修正没有利用--excel参数的bug。
This commit is contained in:
dmy
1
.gitignore
vendored
1
.gitignore
vendored
@@ -8,6 +8,5 @@ wheels/
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# Virtual environments
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.venv
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test_*
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*.xls*
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*.png
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12
.vscode/launch.json
vendored
12
.vscode/launch.json
vendored
@@ -9,9 +9,17 @@
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"name": "Main",
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"type": "debugpy",
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"request": "launch",
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"program": "main.py",
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"program": "src\\main.py",
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"console": "integratedTerminal",
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"args": "--excel .\\data_template_8760-in-use.xlsx"
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"args": "--excel .\\templates\\data_template_8760-in-use2.xlsx"
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},
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{
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"name": "Multi_Scenarios",
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"type": "debugpy",
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"request": "launch",
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"program": "src\\multi_scenario.py",
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"console": "integratedTerminal",
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"args": "--excel .\\templates\\data_template_8760-in-use2.xlsx"
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}
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]
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}
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@@ -9,4 +9,12 @@ dependencies = [
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"numpy>=1.19.0",
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"openpyxl>=3.1.5",
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"pandas>=2.3.3",
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"pyinstaller>=6.17.0",
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"scikit-learn>=1.8.0",
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"seaborn>=0.13.2",
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]
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[dependency-groups]
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dev = [
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"mypy>=1.19.1",
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]
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@@ -9,17 +9,19 @@
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"""
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import numpy as np
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import pandas as pd
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import pandas as pd # type: ignore[import-untyped]
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import matplotlib.pyplot as plt
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from sklearn.cluster import KMeans
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from sklearn.preprocessing import StandardScaler
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from sklearn.metrics import silhouette_score
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from typing import List, Dict, Tuple, Optional, Union
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from sklearn.cluster import KMeans # type: ignore[import-untyped]
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from sklearn.preprocessing import StandardScaler # type: ignore[import-untyped]
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from sklearn.metrics import silhouette_score # type: ignore[import-untyped]
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from typing import List, Dict, Tuple, Optional, Union, Any
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from storage_optimization import optimize_storage_capacity, SystemParameters
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from dataclasses import dataclass
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import seaborn as sns
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import seaborn as sns # type: ignore[import-untyped]
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from datetime import datetime, timedelta
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import warnings
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import os
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from excel_reader import read_excel_data, read_system_parameters, get_optimization_settings
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warnings.filterwarnings('ignore')
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# 设置中文字体
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@@ -57,6 +59,147 @@ class MultiScenarioAnalyzer:
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self.kmeans: KMeans = KMeans(n_clusters=n_clusters, random_state=random_state, n_init=10)
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self.scenario_names: List[str] = []
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@staticmethod
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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（支持多种列名格式）
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required_columns = [
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['光伏出力(MW)', '风电出力(MW)', '负荷需求(MW)'], # 标准格式
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['光伏', '风电', '负荷'], # 简化格式
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['solar', 'wind', 'load'], # 英文格式
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['Solar', 'Wind', 'Load'] # 英文首字母大写
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]
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# 检查是否存在任何一组必需列
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columns_found = False
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for columns in required_columns:
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if all(col in df.columns for col in columns):
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columns_found = True
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break
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if not columns_found:
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print(f"错误：缺少必需的列。支持的列名格式：{required_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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# 找到实际使用的列名
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actual_columns = None
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for columns in required_columns:
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if all(col in df.columns for col in columns):
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actual_columns = columns
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break
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if actual_columns:
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for col in actual_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(self, file_path: str, sheet_name: str = 0, include_parameters: bool = True) -> Dict[str, Any]:
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"""
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从Excel文件读取数据用于多场景分析
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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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include_parameters: 是否同时读取系统参数和经济参数
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Returns:
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包含光伏、风电、负荷数据和可选参数的字典
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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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try:
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# 使用 excel_reader 模块读取数据
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excel_data = read_excel_data(file_path, sheet_name, include_parameters)
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# 提取基本数据
