import pandas as pd import math from scipy.optimize import fsolve def wind_farm_analysis(device_name, area_km2, electricity_price, file_path, velocity_path, T_path, lateral_spacing_factor=5, longitudinal_spacing_factor=10, q=0.02, altitude=11, hub_height=100, Cp=0.45, eta=0.8, cost_per_mw=5000): """ 封装函数：分析风电场的风机数量及各项经济和技术指标 参数： device_name (str): 设备名称 area_km2 (float): 风电场面积（平方公里） electricity_price (float): 电价（元/kWh） file_path (str): 风机参数 Excel 文件路径 velocity_path (str): 风速数据文件路径（12 个月平均风速） T_path (str): 温度数据文件路径（12 个月平均温度） lateral_spacing_factor (float): 横向间距因子（默认为 5D） longitudinal_spacing_factor (float): 纵向间距因子（默认为 10D） q (float): 运维成本占初始投资成本的比例（默认 0.02 表示 2%） altitude (float): 海拔高度（m），默认 11m hub_height (float): 轮毂高度（m），默认 100m Cp (float): 风能利用系数，默认 0.45 eta (float): 总系统效率，默认 0.8 cost_per_mw (float): 每 MW 投资成本（万元/MW），默认 5000 万元/MW 返回： dict: 包含风电场分析结果的字典 """ def estimate_wind_turbine_count(area_km2, blade_diameter): area_m2 = area_km2 * 1_000_000 lateral_spacing = lateral_spacing_factor * blade_diameter longitudinal_spacing = longitudinal_spacing_factor * blade_diameter turbine_area = lateral_spacing * longitudinal_spacing turbine_count = int(area_m2 / turbine_area) print(f"单台风机占地面积: {turbine_area:,} 平方米 " f"(横向间距: {lateral_spacing} 米, 纵向间距: {longitudinal_spacing} 米)") print(f"估算风机数量: {turbine_count} 台") return turbine_count def get_wind_turbine_specs(device_name, file_path): try: df = pd.read_excel(file_path) match = df[df.iloc[:, 0] == device_name] if not match.empty: rated_power = match.iloc[0, 1] / 1000 # kW 转换为 MW swept_area = match.iloc[0, 7] # 扫风面积 blade_diameter = match.iloc[0, 6] # 叶片直径 print(f"找到设备 '{device_name}'，额定功率: {rated_power} MW, " f"扫风面积: {swept_area} m², 叶片直径: {blade_diameter} 米") return rated_power, swept_area, blade_diameter else: raise ValueError(f"未找到设备名称: {device_name}") except FileNotFoundError: raise FileNotFoundError(f"文件未找到: {file_path}") except Exception as e: raise Exception(f"发生错误: {str(e)}") def read_monthly_temperatures(file_path): try: with open(file_path, 'r', encoding='utf-8') as file: temperatures = [float(line.strip()) for line in file.readlines()] if len(temperatures) != 12: raise ValueError(f"温度文件应包含 12 个月的数据，但实际有 {len(temperatures)} 条") return temperatures except Exception as e: raise Exception(f"读取温度文件时出错: {str(e)}") def air_density(altitude, hub_height, T0): z = altitude + hub_height LR = 0.0065 T = T0 - LR * z + 273.15 return (353.05 / T) * math.exp(-0.034 * (z / T)) def wind_power_density(densities, file_path): try: with open(file_path, 'r', encoding='utf-8') as file: wind_speeds = [float(line.strip()) for line in file.readlines()] if len(wind_speeds) != 12: raise ValueError(f"风速文件应包含 12 个月的数据，但实际有 {len(wind_speeds)} 条") sum_rho_v3 = sum(rho * (v ** 3) for rho, v in zip(densities, wind_speeds)) return (1 / (2 * 12)) * sum_rho_v3 except Exception as e: raise Exception(f"读取风速文件时出错: {str(e)}") def estimated_wind_power(num_turbines, rated_power): if not isinstance(num_turbines, int) or