339 lines
15 KiB
Python
339 lines
15 KiB
Python
import json
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import pandas as pd
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import math
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from scipy.optimize import fsolve
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from shapely.geometry import Point, shape
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# 默认文件路径
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PV_EXCEL_PATH = r"./pv_product.xlsx" # 光伏组件数值
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TILT_EXCEL_PATH = r"./倾角_峰值小时数.xlsx" # 倾角和峰值小时数
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GEOJSON_PATH = r"./中国_市.geojson" # GeoJSON 文件
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def calculate_pv_metrics(component_name, electricity_price, pv_number, q, longitude, latitude,
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is_fixed=True, optimize=True, peak_load_hour=16, cost_per_kw=3.4, E_S=1.0, K=0.8):
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"""
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计算光伏项目的各项指标，包括装机容量、年发电量、等效小时数、环境收益和内部收益率（IRR）。
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参数：
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component_name (str): 光伏组件名称，例如 "TWMHF-66HD715"
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electricity_price (float): 电价（元/kWh）
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pv_number (int): 光伏组件数量
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q (float): 运维成本占初始投资成本的比例（例如 0.02 表示 2%）
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longitude (float): 经度
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latitude (float): 纬度
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is_fixed (bool): 是否为固定式支架，True 为固定式，False 为跟踪式
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optimize (bool): 是否优化倾角和方位角
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peak_load_hour (int): 峰值负荷小时，默认 16
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cost_per_kw (float): 每 kW 投资成本（元/kW），默认 3.4 元/kW
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E_S (float): 标准辐射量，默认 1.0
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K (float): 系统效率，默认 0.8
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返回：
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dict: 包含以下结果的字典：
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- component_name: 组件名称
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- tilt: 倾角 (度)
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- azimuth: 方位角 (度)
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- array_distance: 阵列间距 (米)
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- max_power: 单组件最大功率 (Wp)
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- capacity: 装机容量 (kW)
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- peak_sunshine_hours: 峰值日照小时数 (小时/天)
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- single_daily_energy: 一天单个组件发电量 (kWh)
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- annual_energy: 年发电量 (kWh)
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- equivalent_hours: 等效小时数 (小时)
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- coal_reduction: 标准煤减排量 (kg)
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- CO2_reduction: CO₂ 减排量 (kg)
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- SO2_reduction: SO₂ 减排量 (kg)
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- NOX_reduction: NOx 减排量 (kg)
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- IRR: 内部收益率 (%)
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"""
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# 1. 加载 GeoJSON 数据
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def load_geojson(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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data = json.load(file)
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return data
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except FileNotFoundError:
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raise FileNotFoundError(f"未找到GeoJSON文件：{file_path}")
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except Exception as e:
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raise Exception(f"加载GeoJSON出错：{e}")
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# 2. 根据经纬度查找城市
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def find_city_by_coordinates(lat, lon, geojson_data):
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point = Point(lon, lat)
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for feature in geojson_data['features']:
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polygon = shape(feature['geometry'])
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if polygon.contains(point):
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city_name = feature['properties'].get('name', '未知城市')
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# 规范化城市名称（去除“市”或“地区”等后缀）
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if city_name.endswith('市') or city_name.endswith('地区'):
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city_name = city_name[:-1]
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return city_name
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return '未知城市'
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# 3. 从 Excel 获取倾角和峰值日照小时数
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def get_tilt_and_peak_hours(lat, lon, excel_path=TILT_EXCEL_PATH, geojson_path=GEOJSON_PATH):
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try:
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# 加载 GeoJSON 数据
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geojson_data = load_geojson(geojson_path)
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# 查找城市
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city = find_city_by_coordinates(lat, lon, geojson_data)
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# 读取 Excel 文件
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df = pd.read_excel(excel_path)
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if len(df.columns) < 5:
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raise ValueError("Excel文件需包含至少5列：城市、倾角、峰值日照小时数等")
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# 匹配城市名称（前两个字）
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matched_row = df[df.iloc[:, 2].str.startswith(city, na=False)]
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if matched_row.empty:
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raise ValueError(f"未找到匹配的城市：{city}")
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# 提取并转换为浮点数
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tilt = float(matched_row.iloc[0, 3]) # 第四列：倾角
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peak_hours = float(matched_row.iloc[0, 4]) # 第五列：PSH
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return {
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"tilt": tilt,
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"peak_sunshine_hours": peak_hours
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}
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except FileNotFoundError:
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raise FileNotFoundError(f"未找到Excel文件：{excel_path}")
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except ValueError as e:
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raise ValueError(f"Excel数据转换错误：{e}")
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except Exception as e:
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raise Exception(f"读取Excel或GeoJSON出错：{e}")
