A. S. Bedi, Md. Waseem Ahmad, K. Rajawat, S. Anand
{"title":"Optimal utilization of storage systems under real-time pricing","authors":"A. S. Bedi, Md. Waseem Ahmad, K. Rajawat, S. Anand","doi":"10.1109/ICCW.2017.7962812","DOIUrl":null,"url":null,"abstract":"This paper considers the problem of optimal battery usage under real-time pricing scenarios. The problem is formulated as a finite-horizon optimization problem, and solved via an incremental algorithm that is provably optimal in the long run. The proposed approach gives rise to a class of algorithms that utilize the battery state-of-charge to make usage decisions in real-time. The proposed algorithm is simple to implement, easy to modify for a variety use cases, and outperform the state-of-the-art technique such as Markov Decision Process (MDP) based. The robustness and flexibility of the proposed algorithm is tested extensively via numerical studies.","PeriodicalId":6656,"journal":{"name":"2017 IEEE International Conference on Communications Workshops (ICC Workshops)","volume":"73 1","pages":"1141-1146"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Communications Workshops (ICC Workshops)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCW.2017.7962812","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
This paper considers the problem of optimal battery usage under real-time pricing scenarios. The problem is formulated as a finite-horizon optimization problem, and solved via an incremental algorithm that is provably optimal in the long run. The proposed approach gives rise to a class of algorithms that utilize the battery state-of-charge to make usage decisions in real-time. The proposed algorithm is simple to implement, easy to modify for a variety use cases, and outperform the state-of-the-art technique such as Markov Decision Process (MDP) based. The robustness and flexibility of the proposed algorithm is tested extensively via numerical studies.