1.可以读excel文件
2.可以计算8760小时
This commit is contained in:
dmy
@@ -153,7 +153,7 @@ def create_comprehensive_plot(solar_output, wind_output, thermal_output, load_de
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"""
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ax7.text(0.1, 0.5, metrics_text, fontsize=11, verticalalignment='center',
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fontfamily='monospace', bbox=dict(boxstyle='round', facecolor='lightgray', alpha=0.8))
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fontfamily='SimHei', bbox=dict(boxstyle='round', facecolor='lightgray', alpha=0.8))
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# 保存图片
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plt.savefig('comprehensive_analysis.png', dpi=300, bbox_inches='tight')
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266
excel_reader.py
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266
excel_reader.py
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"""
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Excel数据读取模块
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该模块提供从Excel文件中读取8760小时负荷和发电曲线数据的功能。
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作者: iFlow CLI
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创建日期: 2025-12-25
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"""
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import pandas as pd
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import numpy as np
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from typing import Dict, List, Optional, Tuple
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import os
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def validate_excel_data(df: pd.DataFrame, data_type: str = "8760") -> bool:
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"""
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验证Excel数据格式是否正确
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Args:
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df: pandas DataFrame对象
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data_type: 数据类型，"24"或"8760"
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Returns:
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bool: 验证是否通过
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"""
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expected_length = 8760 if data_type == "8760" else 24
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# 检查行数
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if len(df) != expected_length:
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print(f"错误：数据行数应为{expected_length}，实际为{len(df)}")
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return False
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# 检查必需的列
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required_columns = ['光伏出力(MW)', '风电出力(MW)', '火电出力(MW)', '负荷需求(MW)']
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missing_columns = [col for col in required_columns if col not in df.columns]
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if missing_columns:
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print(f"错误：缺少必需的列：{missing_columns}")
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return False
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# 检查数据类型和非负值
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for col in required_columns:
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if not pd.api.types.is_numeric_dtype(df[col]):
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print(f"错误：列'{col}'必须为数值类型")
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return False
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if (df[col] < 0).any():
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print(f"错误：列'{col}'包含负值")
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return False
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return True
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def read_excel_data(file_path: str, sheet_name: str = 0) -> Dict[str, List[float]]:
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"""
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从Excel文件读取8760小时数据
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Args:
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file_path: Excel文件路径
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sheet_name: 工作表名称或索引，默认为第一个工作表
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Returns:
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包含所有数据的字典
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Raises:
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FileNotFoundError: 文件不存在
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ValueError: 数据格式错误
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"""
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# 检查文件是否存在
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if not os.path.exists(file_path):
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raise FileNotFoundError(f"文件不存在：{file_path}")
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try:
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# 读取Excel文件
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df = pd.read_excel(file_path, sheet_name=sheet_name)
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# 自动检测数据类型
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data_type = "8760" if len(df) >= 8760 else "24"
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# 验证数据格式
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if not validate_excel_data(df, data_type):
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raise ValueError("Excel数据格式验证失败")
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# 提取数据并转换为列表
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solar_output = df['光伏出力(MW)'].tolist()
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wind_output = df['风电出力(MW)'].tolist()
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thermal_output = df['火电出力(MW)'].tolist()
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load_demand = df['负荷需求(MW)'].tolist()
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# 如果是24小时数据，扩展到8760小时（重复365天）
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if data_type == "24" and len(df) == 24:
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print("检测到24小时数据，自动扩展到8760小时（重复365天）")
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solar_output = solar_output * 365
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wind_output = wind_output * 365
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thermal_output = thermal_output * 365
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load_demand = load_demand * 365
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return {
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'solar_output': solar_output,
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'wind_output': wind_output,
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'thermal_output': thermal_output,
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'load_demand': load_demand,
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'data_type': data_type,
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'original_length': len(df)
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}
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except Exception as e:
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raise ValueError(f"读取Excel文件失败：{str(e)}")
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def create_excel_template(file_path: str, data_type: str = "8760"):
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"""
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创建Excel数据模板文件
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Args:
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file_path: 保存路径
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data_type: 数据类型，"24"或"8760"
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"""
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# 生成示例数据
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if data_type == "24":
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hours = 24
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# 24小时典型日数据
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solar = [0.0] * 6 + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0] + [0.0] * 6
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wind = [2.0, 3.0, 4.0, 3.0, 2.0, 1.0] * 4
