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3bb8c38050
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FROM python:3.10
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WORKDIR /app
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COPY . /app/
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RUN pip install --upgrade pip -i https://pypi.tuna.tsinghua.edu.cn/simple --no-cache-dir
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RUN pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple --no-cache-dir
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CMD ["python3", "run.py"]
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19
PV/说明.txt
19
PV/说明.txt
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@ -1,19 +0,0 @@
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pv_product.xlsx
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含有光伏组件设备信息，例如设备名称、组件尺寸、最大功率、组件效率等
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倾角_峰值小时数.xlsx：
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照城市（简称）确定安装角度（即倾角）、峰值日照时数，较之前做了变动，增加了一些城市（和市级的json相匹配）一部分没添加数值
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中国_市.geojson：
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市一级别行政json图
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PV_total.py：
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通过城市找倾角和峰值日照时数，对比与PV_total3.py的区别
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PV_total2.py：
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通过nasa的api接口获取10年-23年平均峰值日照时数
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倾角 = 纬度（弧度） × 0.86 + 24
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主要修改：47-97行部分。
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！注意！：需要挂梯子；计算的倾角在高纬度地区较之前较小，峰值日照时数较大。详细对照最后的结果
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PV_total3.py：
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思路：通过经纬度查找市json图对应的所在市区，再通过”倾角_峰值小时数.xlsx“表格查找倾角和峰值日照时数
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主要修改：51-10行部分。
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【Photovoltaic Panel Evaluation System v2.zip】为韩耀朋做的最新一版。
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Binary file not shown.
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import pandas as pd
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import math
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import numpy as np
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import requests
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from scipy.optimize import fsolve
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from tabulate import tabulate
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# 默认文件路径
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PV_EXCEL_PATH = r"./pv_product.xlsx" # 请确保此文件存在或更改为正确路径
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# 地形类型与复杂性因子范围
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TERRAIN_COMPLEXITY_RANGES = {
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"distributed": {
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"耕地": (1.0, 1.2), "裸地": (1.0, 1.2), "草地": (1.1, 1.3),
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"灌木": (1.3, 1.5), "湿地": (1.5, 1.8), "林地": (1.5, 1.8), "建筑": (1.2, 1.5)
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},
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"centralized": {
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"耕地": (1.0, 1.2), "裸地": (1.0, 1.2), "草地": (1.1, 1.3),
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"灌木": (1.3, 1.6), "湿地": (1.5, 1.8), "林地": (1.6, 2.0)
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},
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"floating": {"水域": (1.2, 1.5)}
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}
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# 地形类型与土地可用性、发电效率的映射
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TERRAIN_ADJUSTMENTS = {
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"耕地": {"land_availability": 0.85, "K": 0.8}, "裸地": {"land_availability": 0.85, "K": 0.8},
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"草地": {"land_availability": 0.85, "K": 0.8}, "灌木": {"land_availability": 0.75, "K": 0.75},
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"湿地": {"land_availability": 0.65, "K": 0.75}, "水域": {"land_availability": 0.85, "K": 0.8},
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"林地": {"land_availability": 0.65, "K": 0.7}, "建筑": {"land_availability": 0.6, "K": 0.75}
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}
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# 光伏类型的装机容量上限 (MW/平方千米)
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CAPACITY_LIMITS = {
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"distributed": 25.0, "centralized": 50.0, "floating": 25.0
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}
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# 实际面板间距系数
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PANEL_SPACING_FACTORS = {
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"distributed": 1.5, "centralized": 1.2, "floating": 1.3
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}
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def calculate_psh_average(lat, lon, start_year=2010, end_year=2023):
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"""
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从 NASA POWER API 获取峰值日照小时数（PSH）。
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返回：平均 PSH（小时/天），失败时返回默认值 4.0
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"""
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print("DEBUG: Starting calculate_psh_average (version 2025-04-28)")
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url = "https://power.larc.nasa.gov/api/temporal/monthly/point"
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params = {
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"parameters": "ALLSKY_SFC_SW_DWN",
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"community": "RE",
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"longitude": lon,
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"latitude": lat,
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"format": "JSON",
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"start": str(start_year),
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"end": str(end_year)
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}
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try:
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print(f"DEBUG: Requesting NASA POWER API for lat={lat}, lon={lon}")
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response = requests.get(url, params=params, timeout=10)
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response.raise_for_status()
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print("DEBUG: API response received")
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data = response.json()
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print("DEBUG: Validating API data")
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if "properties" not in data or "parameter" not in data["properties"]:
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print("ERROR: NASA POWER API returned invalid data format")
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return 4.0