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result = {
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'solar_output': excel_data['solar_output'],
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'wind_output': excel_data['wind_output'],
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'load_demand': excel_data['load_demand'],
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'data_type': excel_data['data_type'],
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'original_length': excel_data['original_length']
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}
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# 如果包含参数，则添加到结果中
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if include_parameters:
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if 'system_parameters' in excel_data:
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result['system_parameters'] = excel_data['system_parameters']
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if 'optimization_settings' in excel_data:
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result['optimization_settings'] = excel_data['optimization_settings']
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print(f"成功从Excel读取数据：")
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print(f" - 数据类型：{result['data_type']}小时")
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print(f" - 数据长度：{len(result['solar_output'])} 小时")
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print(f" - 光伏出力范围：{min(result['solar_output']):.2f} - {max(result['solar_output']):.2f} MW")
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print(f" - 风电出力范围：{min(result['wind_output']):.2f} - {max(result['wind_output']):.2f} MW")
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print(f" - 负荷需求范围：{min(result['load_demand']):.2f} - {max(result['load_demand']):.2f} MW")
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if include_parameters and 'system_parameters' in result:
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print(f" - 系统参数：已读取")
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return result
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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 fit_predict_from_excel(self, file_path: str, sheet_name: str = 0,
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find_optimal_k: bool = False, include_parameters: bool = True) -> Tuple[ScenarioResult, Optional[SystemParameters]]:
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"""
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从Excel文件读取数据并执行多场景聚类分析
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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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find_optimal_k: 是否自动寻找最优聚类数
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include_parameters: 是否同时读取系统参数
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Returns:
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场景聚类结果，系统参数（可选）
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"""
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print("开始从Excel文件读取数据...")
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# 读取Excel数据
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excel_data = self.read_excel_data(file_path, sheet_name, include_parameters)
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# 执行多场景聚类分析
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result = self.fit_predict(
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excel_data['solar_output'],
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excel_data['wind_output'],
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excel_data['load_demand'],
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find_optimal_k=find_optimal_k
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)
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# 提取参数
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system_params = excel_data.get('system_parameters') if include_parameters else None
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return result, system_params
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def prepare_multivariate_data(self, solar_output: List[float],
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wind_output: List[float],
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load_demand: List[float]) -> np.ndarray:
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@@ -216,11 +359,11 @@ class MultiScenarioAnalyzer:
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typical_load = load_demand[typical_day_start:end_hour]
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typical_days[f'scenario_{cluster_id}'] = {
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'day_start_hour': typical_day_start,
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'solar_profile': typical_solar,
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'wind_profile': typical_wind,
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'load_profile': typical_load,
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'day_of_year': (typical_day_start // 24) + 1
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'day_start_hour': float(typical_day_start),
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'solar_profile': float(typical_solar[0]) if len(typical_solar) == 1 else typical_solar, # type: ignore
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'wind_profile': float(typical_wind[0]) if len(typical_wind) == 1 else typical_wind, # type: ignore
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'load_profile': float(typical_load[0]) if len(typical_load) == 1 else typical_load, # type: ignore
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'day_of_year': float((typical_day_start // 24) + 1)
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}
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return typical_days
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@@ -295,7 +438,7 @@ class MultiScenarioAnalyzer:
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def plot_scenario_analysis(self, result: ScenarioResult, solar_output: List[float],
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wind_output: List[float], load_demand: List[float],
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save_path: str = None, show_plot: bool = False):
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save_path: Optional[str] = None, show_plot: bool = False) -> None:
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"""
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绘制场景分析图表
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@@ -312,7 +455,7 @@ class MultiScenarioAnalyzer:
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# 1. 场景时间分布
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ax1 = plt.subplot(3, 3, 1)
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hours = np.arange(len(result.cluster_labels))
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colors = plt.cm.Set3(np.linspace(0, 1, result.n_scenarios))
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colors = plt.cm.Set3(np.linspace(0, 1, result.n_scenarios)) # type: ignore[attr-defined]
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for i in range(result.n_scenarios):
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mask = result.cluster_labels == i
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@@ -432,8 +575,8 @@ class MultiScenarioAnalyzer:
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def optimize_storage_for_scenarios(self, result: ScenarioResult, solar_output: List[float],
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wind_output: List[float], load_demand: List[float],
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system_params: SystemParameters = None,
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safety_factor: float = 1.2) -> Dict[str, Dict[str, float]]:
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system_params: Optional[SystemParameters] = None,
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safety_factor: float = 1.2) -> Dict[str, Dict[str, Any]]:
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"""
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对聚类后的场景进行储能配置优化
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@@ -460,7 +603,7 @@ class MultiScenarioAnalyzer:
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print("开始对各场景进行储能配置优化...")
