Optimization of electric vehicle charging facility layout considering the enhancement of renewable energy consumption capacity and improvement of PSO algorithm
{"title":"Optimization of electric vehicle charging facility layout considering the enhancement of renewable energy consumption capacity and improvement of PSO algorithm","authors":"Di Zheng, Baobao Zheng","doi":"10.1186/s42162-025-00514-8","DOIUrl":null,"url":null,"abstract":"<div><p>By arranging the charging facilities of electric vehicles reasonably, electric vehicle users can be guided to charge during the peak period of renewable energy generation, improving their ability to consume this energy. To layout electric vehicle charging facilities, a single charging station optimization configuration model is constructed to provide optimal configuration parameter references for subsequent charging facility layout optimization models. In the optimization model, the study considers charging load calculation, site selection, and capacity determination. To deal with the optimization model, the particle swarm optimization is adopted and improved in three aspects. These three improvements include randomly updating inertia weights, introducing acceleration factors to replace learning factors, and introducing fast non-dominated sorting for better or worse selection, and improving the optimization ability of the algorithm by solving the crowding distance. The results showed that the maximum function values of the designed algorithm were 3.56 × 10<sup>–14</sup>, 5.32 × 10<sup>0</sup>, and 1.08 × 10<sup>1</sup> for unimodal, multimodal, and composite functions, respectively, and the standard deviations of the algorithm were 2.01 × 10<sup>–14</sup>, 3.557 × 10<sup>0</sup>, and 8.56 × 10<sup>–1</sup>, all of which were smaller than comparison algorithms. In a single charging station, the expected values of photovoltaic power generation, energy storage system, and charging piles were 500 kW, 56.45 kW/20163 kW, and 680 kW, respectively. In terms of charging station location and charging facility capacity, there should be 7 charging locations and charging facilities. In summary, the designed model has good performance, and the optimized model can layout charging facilities. The research results can better promote the consumption of renewable energy, lower the construction cost, and optimize the utilization rate of charging facilities.</p></div>","PeriodicalId":538,"journal":{"name":"Energy Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://energyinformatics.springeropen.com/counter/pdf/10.1186/s42162-025-00514-8","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy Informatics","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1186/s42162-025-00514-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Energy","Score":null,"Total":0}
引用次数: 0
Abstract
By arranging the charging facilities of electric vehicles reasonably, electric vehicle users can be guided to charge during the peak period of renewable energy generation, improving their ability to consume this energy. To layout electric vehicle charging facilities, a single charging station optimization configuration model is constructed to provide optimal configuration parameter references for subsequent charging facility layout optimization models. In the optimization model, the study considers charging load calculation, site selection, and capacity determination. To deal with the optimization model, the particle swarm optimization is adopted and improved in three aspects. These three improvements include randomly updating inertia weights, introducing acceleration factors to replace learning factors, and introducing fast non-dominated sorting for better or worse selection, and improving the optimization ability of the algorithm by solving the crowding distance. The results showed that the maximum function values of the designed algorithm were 3.56 × 10–14, 5.32 × 100, and 1.08 × 101 for unimodal, multimodal, and composite functions, respectively, and the standard deviations of the algorithm were 2.01 × 10–14, 3.557 × 100, and 8.56 × 10–1, all of which were smaller than comparison algorithms. In a single charging station, the expected values of photovoltaic power generation, energy storage system, and charging piles were 500 kW, 56.45 kW/20163 kW, and 680 kW, respectively. In terms of charging station location and charging facility capacity, there should be 7 charging locations and charging facilities. In summary, the designed model has good performance, and the optimized model can layout charging facilities. The research results can better promote the consumption of renewable energy, lower the construction cost, and optimize the utilization rate of charging facilities.