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ghi_data = data["properties"]["parameter"].get("ALLSKY_SFC_SW_DWN", {})
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if not ghi_data:
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print("ERROR: No GHI data found in API response")
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return 4.0
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print("DEBUG: Filtering GHI data")
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print(f"DEBUG: Raw GHI data keys: {list(ghi_data.keys())}")
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ghi_data = {k: v for k, v in ghi_data.items() if not k.endswith("13")}
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if not ghi_data:
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print("ERROR: No valid GHI data after filtering")
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return 4.0
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print(f"DEBUG: Filtered GHI data keys: {list(ghi_data.keys())}")
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print("DEBUG: Creating DataFrame")
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df = pd.DataFrame.from_dict(ghi_data, orient="index", columns=["GHI (kWh/m²/day)"])
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print(f"DEBUG: Original DataFrame index: {list(df.index)}")
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print("DEBUG: Reformatting DataFrame index")
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new_index = []
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for k in df.index:
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try:
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# 验证索引格式
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if not (isinstance(k, str) and len(k) == 6 and k.isdigit()):
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print(f"ERROR: Invalid index format for {k}")
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return 4.0
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year = k[:4]
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month = k[-2:]
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formatted_index = f"{year}-{month:0>2}" # 使用 :0>2 确保两位数字
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print(f"DEBUG: Formatting index {k} -> {formatted_index}")
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new_index.append(formatted_index)
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except Exception as e:
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print(f"ERROR: Failed to format index {k}: {e}")
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return 4.0
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df.index = new_index
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print(f"DEBUG: Reformatted DataFrame index: {list(df.index)}")
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if df.empty:
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print("ERROR: DataFrame is empty")
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return 4.0
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print("DEBUG: Calculating PSH")
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df["PSH (hours/day)"] = df["GHI (kWh/m²/day)"]
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if df["PSH (hours/day)"].isna().any():
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print("ERROR: PSH data contains invalid values")
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return 4.0
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print("DEBUG: Grouping by year")
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df['Year'] = df.index.str[:4]
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print(f"DEBUG: Year column: {list(df['Year'])}")
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annual_avg = df.groupby('Year')['PSH (hours/day)'].mean()
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print(f"DEBUG: Annual averages: {annual_avg.to_dict()}")
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if annual_avg.empty:
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print("ERROR: Annual average PSH data is empty")
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return 4.0
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print("DEBUG: Calculating final PSH")
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psh = annual_avg.mean()
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if math.isnan(psh):
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print("ERROR: PSH calculation resulted in NaN")
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return 4.0
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print(f"DEBUG: PSH calculated successfully, value={psh:.2f}")
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print(f"获取成功！平均PSH: {psh:.2f} 小时/天")
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return psh
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except requests.exceptions.RequestException as e:
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print(f"ERROR: NASA POWER API request failed: {e}")
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return 4.0
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except Exception as e:
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print(f"ERROR: Error processing API data: {e}")
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return 4.0
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def calculate_optimal_tilt(lat):
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"""根据纬度计算最佳倾角（单位：度）"""
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try:
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lat_abs = abs(lat)
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if lat_abs < 25:
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optimal_tilt = lat_abs * 0.87
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elif lat_abs <= 50:
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optimal_tilt = lat_abs * 0.76 + 3.1
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else:
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optimal_tilt = lat_abs * 0.5 + 16.3
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return optimal_tilt
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except ValueError as e:
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raise Exception(f"倾角计算错误: {e}")
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def pv_area(panel_capacity, slope_deg, shading_factor=0.1, land_compactness=1.0, terrain_complexity=1.0):
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"""计算单块光伏组件占地面积"""
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base_area = panel_capacity * 6
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slope_factor = 1 + (slope_deg / 50) if slope_deg <= 15 else 1.5
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shade_factor = 1 + shading_factor * 2
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compact_factor = 1 / land_compactness if land_compactness > 0 else 1.5
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terrain_factor = terrain_complexity
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return base_area * slope_factor * shade_factor * compact_factor * terrain_factor
