村庄电动汽车充电桩功率配置优化数据

本数据通过分析村庄不同时段的充电需求特征，为充电基础设施优化提供决策支持。主要应用于：指导运营商根据不同时段的功率匹配度计算结果，动态调整充电桩功率配置；识别高负荷站点优先扩容，发现闲置设备可优化区域；合理分配电力资源，提升整体运营效率。同时可为管理部门提供充电设施使用时段分析报告，助力实现充电资源的精准配置与高效利用。 "1.数据采集与处理 采集企业自有充电桩设备管理数据，包括充电站编号、城市名称、部署区域、充电桩配置情况、统计日期、早高峰总充电量、晚高峰总充电量、低谷时段总充电量、平峰时段总充电量等数据。时段划分为：早高峰（7:00-9:00）、晚高峰（17:00-19:00）、低谷（0:00-6:00）、平峰（其余时段），通过数据清洗剔除＜5分钟充电记录等其他异常值与无效记录。 2.核心计算 计算需求指数： 早高峰需求指数=早高峰总充电量/早高峰时长（2小时） 晚高峰需求指数=晚高峰总充电量/晚高峰时长（2小时） 低谷时段需求指数=低谷时段总充电量×0.3（低谷时段权重）/低谷时段时长（6小时） 平峰时段需求指数=平峰总充电量×0.5（平峰时段权重）/平峰时段时长（14小时） 计算功率匹配度： 功率匹配度=（早高峰需求指数×1.5+晚高峰需求指数×1.2+低谷需求指数+平峰需求指数）/（快充桩总功率+慢充桩总功率） 3.功率匹配度情况分类及充电桩功率配置优化策略 功率匹配度＞1.5：功率匹配度情况分类为""功率不足""，功率配置优化策略：早高峰时段：快充桩锁定100%功率，慢充桩+20%超频；晚高峰时段：快充桩保持100%，慢充桩+10%超频；低谷时段：慢充桩降功30%；平峰时段：维持标准功率 1.0≤匹配度≤1.5：功率匹配度情况分类为""配置基本合理""，功率配置优化策略：早高峰时段：快充桩保持90%功率；晚高峰时段：快充桩保持85%功率；低谷时段：慢充桩降功20%；平峰时段：维持标准功率 匹配度＜1.0：功率匹配度情况分类为""功率过剩""，功率配置优化策略：早高峰时段：快充桩保持80%功率；晚高峰时段：快充桩保持75%功率；低谷时段：全桩降功40%；平峰时段：全桩降功30%"This dataset is developed to provide decision support for the optimization of charging infrastructure by analyzing the charging demand characteristics of villages in different time periods. Its main applications include: guiding operators to dynamically adjust the charging pile power configuration based on the power matching degree calculation results across different time periods; identifying high-load stations for priority expansion, and optimizing regions with idle equipment; rationally allocating power resources to improve overall operational efficiency. It can also provide time-based usage analysis reports of charging facilities for management departments, facilitating the precise allocation and efficient utilization of charging resources. 1. Data Collection and Processing The dataset collects proprietary charging pile management data from enterprises, including charging station ID, city name, deployment area, charging pile configuration, statistical date, total charging volume during morning peak, total charging volume during evening peak, total charging volume during low-demand period, total charging volume during flat-peak period, and other relevant data. The time periods are divided as: morning peak (7:00-9:00), evening peak (17:00-19:00), low-demand period (0:00-6:00), flat-peak period (remaining hours). Data cleaning was conducted to remove outliers and invalid records such as charging records with duration less than 5 minutes. 2. Core Calculations 1) Demand Index Calculation: Morning peak demand index = Total charging volume during morning peak / Duration of morning peak (2 hours) Evening peak demand index = Total charging volume during evening peak / Duration of evening peak (2 hours) Low-demand period demand index = (Total charging volume during low-demand period × 0.3 (weight of low-demand period)) / Duration of low-demand period (6 hours) Flat-peak period demand index = (Total charging volume during flat-peak period × 0.5 (weight of flat-peak period)) / Duration of flat-peak period (14 hours) 2) Power Matching Degree Calculation: Power matching degree = (Morning peak demand index × 1.5 + Evening peak demand index × 1.2 + Low-demand period demand index + Flat-peak period demand index) / (Total power of fast charging piles + Total power of slow charging piles) 3. Power Matching Degree Classification and Charging Pile Power Configuration Optimization Strategies - When power matching degree > 1.5: Classified as "insufficient power". Optimization strategies: During morning peak: Lock fast charging piles at 100% power, overclock slow charging piles by 20%; During evening peak: Maintain fast charging piles at 100% power, overclock slow charging piles by 10%; During low-demand period: Reduce power of slow charging piles by 30%; During flat-peak period: Maintain standard power - When 1.0 ≤ power matching degree ≤ 1.5: Classified as "basically reasonable configuration". Optimization strategies: During morning peak: Maintain fast charging piles at 90% power; During evening peak: Maintain fast charging piles at 85% power; During low-demand period: Reduce power of slow charging piles by 20%; During flat-peak period: Maintain standard power - When power matching degree < 1.0: Classified as "excessive power". Optimization strategies: During morning peak: Maintain fast charging piles at 80% power; During evening peak: Maintain fast charging piles at 75% power; During low-demand period: Reduce power of all piles by 40%; During flat-peak period: Reduce power of all piles by 30%

搜集汇总

背景与挑战

背景概述

该数据集聚焦于村庄电动汽车充电桩的功率配置优化，包含500条每日更新的记录，详细记录了充电站的基本信息、不同时段的充电量数据以及计算得出的需求指数和功率匹配度。其核心特点是通过科学的算法分析充电需求特征，为运营商提供动态调整功率配置的策略，以优化电力资源分配、提升运营效率，并支持充电基础设施的精准管理。