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scenario_optimization_results = {}
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scenario_optimization_results: Dict[Union[int, str], Dict[str, Any]] = {}
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weighted_storage_need = 0.0
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max_storage_need = 0.0
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@@ -549,7 +692,179 @@ class MultiScenarioAnalyzer:
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print(f"最大储能需求: {max_storage_need:.2f} MWh")
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print(f"推荐储能容量: {max_storage_need * safety_factor:.2f} MWh")
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return scenario_optimization_results
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# 转换为字符串键的字典以匹配返回类型
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result_dict: Dict[str, Dict[str, Any]] = {}
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for key, value in scenario_optimization_results.items():
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result_dict[str(key)] = value
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return result_dict
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def optimize_storage_from_excel(self, file_path: str, sheet_name: str = 0,
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find_optimal_k: bool = False, safety_factor: float = 1.2) -> Dict[str, Any]:
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"""
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从Excel文件读取数据并执行多场景聚类分析和储能优化
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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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find_optimal_k: 是否自动寻找最优聚类数
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safety_factor: 安全系数，用于极端场景配置
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Returns:
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包含聚类结果、储能优化结果和参数的完整结果字典
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"""
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print("开始从Excel文件读取数据并执行分析...")
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# 读取Excel数据并执行聚类分析
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result, system_params = self.fit_predict_from_excel(
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file_path, sheet_name, find_optimal_k, include_parameters=True
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)
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# 重新读取数据以获取原始数据
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excel_data = self.read_excel_data(file_path, sheet_name, include_parameters=False)
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# 执行储能优化
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optimization_results = self.optimize_storage_for_scenarios(
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result,
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excel_data['solar_output'],
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excel_data['wind_output'],
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excel_data['load_demand'],
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system_params=system_params,
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safety_factor=safety_factor
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)
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# 返回完整结果
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return {
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'scenario_result': result,
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'optimization_results': optimization_results,
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'system_parameters': system_params,
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'data_info': {
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'data_type': excel_data['data_type'],
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'original_length': excel_data['original_length'],
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'data_length': len(excel_data['solar_output'])
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}
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}
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"""
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对聚类后的场景进行储能配置优化
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Args:
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result: 聚类结果
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solar_output: 光伏出力曲线 (MW)
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wind_output: 风电出力曲线 (MW)
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load_demand: 负荷曲线 (MW)
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system_params: 系统参数，默认为标准参数
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safety_factor: 安全系数，用于极端场景配置
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Returns:
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各场景的储能优化结果
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"""
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if system_params is None:
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system_params = SystemParameters(
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max_curtailment_wind=0.1,
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max_curtailment_solar=0.1,
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max_grid_ratio=0.2,
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storage_efficiency=0.9,
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discharge_rate=1.0,
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charge_rate=1.0
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)
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print("开始对各场景进行储能配置优化...")