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def calculate_pv_potential(available_area_sq_km, component_name, longitude, latitude, slope_deg=10,
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shading_factor=0.1, land_compactness=0.8, terrain_complexity=1.2,
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terrain_type="耕地", pv_type="centralized", land_availability=0.85,
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min_irradiance=800, max_slope=25, electricity_price=0.65, q=0.02,
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is_fixed=True, optimize=True, peak_load_hour=16, cost_per_kw=3.4,
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E_S=1.0, K=0.8, project_lifetime=25, discount_rate=0.06):
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"""计算最小和最大组件数量的光伏系统潜力"""
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# 输入验证
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if available_area_sq_km <= 0:
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raise ValueError("可用面积必须大于0")
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if slope_deg < 0 or slope_deg > max_slope:
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raise ValueError(f"坡度必须在0-{max_slope}度之间")
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# 转换为公顷
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available_area_hectares = available_area_sq_km * 100
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# 验证光伏类型与地形类型
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valid_terrains = TERRAIN_COMPLEXITY_RANGES.get(pv_type, {})
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if terrain_type not in valid_terrains:
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raise ValueError(f"{pv_type} 光伏不支持 {terrain_type} 地形。可选地形：{list(valid_terrains.keys())}")
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# 获取地形调整参数
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terrain_adjustments = TERRAIN_ADJUSTMENTS.get(terrain_type, {"land_availability": 0.85, "K": 0.8})
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adjusted_land_availability = terrain_adjustments["land_availability"] / max(1.0, terrain_complexity)
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adjusted_K = terrain_adjustments["K"] / max(1.0, terrain_complexity)
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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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single_panel_capacity = pv_info["max_power"] / 1000 # kWp
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pv_size = pv_info["pv_size"].split("×")
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panel_length = float(pv_size[0]) / 1000 # 米
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panel_width = float(pv_size[1]) / 1000 # 米
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panel_area_sqm = panel_length * panel_width
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# 获取阵列间距
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tilt, azimuth = get_tilt_and_azimuth(is_fixed, optimize, longitude, latitude, peak_load_hour)
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array_distance = calculate_array_distance(panel_width * 1.1, tilt, latitude)
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spacing_factor = PANEL_SPACING_FACTORS.get(pv_type, 1.2)
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adjusted_array_distance = array_distance * spacing_factor
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# 计算有效面积
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effective_area_hectares = available_area_hectares * adjusted_land_availability
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effective_area_sqm = effective_area_hectares * 10000
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# 计算每MW占地面积
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area_per_mw = 10000 * (1 + slope_deg / 50 if slope_deg <= 15 else 1.5) * (
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1 + shading_factor * 2) * terrain_complexity * spacing_factor
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# 容量密度限制 (kW/m²)
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capacity_density_limit = CAPACITY_LIMITS.get(pv_type, 5.0) / 1000
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max_capacity_by_density = effective_area_sqm * capacity_density_limit
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# 计算单块组件占地面积
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row_spacing = panel_length * math.sin(math.radians(tilt)) + adjusted_array_distance
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effective_panel_area = panel_area_sqm * (row_spacing / panel_length) * 1.2
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# 调整 min/max 布局以确保差异
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min_area_per_panel = effective_panel_area * 0.8 # 密集布局
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max_area_per_panel = effective_panel_area * 1.5 # 稀疏布局
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max_panels = math.floor(effective_area_sqm / min_area_per_panel)
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min_panels = math.floor(effective_area_sqm / max_area_per_panel)
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# 计算装机容量
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max_capacity_raw = calculate_installed_capacity(pv_info["max_power"], max_panels)
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min_capacity_raw = calculate_installed_capacity(pv_info["max_power"], min_panels)
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# 应用容量密度限制
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max_capacity = min(max_capacity_raw, max_capacity_by_density)
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min_capacity = min(min_capacity_raw, max_capacity_by_density * 0.8) # 稀疏布局取80%
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# 检查理论上限
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theoretical_max_capacity_mw = available_area_sq_km * CAPACITY_LIMITS.get(pv_type, 5.0)
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if max_capacity / 1000 > theoretical_max_capacity_mw:
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max_capacity = theoretical_max_capacity_mw * 1000
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max_panels = math.floor(max_capacity * 1000 / pv_info["max_power"])
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if min_capacity / 1000 > theoretical_max_capacity_mw * 0.8:
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min_capacity = theoretical_max_capacity_mw * 1000 * 0.8
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min_panels = math.floor(min_capacity * 1000 / pv_info["max_power"])
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# 计算指标
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min_metrics = calculate_pv_metrics(
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component_name=component_name, electricity_price=electricity_price, pv_number=min_panels,
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q=q, longitude=longitude, latitude=latitude, is_fixed=is_fixed, optimize=optimize,
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peak_load_hour=peak_load_hour, cost_per_kw=cost_per_kw * terrain_complexity, E_S=E_S, K=adjusted_K,
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override_capacity=min_capacity
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)
