A temporal and spatial electric vehicle charging optimization scheme with DSO-EVA coordination framework
Burdens of increasing penetration of electric vehicles (EVs) on distribution systems have attracted wide attention to the research of EV charging coordination. Nevertheless, existing coordination lacks the joint consideration of the temporal-spatial flexibility of EVs, which could reduce load fluctu...
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Elsevier
2024-02-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061523008189 |
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| author | Tingting Xiao Yonggang Peng Chunyu Chen |
| author_facet | Tingting Xiao Yonggang Peng Chunyu Chen |
| author_sort | Tingting Xiao |
| collection | DOAJ |
| description | Burdens of increasing penetration of electric vehicles (EVs) on distribution systems have attracted wide attention to the research of EV charging coordination. Nevertheless, existing coordination lacks the joint consideration of the temporal-spatial flexibility of EVs, which could reduce load fluctuations when meeting the EV charging demands. Moreover, a thorough investigation is required for the coordination of the benefits to the distribution system operator (DSO), EV aggregator (EVA), and EVs. In this paper, a novel EV charging scheme considering the temporal-spatial features of EVs is proposed, achieving the coordination of DSO, EVA, and EVs in two steps. First, a mixed-integer second-order cone programming (MISOCP)-based EV charging schedule is developed for DSO. Second, the temporal-spatial features of EVs are exploited by EVA to track the charging schedule. Specifically, a dynamic charging price mechanism based on active power margin is proposed for public charging facilities and a specific load regulation is designed for private charging facilities. On this basis, the modified adaptive multi-objective particle swarm optimization (AMOPSO) algorithm is proposed, including adaptive flight parameter adjustment and termination mechanisms. Case studies demonstrate the proposed strategy can attenuate load variance and raise EVA revenue. Further, the impact analysis of penalty price and the price elasticity of electricity demand can provide references for stable distribution network operation, higher EVA revenue, and charging cost reduction. |
| format | Article |
| id | doaj-art-699171ea8fc94e3396a9168d7ccb48fe |
| institution | Kabale University |
| issn | 0142-0615 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-699171ea8fc94e3396a9168d7ccb48fe2024-11-25T04:40:44ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152024-02-01156109761A temporal and spatial electric vehicle charging optimization scheme with DSO-EVA coordination frameworkTingting Xiao0Yonggang Peng1Chunyu Chen2Polytechnic Institute, Zhejiang University, Hangzhou 310015, ChinaCollege of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; Corresponding author.School of Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaBurdens of increasing penetration of electric vehicles (EVs) on distribution systems have attracted wide attention to the research of EV charging coordination. Nevertheless, existing coordination lacks the joint consideration of the temporal-spatial flexibility of EVs, which could reduce load fluctuations when meeting the EV charging demands. Moreover, a thorough investigation is required for the coordination of the benefits to the distribution system operator (DSO), EV aggregator (EVA), and EVs. In this paper, a novel EV charging scheme considering the temporal-spatial features of EVs is proposed, achieving the coordination of DSO, EVA, and EVs in two steps. First, a mixed-integer second-order cone programming (MISOCP)-based EV charging schedule is developed for DSO. Second, the temporal-spatial features of EVs are exploited by EVA to track the charging schedule. Specifically, a dynamic charging price mechanism based on active power margin is proposed for public charging facilities and a specific load regulation is designed for private charging facilities. On this basis, the modified adaptive multi-objective particle swarm optimization (AMOPSO) algorithm is proposed, including adaptive flight parameter adjustment and termination mechanisms. Case studies demonstrate the proposed strategy can attenuate load variance and raise EVA revenue. Further, the impact analysis of penalty price and the price elasticity of electricity demand can provide references for stable distribution network operation, higher EVA revenue, and charging cost reduction.http://www.sciencedirect.com/science/article/pii/S0142061523008189Demand responseParticle swarm optimizationTemporal-spatial featureMixed-integer second-order cone programmingElectric vehicle aggregator |
| spellingShingle | Tingting Xiao Yonggang Peng Chunyu Chen A temporal and spatial electric vehicle charging optimization scheme with DSO-EVA coordination framework International Journal of Electrical Power & Energy Systems Demand response Particle swarm optimization Temporal-spatial feature Mixed-integer second-order cone programming Electric vehicle aggregator |
| title | A temporal and spatial electric vehicle charging optimization scheme with DSO-EVA coordination framework |
| title_full | A temporal and spatial electric vehicle charging optimization scheme with DSO-EVA coordination framework |
| title_fullStr | A temporal and spatial electric vehicle charging optimization scheme with DSO-EVA coordination framework |
| title_full_unstemmed | A temporal and spatial electric vehicle charging optimization scheme with DSO-EVA coordination framework |
| title_short | A temporal and spatial electric vehicle charging optimization scheme with DSO-EVA coordination framework |
| title_sort | temporal and spatial electric vehicle charging optimization scheme with dso eva coordination framework |
| topic | Demand response Particle swarm optimization Temporal-spatial feature Mixed-integer second-order cone programming Electric vehicle aggregator |
| url | http://www.sciencedirect.com/science/article/pii/S0142061523008189 |
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