{"title":"Intelligent load shifting strategy for economic operation of islanded microgrid system","authors":"Swarupa Pinninti, Srinivasa Rao Sura","doi":"10.1109/iSSSC56467.2022.10051433","DOIUrl":null,"url":null,"abstract":"This work employs a robust and efficient teaching learning-based optimization (TLBO) to optimally schedule the DGs of a low voltage (LV) off-grid microgrid (MG) system in order to lower the active power production cost of the systems. The topic test systems' cost-effective fitness functions considers efficiency of the distributed energy resources (DERs) that constitutes the MG system. Photovoltaic (PV) systems and wind turbines were selected to share load demand, and the load profiles of various systems were evaluated. The numerical and graphical findings show that TLBO is beneficial in minimizing the generation cost of test systems, surpassing a large list of techniques available in the literature. Furthermore, a strategic demand side management (DSM) technique was implemented to restructure the load demand shifting the elastic loads to reduce the peak demand without altering the daily load demand. The new restructured load demand furthered reduced the generation cost of the system. Non-parametric statistical analysis, and processing time all testify to TLBO's exceptional efficiency in dealing with any dimensions test system.","PeriodicalId":334645,"journal":{"name":"2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC)","volume":"303 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/iSSSC56467.2022.10051433","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
This work employs a robust and efficient teaching learning-based optimization (TLBO) to optimally schedule the DGs of a low voltage (LV) off-grid microgrid (MG) system in order to lower the active power production cost of the systems. The topic test systems' cost-effective fitness functions considers efficiency of the distributed energy resources (DERs) that constitutes the MG system. Photovoltaic (PV) systems and wind turbines were selected to share load demand, and the load profiles of various systems were evaluated. The numerical and graphical findings show that TLBO is beneficial in minimizing the generation cost of test systems, surpassing a large list of techniques available in the literature. Furthermore, a strategic demand side management (DSM) technique was implemented to restructure the load demand shifting the elastic loads to reduce the peak demand without altering the daily load demand. The new restructured load demand furthered reduced the generation cost of the system. Non-parametric statistical analysis, and processing time all testify to TLBO's exceptional efficiency in dealing with any dimensions test system.