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min_lcoe = calculate_lcoe(
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capacity=min_metrics["capacity"], annual_energy=min_metrics["annual_energy"],
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cost_per_kw=cost_per_kw * terrain_complexity, q=q, project_lifetime=project_lifetime,
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discount_rate=discount_rate
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)
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max_metrics = calculate_pv_metrics(
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component_name=component_name, electricity_price=electricity_price, pv_number=max_panels,
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q=q, longitude=longitude, latitude=latitude, is_fixed=is_fixed, optimize=optimize,
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peak_load_hour=peak_load_hour, cost_per_kw=cost_per_kw * terrain_complexity, E_S=E_S, K=adjusted_K,
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override_capacity=max_capacity
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)
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max_lcoe = calculate_lcoe(
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capacity=max_metrics["capacity"], annual_energy=max_metrics["annual_energy"],
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cost_per_kw=cost_per_kw * terrain_complexity, q=q, project_lifetime=project_lifetime,
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discount_rate=discount_rate
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)
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# 警告
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if min_panels == max_panels:
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print(f"警告：最小和最大组件数量相同 ({min_panels})，请检查地形复杂性或面积是否过小")
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if min_panels == 0 or max_panels == 0:
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print(f"警告：组件数量为0，请检查输入参数")
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# 返回结果
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return {
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"min_case": {
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**min_metrics, "lcoe": min_lcoe, "actual_panels": min_panels,
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"available_area_sq_km": available_area_sq_km, "available_area_hectares": available_area_hectares,
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"effective_area_hectares": effective_area_hectares, "panel_area_sqm": max_area_per_panel,
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"terrain_type": terrain_type, "pv_type": pv_type, "theoretical_max_capacity_mw": theoretical_max_capacity_mw
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},
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"max_case": {
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**max_metrics, "lcoe": max_lcoe, "actual_panels": max_panels,
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"available_area_sq_km": available_area_sq_km, "available_area_hectares": available_area_hectares,
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"effective_area_hectares": effective_area_hectares, "panel_area_sqm": min_area_per_panel,
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"terrain_type": terrain_type, "pv_type": pv_type, "theoretical_max_capacity_mw": theoretical_max_capacity_mw
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}
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}
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def calculate_lcoe(capacity, annual_energy, cost_per_kw, q, project_lifetime=25, discount_rate=0.06):
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"""计算平准化度电成本(LCOE)"""
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total_investment = capacity * cost_per_kw * 1000
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annual_om_cost = total_investment * q
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discount_factors = [(1 + discount_rate) ** -t for t in range(1, project_lifetime + 1)]
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discounted_energy = sum(annual_energy * discount_factors[t] for t in range(project_lifetime))
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discounted_costs = total_investment + sum(annual_om_cost * discount_factors[t] for t in range(project_lifetime))
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if discounted_energy == 0:
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return float('inf')
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return discounted_costs / discounted_energy
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|
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def get_pv_product_info(component_name, excel_path=PV_EXCEL_PATH):
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"""从Excel获取光伏组件信息"""
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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:
|
||||
raise ValueError(f"未找到组件：{component_name}")
|
||||
return {
|
||||
"component_name": component_name,
|
||||
"max_power": row.iloc[0, 5],
|
||||
"efficiency": row.iloc[0, 9],
|
||||
"pv_size": row.iloc[0, 3]
|
||||
}
|
||||
except FileNotFoundError:
|
||||
raise FileNotFoundError(f"未找到Excel文件：{excel_path}")
|
||||
except Exception as e:
|
||||
raise Exception(f"读取Excel出错：{e}")
|
||||
|
||||
|
||||
def get_tilt_and_azimuth(is_fixed=True, optimize=True, longitude=116, latitude=None, peak_load_hour=16):
|
||||
"""计算光伏系统的倾角和方位角"""
|
||||
if optimize and latitude is None:
|
||||
raise ValueError("优化模式下需提供纬度")
|
||||
|
||||
if is_fixed:
|
||||
if optimize:
|
||||
tilt = calculate_optimal_tilt(latitude)
|
||||
azimuth = (peak_load_hour - 12) * 15 + (longitude - 116)
|
||||
azimuth = azimuth % 360 if azimuth >= 0 else azimuth + 360
|
||||
else:
|
||||
print("倾角：0°(水平)-90°(垂直) | 方位角：0°(正北)-180°(正南)，顺时针")
|
||||
tilt = float(input("请输入倾角(度)："))
|
||||
azimuth = float(input("请输入方位角(度)："))
|
||||
if not (0 <= tilt <= 90) or not (0 <= azimuth <= 360):
|
||||
raise ValueError("倾角需在0-90°，方位角需在0-360°")
|
||||
else:
|
||||
azimuth = 180
|
||||
if optimize:
|
||||
tilt = calculate_optimal_tilt(latitude)
|
||||
else:
|
||||
print("倾角：0°(水平)-90°(垂直)")
|
||||
tilt = float(input("请输入倾角(度)："))
|
||||
if not (0 <= tilt <= 90):
|
||||
raise ValueError("倾角需在0-90°")
|
||||
return tilt, azimuth
|
||||
|
||||
|
||||
def calculate_array_distance(L, tilt, latitude):
|
||||
"""计算阵列间距"""
|
||||
beta_rad = math.radians(tilt)
|
||||
phi_rad = math.radians(latitude)
|
||||
return (L * math.cos(beta_rad) +
|
||||
L * math.sin(beta_rad) * 0.707 * math.tan(phi_rad) +
|
||||
0.4338 * math.tan(phi_rad))
|
||||
|
||||
|
||||
def calculate_equivalent_hours(P, P_r):
|
||||
"""计算等效小时数"""
|
||||
if P_r == 0:
|
||||
raise ValueError("额定功率不能为 0")
|
||||
return P / P_r
|
||||
|
||||
|
||||
def calculate_installed_capacity(max_power, num_components):
|
||||
"""计算装机容量"""
|
||||
if max_power < 0 or num_components < 0 or not isinstance(num_components, int):
|
||||
raise ValueError("功率和数量需为非负数，数量需为整数")
|
||||
return (max_power * num_components) / 1000 # 单位：kW
|
||||
|
||||
|
||||
def calculate_annual_energy(peak_hours, capacity, E_S=1.0, K=0.8):
|
||||
"""计算年发电量"""
|
||||
if any(x < 0 for x in [peak_hours, capacity]) or E_S <= 0 or not 0 <= K <= 1:
