基于负荷预测的居民小区电动汽车多目标充电调度策略

赵锴; 钱忠; 殷展; 宗梦凡; 安硕; 张泽成

赵锴,钱忠,殷展,宗梦凡,安硕,张泽成.基于负荷预测的居民小区电动汽车多目标充电调度策略[J].电测与仪表,2026,63(6):92-100.
Zhaokai,Qianzhong,Yinzhan,Zongmengfan,Anshuo,Zhangzecheng.Multi-objective charging scheduling strategy for electric vehicles in residential communities based on load forecasting[J].Electrical Measurement & Instrumentation,2026,63(6):92-100.

基于负荷预测的居民小区电动汽车多目标充电调度策略

Multi-objective charging scheduling strategy for electric vehicles in residential communities based on load forecasting

DOI：10.19753/j.issn1001-1390.2026.06.010

中文关键词: 高斯回归负荷预测居民小区充电调度粒子群算法

英文关键词:Gaussian regression, load forecasting, residential community, charging scheduling, particle swarm optimization algorithm

基金项目:国网上海市电力公司科技项目(520931230005)

作者中文名	作者英文名	单位
赵锴	Zhaokai	国网上海市电力公司嘉定供电公司
钱忠	Qianzhong	国网上海市电力公司嘉定供电公司
殷展	Yinzhan	国网上海市电力公司嘉定供电公司
宗梦凡	Zongmengfan	国网上海市电力公司嘉定供电公司
安硕	Anshuo	华升科技集团有限公司
张泽成	Zhangzecheng	上海电力大学

摘要点击次数: 125

中文摘要:

为了解决电动汽车在居民小区内无序充电所导致的配电网负荷波动和稳定性问题，文中研究提出了一种基于高斯回归负荷预测和分时电价优化的居民小区电动汽车充电调度策略。通过分析某小区的真实数据，得出用户的用电和用车习惯，在此基础上，引入“同天”预测的预测方法，使用前两周的历史数据作为训练，通过高斯回归预测模型，对居民小区基础负荷和电动汽车充电需求进行预测。进一步结合分时电价机制，构建了一个旨在最小化配电网负荷方差和用户充电费用的多目标优化模型，采用粒子群算法实现了对目标函数的求解，并对无序充电与有序充电条件下的配电网性能进行了比较分析。仿真结果表明使用前两周的数据既可以确保预测模型基于最新的负荷信息，同时可以减少模型的计算复杂度，提高预测的准确性和实时性。同时，文中所提策略不仅降低了配电网的负荷波动和峰谷差，还减少了用户的充电成本，实现了负荷曲线的“削峰填谷”。

英文摘要:

To address the issues of load fluctuation and stability in the distribution network caused by unordered charging of electric vehicles (EVs) in residential communities, this paper proposes an EV charging scheduling strategy for residential communities based on Gaussian regression load forecasting and time-of-use (TOU) electricity pricing optimization. By analyzing the real data from a residential community, the electricity consumption and vehicle usage habits of users are identified. On this basis, a "same-day" forecasting approach is introduced, utilizing the historical data from the preceding two weeks as training data. Through the Gaussian regression prediction model, the base load of the residential community and the EV charging demand are predicted. Furthermore, integrating the TOU electricity pricing mechanism, a multi-objective optimization model aimed at minimizing the variance of the distribution network load and user charging costs is constructed. The particle swarm optimization (PSO) algorithm is employed to solve the objective function, and a comparative analysis of the distribution network performance under unordered and ordered charging conditions is conducted. The simulation results indicate that using data from the preceding two weeks can ensure that the prediction model is based on the latest load information while reducing the computational complexity of the model, thereby improving the accuracy and real-time performance of the predictions. Additionally, the proposed strategy in this paper not only reduces the load fluctuation and peak-to-trough difference in the distribution network but also decreases the charging costs for users, achieving the “peak shaving and valley filling” of the load curve.

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