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# 4. 计算倾角和方位角
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def get_tilt_and_azimuth(is_fixed=True, optimize=True, longitude=116, latitude=None, peak_load_hour=16):
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if optimize and latitude is None:
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raise ValueError("优化模式下需提供纬度")
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if is_fixed:
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if optimize:
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# 从 Excel 获取倾角
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tilt_data = get_tilt_and_peak_hours(latitude, longitude)
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tilt = tilt_data["tilt"]
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if not (0 <= tilt <= 90):
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raise ValueError(f"Excel中的倾角 {tilt:.2f}° 超出合理范围（0°-90°）")
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azimuth = (peak_load_hour - 12) * 15 + (longitude - 116) # 方位角公式
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azimuth = azimuth % 360 if azimuth >= 0 else azimuth + 360
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else:
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print("倾角：0°(水平)-90°(垂直) | 方位角：0°(正北)-180°(正南)，顺时针")
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tilt = float(input("请输入倾角(度)："))
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azimuth = float(input("请输入方位角(度)："))
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if not (0 <= tilt <= 90) or not (0 <= azimuth <= 360):
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raise ValueError("倾角需在0-90°，方位角需在0-360°")
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else: # 跟踪式
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azimuth = 180 # 固定朝南
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if optimize:
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tilt_data = get_tilt_and_peak_hours(latitude, longitude)
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tilt = tilt_data["tilt"]
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if not (0 <= tilt <= 90):
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raise ValueError(f"Excel中的倾角 {tilt:.2f}° 超出合理范围（0°-90°）")
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else:
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print("倾角：0°(水平)-90°(垂直)")
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tilt = float(input("请输入倾角(度)："))
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if not (0 <= tilt <= 90):
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raise ValueError("倾角需在0-90°")
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return tilt, azimuth
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# 5. 获取光伏组件信息
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def get_pv_product_info(component_name, excel_path=PV_EXCEL_PATH):
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try:
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df = pd.read_excel(excel_path)
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if len(df.columns) < 10:
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raise ValueError("Excel文件需包含至少10列：组件名称、尺寸、功率等")
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row = df[df.iloc[:, 1] == component_name]
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if row.empty:
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raise ValueError(f"未找到组件：{component_name}")
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return {
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"component_name": component_name,
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"max_power": row.iloc[0, 5],
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"efficiency": row.iloc[0, 9],
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"pv_size": row.iloc[0, 3]
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}
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except FileNotFoundError:
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raise FileNotFoundError(f"未找到Excel文件：{excel_path}")
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except Exception as e:
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raise Exception(f"读取Excel出错：{e}")
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# 6. 计算光伏阵列间距
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def calculate_array_distance(L, tilt, latitude):
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beta_rad = math.radians(tilt)
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phi_rad = math.radians(latitude)
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return (L * math.cos(beta_rad) +
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L * math.sin(beta_rad) * 0.707 * math.tan(phi_rad) +
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0.4338 * math.tan(phi_rad))
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# 7. 计算等效小时数
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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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h = P / P_r # 单位换算
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return h
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# 8. 计算装机容量
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def calculate_installed_capacity(max_power, num_components):
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if max_power < 0 or num_components < 0 or not isinstance(num_components, int):
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raise ValueError("功率和数量需为非负数，数量需为整数")
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return (max_power * num_components) / 1000 # 单位：kW
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# 9. 计算年发电量
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def calculate_annual_energy(peak_hours, capacity, E_S=E_S, K=K):
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if any(x < 0 for x in [peak_hours, capacity]) or E_S <= 0 or not 0 <= K <= 1:
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raise ValueError("输入参数需满足：辐射量、容量≥0，E_S>0，K∈[0,1]")
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return peak_hours * 365 * (capacity / E_S) * K # 单位：kWh
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# 10. 计算环境收益
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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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# 11. 计算净现值和内部收益率
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def calculate_reference_yield(E_p, electricity_price, IC, q, n=25):
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if E_p < 0 or electricity_price < 0 or IC <= 0 or not 0 <= q <= 1:
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raise ValueError("发电量、电价≥0，投资成本>0，回收比例∈[0,1]")
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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，请检查输入")
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npv = npv_equation(irr, E_p, electricity_price, IC, q)
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return {"NPV": npv, "IRR": irr * 100}
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# 主计算流程
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try:
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# 验证经纬度
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if not (-90 <= latitude <= 90):
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raise ValueError("纬度必须在 -90 到 90 之间")
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if not (-180 <= longitude <= 180):
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raise ValueError("经度必须在 -180 到 180 之间")