|
||||
raise ValueError("输入参数需满足：辐射量、容量≥0，E_S>0，K∈[0,1]")
|
||||
return peak_hours * 365 * (capacity / E_S) * K # 单位：kWh
|
||||
|
||||
|
||||
def calculate_environmental_benefits(E_p_million_kwh):
|
||||
"""计算环境收益"""
|
||||
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=25):
|
||||
"""计算净现值(NPV)和内部收益率(IRR)"""
|
||||
if E_p < 0 or electricity_price < 0 or IC <= 0 or not 0 <= q <= 1:
|
||||
raise ValueError("发电量、电价≥0，投资成本>0，回收比例∈[0,1]")
|
||||
|
||||
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，请检查输入")
|
||||
npv = npv_equation(irr, E_p, electricity_price, IC, q)
|
||||
return {"NPV": npv, "IRR": irr * 100}
|
||||
|
||||
|
||||
def calculate_pv_metrics(component_name, electricity_price, pv_number, q, longitude, latitude,
|
||||
is_fixed=True, optimize=True, peak_load_hour=16, cost_per_kw=3.4, E_S=1.0, K=0.8,
|
||||
override_capacity=None):
|
||||
"""计算光伏项目的各项指标"""
|
||||
try:
|
||||
tilt, azimuth = get_tilt_and_azimuth(is_fixed, optimize, longitude, latitude, peak_load_hour)
|
||||
pv_info = get_pv_product_info(component_name)
|
||||
width_mm = float(pv_info["pv_size"].split("×")[1])
|
||||
L = (width_mm / 1000) * 1.1
|
||||
array_distance = calculate_array_distance(L, tilt, latitude)
|
||||
max_power = pv_info["max_power"]
|
||||
|
||||
# 使用提供的容量或计算容量
|
||||
if override_capacity is not None:
|
||||
capacity = override_capacity
|
||||
else:
|
||||
capacity = calculate_installed_capacity(max_power, pv_number)
|
||||
|
||||
# 使用NASA API获取峰值日照小时数
|
||||
peak_hours = calculate_psh_average(latitude, longitude)
|
||||
single_daily_energy = peak_hours * (capacity / pv_number) * K if pv_number > 0 else 0
|
||||
E_p = calculate_annual_energy(peak_hours, capacity, E_S, K)
|
||||
h = calculate_equivalent_hours(E_p, capacity) if capacity > 0 else 0
|
||||
E_p_million_kwh = E_p / 1000000
|
||||
env_benefits = calculate_environmental_benefits(E_p_million_kwh)
|
||||
IC = capacity * cost_per_kw * 1000
|
||||
ref_yield = calculate_reference_yield(E_p, electricity_price, IC, q)
|
||||
return {
|
||||
"longitude": longitude,
|
||||
"latitude": latitude,
|
||||
"component_name": component_name,
|
||||
"tilt": tilt,
|
||||
"azimuth": azimuth,
|
||||
"array_distance": array_distance,
|
||||
"max_power": max_power,
|
||||
"capacity": capacity,
|
||||
"peak_sunshine_hours": peak_hours,
|
||||
"single_daily_energy": single_daily_energy,
|
||||
"annual_energy": E_p,
|
||||
"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": ref_yield["IRR"]
|
||||
}
|
||||
except Exception as e:
|
||||
raise Exception(f"计算过程中发生错误: {str(e)}")
|
||||
|
||||
|
||||
def print_result(min_case, max_case):
|
||||
"""优化输出格式，使用表格展示结果"""
|
||||
headers = ["指标", "最小组件数量", "最大组件数量"]
|
||||
table_data = [
|
||||
["经度", f"{min_case['longitude']:.2f}", f"{max_case['longitude']:.2f}"],
|
||||
["纬度", f"{min_case['latitude']:.2f}", f"{max_case['latitude']:.2f}"],
|
||||
["光伏类型", min_case["pv_type"], max_case["pv_type"]],
|
||||
["地形类型", min_case["terrain_type"], max_case["terrain_type"]],
|
||||
["组件型号", min_case["component_name"], max_case["component_name"]],
|
||||
["识别面积 (平方千米)", f"{min_case['available_area_sq_km']:.2f}", f"{max_case['available_area_sq_km']:.2f}"],
|
||||
["识别面积 (公顷)", f"{min_case['available_area_hectares']:.2f}", f"{max_case['available_area_hectares']:.2f}"],
|
||||
["有效面积 (公顷)", f"{min_case['effective_area_hectares']:.2f}", f"{max_case['effective_area_hectares']:.2f}"],
|
||||
["理论最大容量 (MW)", f"{min_case['theoretical_max_capacity_mw']:.2f}",
|
||||
f"{max_case['theoretical_max_capacity_mw']:.2f}"],
|
||||
["单块组件占地 (m²)", f"{min_case['panel_area_sqm']:.2f}", f"{max_case['panel_area_sqm']:.2f}"],
|
||||
["组件数量", f"{min_case['actual_panels']:,}", f"{max_case['actual_panels']:,}"],
|
||||
["倾角 (度)", f"{min_case['tilt']:.2f}", f"{max_case['tilt']:.2f}"],
|
||||
["方位角 (度)", f"{min_case['azimuth']:.2f}", f"{max_case['azimuth']:.2f}"],
|
||||
["阵列间距 (m)", f"{min_case['array_distance']:.2f}", f"{max_case['array_distance']:.2f}"],
|
||||
["单块功率 (Wp)", f"{min_case['max_power']}", f"{max_case['max_power']}"],
|
||||
["装机容量 (MW)", f"{min_case['capacity'] / 1000:.2f}", f"{max_case['capacity'] / 1000:.2f}"],
|
||||
["峰值日照 (小时/天)", f"{min_case['peak_sunshine_hours']:.2f}", f"{max_case['peak_sunshine_hours']:.2f}"],
|
||||
["年发电量 (kWh)", f"{min_case['annual_energy']:,.0f}", f"{max_case['annual_energy']:,.0f}"],
|
||||
["等效小时数", f"{min_case['equivalent_hours']:.2f}", f"{max_case['equivalent_hours']:.2f}"],
|
||||
["LCOE (元/kWh)", f"{min_case['lcoe']:.4f}", f"{max_case['lcoe']:.4f}"],
|
||||
["标准煤减排 (kg)", f"{min_case['coal_reduction']:,.0f}", f"{max_case['coal_reduction']:,.0f}"],
|
||||
["CO₂减排 (kg)", f"{min_case['CO2_reduction']:,.0f}", f"{max_case['CO2_reduction']:,.0f}"],
|
||||
["SO₂减排 (kg)", f"{min_case['SO2_reduction']:,.0f}", f"{max_case['SO2_reduction']:,.0f}"],
|
||||
["NOx减排 (kg)", f"{min_case['NOX_reduction']:,.0f}", f"{max_case['NOX_reduction']:,.0f}"],
|
||||
["IRR (%)", f"{min_case['IRR']:.2f}", f"{max_case['IRR']:.2f}"]
|
||||
]
|
||||
print("\n===== 光伏系统潜力评估结果 =====")
|
||||
print(tabulate(table_data, headers=headers, tablefmt="grid"))
|
||||
|
||||
|
||||
# 主程序
|
||||
if __name__ == "__main__":
|
||||
while True:
|
||||
try:
|
||||
# 输入参数
|
||||
print("\n======= 光伏系统潜力评估 =======")
|
||||
print("请输入以下必要参数：")
|
||||
|
||||
# 输入经纬度
|
||||
latitude = float(input("请输入纬度（-90到90，例如39.9）："))
|
||||
if not -90 <= latitude <= 90:
|
||||
raise ValueError("纬度必须在-90到90之间")
|
||||
|
||||
longitude = float(input("请输入经度（-180到180，例如116.4）："))
|
||||
if not -180 <= longitude <= 180:
|
||||
raise ValueError("经度必须在-180到180之间")
|
||||
|
||||
# 输入可用面积
|
||||
available_area_sq_km = float(input("请输入识别面积（平方千米，例如10）："))
|
||||
if available_area_sq_km <= 0:
|
||||
raise ValueError("识别面积必须大于0")
|
||||
|
||||
# 输入光伏类型
|
||||
pv_type = input("请输入光伏类型（distributed, centralized, floating）：").lower()
|
||||
if pv_type not in ["distributed", "centralized", "floating"]:
|
||||
raise ValueError("光伏类型必须是 distributed, centralized 或 floating")
|
||||
|
||||
# 输入地形类型
|
||||
valid_terrains = list(TERRAIN_COMPLEXITY_RANGES.get(pv_type, {}).keys())
|
||||
print(f"支持的地形类型：{valid_terrains}")
|
||||
terrain_type = input("请输入地形类型：")
|
||||
if terrain_type not in valid_terrains:
|
||||
raise ValueError(f"不支持的地形类型：{terrain_type}")
|
||||
|
||||
# 输入组件型号
|
||||
component_name = input("请输入光伏组件型号（需在Excel中存在，例如M10-72H）：")
|
||||
|
||||
# 输入其他参数
|
||||
slope_deg = float(input("请输入地形坡度（度，0-25，例如10）："))
|
||||
if not 0 <= slope_deg <= 25:
|
||||
raise ValueError("坡度必须在0-25度之间")
|
||||
|
||||
terrain_complexity = float(input("请输入地形复杂性因子（参考范围1.0-2.0，例如1.2）："))
|
||||
min_complexity, max_complexity = TERRAIN_COMPLEXITY_RANGES[pv_type][terrain_type]
|
||||
if not min_complexity <= terrain_complexity <= max_complexity:
|
||||
raise ValueError(f"地形复杂性因子必须在 {min_complexity}-{max_complexity} 之间")
|
||||
|
||||
electricity_price = float(input("请输入电价（元/kWh，例如0.65）："))
|
||||
if electricity_price < 0:
|
||||
raise ValueError("电价必须非负")
|
||||
|
||||
# 计算光伏潜力
|
||||
result = calculate_pv_potential(
|
||||
available_area_sq_km=available_area_sq_km,
|
||||
component_name=component_name,
|
||||
longitude=longitude,
|
||||
latitude=latitude,
|
||||
slope_deg=slope_deg,
|
||||
terrain_complexity=terrain_complexity,
|
||||
terrain_type=terrain_type,
|
||||
pv_type=pv_type,
|
||||
electricity_price=electricity_price
|
||||
)
|
||||
|
||||
# 输出结果
|
||||
