计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (2): 242-252.doi: 10.11896/jsjkx.240200018

• 人工智能 • 上一篇    下一篇

面向时变电价的电池充电功率与用户分配联合调度算法

万得胜1,2, 陈豪1,2, 程文辉1,2, 高云龙3   

  1. 1 重庆大学计算机学院 重庆 400044
    2 信息物理社会可信服务计算教育部重点实验室(重庆大学) 重庆 400044
    3 国家工业信息安全发展研究中心 北京 100040
  • 收稿日期:2024-02-04 修回日期:2024-06-27 出版日期:2025-02-15 发布日期:2025-02-17
  • 通讯作者: 高云龙(pkugaoyunlong@sina.com)
  • 作者简介:(3135488227@qq.com)

Joint Scheduling Algorithm of Battery Charging Power and User Allocation for Time-varyingElectricity Prices

WAN Desheng1,2, CHEN Hao1,2, CHENG Wenhui1,2, GAO Yunlong3   

  1. 1 College of Computer Science,Chongqing University,Chongqing 400044,China
    2 Key Laboratory of Dependable Service Computing in Cyber Physical Society(Chongqing University),Ministry of Education,Chongqing 400044,China
    3 China Industrial Control Systems Cyber Emergency Response Team,Beijing 100040,China
  • Received:2024-02-04 Revised:2024-06-27 Online:2025-02-15 Published:2025-02-17
  • About author:WAN Desheng,born in 1995,master.His main research interests include edge computing and urban computing.
    GAO Yunlong,born in 1987,Ph.D,engineer.His main research interests include artificial intelligence,big data,and cloud computing.

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摘要： 随着电动摩托车在全球范围内迅速增长,电池交换站因其快速补能、便利性及安全性等优势而被广泛推广。然而,大部分站点面临充电成本高的问题,这来源于运营商的充电策略和用户的换电行为。鉴于电池交换站充电功率可调节的特点,且大量电池在充电过程中存在空闲时间,合理利用上述特点有望降低充电成本。因此,以换电业务中的电池和用户为研究对象,重点研究电池交换站中电池的充电功率调节和用户分配问题。主要面临以下挑战:首先,电池充电功率调节策略受到时变电价和用户分配的双重影响;进一步地,电池和用户的分配方案既要考虑满足不同时间段的用户换电需求,又需考虑单体电池在不同充电功率调节策略下产生的充电成本。因此,电池充电功率调节和用户分配相互影响,是一个互为耦合的问题。为了应对上述挑战,提出了一种面向时变电价的电池充电功率调节和用户分配的联合调度算法。首先,利用贪心策略为每个电池设计充电功率调节方案;然后,综合单体电池的充电成本和换出电量,基于遗传特性为电池匹配最优用户,从而最小化电池交换站的总充电成本;最后,基于成都市44个电池交换站和7 334块电池2年的大规模数据集,对所提方法进行了全面评估。实验结果表明,与3种对比算法相比,所提算法的总充电成本平均降低20.8%。

关键词: 电池交换站, 充电成本, 功率调节, 遗传特性, 用户分配

Abstract: With the global proliferation of electric motorcycles,battery-swapping stations have gained widespread attention due to their advantages,such as swift replenishment,convenience,and safety.Nonetheless,a predominant challenge most stations face is the elevated cost of charging.It is derived from the charging strategies for battery and user behavior during battery swaps.Leve-raging the adjustable charging power of batteries at swapping stations and the idle time of numerous batteries during the charging process is anticipated to reduce charging costs judiciously.Hence,this paper concentrates on the batteries and users within the battery swapping industry,specifically delving into the intricacies of adjusting the charging power of batteries and the allocation of users in battery swapping stations.The primary challenges encompass the dual influence of time-varying electricity prices and user allocation on battery charging power adjustment strategies.Furthermore,the battery and user allocation schemes must cater to users' battery swapping requirements across different time periods while also consider the charging costs incurred by individual batteries under varying charging power adjustment strategies.Consequently,adjusting battery charging power and user allocation are interlinked and form a mutually dependent problem.To this end,we propose a comprehensive scheduling algorithm for adjusting battery charging power and user allocation,taking into account the time-varying electricity prices.Firstly,a greedy strategy is initially applied to formulate a charging power adjustment plan for each battery.Then,considering the charging costs and the energy swapped out by individual batteries,an algorithm based on genetic characteristics is utilized to match batteries with optimal users,thereby minimizing the overall charging costs of the battery swapping station.Finally,the proposed method undergoes a thorough evaluation using a large-scale dataset from 44 battery swapping stations and 7 334 batteries in Chengdu,spanning two years.Experimental results demonstrate that,on average,the total charging costs of the proposed algorithm are reduced by 20.8% compared to the three baselines.

Key words: Battery swapping stations, Charging costs, Power regulation, Genetic characteristics, User allocation

中图分类号: 

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