{"title":"Optimal scheduling of electric vehicle using dual decomposition","authors":"Jinfeng Yang, Zaiyue Yang","doi":"10.1109/CCDC.2014.6852522","DOIUrl":null,"url":null,"abstract":"This paper concentrates on the optimal scheduling of electric vehicle (EV). The EV is scheduled for both operating stage and non-operating stage. With full consideration of operating income, regulation revenue and the owner's habit, the cost minimization problem is formulated as a convex programming with coupling constrains. Dual decomposition is utilized to obtain the global optimal solution. Then, a modified online approach is introduced to alleviate the impact of price prediction error. The simulation reveals that our algorithms can reduce the cost to a large extent. Furthermore, it is demonstrated that the online scheduling scheme can achieve a similar performance compared with the optimal scheduling scheme having full knowledge of real time prices (RTP).","PeriodicalId":380818,"journal":{"name":"The 26th Chinese Control and Decision Conference (2014 CCDC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 26th Chinese Control and Decision Conference (2014 CCDC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCDC.2014.6852522","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
This paper concentrates on the optimal scheduling of electric vehicle (EV). The EV is scheduled for both operating stage and non-operating stage. With full consideration of operating income, regulation revenue and the owner's habit, the cost minimization problem is formulated as a convex programming with coupling constrains. Dual decomposition is utilized to obtain the global optimal solution. Then, a modified online approach is introduced to alleviate the impact of price prediction error. The simulation reveals that our algorithms can reduce the cost to a large extent. Furthermore, it is demonstrated that the online scheduling scheme can achieve a similar performance compared with the optimal scheduling scheme having full knowledge of real time prices (RTP).