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scenario_optimization_results: Dict[Union[int, str], Dict[str, Any]] = {}
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weighted_storage_need = 0.0
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max_storage_need = 0.0
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for scenario_id in range(result.n_scenarios):
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print(f"\n优化场景 {scenario_id + 1}: {result.scenario_names[scenario_id]}")
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# 提取场景数据
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scenario_solar, scenario_wind, scenario_load, duration = self._extract_scenario_data(
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solar_output, wind_output, load_demand, result.cluster_labels, scenario_id
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)
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if duration == 0:
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print(f" 警告：场景 {scenario_id + 1} 没有数据点")
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continue
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print(f" 场景持续时间: {duration} 小时")
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# 确保数据长度符合储能优化要求（24小时或8760小时）
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if duration < 24:
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# 短于24小时的数据扩展到24小时
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repeats = (24 // duration) + 1
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scenario_solar = (scenario_solar * repeats)[:24]
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scenario_wind = (scenario_wind * repeats)[:24]
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scenario_load = (scenario_load * repeats)[:24]
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duration = 24
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print(f" 扩展为24小时数据进行优化")
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elif duration == 8760:
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# 如果已经是8760小时，保持不变
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print(f" 使用完整的年度数据进行优化")
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else:
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# 介于24和8760之间的情况，需要截断或扩展到24小时（更合理的处理）
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if duration > 24:
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scenario_solar = scenario_solar[:24]
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scenario_wind = scenario_wind[:24]
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scenario_load = scenario_load[:24]
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duration = 24
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print(f" 截取24小时数据进行优化")
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else:
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# 如果介于两者之间，确保是24小时
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scenario_solar = scenario_solar[:duration]
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scenario_wind = scenario_wind[:duration]
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scenario_load = scenario_load[:duration]
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# 执行储能优化
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# 创建对应的热电输出（这里设置为0，表示纯电热系统）
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thermal_output = [0.0] * duration
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optimization_result = optimize_storage_capacity(
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scenario_solar, scenario_wind, thermal_output, scenario_load, system_params
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)
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# 存储结果
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scenario_optimization_results[scenario_id] = {
