import pandas as pd import math from scipy.optimize import fsolve import requests import json import os def wind_farm_analysis(device_name, area_km2, electricity_price, file_path, latitude, longitude, lateral_spacing_factor=5, longitudinal_spacing_factor=10, q=0.02, altitude=11, hub_height=100, Cp=0.45, eta=0.8, cost_per_kw=5): """ 封装函数：分析风电场的风机数量及各项经济和技术指标，使用 NASA POWER API 获取风速和温度数据 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 latitude (float): 目标地点的纬度（度） longitude (float): 目标地点的经度（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] 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} KW, " 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 fetch_nasa_data(latitude, longitude, start_year="2023", end_year="2023"): """ 从 NASA POWER API 获取 12 个月的平均气温和风速数据，不保存缓存 """ try: url = (f"https://power.larc.nasa.gov/api/temporal/monthly/point?" f"parameters=T2M,WS10M&community=RE&longitude={longitude}&latitude={latitude}&" f"start={start_year}&end={end_year}&format=JSON") response = requests.get(url) response.raise_for_status() # 检查请求是否成功 data = response.json() # 提取气温和风速数据 year = start_year temperatures = [data["properties"]["parameter"]["T2M"][f"{year}{str(i).zfill(2)}"] for i in range(1, 13)] wind_speeds = [data["properties"]["parameter"]["WS10M"][f"{year}{str(i).zfill(2)}"] for i in range(1, 13)] # 检查数据完整性 if len(temperatures) != 12 or len(wind_speeds) != 12: raise ValueError("NASA 数据不完整，未包含 12 个月的数据") return temperatures, wind_speeds except requests.exceptions.HTTPError as http_err: raise Exception(f"HTTP 错误: {str(http_err)}\n响应内容: {response.text}") except Exception as e: raise Exception(f"从 NASA POWER API 获取数据时出错: {str(e)}") def adjust_wind_speed(v_10m, h_ref=10, h_hub=100, alpha=0.143): """根据风切变公式调整风速：v_hub = v_ref * (h_hub/h_ref)^alpha""" return v_10m * (h_hub / h_ref) ** alpha 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, wind_speeds): sum_rho_v3 = sum(rho * (v ** 3) for rho, v in zip(densities, wind_speeds)) return (1 / (2 * 12)) * sum_rho_v3 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,num_turbines): return w * S * Cp * 8760 * eta *num_turbines def calculate_equivalent_hours(P, P_r): if P_r == 0: raise ValueError("额定功率不能为 0") #传入的P(发电量)wh,P_r(额定功率)Kw return (P / 1000) / P_r def calculate_environmental_benefits(E_p_million_kwh): if E_p_million_kwh < 0: raise ValueError("年发电量需≥0") return { "coal_reduction": E_p_million_kwh * 0.404 * 10, "CO2_reduction": E_p_million_kwh * 0.977 * 10, "SO2_reduction": E_p_million_kwh * 0.03 * 10, "NOX_reduction": E_p_million_kwh * 0.015 * 10 } 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 # 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) # 从 NASA POWER API 获取气温和风速数据 monthly_temps, wind_speeds = fetch_nasa_data(latitude, longitude, start_year="2023", end_year="2023") # 调整风速到轮毂高度 wind_speeds = [adjust_wind_speed(v) for v in wind_speeds] # 计算空气密度 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 w/m2 wpd = wind_power_density(densities, wind_speeds) # 计算装机容量 KW total_power = estimated_wind_power(num_turbines, rated_power) # 计算初始投资成本 IC = total_power * cost_per_kw * 1000 # 计算年发电量 Wh P_test = calculate_power_output(swept_area, wpd, Cp, eta,num_turbines) # 计算等效小时数 年发电量(Wh)/额定功率(KW) h = calculate_equivalent_hours(P_test, rated_power) # 计算环境收益（转换为万 kWh） E_p_million_kwh = P_test / 10000000 # 转换为 万 kWh env_benefits = calculate_environmental_benefits(E_p_million_kwh) # 计算 IRR P_test_IRR = P_test/1000 irr = calculate_reference_yield(P_test_IRR, 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 /1000, #变为了MW "annual_power_output": P_test/10000000 , # 万 kWh "equivalent_hours": h, "coal_reduction": env_benefits["coal_reduction"], "CO2_reduction": env_benefits["CO2_reduction"], "SO2_reduction": env_benefits["SO2_reduction"], "NOX_reduction": env_benefits["NOX_reduction"], "IRR": irr } # 主程序 if __name__ == "__main__": file_path = r"/home/zhaojh/workspace/GreenTransPowerCalculate/wind/wind_product.xlsx" device_name = 'GW165-4.0' area_km2 = 10 electricity_price = 0.6 latitude = 39 longitude = 116 result = wind_farm_analysis( device_name=device_name, area_km2=area_km2, electricity_price=electricity_price, file_path=file_path, latitude=latitude, longitude=longitude ) print(f"\n设备: {result['device']}") print(f"额定功率: {result['rated_power']:.2f} KW") 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"CO₂减排量：{result['CO2_reduction']:,.0f} kg") print(f"SO₂减排量：{result['SO2_reduction']:,.0f} kg") print(f"NOx减排量：{result['NOX_reduction']:,.0f} kg") print(f"内部收益率 IRR: {result['IRR']:.2f}%")