192 lines
8.5 KiB
Python
192 lines
8.5 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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def wind_farm_analysis(device_name, area_km2, electricity_price, file_path, velocity_path, T_path,
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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_mw=5000):
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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
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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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velocity_path (str): 风速数据文件路径（12 个月平均风速）
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T_path (str): 温度数据文件路径（12 个月平均温度）
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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] / 1000 # kW 转换为 MW
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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} MW, "
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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 read_monthly_temperatures(file_path):
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try:
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with open(file_path, 'r', encoding='utf-8') as file:
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temperatures = [float(line.strip()) for line in file.readlines()]
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if len(temperatures) != 12:
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raise ValueError(f"温度文件应包含 12 个月的数据，但实际有 {len(temperatures)} 条")
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return temperatures
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except Exception as e:
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raise Exception(f"读取温度文件时出错: {str(e)}")
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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, file_path):
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try:
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with open(file_path, 'r', encoding='utf-8') as file:
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wind_speeds = [float(line.strip()) for line in file.readlines()]
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if len(wind_speeds) != 12:
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raise ValueError(f"风速文件应包含 12 个月的数据，但实际有 {len(wind_speeds)} 条")
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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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except Exception as e:
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raise Exception(f"读取风速文件时出错: {str(e)}")
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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):
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return w * S * Cp * 8760 * eta
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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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return (P / 1000) / (P_r * 1000)
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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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try:
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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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# 读取温度数据并计算空气密度
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monthly_temps = read_monthly_temperatures(T_path)
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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
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wpd = wind_power_density(densities, velocity_path)
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# 计算装机容量
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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_mw * 1000000
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# 计算年发电量
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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
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h = calculate_equivalent_hours(P_test, rated_power)
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# 计算 IRR
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irr = calculate_reference_yield(P_test, 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,
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"annual_power_output": P_test / 10000000, # 万 kWh
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"equivalent_hours": h,
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"IRR": irr
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}
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except Exception as e:
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raise Exception(f"风电场分析出错: {str(e)}")
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# 主程序
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if __name__ == "__main__":
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file_path = r".\wind_product.xlsx"
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velocity_path = r".\wind_speed.txt"
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T_path = r".\temperature.txt"
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device_name = input("请输入设备名称: ")
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area_km2 = float(input("请输入风电场面积（平方公里）: "))
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electricity_price = float(input("请输入电价（元/kWh）: "))
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try:
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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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velocity_path=velocity_path,
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T_path=T_path
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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} MW")
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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"内部收益率 IRR: {result['IRR']:.2f}%")
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except Exception as e:
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print(f"错误: {str(e)}") |