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'scenario_name': result.scenario_names[scenario_id],
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'duration': duration,
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'required_storage': optimization_result['required_storage_capacity'],
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'curtailment_wind': optimization_result['total_curtailment_wind_ratio'],
|
||||
'curtailment_solar': optimization_result['total_curtailment_solar_ratio'],
|
||||
'grid_ratio': optimization_result['total_grid_feed_in_ratio'],
|
||||
'frequency': result.scenario_frequencies[scenario_id],
|
||||
'energy_balance_check': optimization_result['energy_balance_check']
|
||||
}
|
||||
|
||||
# 累计计算
|
||||
frequency = result.scenario_frequencies[scenario_id]
|
||||
storage_need = optimization_result['required_storage_capacity']
|
||||
weighted_storage_need += storage_need * frequency
|
||||
max_storage_need = max(max_storage_need, storage_need)
|
||||
|
||||
print(f" 所需储能容量: {storage_need:.2f} MWh")
|
||||
print(f" 弃风率: {optimization_result['total_curtailment_wind_ratio']:.3f}")
|
||||
print(f" 弃光率: {optimization_result['total_curtailment_solar_ratio']:.3f}")
|
||||
print(f" 上网比例: {optimization_result['total_grid_feed_in_ratio']:.3f}")
|
||||
|
||||
# 添加汇总信息
|
||||
scenario_optimization_results['summary'] = {
|
||||
'weighted_average_storage': weighted_storage_need,
|
||||
'maximum_storage_need': max_storage_need,
|
||||
'recommended_capacity': max_storage_need * safety_factor,
|
||||
'safety_factor': safety_factor,
|
||||
'n_scenarios': result.n_scenarios
|
||||
}
|
||||
|
||||
print(f"\n储能配置优化完成！")
|
||||
print(f"加权平均储能需求: {weighted_storage_need:.2f} MWh")
|
||||
print(f"最大储能需求: {max_storage_need:.2f} MWh")
|
||||
print(f"推荐储能容量: {max_storage_need * safety_factor:.2f} MWh")
|
||||
|
||||
# 转换为字符串键的字典以匹配返回类型
|
||||
result_dict: Dict[str, Dict[str, Any]] = {}
|
||||
for key, value in scenario_optimization_results.items():
|
||||
result_dict[str(key)] = value
|
||||
|
||||
return result_dict
|
||||
|
||||
def _extract_scenario_data(self, solar_output: List[float], wind_output: List[float],
|
||||
load_demand: List[float], cluster_labels: np.ndarray,
|
||||
@@ -575,7 +890,7 @@ class MultiScenarioAnalyzer:
|
||||
|
||||
return scenario_solar, scenario_wind, scenario_load, duration
|
||||
|
||||
def print_storage_optimization_summary(self, optimization_results: Dict[str, Dict[str, float]]):
|
||||
def print_storage_optimization_summary(self, optimization_results: Dict[str, Dict[str, Any]]) -> None:
|
||||
"""
|
||||
打印储能配置优化汇总结果
|
||||
|
||||
@@ -591,7 +906,7 @@ class MultiScenarioAnalyzer:
|
||||
print("-" * 80)
|
||||
|
||||
# 各场景数据
|
||||
n_scenarios = optimization_results['summary']['n_scenarios']
|
||||
n_scenarios = int(optimization_results['summary']['n_scenarios'])
|
||||
for scenario_id in range(n_scenarios):
|
||||
if str(scenario_id) in optimization_results:
|
||||
result = optimization_results[str(scenario_id)]
|
||||
@@ -622,7 +937,7 @@ class MultiScenarioAnalyzer:
|
||||
|
||||
def export_scenario_results(self, result: ScenarioResult, solar_output: List[float],
|
||||
wind_output: List[float], load_demand: List[float],
|
||||
filename: str = None) -> str:
|
||||
filename: Optional[str] = None) -> str:
|
||||
"""
|
||||
导出场景分析结果到Excel
|
||||
|
||||
@@ -656,7 +971,7 @@ class MultiScenarioAnalyzer:
|
||||
})
|
||||
|
||||
# 场景统计汇总
|
||||
summary_data: List[Dict[str, str]] = []
|
||||
summary_data: List[Dict[str, Any]] = []
|
||||
for i in range(result.n_scenarios):
|
||||
stats = result.scenario_stats[f'scenario_{i}']
|
||||
summary_data.append({
|
||||
@@ -674,18 +989,26 @@ class MultiScenarioAnalyzer:
|
||||
summary_df = pd.DataFrame(summary_data)
|
||||
|
||||
# 典型日数据
|
||||
typical_data: List[Dict[str, float]] = []
|
||||
typical_data: List[Dict[str, Any]] = []
|
||||
for i in range(result.n_scenarios):
|
||||
if f'scenario_{i}' in result.typical_days:
