Charging and Discharging Scheduling for Electric Vehicles Based on Improved Multi-objective Chaotic Particle Swarm Optimization

doi:10.12068/j.issn.1005-3026.2025.20240014

Abstract

Abstract:

To address the issue of charging and discharging scheduling for EVs（electric vehicles）， an orderly charging and discharging algorithm that considered users’ comprehensive satisfaction was proposed. Firstly， a large-scale orderly charging and discharging model for EVs was constructed， and users’ comprehensive satisfaction was quantified. Secondly， an improved multi-objective role partitioning chaotic particle swarm optimization（IMRPC-PSO） algorithm was proposed to solve the problems of insufficient diversity and being trapped in local optimal in traditional methods. According to the performance of particles， the roles of elite particles， general particles， and learning particles were assigned， which respectively implement diversity strategies of maintaining search， developing search， and learning search. Each particle searched the optimization space according to its assigned role. To avoid falling into local optimal， a chaotic sequence perturbation was added after the initialization of each iteration. Finally， the performance of the proposed algorithm was compared with that of the other five multi-objective optimization algorithms through case simulation. The results show that IMRPC-PSO is superior to other algorithms in solving the problem of orderly charging and discharging of EVs， verifying the effectiveness and feasibility of the proposed algorithm.

Key words: electric vehicle（EV）, orderly charging and discharging, Tent chaotic sequence perturbation, particle swarm optimization（PSO） algorithm, multi-objective optimization

CLC Number:

TP 273

Zhi-ao CAO, Chen-shuo MA. Charging and Discharging Scheduling for Electric Vehicles Based on Improved Multi-objective Chaotic Particle Swarm Optimization[J]. Journal of Northeastern University(Natural Science), 2025, 46(9): 1-8.

Figures/Tables 8

References 15

[1]	He Y F， Venkatesh B， Guan L. Optimal scheduling for charging and discharging of electric vehicles［J］. IEEE Transactions on Smart Grid， 2012， 3（3）： 1095-1105.
[2]	Dang S P， Odonde A， Mirza T， et al. Sustainable energy management： an analysis report of the impacts of electric vehicles［C］//2014 14th International Conference on Environment and Electrical Engineering. Krakow， 2014： 318-322.
[3]	Wei H Q， Zhang Y T， Wang Y Z， et al. Planning integrated energy systems coupling V2G as a flexible storage［J］. Energy， 2022， 239： 122215.
[4]	Shafiq S， Al-Awami A T. Reliability and economic assessment of renewable micro-grid with V2G electric vehicles coordination［C］//2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies （AEECT）. Amman， 2015： 1-6.
[5]	Ahmadi S， Arabani H P， Haghighi D A， et al. Optimal use of vehicle-to-grid technology to modify the load profile of the distribution system［J］. Journal of Energy Storage， 2020， 31： 101627.
[6]	Zhang Q， Hu Y， Tan W Y， et al. Dynamic time-of-use pricing strategy for electric vehicle charging considering user satisfaction degree［J］. Applied Sciences， 2020， 10（9）： 3247.
[7]	Marini F， Walczak B. Particle swarm optimization （PSO）： a tutorial［J］. Chemometrics and Intelligent Laboratory Systems， 2015， 149： 153-165.
[8]	Coello C A， Lechuga M S. MOPSO： a proposal for multiple objective particle swarm optimization［C］//Proceedings of the 2002 Congress on Evolutionary Computation. Honolulu， 2002： 1051-1056.
[9]	Breunig M M， Kriegel H P， Ng R T， et al. LOF： identifying density-based local outliers［C］//Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. Dallas， 2000： 93-104.
[10]	吕鑫，慕晓冬，张钧，等. 混沌麻雀搜索优化算法［J］. 北京航空航天大学学报， 2021， 47（8）： 1712-1720.
	Xin Lyu， Mu Xiao-dong， Zhang Jun， et al. Chaos sparrow search optimization algorithm［J］. Journal of Beijing University of Aeronautics and Astronautics， 2021， 47（8）： 1712-1720.
[11]	王万良，金雅文，陈嘉诚，等. 多角色多策略多目标粒子群优化算法［J］. 浙江大学学报（工学版）， 2022， 56（3）： 531-541.
	Wang Wan-liang， Jin Ya-wen， Chen Jia-cheng， et al. Multi-objective particle swarm optimization algorithm with multi-role and multi-strategy［J］. Journal of Zhejiang University （Engineering Science）， 2022， 56（3）： 531-541.
[12]	Becker T A， Sidhu I， Tenderich B. Electric vehicles in the United States： a new model with forecasts to 2030［M］. Berkeley： University of California， 2009.
[13]	Borhanazad H， Mekhilef S， Gounder-Ganapathy V， et al. Optimization of micro-grid system using MOPSO［J］. Renewable Energy， 2014， 71： 295-306.
[14]	Adham A M， Mohd-Ghazali N， Ahmad R. Optimization of an ammonia-cooled rectangular microchannel heat sink using multi-objective non-dominated sorting genetic algorithm （NSGA2）［J］. Heat and Mass Transfer， 2012， 48（10）： 1723-1733.
[15]	Karaboga D， Basturk B. A powerful and efficient algorithm for numerical function optimization： artificial bee colony （ABC） algorithm［J］. Journal of Global Optimization， 2007， 39（3）： 459-471.

算法	负载/kW	用户满意度
IMRPC-PSO	210.661	5.23
MOPSO^[13]	218.723	5.28
CMOPSO^[10]	211.076	5.27
MMMOPSO^[7]	218.746	5.26
NSGA2^[14]	214.710	5.28
ABC^[15]	242.603	5.59

算法	负载/kW	用户满意度
IMRPC-PSO	210.661	5.23
MOPSO^[13]	218.723	5.28
CMOPSO^[10]	211.076	5.27
MMMOPSO^[7]	218.746	5.26
NSGA2^[14]	214.710	5.28
ABC^[15]	242.603	5.59

算法	负载/kW	用户综合满意度
IMRPC-PSO	2 434.919	-32.8
MOPSO^[13]	4 322.687	-29.2
CMOPSO^[10]	3 594.713	-33.1
MMMOPSO^[7]	2 961.451	-32.2
NSGA2^[14]	4 103.258	-34.0
ABC^[15]	2 193.556	-40.7

算法	负载/kW	用户综合满意度
IMRPC-PSO	2 434.919	-32.8
MOPSO^[13]	4 322.687	-29.2
CMOPSO^[10]	3 594.713	-33.1
MMMOPSO^[7]	2 961.451	-32.2
NSGA2^[14]	4 103.258	-34.0
ABC^[15]	2 193.556	-40.7

算法	负载/kW	用户综合满意度
IMRPC-PSO	4 720.144	-52.2
MOPSO^[13]	8 182.282	-36.4
CMOPSO^[10]	5 657.921	-51.2
MMMOPSO^[7]	7 753.242	-37.1
NSGA2^[14]	8 591.319	-33.6
ABC^[15]	5 413.999	-36.5