print_result(result["min_case"], result["max_case"])
|
||||
|
||||
# 询问是否继续
|
||||
if input("\n是否继续评估？(y/n)：").lower() != 'y':
|
||||
break
|
||||
|
||||
except ValueError as ve:
|
||||
print(f"输入错误：{ve}")
|
||||
except FileNotFoundError as fe:
|
||||
print(f"文件错误：{fe}")
|
||||
except Exception as e:
|
||||
print(f"发生错误：{e}")
|
||||
print("请重新输入参数或检查错误。\n")
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,19 @@
|
|||
目的：修改之前代码，其中输入经纬度进行倾角和峰值日照时数的查询（不通过城市）。
|
||||
|
||||
【1】文件夹：
|
||||
pv_product.xlsx
|
||||
为组件，相较之前没有改变
|
||||
倾角_峰值小时数.xlsx：
|
||||
为通过城市查的倾角和峰值时数，做了变动，增加了一些城市（和市级的json相匹配）一部分没添加数值
|
||||
中国_市.geojson：
|
||||
市一级别行政json图
|
||||
PV_total3.py：
|
||||
思路：通过经纬度查找市json图对应的所在市区，再通过”倾角_峰值小时数.xlsx“表格查找倾角和峰值日照时数
|
||||
主要修改：51-10行部分。
|
||||
|
||||
【2】文件夹：
|
||||
PV_total2.py：
|
||||
通过nasa的api接口获取10年-23年平均峰值日照时数
|
||||
倾角 = 纬度（弧度） × 0.86 + 24
|
||||
主要修改：47-97行部分。
|
||||
！注意！：需要挂梯子；计算的倾角在高纬度地区较之前较小，峰值日照时数较大。详细对照最后的结果
|
||||
Binary file not shown.
Binary file not shown.
|
|
@ -1,12 +0,0 @@
|
|||
{
|
||||
"default_terrain_complexity": {
|
||||
"耕地": 1.1,
|
||||
"裸地": 1.1,
|
||||
"草地": 1.2,
|
||||
"灌木": 1.4,
|
||||
"湿地": 1.65,
|
||||
"林地": 1.65,
|
||||
"建筑": 1.35,
|
||||
"水域": 1.35
|
||||
}
|
||||
}
|
||||
102
readme.md
102
readme.md
|
|
@ -1,102 +0,0 @@
|
|||
# 文件夹说明
|
||||
|
||||
### 【PV文件夹】
|
||||
- 分为了**两个运行代码**（pv_total2.py和pv_total3.py），查看说明.txt文件。
|
||||
|
||||
|
||||
#### **pv_total2.py**
|
||||
|
||||
- **输入参数**：
|
||||
- 请输入纬度（-90 到 90，例如 39.9042）：39
|
||||
- 请输入经度（-180 到 180，例如 116.4074）：116
|
||||
- 组件名称：TWMND-72HD580
|
||||
- 电价：0.6 元/kWh
|
||||
- 光伏支架：固定式
|
||||
- 是否优化倾角和方位角：是
|
||||
- **输出结果**：
|
||||
- 组件名称：TWMND-72HD580
|
||||
- 倾角：24.59° | 方位角：60.00°
|
||||
- 阵列间距：5.56 米
|
||||
- 单个组件最大功率（Wp）：580
|
||||
- 装机容量：696.00 kW
|
||||
- 峰值日照小时数：3.99 小时/天
|
||||
- 一天单个组件发电量：2 kWh
|
||||
- 年发电量：810,000 kWh
|
||||
- 等效小时数：1164 小时
|
||||
- **环境收益**：
|
||||
- 标准煤减排量：3 kg
|
||||
- CO₂减排量：8 kg
|
||||
- SO₂减排量：0 kg
|
||||
- NOx减排量：0 kg
|
||||
- 内部收益率 IRR：22.39%
|
||||
|
||||
#### **pv_total3.py**
|
||||
- **输入参数**：
|
||||
- 请输入纬度（-90 到 90，例如 39.9042）：39
|
||||
- 请输入经度（-180 到 180，例如 116.4074）：116
|
||||
- 组件名称：TWMND-72HD580
|
||||
- 电价：0.6 元/kWh
|
||||
- 光伏支架：固定式
|
||||
- 是否优化倾角和方位角：是
|
||||
- **输出结果**：
|
||||
- 组件名称：TWMND-72HD580
|
||||
- 倾角：32.00° | 方位角：60.00°
|
||||
- 阵列间距：5.57 米
|
||||
- 单个组件最大功率（Wp）：580
|
||||
- 装机容量：696.00 kW
|
||||
- 峰值日照小时数：4.10 小时/天
|
||||
- 一天单个组件发电量：2 kWh
|
||||
- 年发电量：33,251 kWh
|
||||
- 等效小时数：1197 小时
|
||||
- **环境收益**：
|
||||
- 标准煤减排量：3 kg
|
||||
- CO₂减排量：8 kg
|
||||
- SO₂减排量：0 kg
|
||||
- NOx减排量：0 kg
|
||||
- 内部收益率 IRR：23.00%
|
||||
|
||||
- **解释说明**：
|
||||
-两个代码执行看文件夹中的说明（根据需要使用）。倾角和峰值日照时数不同。
|
||||
- 组件名称：从 `pv_product.xlsx` 中获取
|
||||
- 光伏支架：分为固定式和跟踪式
|
||||
- 是否优化：如果优化，则按照 `倾角_峰值小时数.xlsx` 中的数据
|
||||
- 固定式/跟踪式 + 优化/不优化可以随意组合
|
||||
- 光伏个数：暂时按照默认值 1200（区域内可安装光伏个数），后续韩耀朋优化
|
||||
|
||||
---
|
||||
|
||||
### 【Wind文件夹】
|
||||
|
||||
#### **temperature.txt**
|
||||
- 某地区12个月份的平均气温（后续需要替换为实际温度）
|
||||
|
||||
#### **wind_product.xlsx**
|
||||
- 含有风机设备信息，例如设备名称、额定功率、叶轮直径、扫风面积和轮毂高度等
|
||||
|
||||
#### **wind_speed.txt**
|
||||
- 某地区12个月的平均风速（后续需要替换为实际风速）
|
||||
|
||||
#### **wind_total.py**
|
||||
- **风机相关封装函数**：
|
||||
- **注意**：
|
||||
- 需要输入：设备名称、风电场面积（后续可替换为预测值）、当地电价
|
||||
- **示例**：
|
||||
- 输入：
|
||||
- 请输入设备名称: GWH191-4.0
|
||||
- 请输入风电场面积（平方公里）: 10
|
||||
- 请输入电价（元/kWh）: 0.6
|
||||
- 输出：
|
||||
- 找到设备 'GWH191-4.0'，额定功率: 4.0 MW, 扫风面积: 28652 m², 叶片直径: 191 米
|
||||
- 单台风机占地面积: 1,824,050 平方米 (横向间距: 955 米, 纵向间距: 1910 米)
|
||||
- 估算风机数量: 5 台
|
||||
- 设备: GWH191-4.0
|
||||
- 额定功率: 4.00 MW
|
||||
- 扫风面积: 28652.00 m^2
|
||||
- 叶片直径: 191.00 m
|
||||
- 风机数量: 5 台
|
||||
- 平均空气密度: 1.205 kg/m^3
|
||||
- 风功率密度: 63.93 W/m^2
|
||||
- 项目装机容量: 20.00 MW
|
||||
- 年发电量: 2888.404 万 kWh
|
||||
- 等效小时数: 7221.01 小时
|
||||
- 内部收益率 IRR: 18.70%
|
||||
|
|
@ -1,9 +0,0 @@
|
|||
numpy==1.22.0
|
||||
pandas==1.5.3
|
||||
scikit_learn==1.2.1
|
||||
xlrd==2.0.1
|
||||
logzero==1.7.0
|
||||
scipy==1.11.4
|
||||
flask==3.1.0
|
||||
requests==2.31.0
|
||||
openpyxl==3.1.2
|
||||
90
run.py
90
run.py
|
|
@ -1,90 +0,0 @@
|
|||
# -*-coding:utf-8-*-
|
||||
import os
|
||||
import json
|
||||
from flask import Flask, request, make_response, jsonify
|
||||
from logzero import logger
|
||||
|
||||
current_path = os.path.dirname(os.path.abspath(__file__)) # for local
|
||||
wind_product_path = f"{current_path}/wind/wind_product.xlsx"
|
||||
# current_path = "/app" # for docker
|
||||
logger.info(f"{current_path}")
|
||||
|
||||
|
||||
app = Flask(__name__)
|
||||
from pv.eva_pv import calculate_pv_potential, get_slope_from_api
|
||||
from wind.wind_total import wind_farm_analysis
|
||||
|
||||
terrain_config = {
|
||||
"耕地": 1.1, "裸地": 1.1, "草地": 1.2, "灌木": 1.4,
|
||||
"湿地": 1.65, "林地": 1.65, "建筑": 1.35, "水域": 1.35
|
||||
}
|
||||
|
||||
@app.route('/pv_power/', methods=["POST"])
|
||||
def get_pv_potential():
|
||||
resp_info = dict()
|
||||
if request.method == "POST":
|
||||
logger.info(request.get_json())
|
||||
latitude = request.json.get('latitude')
|
||||
longitude = request.json.get('longitude')
|
||||
available_area_sq_km = float(request.json.get('available_area_sq_km'))
|
||||
pv_type = request.json.get('pv_type')
|
||||
terrain_type = request.json.get('terrain_type')
|
||||
component_name = request.json.get('component_name')
|
||||
electricity_price = float(request.json.get('electricity_price'))
|
||||
terrain_complexity = terrain_config.get(terrain_type)
|
||||
try:
|
||||
# 通过 API 获取坡度
|
||||
slope_deg = get_slope_from_api(latitude, longitude)
|
||||
logger.info(f"使用参数：坡度={slope_deg:.2f}°，地形复杂性因子={terrain_complexity}")
|
||||
pv_potential = calculate_pv_potential(
|
||||
available_area_sq_km=available_area_sq_km,
|
||||
component_name=component_name,
|
||||
longitude=longitude,
|
||||
latitude=latitude,
|
||||
slope_deg=slope_deg,
|
||||
terrain_complexity=terrain_complexity,
|
||||
pv_type=pv_type,
|
||||
terrain_type=terrain_type,
|
||||
electricity_price=electricity_price
|
||||
)
|
||||
resp_info["code"] = 200
|
||||
resp_info["data"] = pv_potential
|
||||
except Exception as e:
|
||||
logger.info(e)
|
||||
resp_info["code"] = 406
|
||||
resp_info["data"] = str(e)
|
||||
resp = make_response(json.dumps(resp_info))
|
||||
resp.status_code = 200
|
||||
return resp
|
||||
|
||||
@app.route('/wind_power/', methods=["POST"])
|
||||
def get_wind_potential():
|
||||
resp_info = dict()
|
||||
if request.method == "POST":
|
||||
area_km2 = float(request.json.get('available_area_sq_km'))
|
||||
device_name = request.json.get('component_name')
|
||||
electricity_price = float(request.json.get('electricity_price'))
|
||||
v_avg = float(request.json.get("velocity_avg"))
|
||||
t_avg = float(request.json.get("temp_avg"))
|
||||
try:
|
||||
wind_potential = wind_farm_analysis(
|
||||
device_name=device_name,
|
||||
area_km2=area_km2,
|
||||
electricity_price=electricity_price,
|
||||
file_path=wind_product_path,
|
||||
velocity_avg=v_avg,
|
||||
T_avg=t_avg
|
||||
)
|
||||
resp_info["code"] = 200
|
||||
resp_info["data"] = wind_potential
|
||||
except Exception as e:
|
||||
logger.info(e)
|
||||
resp_info["code"] = 406
|
||||
resp_info["data"] = str(e)
|
||||
resp = make_response(json.dumps(resp_info))
|
||||
resp.status_code = 200
|
||||
return resp
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
app.run(port=12123, host="0.0.0.0", debug=False)
|
||||
Binary file not shown.