|
||||
typical = result.typical_days[f'scenario_{i}']
|
||||
for hour in range(len(typical['solar_profile'])):
|
||||
solar_profile = typical['solar_profile']
|
||||
if isinstance(solar_profile, (list, tuple)):
|
||||
profile_len = len(solar_profile)
|
||||
else:
|
||||
profile_len = 24 # 默认24小时
|
||||
for hour in range(profile_len):
|
||||
solar_val = typical['solar_profile'][hour] if isinstance(typical['solar_profile'], (list, tuple)) else typical['solar_profile'] # type: ignore
|
||||
wind_val = typical['wind_profile'][hour] if isinstance(typical['wind_profile'], (list, tuple)) else typical['wind_profile'] # type: ignore
|
||||
load_val = typical['load_profile'][hour] if isinstance(typical['load_profile'], (list, tuple)) else typical['load_profile'] # type: ignore
|
||||
typical_data.append({
|
||||
'场景': result.scenario_names[i],
|
||||
'典型日': f"第{typical['day_of_year']}天",
|
||||
'典型日': f"第{int(typical['day_of_year'])}天",
|
||||
'小时': hour + 1,
|
||||
'光伏典型出力(MW)': typical['solar_profile'][hour],
|
||||
'风电典型出力(MW)': typical['wind_profile'][hour],
|
||||
'负荷典型需求(MW)': typical['load_profile'][hour]
|
||||
'光伏典型出力(MW)': solar_val,
|
||||
'风电典型出力(MW)': wind_val,
|
||||
'负荷典型需求(MW)': load_val
|
||||
})
|
||||
typical_df = pd.DataFrame(typical_data)
|
||||
|
||||
@@ -786,6 +1109,101 @@ def demo_multi_scenario_analysis():
|
||||
return result
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 运行演示
|
||||
demo_multi_scenario_analysis()
|
||||
import sys
|
||||
|
||||
# 检查命令行参数
|
||||
if len(sys.argv) > 1 and sys.argv[1] == "--excel":
|
||||
if len(sys.argv) > 2 and sys.argv[2]:
|
||||
# 使用指定的Excel文件
|
||||
excel_file = sys.argv[2]
|
||||
if not os.path.exists(excel_file):
|
||||
print(f"错误: Excel文件 '{excel_file}' 不存在")
|
||||
sys.exit(1)
|
||||
|
||||
print(f"=== 使用指定Excel文件进行多场景分析 ===\n")
|
||||
print(f"Excel文件: {excel_file}")
|
||||
|
||||
# 使用MultiScenarioAnalyzer从Excel读取数据并分析
|
||||
analyzer = MultiScenarioAnalyzer(n_clusters=8)
|
||||
|
||||
# 执行完整分析（聚类+储能优化）
|
||||
complete_results = analyzer.optimize_storage_from_excel(
|
||||
excel_file,
|
||||
find_optimal_k=True,
|
||||
safety_factor=1.2
|
||||
)
|
||||
|
||||
# 提取结果
|
||||
scenario_result = complete_results['scenario_result']
|
||||
optimization_results = complete_results['optimization_results']
|
||||
system_params = complete_results['system_parameters']
|
||||
data_info = complete_results['data_info']
|
||||
|
||||
# 输出聚类分析结果
|
||||
print(f"\n=== 聚类分析结果 ===")
|
||||
print(f"数据类型: {data_info['data_type']}小时")
|
||||
print(f"数据长度: {data_info['data_length']}小时")
|
||||
print(f"识别场景数: {scenario_result.n_scenarios}")
|
||||
print(f"轮廓系数: {scenario_result.silhouette_score:.3f}")
|
||||
|
||||
# 输出系统参数
|
||||
if system_params:
|
||||
print(f"\n=== 系统参数 ===")
|
||||
print(f"最大弃风率: {system_params.max_curtailment_wind}")
|
||||
print(f"最大弃光率: {system_params.max_curtailment_solar}")
|
||||
print(f"最大上网电量比例: {system_params.max_grid_ratio}")
|
||||
print(f"储能效率: {system_params.storage_efficiency}")
|
||||
print(f"放电倍率: {system_params.discharge_rate}")
|
||||
print(f"充电倍率: {system_params.charge_rate}")
|
||||
if system_params.max_storage_capacity:
|
||||
print(f"最大储能容量: {system_params.max_storage_capacity} MWh")
|
||||
else:
|
||||
print(f"最大储能容量: 无限制")
|
||||
|
||||
# 输出储能优化结果
|
||||
print(f"\n=== 储能优化结果 ===")
|
||||
analyzer.print_storage_optimization_summary(optimization_results)
|
||||
|
||||
# 重新读取数据以生成图表
|
||||
excel_data = analyzer.read_excel_data(excel_file, include_parameters=False)
|
||||
|
||||
# 生成分析图表
|
||||
print(f"\n生成场景分析图表...")
|
||||
base_filename = os.path.splitext(os.path.basename(excel_file))[0]
|
||||
analyzer.plot_scenario_analysis(
|
||||
scenario_result,
|
||||
excel_data['solar_output'],
|
||||
excel_data['wind_output'],
|
||||
excel_data['load_demand'],
|
||||
save_path=f"images/{base_filename}_analysis.png"
|
||||
)
|
||||
|
||||
# 导出场景分析结果
|
||||
print(f"\n导出分析结果...")
