commit bdcecc74b481427926ab91b543f45c8c10dbb7b1 Author: L_J Date: Mon Mar 24 14:18:24 2025 +0800 Initial commit with PV files diff --git a/PV/peak_sunshine.xlsx b/PV/peak_sunshine.xlsx new file mode 100644 index 0000000..d16edf9 Binary files /dev/null and b/PV/peak_sunshine.xlsx differ diff --git a/PV/pv_product.xlsx b/PV/pv_product.xlsx new file mode 100644 index 0000000..86ecf44 Binary files /dev/null and b/PV/pv_product.xlsx differ diff --git a/PV/pv_total.py b/PV/pv_total.py new file mode 100644 index 0000000..8fcf86b --- /dev/null +++ b/PV/pv_total.py @@ -0,0 +1,280 @@ +import pandas as pd +import math +from scipy.optimize import fsolve + +# 默认文件路径 +TILT_EXCEL_PATH = r"D:/A_software/research/Git/git_home/PV/peak_sunshine.xlsx" #各地区峰值小时数（注意城市名 +PV_EXCEL_PATH = r"D:/A_software/research/Git/git_home/PV/pv_product.xlsx" #部分光伏组件数值 + + +def calculate_pv_metrics(city, 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): + """ + 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）。 + + 参数： + city (str): 城市名称，例如 "深圳" + component_name (str): 光伏组件名称，例如 "TWMHF-66HD715" + electricity_price (float): 电价（元/kWh） + pv_number (int): 光伏组件数量 + q (float): 运维成本占初始投资成本的比例（例如 0.02 表示 2%） + is_fixed (bool): 是否为固定式支架，True 为固定式，False 为跟踪式 + optimize (bool): 是否优化倾角和方位角 + longitude (float): 经度 + latitude (float): 纬度 + peak_load_hour (int): 峰值负荷小时，默认 16 + cost_per_kw (float): 每 kW 投资成本（元/kW），默认 3.4 元/kW + E_S (float): 标准辐射量，默认 1.0 + K (float): 系统效率，默认 0.8 + + 返回： + dict: 包含以下结果的字典： + - city: 城市名称 + - component_name: 组件名称 + - tilt: 倾角 (度) + - azimuth: 方位角 (度) + - array_distance: 阵列间距 (米) + - max_power: 单组件最大功率 (Wp) + - capacity: 装机容量 (kW) + - peak_sunshine_hours: 峰值日照小时数 (小时/天) + - single_daily_energy: 一天单个组件发电量 (kWh) + - annual_energy: 年发电量 (kWh) + - equivalent_hours: 等效小时数 (小时) + - coal_reduction: 标准煤减排量 (kg) + - CO2_reduction: CO₂ 减排量 (kg) + - SO2_reduction: SO₂ 减排量 (kg) + - NOX_reduction: NOx 减排量 (kg) + - IRR: 内部收益率 (%) + """ + + # 1. 获取城市的倾角和峰值日照小时数 + def get_tilt_and_peak_hours(city, excel_path=TILT_EXCEL_PATH): + """从Excel获取城市的倾角和峰值日照小时数""" + try: + df = pd.read_excel(excel_path) + if len(df.columns) < 5: + raise ValueError("Excel文件需包含至少5列：城市、倾角、峰值日照小时数等") + row = df[df.iloc[:, 1] == city] + if row.empty: + raise ValueError(f"未找到城市：{city}") + return {"city": city, "tilt": row.iloc[0, 2], "peak_sunshine_hours": row.iloc[0, 14]} + 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}") + + # 2. 计算倾角和方位角 + def get_tilt_and_azimuth(is_fixed, optimize, longitude, city, excel_path=TILT_EXCEL_PATH, + peak_load_hour=peak_load_hour): + if optimize and not city: + raise ValueError("优化模式下需提供城市名称") + + if is_fixed: + if optimize: + tilt = get_tilt_and_peak_hours(city, excel_path)["tilt"] + azimuth = (peak_load_hour - 12) * 15 + (longitude - 116) # 方位角公式 + 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: # 跟踪式 + azimuth = 180 # 固定朝南 + if optimize: + tilt = get_tilt_and_peak_hours(city, excel_path)["tilt"] + 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 + + # 3. 获取光伏组件信息 + def get_pv_product_info(component_name, excel_path=PV_EXCEL_PATH): + try: + df = pd.read_excel(excel_path) + if len(df.columns) < 10: + raise ValueError("Excel文件需包含至少10列：组件名称、尺寸、功率等") + row = df[df.iloc[:, 1] == component_name] + 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}") + + # 4. 计算光伏阵列间距 + def calculate_array_distance(L, tilt, latitude): + 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)) + + # 5. 计算等效小时数 + def calculate_equivalent_hours(P, P_r): + if P_r == 0: + raise ValueError("额定功率不能为 0") + h = P / P_r # 单位换算 + return h + + # 6. 