Binary file not shown.
254
wind/wind2.py
254
wind/wind2.py
|
|
@ -1,254 +0,0 @@
|
|||
import pandas as pd
|
||||
import math
|
||||
|
||||
def wind_farm_analysis(device_name, area_km2, file_path, avg_temp, avg_wind_speed,
|
||||
lateral_spacing_factor=5, longitudinal_spacing_factor=10, altitude=11,
|
||||
hub_height=100, Cp=0.45, eta=0.8):
|
||||
"""
|
||||
封装函数：分析风电场的风机数量及各项经济和技术指标，直接输入年平均气温和年平均风速
|
||||
参数：
|
||||
device_name (str): 风力发电机型号名称
|
||||
area_km2 (float): 风电场面积（平方公里）
|
||||
file_path (str): 包含风机参数的Excel文件路径
|
||||
avg_temp (float): 年平均气温（摄氏度）
|
||||
avg_wind_speed (float): 年平均风速（m/s）
|
||||
lateral_spacing_factor (float): 横向间距因子（默认为5倍叶片直径，5D）
|
||||
longitudinal_spacing_factor (float): 纵向间距因子（默认为10倍叶片直径，10D）
|
||||
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（功率系数），默认0.45，反映风能转换效率
|
||||
eta (float): 总系统效率（包括机械和电气效率），默认0.8
|
||||
返回：
|
||||
dict: 包含风电场分析结果的字典，包括装机容量、发电量、环境效益等
|
||||
"""
|
||||
def estimate_wind_turbine_count(area_km2, blade_diameter):
|
||||
"""
|
||||
估算风电场可容纳的风机数量，基于面积和风机间距
|
||||
参数：
|
||||
area_km2 (float): 风电场面积（平方公里）
|
||||
blade_diameter (float): 风机叶片直径（m）
|
||||
|
||||
返回：
|
||||
int: 估算的风机数量
|
||||
"""
|
||||
# 将面积从平方公里转换为平方米
|
||||
area_m2 = area_km2 * 1_000_000
|
||||
# 计算横向和纵向间距（以叶片直径为单位）
|
||||
lateral_spacing = lateral_spacing_factor * blade_diameter
|
||||
longitudinal_spacing = longitudinal_spacing_factor * blade_diameter
|
||||
# 单台风机占地面积 = 横向间距 * 纵向间距
|
||||
turbine_area = lateral_spacing * longitudinal_spacing
|
||||
# 风机数量 = 总面积 / 单台风机占地面积（取整数）
|
||||
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):
|
||||
"""
|
||||
从Excel文件中获取指定风机的参数
|
||||
参数：
|
||||
device_name (str): 风机型号名称
|
||||
file_path (str): Excel文件路径
|
||||
返回：
|
||||
tuple: 额定功率（kW）、扫风面积（m²）、叶片直径（m）
|
||||
"""
|
||||
try:
|
||||
# 读取Excel文件
|
||||
df = pd.read_excel(file_path)
|
||||
# 查找匹配的设备名称
|
||||
match = df[df.iloc[:, 0] == device_name]
|
||||
if not match.empty:
|
||||
rated_power = match.iloc[0, 1] # 额定功率（kW）
|
||||
swept_area = match.iloc[0, 7] # 扫风面积（m²）
|
||||
blade_diameter = match.iloc[0, 6] # 叶片直径（m）
|
||||
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 air_density(altitude, hub_height, T0):
|
||||
"""
|
||||
计算空气密度，考虑海拔和轮毂高度的影响
|
||||
参数：
|
||||
altitude (float): 海拔高度（m）
|
||||
hub_height (float): 轮毂高度（m）
|
||||
T0 (float): 地面平均气温（摄氏度）
|
||||
|
||||
返回：
|
||||
float: 空气密度（kg/m³）
|
||||
公式：
|
||||
ρ = (353.05 / T) * exp(-0.034 * (z / T))
|
||||
其中 T = T0 - LR * z + 273.15（T0为地面温度，LR为温度递减率，z为总高度）
|
||||
"""
|
||||
# 计算总高度（海拔 + 轮毂高度）
|
||||
z = altitude + hub_height
|
||||
# 温度递减率（lapse rate），每升高1米温度降低0.0065°C
|
||||
LR = 0.0065
|
||||
# 计算绝对温度（K），考虑高度引起的温度变化
|
||||
T = T0 - LR * z + 273.15
|
||||
# 计算空气密度
|
||||
return (353.05 / T) * math.exp(-0.034 * (z / T))
|
||||
|
||||
def wind_power_density(density, velocity_avg):
|
||||
"""
|
||||
计算风功率密度（单位面积的风能功率）
|
||||
参数：
|
||||
density (float): 空气密度（kg/m³）
|
||||
velocity_avg (float): 平均风速（m/s）
|
||||
返回：
|
||||
float: 风功率密度（W/m²）
|
||||
公式：
|
||||
P = 0.5 * ρ * v³（按照年平均风速来算）
|
||||
"""
|
||||
return 0.5 * density * velocity_avg**3
|
||||
|
||||
def estimated_wind_power(num_turbines, rated_power):
|
||||
"""
|
||||
计算风电场总装机容量
|
||||
|
||||
参数：
|
||||
num_turbines (int): 风机数量
|
||||
rated_power (float): 单台风机额定功率（kW）
|
||||
返回：
|
||||
float: 总装机容量（kW）
|
||||
"""
|
||||
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):
|
||||
"""
|
||||
计算风电场年发电量
|
||||
|
||||
参数：
|
||||
S (float): 扫风面积（m²）
|
||||
w (float): 风功率密度（W/m²）
|
||||
Cp (float): 风能利用系数
|
||||
eta (float): 系统效率
|
||||
num_turbines (int): 风机数量
|
||||
返回：
|
||||
float: 年发电量（Wh）
|
||||
公式：
|
||||
E = w * S * Cp * 8760 * η * N (N为风机个数）
|
||||
其中 8760 为一年小时数
|
||||
"""
|
||||
return w * S * Cp * 8760 * eta * num_turbines
|
||||
|
||||
def calculate_equivalent_hours(P, P_r):
|
||||
"""
|
||||
计算等效满负荷小时数
|
||||
参数：
|
||||
P (float): 年发电量（Wh）
|
||||
P_r (float): 单台风机额定功率（kW）
|
||||
返回：
|
||||
float: 等效小时数（小时）
|
||||
"""
|
||||
if P_r == 0:
|
||||
raise ValueError("额定功率不能为 0")
|
||||
return (P / 1000) / P_r
|
||||
|
||||
def calculate_environmental_benefits(E_p_million_kwh):
|
||||
"""
|
||||
计算环境效益（减排量）
|
||||
|
||||
参数：
|
||||
E_p_million_kwh (float): 年发电量（万kWh）
|
||||
|
||||
返回：
|
||||
dict: 包含标准煤、CO₂、SO₂、NOx减排量的字典
|
||||
|
||||
假设：
|
||||
每万kWh可节约标准煤0.404吨，减排CO₂ 0.977吨，SO₂ 0.03吨，NOx 0.015吨
|
||||
"""
|
||||
if E_p_million_kwh < 0:
|
||||
raise ValueError("年发电量需≥0")
|
||||
return {
|
||||
"coal_reduction": E_p_million_kwh * 0.404 * 10, # kg
|
||||
"CO2_reduction": E_p_million_kwh * 0.977 * 10, # kg
|
||||
"SO2_reduction": E_p_million_kwh * 0.03 * 10, # kg
|
