{"title":"Cost-Effective Peak Shaving Strategy Based on Clustering and XGBoost Algorithm","authors":"Sol Lim, Rahma Gantassi, Yonghoon Choi","doi":"10.1109/ICAIIC57133.2023.10067091","DOIUrl":null,"url":null,"abstract":"In a cost-effective peak shaving strategy, clustering and machine learning algorithm can be used to set optimal peak shaving time zone for each load. Energy Storage System (ESS) charge amount is determined with load prediction data through machine learning model, and the peak shaving time zone is adjusted flexibly according to load patterns for each cluster. It is possible to prevent ESS from being overcharged or undercharged through load prediction. In addition, rather than applying peak shaving collectively at the on-peak time, efficient operation of the power grid can be expected by adjusting the time zone flexibly for each power usage pattern. The effectiveness of the proposed system model is to be proved through changes in electricity cost depending on whether it is introduced or not.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAIIC57133.2023.10067091","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 a cost-effective peak shaving strategy, clustering and machine learning algorithm can be used to set optimal peak shaving time zone for each load. Energy Storage System (ESS) charge amount is determined with load prediction data through machine learning model, and the peak shaving time zone is adjusted flexibly according to load patterns for each cluster. It is possible to prevent ESS from being overcharged or undercharged through load prediction. In addition, rather than applying peak shaving collectively at the on-peak time, efficient operation of the power grid can be expected by adjusting the time zone flexibly for each power usage pattern. The effectiveness of the proposed system model is to be proved through changes in electricity cost depending on whether it is introduced or not.