吉林大学学报(信息科学版) ›› 2025, Vol. 43 ›› Issue (4): 894-902.

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基于浣熊优化算法的居民社区电动汽车优化调度

 周 斌1, 武 斌2, 张智达3, 李少雄3   

  1. 1. 国网电力科学研究院有限公司充电设施产品部,南京211106; 2. 国家电网有限公司营销部,北京100031;3. 国网天津市电力公司 营销部市场处,天津300010
  • 收稿日期:2024-07-28 出版日期:2025-08-15 发布日期:2025-08-15
  • 作者简介:周斌(1976— ), 男, 江苏武进人, 国电南瑞南京控制系统有限公司高级工程师, 硕士, 主要从事电动汽车充换电技术研究, (Tel)86-17331208962 (E-mail)953219675@ qq. com。
  • 基金资助:
    国家电网有限公司科技基金资助项目(5400-202312239A-1-1-ZN)

Optimal Scheduling of Electric Vehicles in Residential Communities Based on Coati-Optimization Algorithm

 ZHOU Bin1, WU Bin2, ZHANG Zhida3, LI Shaoxiong3   

  1. 1. Charging Facilities Product Department, State Grid Electric Power Research Institute, Nanjing 211106, China; 2. Marketing Department, State Grid Corporation of China, Beijing 100031, China; 3. Marketing Department Marketing Division, State Grid Tianjin Electric Power Company, Tianjin 300010, China
  • Received:2024-07-28 Online:2025-08-15 Published:2025-08-15

摘要: 针对居民社区电动汽车(EV: Electric Vehicles)用户基础负荷与 EV 无序充电负荷叠加造成配电网负荷峰上加峰冶的问题,提出了基于浣熊优化算法(COA: Coati Optimization Algorithm)的居民社区 EV 有序充放电策略。首先,基于云边协同理论及大数据技术,建立配电网、充电站运营商、智能充电桩与EV用户信息全面互联互通的云边端协同优化调度框架;其次, 提出考虑用户可接受最小利润或最大成本的EV用户充电调度机制; 然后,从电网侧与用户侧的角度建立双层多目标有序充放电优化调控模型;最后,以居民区中EV负荷数据为例,提出采用COA对模型进行求解。仿真结果验证了所提出的模型与方法的有效性和优越性, 可以较好地实现负荷削峰填谷,同时提高了用户充电体验。

关键词: 电动汽车, 云边协同调度框架, 电动汽车调度机制, 有序充放电, 浣熊算法

Abstract: In order to solve the problem of “peak-on-peak” load in distribution networks caused by the superposition of EV ( Electric Vehicle) users’ base load and unordered EV charging load in residential communities, the following solution is proposed Firstly, based on cloud-edge collaboration theory and big data technology, a cloud-edge collaborative optimization scheduling framework is established for comprehensive interconnection of distribution networks, charging station operators, intelligent charging piles, and EV user information. Secondly, an EV user charging scheduling mechanism considering the minimum profit or maximum cost acceptable to users is proposed. Then, a two-layer multi-objective orderly charge and discharge optimization regulation model is established from the perspectives of both the grid side and the user side. Finally, taking EV load data in residential areas as an example, the COA(Coati Optimization Algorithm) is proposed to solve the model. The simulation results verify the effectiveness and superiority of the proposed model and method. It can achieve better peak cutting and valley filling, and improve the user’s charging experience. 

Key words: electric vehicles, cloud edge cooperative scheduling framework, electric vehicle scheduling mechanism, orderly charge and dis-charge, coati optimization algorithm 

中图分类号: 

  • TP14