上海交通大学学报

上海交通大学学报 >

2023 , Vol. 57 >Issue 1: 1 - 9

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2021.499

新型电力系统与综合能源

随机环境下电动汽车充电实时管理与优化控制算法

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  • 1.广西师范大学 电子与信息工程学院, 广西壮族自治区 桂林 541004
    2.广西电网有限责任公司电力科学研究院,南宁 530000
刘迪迪(1980-),副教授,博士,主要研究方向为电力系统控制、随机网络优化.

收稿日期: 2021-12-07

  修回日期: 2022-02-12

  网络出版日期: 2022-10-31

基金资助

国家自然科学基金项目(62061006);国家自然科学基金项目(12162005)

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Management and Optimal Control Algorithm for Electric Vehicle Charging in Random Environment

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  • 1. College of Electronic and Information Engineering, Guangxi Normal University, Guilin 541004, Guangxi Zhuang Autonomous Region, China
    2. Power Research Institute of Guangxi Power Grid Co., Ltd., Nanning 530000, China

Received date: 2021-12-07

  Revised date: 2022-02-12

  Online published: 2022-10-31

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摘要

电动汽车的规模日益壮大,对其充电行为进行自适应管理成为亟待解决的问题.从充电服务商的角度出发,协同可再生能源和储能设备,并计及电网的时变电价和电动汽车充电可容忍时延,基于Lyapunov优化理论提出随机环境下的电动汽车充电实时管理和优化控制算法,旨在最大化充电服务商的利益,即最小化购电成本.理论性能分析证明,所提算法无需可再生能源出力、充电需求和时变电价的先验统计信息,就能使优化目标趋近最优值.仿真结果表明,该算法可以有效减少充电服务商的购电成本,相比于基准贪婪算法可降低27.3%.

关键词： 电动汽车; 充电调度; 随机环境; 智能电网; 可再生能源

本文引用格式

刘迪迪, 杨益菲, 杨玉荟, 邹艳丽, 王小华, 黎新 . 随机环境下电动汽车充电实时管理与优化控制算法[J]. 上海交通大学学报, 2023 , 57(1) : 1 -9 . DOI: 10.16183/j.cnki.jsjtu.2021.499

Abstract

With the increasing scale of electric vehicles (EVs), the adaptive management of its charging behavior becomes an urgent problem to be solved. From the point of view of charging service provider, an online management algorithm for EV charging is proposed based on the Lyapunov optimization theory under the random environment in this paper, considering renewable sources energy, storage equipment, time-varying electricity price, and the tolerable delay of EV, with an aim of maximizing the benefits of charging service providers (i.e., minimizing the cost of electricity purchased). The performance of the proposed algorithm is analyzed to verify that it can achieve near-optimal optimization results without any a priori statistical information about the system inputs (renewable energy generation, charging demand, and time-varying electricity price). The simulation results show that the proposed algorithm can effectively reduce the economic cost by 27.3% compared with the benchmark algorithm.

Key words： electric vehicles (EVs); charging schedule; smart grid; random environment; renewable energy source

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