换为年容量
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wind_capex_cost = sum(wind_output) * econ_params.wind_capex / len(wind_output) * 8760
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storage_capex_cost = storage_capacity * econ_params.storage_capex
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total_capex = solar_capex_cost + wind_capex_cost + storage_capex_cost
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# 计算年运维成本
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solar_om_cost = sum(solar_output) * econ_params.solar_om / len(solar_output) * 8760
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wind_om_cost = sum(wind_output) * econ_params.wind_om / len(wind_output) * 8760
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storage_om_cost = storage_capacity * econ_params.storage_om
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total_om_cost = solar_om_cost + wind_om_cost + storage_om_cost
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# 计算年电费成本
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annual_electricity_cost = grid_purchase * econ_params.electricity_price
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# 计算年发电量
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total_generation = sum(solar_output) + sum(wind_output) + sum(thermal_output)
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renewable_generation = sum(solar_output) + sum(wind_output) - total_curtailment
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# 计算LCOE
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lcoe = calculate_lcoe(
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total_capex, total_om_cost, annual_electricity_cost,
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renewable_generation, econ_params.project_lifetime, econ_params.discount_rate
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)
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# 计算NPV
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annual_costs = [total_om_cost + annual_electricity_cost] * econ_params.project_lifetime
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npv = calculate_npv([total_capex] + annual_costs, econ_params.discount_rate)
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# 计算新能源消纳比例
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renewable_ratio = (renewable_generation / sum(load_demand) * 100) if sum(load_demand) > 0 else 0
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return {
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'total_capex': total_capex,
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'total_om_cost': total_om_cost * econ_params.project_lifetime,
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'total_electricity_cost': annual_electricity_cost * econ_params.project_lifetime,
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'total_lcoe': lcoe,
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'total_npv': npv,
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'total_curtailment': total_curtailment,
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'grid_purchase': grid_purchase,
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'grid_feed_in': grid_feed_in,
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'renewable_ratio': renewable_ratio,
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'storage_capacity': storage_capacity,
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'charge_rate': charge_rate,
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'discharge_rate': discharge_rate
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}
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def optimize_storage_economic(
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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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econ_params: EconomicParameters,
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system_params: SystemParameters,
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storage_capacity_range: Tuple[float, float] = (0, 1000),
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rate_range: Tuple[float, float] = (0.1, 2.0),
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max_iterations: int = 100,
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tolerance: float = 0.01
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) -> OptimizationResult:
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"""
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经济指标优化主函数
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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: 负荷需求曲线 (MW)
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econ_params: 经济参数
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system_params: 系统参数
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storage_capacity_range: 储能容量搜索范围 (MWh)
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rate_range: 充放电倍率搜索范围
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max_iterations: 最大迭代次数
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tolerance: 收敛容差
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Returns:
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优化结果
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"""
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print("开始经济指标优化...")
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best_result = None
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best_npv = float('inf')
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# 简化的网格搜索优化
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for iteration in range(max_iterations):
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# 在搜索范围内随机采样
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storage_capacity = np.random.uniform(storage_capacity_range[0], storage_capacity_range[1])
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charge_rate = np.random.uniform(rate_range[0], rate_range[1])
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discharge_rate = np.random.uniform(rate_range[0], rate_range[1])
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# 评估当前配置
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current_result = evaluate_objective(
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solar_output, wind_output, thermal_output, load_demand,
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storage_capacity, charge_rate, discharge_rate,
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econ_params, system_params
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)
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# 更新最优解
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if current_result['total_npv'] < best_npv:
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best_npv = current_result['total_npv']
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best_result = current_result
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# 输出进度
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if (iteration + 1) % 10 == 0:
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print(f"迭代 {iteration + 1}/{max_iterations}, 当前最优NPV: {best_npv:.2f}元")
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# 在最优解附近进行精细搜索
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if best_result is not None:
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print("在最优解附近进行精细搜索...")
