231 lines
9.8 KiB
Python
231 lines
9.8 KiB
Python
import pandas as pd
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import math
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from scipy.optimize import fsolve
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import requests
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import json
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import os
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def wind_farm_analysis(device_name, area_km2, electricity_price, file_path, latitude, longitude,
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lateral_spacing_factor=5, longitudinal_spacing_factor=10, q=0.02, altitude=11,
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hub_height=100, Cp=0.45, eta=0.8, cost_per_kw=5):
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"""
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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
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参数：
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device_name (str): 设备名称
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area_km2 (float): 风电场面积（平方公里）
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electricity_price (float): 电价（元/kWh）
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file_path (str): 风机参数 Excel 文件路径
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latitude (float): 目标地点的纬度（度）
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longitude (float): 目标地点的经度（度）
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lateral_spacing_factor (float): 横向间距因子（默认为 5D）
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longitudinal_spacing_factor (float): 纵向间距因子（默认为 10D）
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q (float): 运维成本占初始投资成本的比例（默认 0.02 表示 2%）
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altitude (float): 海拔高度（m），默认 11m
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hub_height (float): 轮毂高度（m），默认 100m
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Cp (float): 风能利用系数，默认 0.45
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eta (float): 总系统效率，默认 0.8
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cost_per_mw (float): 每 MW 投资成本（万元/MW），默认 5000 万元/MW
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返回：
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dict: 包含风电场分析结果的字典
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"""
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def estimate_wind_turbine_count(area_km2, blade_diameter):
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area_m2 = area_km2 * 1_000_000
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lateral_spacing = lateral_spacing_factor * blade_diameter
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longitudinal_spacing = longitudinal_spacing_factor * blade_diameter
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turbine_area = lateral_spacing * longitudinal_spacing
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turbine_count = int(area_m2 / turbine_area)
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print(f"单台风机占地面积: {turbine_area:,} 平方米 "
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f"(横向间距: {lateral_spacing} 米, 纵向间距: {longitudinal_spacing} 米)")
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print(f"估算风机数量: {turbine_count} 台")
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return turbine_count
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def get_wind_turbine_specs(device_name, file_path):
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try:
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df = pd.read_excel(file_path)
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match = df[df.iloc[:, 0] == device_name]
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if not match.empty:
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rated_power = match.iloc[0, 1]
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swept_area = match.iloc[0, 7] # 扫风面积
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blade_diameter = match.iloc[0, 6] # 叶片直径
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print(f"找到设备 '{device_name}'，额定功率: {rated_power} KW, "
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f"扫风面积: {swept_area} m², 叶片直径: {blade_diameter} 米")
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return rated_power, swept_area, blade_diameter
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else:
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raise ValueError(f"未找到设备名称: {device_name}")
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except FileNotFoundError:
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raise FileNotFoundError(f"文件未找到: {file_path}")
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except Exception as e:
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raise Exception(f"发生错误: {str(e)}")
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def fetch_nasa_data(latitude, longitude, start_year="2023", end_year="2023"):
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"""
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从 NASA POWER API 获取 12 个月的平均气温和风速数据，不保存缓存
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"""
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try:
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url = (f"https://power.larc.nasa.gov/api/temporal/monthly/point?"
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f"parameters=T2M,WS10M&community=RE&longitude={longitude}&latitude={latitude}&"
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f"start={start_year}&end={end_year}&format=JSON")
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response = requests.get(url)
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response.raise_for_status() # 检查请求是否成功
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data = response.json()
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# 提取气温和风速数据
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year = start_year
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temperatures = [data["properties"]["parameter"]["T2M"][f"{year}{str(i).zfill(2)}"] for i in range(1, 13)]
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wind_speeds = [data["properties"]["parameter"]["WS10M"][f"{year}{str(i).zfill(2)}"] for i in range(1, 13)]
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# 检查数据完整性
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if len(temperatures) != 12 or len(wind_speeds) != 12:
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raise ValueError("NASA 数据不完整，未包含 12 个月的数据")
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return temperatures, wind_speeds
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except requests.exceptions.HTTPError as http_err:
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raise Exception(f"HTTP 错误: {str(http_err)}\n响应内容: {response.text}")
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except Exception as e:
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raise Exception(f"从 NASA POWER API 获取数据时出错: {str(e)}")
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def adjust_wind_speed(v_10m, h_ref=10, h_hub=100, alpha=0.143):
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"""根据风切变公式调整风速：v_hub = v_ref * (h_hub/h_ref)^alpha"""
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return v_10m * (h_hub / h_ref) ** alpha
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def air_density(altitude, hub_height, T0):
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z = altitude + hub_height
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LR = 0.0065
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T = T0 - LR * z + 273.15
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return (353.05 / T) * math.exp(-0.034 * (z / T))
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def wind_power_density(densities, wind_speeds):
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sum_rho_v3 = sum(rho * (v ** 3) for rho, v in zip(densities, wind_speeds))
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return (1 / (2 * 12)) * sum_rho_v3
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def estimated_wind_power(num_turbines, rated_power):
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if not isinstance(num_turbines, int) or num_turbines < 0:
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raise ValueError("风机数量必须为非负整数")
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return rated_power * num_turbines
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def calculate_power_output(S, w, Cp, eta,num_turbines):
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return w * S * Cp * 8760 * eta *num_turbines
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def calculate_equivalent_hours(P, P_r):
