基于改进多目标混沌粒子群优化的电动汽车充放电调度

doi:10.12068/j.issn.1005-3026.2025.20240014

摘要/Abstract

摘要：

针对电动汽车充放电调度问题，提出一种考虑用户综合满意度的有序充放电算法.首先，构建了大规模电动汽车有序充放电模型，并量化用户综合满意度；其次，提出了一种基于改进多目标策略多样性混沌序列扰动粒子群优化(improved multi-objective role partitioning chaotic particle swarm optimization, IMRPC-PSO)算法以解决传统方法中多样性不足和易陷入局部最优的问题.根据粒子性能，给粒子赋予精英粒子、一般粒子和学习粒子的角色，并分别执行保持搜索、发展搜索和学习搜索的多样性策略.每个粒子根据其角色寻优搜索空间；为避免陷入局部最优，在每次迭代初始化后加入混沌序列扰动.最后，通过案例仿真对比所提算法与其余5种多目标优化算法的性能，结果显示IMRPC-PSO在解决电动汽车有序充放电问题上优于其他算法，验证了该算法的有效性和可行性.

关键词: 电动汽车, 有序充放电, Tent混沌序列扰动, 粒子群优化算法, 多目标优化

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

中图分类号:

TP 273

曹知奥, 马晨硕. 基于改进多目标混沌粒子群优化的电动汽车充放电调度[J]. 东北大学学报（自然科学版）, 2025, 46(9): 1-8.

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.

图/表 8

图1 IMRPC-PSO算法流程图

Fig.1 Flowchart of the IMRPC-PSO algorithm

表1 不同算法求解100辆EV问题的结果比较 (with 100 EVs using different algorithms)

Table 1 Comparison of results in solving problems

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

表2 不同算法求解500辆EV问题的结果比较 (with 500 EVs using different algorithms)

Table 2 Comparison of results in solving problems

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

表3 不同算法求解1 000辆EV问题的结果比较 (with 1 000 EVs using different algorithms)

Table 3 Comparison of results in solving problems

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

图2 110辆EV负载仿真结果图（a）—200次迭代图像；（b）—90~130次迭代图像.

Fig.2 Load simulation results of 110 EVs

图3 110辆EV用户满意度仿真结果图（a）—200次迭代图像；（b）—90~130次迭代图像.

Fig.3 Simulation results of user’s satisfaction for 110 EVs

图4 110辆EV BestCost仿真结果图（a）—200次迭代图像；（b）—90~130次迭代图像.

Fig.4 BestCost simulation results for 110 EVs

表4 不同算法求解110辆EV问题的结果比较 (with 110 EVs using different algorithms)

Table 4 Comparison of results in solving problems

算法	Tent	BestCost
IMRPC-PSO	是	10.8
MOPSO^[13]	否	11.4
CMOPSO^[10]	是	11.0
MMMOPSO^[7]	否	11.7
NSGA2^[14]	否	22.9
ABC^[15]	否	16.1

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