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# 获取倾角和方位角
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tilt, azimuth = get_tilt_and_azimuth(
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is_fixed=is_fixed,
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optimize=optimize,
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longitude=longitude,
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latitude=latitude,
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peak_load_hour=peak_load_hour
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)
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# 获取峰值日照小时数
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peak_hours = get_tilt_and_peak_hours(latitude, longitude)["peak_sunshine_hours"]
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# 获取组件信息
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pv_info = get_pv_product_info(component_name)
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width_mm = float(pv_info["pv_size"].split("×")[1])
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L = (width_mm / 1000) * 4
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array_distance = calculate_array_distance(L, tilt, latitude)
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# 计算装机容量
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max_power = pv_info["max_power"]
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capacity = calculate_installed_capacity(max_power, pv_number) # 单位：kW
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# 计算一天单个组件发电量
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single_daily_energy = peak_hours * (capacity / pv_number) * K # 单位：kWh
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# 计算年发电量
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E_p = calculate_annual_energy(peak_hours, capacity, E_S, K) # 单位：kWh
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# 计算等效小时数
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h = calculate_equivalent_hours(E_p, capacity) # P_r 单位为 kW，E_p 单位为 kWh
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# 计算环境收益（转换为百万 kWh）
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E_p_million_kwh = E_p / 1000000 # 转换为百万 kWh
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env_benefits = calculate_environmental_benefits(E_p_million_kwh)
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# 计算初始投资成本
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IC = capacity * cost_per_kw * 1000 # 单位：元
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# 计算 IRR
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ref_yield = calculate_reference_yield(E_p, electricity_price, IC, q)
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# 返回结果
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return {
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"component_name": component_name,
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"tilt": tilt,
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"azimuth": azimuth,
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"array_distance": array_distance,
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"max_power": max_power,
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"capacity": capacity,
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"peak_sunshine_hours": peak_hours,
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"single_daily_energy": single_daily_energy,
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"annual_energy": E_p,
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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": ref_yield["IRR"]
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}
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except Exception as e:
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raise Exception(f"计算过程中发生错误: {str(e)}")
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# 示例用法
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if __name__ == "__main__":
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try:
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# 输入并验证经纬度
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latitude = float(input("请输入纬度（-90 到 90，例如 39.9042）："))
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if not (-90 <= latitude <= 90):
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raise ValueError("纬度必须在 -90 到 90 之间")
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longitude = float(input("请输入经度（-180 到 180，例如 116.4074）："))
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if not (-180 <= longitude <= 180):
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raise ValueError("经度必须在 -180 到 180 之间")
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component_name = input("请输入组件名称：")
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electricity_price = float(input("请输入电价（元/kWh）："))
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pv_number = 1200 # 固定组件数量
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q = 0.02 # 运维成本占初始投资成本的比例
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choice = input("选择光伏支架（固定式/跟踪式）：")
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optimize = input("是否优化倾角和方位角（是/否）：")
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# 调用主函数
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result = calculate_pv_metrics(
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component_name=component_name,
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electricity_price=electricity_price,
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pv_number=pv_number,
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q=q,
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is_fixed=(choice == "固定式"),
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optimize=(optimize.lower() == "是"),
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longitude=longitude,
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latitude=latitude
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)
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# 打印结果
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print("\n")
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print(f"组件名称：{result['component_name']}")
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print(f"倾角：{result['tilt']:.2f}° | 方位角：{result['azimuth']:.2f}°")
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print(f"阵列间距：{result['array_distance']:.2f} 米")
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print(f"单个组件最大功率（Wp）：{result['max_power']}")
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print(f"装机容量：{result['capacity']:.2f} kW")
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print(f"峰值日照小时数：{result['peak_sunshine_hours']:.2f} 小时/天")
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print(f"一天单个组件发电量：{result['single_daily_energy']:.0f} kWh")
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print(f"年发电量：{result['annual_energy']:,.0f} kWh")
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print(f"等效小时数：{result['equivalent_hours']:.0f} 小时")
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print("环境收益：")
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print(f"标准煤减排量：{result['coal_reduction']:,.0f} kg")
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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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except ValueError as e:
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print(f"输入错误：{e}")
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except Exception as e:
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print(f"错误：{e}") |