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thermal = [5.0] * 24
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load = [3.0, 4.0, 5.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 18.0,
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16.0, 14.0, 12.0, 10.0, 8.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 2.0]
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description = "24小时典型日数据模板"
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else:
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hours = 8760
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# 生成8760小时的模拟数据（基于日模式加季节变化）
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daily_solar = [0.0] * 6 + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0] + [0.0] * 6
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daily_wind = [2.0, 3.0, 4.0, 3.0, 2.0, 1.0] * 4
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daily_thermal = [5.0] * 24
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daily_load = [3.0, 4.0, 5.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 18.0,
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16.0, 14.0, 12.0, 10.0, 8.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 2.0]
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solar = []
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wind = []
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thermal = []
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load = []
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np.random.seed(42) # 确保可重复性
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for day in range(365):
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# 季节性因子
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season_factor = 1.0 + 0.3 * np.sin(2 * np.pi * day / 365)
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for hour in range(24):
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# 添加随机变化
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solar_variation = 1.0 + 0.2 * (np.random.random() - 0.5)
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wind_variation = 1.0 + 0.3 * (np.random.random() - 0.5)
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load_variation = 1.0 + 0.1 * (np.random.random() - 0.5)
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solar.append(daily_solar[hour] * season_factor * solar_variation)
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wind.append(daily_wind[hour] * wind_variation)
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thermal.append(daily_thermal[hour])
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load.append(daily_load[hour] * (2.0 - season_factor) * load_variation)
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description = "8760小时全年数据模板"
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# 创建DataFrame
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df = pd.DataFrame({
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'小时': range(1, hours + 1),
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'光伏出力(MW)': solar,
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'风电出力(MW)': wind,
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'火电出力(MW)': thermal,
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'负荷需求(MW)': load
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})
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# 保存到Excel
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with pd.ExcelWriter(file_path, engine='openpyxl') as writer:
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df.to_excel(writer, sheet_name='数据', index=False)
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# 添加说明工作表
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description_df = pd.DataFrame({
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'项目': ['数据说明', '数据类型', '时间范围', '单位', '注意事项'],
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'内容': [
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description,
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f'{data_type}小时电力数据',
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f'1-{hours}小时',
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'MW (兆瓦)',
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'所有数值必须为非负数'
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]
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})
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description_df.to_excel(writer, sheet_name='说明', index=False)
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print(f"Excel模板已创建：{file_path}")
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def analyze_excel_data(file_path: str) -> Dict[str, float]:
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"""
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分析Excel数据的基本统计信息
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Args:
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file_path: Excel文件路径
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Returns:
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包含统计信息的字典
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"""
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try:
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data = read_excel_data(file_path)
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solar = data['solar_output']
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wind = data['wind_output']
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thermal = data['thermal_output']
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load = data['load_demand']
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return {
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'data_length': len(solar),
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'total_solar': sum(solar),
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'total_wind': sum(wind),
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'total_thermal': sum(thermal),
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'total_generation': sum(solar) + sum(wind) + sum(thermal),
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'total_load': sum(load),
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'max_solar': max(solar),
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'max_wind': max(wind),
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'max_thermal': max(thermal),
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'max_load': max(load),
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'avg_solar': np.mean(solar),
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'avg_wind': np.mean(wind),
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'avg_thermal': np.mean(thermal),
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'avg_load': np.mean(load)
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}
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except Exception as e:
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print(f"分析数据失败：{str(e)}")
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return {}
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def main():
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"""主函数，演示Excel数据读取功能"""
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print("=== Excel数据读取模块演示 ===")
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# 创建模板文件
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template_8760 = "data_template_8760.xlsx"
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template_24 = "data_template_24.xlsx"
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print("\n1. 创建Excel模板文件...")
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create_excel_template(template_8760, "8760")
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create_excel_template(template_24, "24")
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# 分析模板数据
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print(f"\n2. 分析{template_8760}数据...")
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stats = analyze_excel_data(template_8760)
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if stats:
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print("数据统计信息：")
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for key, value in stats.items():
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print(f" {key}: {value:.2f}")
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print(f"\n3. 演示读取{template_24}数据...")