num_turbines < 0: raise ValueError("风机数量必须为非负整数") return rated_power * num_turbines def calculate_power_output(S, w, Cp, eta): return w * S * Cp * 8760 * eta def calculate_equivalent_hours(P, P_r): if P_r == 0: raise ValueError("额定功率不能为 0") return (P / 1000) / (P_r * 1000) def calculate_reference_yield(E_p, electricity_price, IC, q, n=20): def npv_equation(irr, p, w, ic, q_val, n=n): term1 = (1 + irr) ** (-1) term2 = irr * (1 + irr) ** (-1) if irr != 0 else float('inf') pv_revenue = p * w * (term1 / term2) * (1 - (1 + irr) ** (-n)) pv_salvage = q_val * ic * (term1 / term2) * (1 - (1 + irr) ** (-n)) return pv_revenue - ic + pv_salvage irr_guess = 0.1 irr = float(fsolve(npv_equation, irr_guess, args=(E_p, electricity_price, IC, q))[0]) if not 0 <= irr <= 1: raise ValueError(f"IRR计算结果{irr:.4f}不合理") return irr * 100 try: # 获取设备信息 rated_power, swept_area, blade_diameter = get_wind_turbine_specs(device_name, file_path) # 估算风机数量 num_turbines = estimate_wind_turbine_count(area_km2, blade_diameter) # 读取温度数据并计算空气密度 monthly_temps = read_monthly_temperatures(T_path) densities = [air_density(altitude, hub_height, T0) for T0 in monthly_temps] avg_density = sum(densities) / len(densities) # 计算风功率密度 wpd = wind_power_density(densities, velocity_path) # 计算装机容量 total_power = estimated_wind_power(num_turbines, rated_power) # 计算初始投资成本 IC = total_power * cost_per_mw * 1000000 # 计算年发电量 P_test = calculate_power_output(swept_area, wpd, Cp, eta) * num_turbines # 计算等效小时数 h = calculate_equivalent_hours(P_test, rated_power) # 计算 IRR irr = calculate_reference_yield(P_test, electricity_price, IC, q) # 返回结果 return { "device": device_name, "rated_power": rated_power, "swept_area": swept_area, "blade_diameter": blade_diameter, "num_turbines": num_turbines, "avg_density": avg_density, "wpd": wpd, "total_power": total_power, "annual_power_output": P_test / 10000000, # 万 kWh "equivalent_hours": h, "IRR": irr } except Exception as e: raise Exception(f"风电场分析出错: {str(e)}") # 主程序 if __name__ == "__main__": file_path = r".\wind_product.xlsx" velocity_path = r".\wind_speed.txt" T_path = r".\temperature.txt" device_name = input("请输入设备名称: ") area_km2 = float(input("请输入风电场面积（平方公里）: ")) electricity_price = float(input("请输入电价（元/kWh）: ")) try: result = wind_farm_analysis( device_name=device_name, area_km2=area_km2, electricity_price=electricity_price, file_path=file_path, velocity_path=velocity_path, T_path=T_path ) print(f"\n设备: {result['device']}") print(f"额定功率: {result['rated_power']:.2f} MW") print(f"扫风面积: {result['swept_area']:.2f} m^2") print(f"叶片直径: {result['blade_diameter']:.2f} m") print(f"风机数量: {result['num_turbines']} 台") print(f"平均空气密度: {result['avg_density']:.3f} kg/m^3") print(f"风功率密度: {result['wpd']:.2f} W/m^2") print(f"项目装机容量: {result['total_power']:.2f} MW") print(f"年发电量: {result['annual_power_output']:.3f} 万 kWh") print(f"等效小时数: {result['equivalent_hours']:.2f} 小时") print(f"内部收益率 IRR: {result['IRR']:.2f}%") except Exception as e: print(f"错误: {str(e)}")