|
||||
excel_file_output = analyzer.export_scenario_results(
|
||||
scenario_result,
|
||||
excel_data['solar_output'],
|
||||
excel_data['wind_output'],
|
||||
excel_data['load_demand'],
|
||||
filename=f"{base_filename}_results.xlsx"
|
||||
)
|
||||
|
||||
print(f"\n=== 分析完成 ===")
|
||||
print(f"输入文件: {excel_file}")
|
||||
print(f"分析图表: images/{base_filename}_analysis.png")
|
||||
print(f"结果文件: {excel_file_output}")
|
||||
else:
|
||||
print("错误: 请指定Excel文件路径")
|
||||
print("用法: python multi_scenario.py --excel <文件路径>")
|
||||
sys.exit(1)
|
||||
else:
|
||||
# 运行基本演示
|
||||
demo_multi_scenario_analysis()
|
||||
|
||||
print("\n" + "="*60)
|
||||
print("提示: 使用 'python multi_scenario.py --excel <文件路径>' 可以分析指定的Excel文件")
|
||||
print("="*60)
|
||||
@@ -10,7 +10,7 @@
|
||||
|
||||
import numpy as np
|
||||
import math
|
||||
from typing import List, Dict, Tuple, Optional
|
||||
from typing import List, Dict, Tuple, Optional, Any
|
||||
from dataclasses import dataclass
|
||||
|
||||
|
||||
@@ -351,7 +351,7 @@ def check_constraints(
|
||||
solar_output: List[float],
|
||||
wind_output: List[float],
|
||||
thermal_output: List[float],
|
||||
balance_result: Dict[str, List[float]],
|
||||
balance_result: Dict[str, Any],
|
||||
params: SystemParameters
|
||||
) -> Dict[str, float]:
|
||||
"""
|
||||
@@ -373,9 +373,25 @@ def check_constraints(
|
||||
total_solar_potential = sum(solar_output)
|
||||
total_thermal = sum(thermal_output)
|
||||
|
||||
total_curtailed_wind = sum(balance_result['curtailed_wind'])
|
||||
total_curtailed_solar = sum(balance_result['curtailed_solar'])
|
||||
total_grid_feed_in = sum(balance_result['grid_feed_in'])
|
||||
# 确保数据是列表类型
|
||||
curtailed_wind = balance_result['curtailed_wind']
|
||||
curtailed_solar = balance_result['curtailed_solar']
|
||||
grid_feed_in = balance_result['grid_feed_in']
|
||||
|
||||
if isinstance(curtailed_wind, (list, tuple)):
|
||||
total_curtailed_wind = sum(curtailed_wind)
|
||||
else:
|
||||
total_curtailed_wind = float(curtailed_wind) if isinstance(curtailed_wind, (int, float)) else 0.0
|
||||
|
||||
if isinstance(curtailed_solar, (list, tuple)):
|
||||
total_curtailed_solar = sum(curtailed_solar)
|
||||
else:
|
||||
total_curtailed_solar = float(curtailed_solar) if isinstance(curtailed_solar, (int, float)) else 0.0
|
||||
|
||||
if isinstance(grid_feed_in, (list, tuple)):
|
||||
total_grid_feed_in = sum(grid_feed_in)
|
||||
else:
|
||||
total_grid_feed_in = float(grid_feed_in) if isinstance(grid_feed_in, (int, float)) else 0.0
|
||||
|
||||
# 实际发电量（考虑弃风弃光）
|
||||
actual_wind_generation = total_wind_potential - total_curtailed_wind
|
||||
@@ -545,7 +561,8 @@ def optimize_storage_capacity(
|
||||
|
||||
base_curtailed = sum(base_result['curtailed_wind']) + sum(base_result['curtailed_solar'])
|
||||
best_curtailed = base_curtailed
|
||||
best_result = {**base_result, **check_constraints(solar_output, wind_output, thermal_output, base_result, params)}
|
||||
constraint_results = check_constraints(solar_output, wind_output, thermal_output, base_result, params)
|
||||
best_result: Dict[str, Any] = {**base_result, **constraint_results}
|
||||
|
||||
print(f"基准容量 {best_capacity:.2f} MWh 的弃电量: {best_curtailed:.2f} MWh")
|
||||
|
||||
@@ -609,7 +626,7 @@ def optimize_storage_capacity(
|
||||
if curtailed < best_curtailed:
|
||||
best_curtailed = curtailed
|
||||
best_capacity = capacity
|
||||
best_result = {**result, **constraint_results}
|
||||
best_result = {**result, **constraint_results} # type: ignore
|
||||
print(f" 发现更优容量: {best_capacity} MWh, 弃电量: {best_curtailed:.2f} MWh")
|
||||
|
||||