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try:
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data = read_excel_data(template_24)
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print(f"成功读取数据，类型：{data['data_type']}")
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print(f"光伏出力前10小时：{data['solar_output'][:10]}")
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print(f"风电出力前10小时：{data['wind_output'][:10]}")
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print(f"负荷需求前10小时：{data['load_demand'][:10]}")
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except Exception as e:
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print(f"读取失败：{str(e)}")
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print("\n=== 演示完成 ===")
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print("模板文件已创建，您可以根据实际数据修改Excel文件。")
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if __name__ == "__main__":
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main()
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74
main.py
74
main.py
@@ -10,6 +10,7 @@
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import matplotlib.pyplot as plt
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import numpy as np
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from storage_optimization import optimize_storage_capacity, SystemParameters
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from excel_reader import read_excel_data, create_excel_template, analyze_excel_data
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# 设置中文字体
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plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
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@@ -27,6 +28,12 @@ def plot_system_curves(solar_output, wind_output, thermal_output, load_demand, r
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load_demand: 负荷曲线 (MW) - 支持24小时或8760小时
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result: 优化结果字典
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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# 设置中文字体
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plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']
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plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
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hours = np.arange(len(solar_output))
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data_length = len(solar_output)
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@@ -160,12 +167,56 @@ def main():
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import sys
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# 检查命令行参数
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use_yearly_data = len(sys.argv) > 1 and sys.argv[1] == '--yearly'
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if len(sys.argv) < 2:
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print_usage()
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return
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if use_yearly_data:
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command = sys.argv[1]
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if command == '--yearly':
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print("生成8760小时全年数据...")
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solar_output, wind_output, thermal_output, load_demand = generate_yearly_data()
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print(f"数据长度: {len(solar_output)} 小时")
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elif command == '--excel':
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if len(sys.argv) < 3:
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print("错误：请指定Excel文件路径")
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print("用法：python main.py --excel <文件路径>")
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return
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excel_file = sys.argv[2]
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print(f"从Excel文件读取数据：{excel_file}")
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try:
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data = read_excel_data(excel_file)
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solar_output = data['solar_output']
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wind_output = data['wind_output']
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thermal_output = data['thermal_output']
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load_demand = data['load_demand']
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print(f"成功读取{data['data_type']}小时数据")
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print(f"原始数据长度：{data['original_length']}小时")
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print(f"处理后数据长度：{len(solar_output)}小时")
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# 显示数据统计
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stats = analyze_excel_data(excel_file)
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if stats:
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print("\n数据统计：")
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print(f" 总发电量: {stats['total_generation']:.2f} MW")
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print(f" 总负荷: {stats['total_load']:.2f} MW")
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print(f" 最大光伏出力: {stats['max_solar']:.2f} MW")
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print(f" 最大风电出力: {stats['max_wind']:.2f} MW")
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print(f" 最大负荷: {stats['max_load']:.2f} MW")
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except Exception as e:
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print(f"读取Excel文件失败：{str(e)}")
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return
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elif command == '--create-template':
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template_type = sys.argv[2] if len(sys.argv) > 2 else "8760"
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template_file = f"data_template_{template_type}.xlsx"
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print(f"创建{template_type}小时Excel模板：{template_file}")
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create_excel_template(template_file, template_type)
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return
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else:
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print("使用24小时示例数据...")
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# 示例数据
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@@ -174,8 +225,9 @@ def main():
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thermal_output = [5.0] * 24
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load_demand = [3.0, 4.0, 5.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 18.0,
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16.0, 14.0, 12.0, 10.0, 8.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 2.0]
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# 系统参数
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# 系统参数
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params = SystemParameters(
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max_curtailment_wind=0.1,
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max_curtailment_solar=0.1,
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@@ -198,5 +250,19 @@ def main():
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print("\n曲线图已保存为 'system_curves.png'")
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def print_usage():
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"""打印使用说明"""
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print("多能互补系统储能容量优化程序")
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print("\n使用方法：")
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print(" python main.py --excel <文件路径> # 从Excel文件读取数据")
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print(" python main.py --yearly # 使用8760小时全年数据")
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print(" python main.py --create-template [类型] # 创建Excel模板(24或8760)")
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print(" python main.py # 使用24小时示例数据")
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print("\n示例：")
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print(" python main.py --excel data.xlsx")
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print(" python main.py --create-template 8760")
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print(" python main.py --create-template 24")
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if __name__ == "__main__":
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main()
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