||||
"NOX_reduction": E_p_million_kwh * 0.015 * 10 # kg
|
||||
}
|
||||
|
||||
# 获取风机参数
|
||||
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)
|
||||
|
||||
# 计算空气密度
|
||||
avg_density = air_density(altitude, hub_height, avg_temp)
|
||||
|
||||
# 计算风功率密度（W/m²）
|
||||
wpd = wind_power_density(avg_density, avg_wind_speed)
|
||||
|
||||
# 计算总装机容量（kW）
|
||||
total_power = estimated_wind_power(num_turbines, rated_power)
|
||||
|
||||
# 计算年发电量（Wh）
|
||||
P_test = calculate_power_output(swept_area, wpd, Cp, eta, num_turbines)
|
||||
|
||||
# 计算等效满负荷小时数
|
||||
h = calculate_equivalent_hours(P_test, rated_power)
|
||||
|
||||
# 转换为万kWh以计算环境效益
|
||||
E_p_million_kwh = P_test / 10000000
|
||||
env_benefits = calculate_environmental_benefits(E_p_million_kwh)
|
||||
|
||||
# 返回结果字典
|
||||
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, # 转换为MW
|
||||
"annual_power_output": P_test / 10000000, # 转换为万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"]
|
||||
}
|
||||
|
||||
# 主程序
|
||||
if __name__ == "__main__":
|
||||
# 定义输入参数
|
||||
file_path = r".\wind_product.xlsx" # 风机参数文件路径
|
||||
device_name = 'GW165-4.0' # 风机型号
|
||||
area_km2 = 10 # 风电场面积（平方公里）
|
||||
avg_temp = 13.0 # 年平均气温（摄氏度）
|
||||
avg_wind_speed = 6 # 年平均风速（m/s）
|
||||
|
||||
# 调用风电场分析函数
|
||||
result = wind_farm_analysis(
|
||||
device_name=device_name,
|
||||
area_km2=area_km2,
|
||||
file_path=file_path,
|
||||
avg_temp=avg_temp,
|
||||
avg_wind_speed=avg_wind_speed
|
||||
)
|
||||
|
||||
# 输出结果
|
||||
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"标准煤减排量：{result['coal_reduction']:,.0f} kg")
|
||||
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")
|
||||
Binary file not shown.
|
|
@ -1,204 +0,0 @@
|
|||
import pandas as pd
|
||||
import math
|
||||
from scipy.optimize import fsolve
|
||||
import os
|
||||
import sys
|
||||
|
||||
# 获取当前文件的绝对路径
|
||||
current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
print(current_dir)
|
||||
# 添加当前目录到sys.path
|
||||
sys.path.append(current_dir)
|
||||
|
||||
def wind_farm_analysis(device_name, area_km2, electricity_price, file_path, velocity_avg, T_avg,
|
||||
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_avg (float): 全年平均风速
|
||||
T_path (str): 全年平均温度
|
||||
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]
|
||||
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 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, velocity_avg):
|
||||
rho_v3 = densities * velocity_avg
|
||||
return 0.5 * 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):
|
||||
# 瓦时
|
||||
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
|
||||
|
||||
def calculate_environmental_benefits(E_p_million_kwh):
|
||||
"""计算环境收益"""
|
||||
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)
|
||||
|
||||
# 读取温度数据并计算空气密度
|
||||
avg_density = air_density(altitude, hub_height, T_avg)
|
||||
|
||||
# 计算风功率密度
|
||||
wpd = wind_power_density(avg_density, velocity_avg)
|
||||
|
||||
# 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 kwh
|
||||
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
|
||||
P_test_IRR = P_test/1000
|
||||
irr = calculate_reference_yield(P_test_IRR, electricity_price, IC, q)
|
||||
|
||||
env_benefits = calculate_environmental_benefits((P_test / 10000000))
|
||||
|
||||
# 返回结果
|
||||
out = {
|
||||
"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
|
||||
}
|
||||
out.update(env_benefits)
|
||||
return out
|
||||
|
||||
# 主程序
|
||||
if __name__ == "__main__":
|
||||
file_path = f"{current_dir}/wind_product.xlsx"
|
||||
v_avg = 4.2
|
||||
tavg = 15
|
||||
|
||||
device_name = "GW165-5.2"
|
||||
area_km2 = 23.2
|
||||
electricity_price = 0.45
|
||||
|
||||
result = wind_farm_analysis(
|
||||
device_name=device_name,
|
||||
area_km2=area_km2,
|
||||
electricity_price=electricity_price,
|
||||
file_path=file_path,
|
||||
velocity_avg=v_avg,
|
||||
T_avg=tavg
|
||||
)
|
||||
print(result)
|
||||
"""
|
||||
{
|
||||
"code": 200,
|
||||
"data": {
|
||||
"device": "GW165-5.2",
|
||||
"rated_power": 5.2,
|
||||
"swept_area": 21382,
|
||||
"blade_diameter": 165,
|
||||
"num_turbines": 23,
|
||||
"avg_density": 1.2118668826686871,
|
||||
"wpd": 1.9995803564033336,
|
||||
"total_power": 119.60000000000001,
|
||||
"annual_power_output": 310.11418354861905,
|
||||
"equivalent_hours": 596.3734299011904,
|
||||
"IRR": 9.985793133871693,
|
||||
"coal_reduction": 12528.61301536421,
|
||||
"CO2_reduction": 30298.155732700077,
|
||||
"SO2_reduction": 930.342550645857,
|
||||
"NOX_reduction": 465.1712753229285
|
||||
}
|
||||
}
|
||||
"""
|
||||
Loading…
Reference in New Issue