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best_result = fine_tune_optimization(
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solar_output, wind_output, thermal_output, load_demand,
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best_result, econ_params, system_params,
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storage_capacity_range, rate_range
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)
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return best_result
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def fine_tune_optimization(
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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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initial_result: Dict,
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econ_params: EconomicParameters,
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system_params: SystemParameters,
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storage_capacity_range: Tuple[float, float],
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rate_range: Tuple[float, float]
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) -> OptimizationResult:
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"""
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在最优解附近进行精细搜索
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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: 负荷需求曲线 (MW)
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initial_result: 初始优化结果
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econ_params: 经济参数
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system_params: 系统参数
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storage_capacity_range: 储能容量搜索范围
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rate_range: 充放电倍率搜索范围
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Returns:
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精细优化结果
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"""
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best_result = initial_result
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best_npv = initial_result['total_npv']
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# 在最优解附近进行小范围搜索
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search_range = 0.1 # 搜索范围为最优值的±10%
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for storage_capacity in np.linspace(
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max(initial_result['storage_capacity'] * (1 - search_range), 0),
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min(initial_result['storage_capacity'] * (1 + search_range), storage_capacity_range[1]),
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20
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):
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for charge_rate in np.linspace(
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max(initial_result['charge_rate'] * (1 - search_range), rate_range[0]),
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min(initial_result['charge_rate'] * (1 + search_range), rate_range[1]),
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10
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):
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for discharge_rate in np.linspace(
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max(initial_result['discharge_rate'] * (1 - search_range), rate_range[0]),
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min(initial_result['discharge_rate'] * (1 + search_range), rate_range[1]),
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10
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):
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current_result = evaluate_objective(
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solar_output, wind_output, thermal_output, load_demand,
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storage_capacity, charge_rate, discharge_rate,
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econ_params, system_params
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)
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if current_result['total_npv'] < best_npv:
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best_npv = current_result['total_npv']
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best_result = current_result
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# 转换为OptimizationResult格式
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return OptimizationResult(
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storage_capacity=best_result['storage_capacity'],
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charge_rate=best_result['charge_rate'],
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discharge_rate=best_result['discharge_rate'],
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total_capex=best_result['total_capex'],
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total_om_cost=best_result['total_om_cost'],
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total_electricity_cost=best_result['total_electricity_cost'],
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total_lcoe=best_result['total_lcoe'],
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total_npv=best_result['total_npv'],
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total_curtailment=best_result['total_curtailment'],
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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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renewable_ratio=best_result['renewable_ratio']
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)
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def create_economic_report(
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result: OptimizationResult,
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econ_params: EconomicParameters,
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filename: str = None
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) -> str:
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"""
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创建经济优化报告
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Args:
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result: 优化结果
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econ_params: 经济参数
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filename: 输出文件名
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Returns:
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报告文件路径
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"""
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if filename is None:
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from datetime import datetime
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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filename = f"economic_optimization_report_{timestamp}.xlsx"
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print(f"\n正在生成经济优化报告: {filename}")
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# 创建报告数据
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report_data = {
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'指标': [
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'储能容量 (MWh)',
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'充电倍率 (C-rate)',
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'放电倍率 (光伏建设成本 (元/MW)',
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'光伏建设成本 (元)',
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'风电建设成本 (元)',
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'储能建设成本 (元)',
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'总建设成本 (元)',
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'年运维成本 (元)',
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'总运维成本 (元)',
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'年电费成本 (元)',
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'总电费成本 (元)',
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'LCOE (元/MWh)',
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'NPV (元)',
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'总弃风弃光量 (MWh)',
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'总购电量 (MWh)',
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'总上网电量 (MWh)',
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'新能源消纳比例 (%)'
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],
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'数值': [
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f"{result.storage_capacity:.2f}",
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f"{result.charge_rate:.2f}",
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f"{result.discharge_rate:.2f}",
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f"{result.total_capex * 0.3:.2f}", # 光伏建设成本
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f"{result.total_capex * 0.6:.2f}", # 风电建设成本
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f"{result.total_capex * 0.1:.2f}", # 储能建设成本
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f"{result.total_capex:.2f}",
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f"{result.total_om_cost / econ_params.project_lifetime:.2f}",
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f"{result.total_om_cost:.2f}",
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f"{result.total_electricity_cost / econ_params.project_lifetime:.2f}",
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f"{result.total_electricity_cost:.2f}",
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f"{result.total_lcoe:.2f}",
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f"{result.total_npv:.2f}",
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f"{result.total_curtailment:.2f}",
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f"{result.grid_purchase:.2f}",
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f"{result.grid_feed_in:.2f}",
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f"{result.renewable_ratio:.2f}"
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]
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}
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# 创建参数数据
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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()
|
||||
Reference in New Issue
Block a user