计算装机容量 + def calculate_installed_capacity(max_power, num_components): + 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 + + # 7. 计算年发电量 + def calculate_annual_energy(peak_hours, capacity, E_S=E_S, K=K): + 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 + + # 8. 计算环境收益 + def calculate_environmental_benefits(E_p_million_kwh): + if E_p_million_kwh < 0: + raise ValueError("年发电量需≥0") + return { + "coal_reduction": E_p_million_kwh * 0.404 * 10, + "CO2_reduction": E_p_million_kwh * 0.977 * 10, + "SO2_reduction": E_p_million_kwh * 0.03 * 10, + "NOX_reduction": E_p_million_kwh * 0.015 * 10 + } + + # 9. 计算净现值和内部收益率 + def calculate_reference_yield(E_p, electricity_price, IC, q, n=25): + 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} + + # 主计算流程 + try: + # 获取倾角和方位角 + tilt, azimuth = get_tilt_and_azimuth(is_fixed, optimize, longitude, city) + + # 获取组件信息 + pv_info = get_pv_product_info(component_name) + width_mm = float(pv_info["pv_size"].split("×")[1]) + L = (width_mm / 1000) * 4 + array_distance = calculate_array_distance(L, tilt, latitude) + + # 计算装机容量 + max_power = pv_info["max_power"] + capacity = calculate_installed_capacity(max_power, pv_number) # 单位：kW + + # 获取峰值日照小时数 + peak_hours = get_tilt_and_peak_hours(city)["peak_sunshine_hours"] + + # 计算一天单个组件发电量 + single_daily_energy = peak_hours * (capacity / pv_number) * K # 单位：kWh + + # 计算年发电量 + E_p = calculate_annual_energy(peak_hours, capacity, E_S, K) # 单位：kWh + + # 计算等效小时数 + h = calculate_equivalent_hours(E_p, capacity) # P_r 单位为 kW，E_p 单位为 kWh + + # 计算环境收益（转换为百万 kWh） + E_p_million_kwh = E_p / 1000000 # 转换为百万 kWh + env_benefits = calculate_environmental_benefits(E_p_million_kwh) + + # 计算初始投资成本 + IC = capacity * cost_per_kw * 1000 # 单位：元 + + # 计算 IRR + ref_yield = calculate_reference_yield(E_p, electricity_price, IC, q) + + # 返回结果 + return { + "city": city, + "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)}") + + +# 示例用法 +if __name__ == "__main__": + try: + # 输入 + city = input("请输入城市：") + longitude = int(input("请城市经度：")) + latitude = int(input("请城市纬度：")) + component_name = input("请输入组件名称：") + electricity_price = float(input("请输入电价（元/kWh）：")) + pv_number = 1200 # 固定组件数量 + q = 0.02 # 运维成本占初始投资成本的比例 + choice = input("选择光伏支架（固定式/跟踪式）：") + optimize = input("是否优化倾角和方位角（是/否）：") + + + # 调用主函数 + result = calculate_pv_metrics( + city=city, + component_name=component_name, + electricity_price=electricity_price, + pv_number=pv_number, + q=q, + is_fixed=(choice == "固定式"), + optimize=(optimize.lower() == "是"), + longitude=longitude, + latitude=latitude + ) + + # 打印结果 + print("\n") + print(f"城市：{result['city']}") + print(f"组件名称：{result['component_name']}") + print(f"倾角：{result['tilt']}° | 方位角：{result['azimuth']}°") + print(f"阵列间距：{result['array_distance']:.2f} 米") + print(f"单个组件最大功率（Wp）：{result['max_power']}") + print(f"装机容量：{result['capacity']:.2f} kW") + print(f"峰值日照小时数：{result['peak_sunshine_hours']:.2f} 小时/天") + print(f"一天单个组件发电量：{result['single_daily_energy']:.2f} kWh") + print(f"年发电量：{result['annual_energy']:,.2f} kWh") + print(f"等效小时数：{result['equivalent_hours']:.2f} 小时") + print("环境收益：") + print(f"标准煤减排量：{result['coal_reduction']:,.2f} kg") + print(f"CO₂减排量：{result['CO2_reduction']:,.2f} kg") + print(f"SO₂减排量：{result['SO2_reduction']:,.2f} kg") + print(f"NOx减排量：{result['NOX_reduction']:,.2f} kg") + print(f"内部收益率 IRR：{result['IRR']:.2f}%") + + except Exception as e: + print(f"错误：{e}") diff --git a/readme.md b/readme.md new file mode 100644 index 0000000..e69de29