# 每处理10个容量值输出一次进度
|
||||
@@ -647,7 +664,7 @@ def optimize_storage_capacity(
|
||||
adjusted_constraint_results = check_constraints(solar_output, wind_output, thermal_output, adjusted_result, params)
|
||||
|
||||
# 更新结果
|
||||
best_result = {**adjusted_result, **adjusted_constraint_results}
|
||||
best_result = {**adjusted_result, **adjusted_constraint_results} # type: ignore
|
||||
best_capacity = rounded_capacity
|
||||
best_curtailed = sum(adjusted_result['curtailed_wind']) + sum(adjusted_result['curtailed_solar'])
|
||||
|
||||
@@ -658,11 +675,20 @@ def optimize_storage_capacity(
|
||||
# 添加能量平衡校验
|
||||
total_generation = sum(thermal_output) + sum(wind_output) + sum(solar_output)
|
||||
total_consumption = sum(load_demand)
|
||||
total_curtailed = sum(best_result['curtailed_wind']) + sum(best_result['curtailed_solar'])
|
||||
total_grid = sum(best_result['grid_feed_in'])
|
||||
total_charge = sum(best_result['charge_profile'])
|
||||
total_discharge = sum(best_result['discharge_profile'])
|
||||
storage_net_change = best_result['storage_profile'][-1] - best_result['storage_profile'][0]
|
||||
|
||||
# 确保best_result中的数据是列表类型
|
||||
curtailed_wind_list = best_result['curtailed_wind'] if isinstance(best_result['curtailed_wind'], list) else list(best_result['curtailed_wind'])
|
||||
curtailed_solar_list = best_result['curtailed_solar'] if isinstance(best_result['curtailed_solar'], list) else list(best_result['curtailed_solar'])
|
||||
grid_feed_in_list = best_result['grid_feed_in'] if isinstance(best_result['grid_feed_in'], list) else list(best_result['grid_feed_in'])
|
||||
charge_profile_list = best_result['charge_profile'] if isinstance(best_result['charge_profile'], list) else list(best_result['charge_profile'])
|
||||
discharge_profile_list = best_result['discharge_profile'] if isinstance(best_result['discharge_profile'], list) else list(best_result['discharge_profile'])
|
||||
storage_profile_list = best_result['storage_profile'] if isinstance(best_result['storage_profile'], list) else list(best_result['storage_profile'])
|
||||
|
||||
total_curtailed = sum(curtailed_wind_list) + sum(curtailed_solar_list)
|
||||
total_grid = sum(grid_feed_in_list)
|
||||
total_charge = sum(charge_profile_list)
|
||||
total_discharge = sum(discharge_profile_list)
|
||||
storage_net_change = storage_profile_list[-1] - storage_profile_list[0]
|
||||
|
||||
# 能量平衡校验：发电量 + 放电量/效率 = 负荷 + 充电量*效率 + 弃风弃光 + 上网电量
|
||||
# 考虑储能充放电效率的能量平衡
|
||||
@@ -695,14 +721,14 @@ def optimize_storage_capacity(
|
||||
print(f" SOC差值: {soc_initial_final_diff:.4f} MWh")
|
||||
|
||||
# 最终结果
|
||||
result = {
|
||||
result: Dict[str, Any] = {
|
||||
'required_storage_capacity': best_capacity,
|
||||
'storage_profile': best_result['storage_profile'],
|
||||
'charge_profile': best_result['charge_profile'],
|
||||
'discharge_profile': best_result['discharge_profile'],
|
||||
'curtailed_wind': best_result['curtailed_wind'],
|
||||
'curtailed_solar': best_result['curtailed_solar'],
|
||||
'grid_feed_in': best_result['grid_feed_in'],
|
||||
'storage_profile': storage_profile_list,
|
||||
'charge_profile': charge_profile_list,
|
||||
'discharge_profile': discharge_profile_list,
|
||||
'curtailed_wind': curtailed_wind_list,
|
||||
'curtailed_solar': curtailed_solar_list,
|
||||
'grid_feed_in': grid_feed_in_list,
|
||||
'total_curtailment_wind_ratio': best_result['total_curtailment_wind_ratio'],
|
||||
'total_curtailment_solar_ratio': best_result['total_curtailment_solar_ratio'],
|
||||
'total_grid_feed_in_ratio': best_result['total_grid_feed_in_ratio'],
|
||||
|
||||
Reference in New Issue
Block a user