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if P_r == 0:
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raise ValueError("额定功率不能为 0")
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#传入的P(发电量)wh,P_r(额定功率)Kw
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return (P / 1000) / P_r
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def calculate_environmental_benefits(E_p_million_kwh):
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if E_p_million_kwh < 0:
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raise ValueError("年发电量需≥0")
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return {
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"coal_reduction": E_p_million_kwh * 0.404 * 10,
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"CO2_reduction": E_p_million_kwh * 0.977 * 10,
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"SO2_reduction": E_p_million_kwh * 0.03 * 10,
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"NOX_reduction": E_p_million_kwh * 0.015 * 10
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}
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def calculate_reference_yield(E_p, electricity_price, IC, q, n=20):
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def npv_equation(irr, p, w, ic, q_val, n=n):
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term1 = (1 + irr) ** (-1)
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term2 = irr * (1 + irr) ** (-1) if irr != 0 else float('inf')
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pv_revenue = p * w * (term1 / term2) * (1 - (1 + irr) ** (-n))
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pv_salvage = q_val * ic * (term1 / term2) * (1 - (1 + irr) ** (-n))
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return pv_revenue - ic + pv_salvage
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irr_guess = 0.1
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irr = float(fsolve(npv_equation, irr_guess, args=(E_p, electricity_price, IC, q))[0])
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if not 0 <= irr <= 1:
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raise ValueError(f"IRR计算结果{irr:.4f}不合理")
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return irr * 100
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# 获取设备信息
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rated_power, swept_area, blade_diameter = get_wind_turbine_specs(device_name, file_path)
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# 估算风机数量
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num_turbines = estimate_wind_turbine_count(area_km2, blade_diameter)
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# 从 NASA POWER API 获取气温和风速数据
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monthly_temps, wind_speeds = fetch_nasa_data(latitude, longitude, start_year="2023", end_year="2023")
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# 调整风速到轮毂高度
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wind_speeds = [adjust_wind_speed(v) for v in wind_speeds]
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# 计算空气密度
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densities = [air_density(altitude, hub_height, T0) for T0 in monthly_temps]
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avg_density = sum(densities) / len(densities)
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# 计算风功率密度 w/m2
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wpd = wind_power_density(densities, wind_speeds)
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# 计算装机容量 KW
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total_power = estimated_wind_power(num_turbines, rated_power)
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# 计算初始投资成本
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IC = total_power * cost_per_kw * 1000
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# 计算年发电量 Wh
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P_test = calculate_power_output(swept_area, wpd, Cp, eta,num_turbines)
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# 计算等效小时数 年发电量(Wh)/额定功率(KW)
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h = calculate_equivalent_hours(P_test, rated_power)
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# 计算环境收益（转换为万 kWh）
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E_p_million_kwh = P_test / 10000000 # 转换为 万 kWh
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env_benefits = calculate_environmental_benefits(E_p_million_kwh)
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# 计算 IRR
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P_test_IRR = P_test/1000
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irr = calculate_reference_yield(P_test_IRR, electricity_price, IC, q)
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# 返回结果
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return {
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"device": device_name,
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"rated_power": rated_power,
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"swept_area": swept_area,
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"blade_diameter": blade_diameter,
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"num_turbines": num_turbines,
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"avg_density": avg_density,
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"wpd": wpd,
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"total_power": total_power /1000, #变为了MW
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"annual_power_output": P_test/10000000 , # 万 kWh
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"equivalent_hours": h,
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"coal_reduction": env_benefits["coal_reduction"],
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"CO2_reduction": env_benefits["CO2_reduction"],
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"SO2_reduction": env_benefits["SO2_reduction"],
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"NOX_reduction": env_benefits["NOX_reduction"],
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"IRR": irr
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}
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# 主程序
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if __name__ == "__main__":
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file_path = r"/home/zhaojh/workspace/GreenTransPowerCalculate/wind/wind_product.xlsx"
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device_name = 'GW165-4.0'
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area_km2 = 10
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electricity_price = 0.6
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latitude = 39
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longitude = 116
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result = wind_farm_analysis(
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device_name=device_name,
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area_km2=area_km2,
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electricity_price=electricity_price,
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file_path=file_path,
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latitude=latitude,
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longitude=longitude
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)
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print(f"\n设备: {result['device']}")
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print(f"额定功率: {result['rated_power']:.2f} KW")
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print(f"扫风面积: {result['swept_area']:.2f} m^2")
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print(f"叶片直径: {result['blade_diameter']:.2f} m")
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print(f"风机数量: {result['num_turbines']} 台")
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print(f"平均空气密度: {result['avg_density']:.3f} kg/m^3")
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print(f"风功率密度: {result['wpd']:.2f} W/m^2")
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print(f"项目装机容量: {result['total_power']:.2f} MW")
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print(f"年发电量: {result['annual_power_output']:.3f} 万 kWh")
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print(f"等效小时数: {result['equivalent_hours']:.2f} 小时")
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print(f"CO₂减排量：{result['CO2_reduction']:,.0f} kg")
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print(f"SO₂减排量：{result['SO2_reduction']:,.0f} kg")
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print(f"NOx减排量：{result['NOX_reduction']:,.0f} kg")
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print(f"内部收益率 IRR: {result['IRR']:.2f}%")
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