{"title":"分时电价下家用电动汽车最优充电计划","authors":"Ruoyun Hu, Qi Ding, Qingjuan Wang, Ran Shen, Yifan Wang, Taoyi Qi","doi":"10.1109/CEEPE55110.2022.9783267","DOIUrl":null,"url":null,"abstract":"With the development of the Electric Vehicle (EV), the charging demand increases rapidly. To release the electricity supply pressure, time of use price is implemented in some cities to transfer the charging demand of household EVs from peak period to valley period. However, plenty of EVs choose to charge intensively at the beginning of valley period, which causes a new load peak and wastes the potential of peak shaving and valley filling. To address the problem, this paper proposed the optimal charging scheduling strategy for household EVs under time of use price. Firstly, the charging model and process of the EV are developed to describe the various charging demands accurately. Subsequently, EVs with optimization potential are screened to improve the efficiency of time scheduling. In order to shorten the peak-valley difference of the residential load, the charging periods of EVs are optimized utilizing the genetic algorithm. Finally, based on the actual data of residential load and EVs, the time scheduling simulation is studied to show the optimization performance. By making full use of the peak-shaving and valley-filling capacity of EVs, the simulation results proved the effectiveness of the proposed method on charging time scheduling, the peak load caused by centralized charging demands decreased. Besides, the residential power during the peak period is effectively reduced and the power during the valley period is improved.","PeriodicalId":118143,"journal":{"name":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimal Charging Scheduling for Household Electric Vehicles under TOU Prices\",\"authors\":\"Ruoyun Hu, Qi Ding, Qingjuan Wang, Ran Shen, Yifan Wang, Taoyi Qi\",\"doi\":\"10.1109/CEEPE55110.2022.9783267\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the development of the Electric Vehicle (EV), the charging demand increases rapidly. To release the electricity supply pressure, time of use price is implemented in some cities to transfer the charging demand of household EVs from peak period to valley period. However, plenty of EVs choose to charge intensively at the beginning of valley period, which causes a new load peak and wastes the potential of peak shaving and valley filling. To address the problem, this paper proposed the optimal charging scheduling strategy for household EVs under time of use price. Firstly, the charging model and process of the EV are developed to describe the various charging demands accurately. Subsequently, EVs with optimization potential are screened to improve the efficiency of time scheduling. In order to shorten the peak-valley difference of the residential load, the charging periods of EVs are optimized utilizing the genetic algorithm. Finally, based on the actual data of residential load and EVs, the time scheduling simulation is studied to show the optimization performance. By making full use of the peak-shaving and valley-filling capacity of EVs, the simulation results proved the effectiveness of the proposed method on charging time scheduling, the peak load caused by centralized charging demands decreased. Besides, the residential power during the peak period is effectively reduced and the power during the valley period is improved.\",\"PeriodicalId\":118143,\"journal\":{\"name\":\"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)\",\"volume\":\"43 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-04-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEEPE55110.2022.9783267\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 5th International Conference on Energy, Electrical and Power Engineering (CEEPE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEEPE55110.2022.9783267","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimal Charging Scheduling for Household Electric Vehicles under TOU Prices
With the development of the Electric Vehicle (EV), the charging demand increases rapidly. To release the electricity supply pressure, time of use price is implemented in some cities to transfer the charging demand of household EVs from peak period to valley period. However, plenty of EVs choose to charge intensively at the beginning of valley period, which causes a new load peak and wastes the potential of peak shaving and valley filling. To address the problem, this paper proposed the optimal charging scheduling strategy for household EVs under time of use price. Firstly, the charging model and process of the EV are developed to describe the various charging demands accurately. Subsequently, EVs with optimization potential are screened to improve the efficiency of time scheduling. In order to shorten the peak-valley difference of the residential load, the charging periods of EVs are optimized utilizing the genetic algorithm. Finally, based on the actual data of residential load and EVs, the time scheduling simulation is studied to show the optimization performance. By making full use of the peak-shaving and valley-filling capacity of EVs, the simulation results proved the effectiveness of the proposed method on charging time scheduling, the peak load caused by centralized charging demands decreased. Besides, the residential power during the peak period is effectively reduced and the power during the valley period is improved.