Yang Zhang, Yunfei Ma, Shuang Zhang, Lixian Chen, Hongda Liu
{"title":"Building-level Demand-side Energy Management Based on Game Theory","authors":"Yang Zhang, Yunfei Ma, Shuang Zhang, Lixian Chen, Hongda Liu","doi":"10.1109/ICMA54519.2022.9856118","DOIUrl":null,"url":null,"abstract":"In this paper, an improved building-level demand-side management method is proposed based on load classification and real-time electricity pricing. Firstly, the loads are classified into uncontrollable loads, interruptible loads and transferable loads. The uncontrollable loads are forecasted as a part of the system state. The interruptible loads are controlled all the time. The transferable loads can be managed to work in proper periods in a day. Then, the user’s electricity consumption is managed by the energy consumption controller, which solves a game problem between the user and the others. At last, we generate users’ electricity consumption conditions using the Monte Carlo method and simulate the proposed managed system. Proved by experiments, the proposed method effectively reduces both the system cost and the users’ payment, and improves the temporal distribution of the system load.","PeriodicalId":120073,"journal":{"name":"2022 IEEE International Conference on Mechatronics and Automation (ICMA)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Mechatronics and Automation (ICMA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMA54519.2022.9856118","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, an improved building-level demand-side management method is proposed based on load classification and real-time electricity pricing. Firstly, the loads are classified into uncontrollable loads, interruptible loads and transferable loads. The uncontrollable loads are forecasted as a part of the system state. The interruptible loads are controlled all the time. The transferable loads can be managed to work in proper periods in a day. Then, the user’s electricity consumption is managed by the energy consumption controller, which solves a game problem between the user and the others. At last, we generate users’ electricity consumption conditions using the Monte Carlo method and simulate the proposed managed system. Proved by experiments, the proposed method effectively reduces both the system cost and the users’ payment, and improves the temporal distribution of the system load.