考虑光伏发电功率预测的电动汽车充电优化调控策略
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(青岛理工大学信息与控制工程学院 ,山东 青岛 266520)

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通讯作者:

马兆兴(1982—),男,博士,讲师,主要从事电力系统分析、运行以及综合能源系统规划等研究;E-mail:mazhaoxingapple@126.com

中图分类号:

TM73

基金项目:

国家自然科学基金(62203248);电网运行风险防御技术与装备全国重点实验室资助项目(SGNR0000KJJS2302137)


Charging optimization and regulation strategy for electric vehicles considering photovoltaic power prediction
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(School of Information and Control Engineering , Qingdao University of Technology , Qingdao 266520, China)

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    摘要:

    规模化电动汽车和分布式电源的普及给电力系统带来了新的挑战,负荷需求和电源供应的波动性与不确定性增加,使得配电网络运行面临较大压力。提出了一种考虑光伏功率消纳的电动汽车充电控制策略。首先,采用蒙特卡洛方法对电动汽车用户的用电特征及充电方式进行模拟分析;其次,基于门控循环神经网络理论建立光伏发电预测模型;最后,以光伏消纳最大和用户充电成本最低为充电控制策略目标,采用改进蜣螂优化算法获得了最佳充电策略。仿真结果表明,该充电控制策略在促进光伏功率消纳方面具有有效性和可行性,能实现电动汽车的有序充电。

    Abstract:

    The widespread adoption of electric vehicles and distributed power sources poses new challenges to power systems,as increased volatility and uncertainty in both load demand and power supply place greater pressure on distribution network operation.To address these challenges,an electric vehicle charging control strategy considering photovoltaic power accommodation is proposed.First,the Monte Carlo method is employed to simulate and analyze the electricity consumption characteristics and charging behaviors of electric vehicle users.Second,a photovoltaic power generation forecasting model is developed based on gated recurrent unit networks.Finally,an orderly charging control strategy for electric vehicles is proposed,with the objectives of maximum photovoltaic accommodation and minimum charging cost for users,and the improved dung beetle optimization algorithm is used to obtain the optimal charging strategy.The simulation results show that the proposed charging control strategy is effective and feasible in promoting the accommodation of photovoltaic power,and realizes the orderly charging of electric vehicles.

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宋双林,马兆兴,王晶,等.考虑光伏发电功率预测的电动汽车充电优化调控策略[J].电力科学与技术学报,2026,41(1):98-107.
SONG Shuanglin, MA Zhaoxing, WANG Jing, et al. Charging optimization and regulation strategy for electric vehicles considering photovoltaic power prediction[J]. Journal of Electric Power Science and Technology,2026,41(1):98-107.

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  • 收稿日期:2024-11-12
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  • 在线发布日期: 2026-02-11
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