Ahmad Niknami , Mohammad Tolou Askari , Meysam Amir Ahmadi , Majid Babaei Nik , Mahmoud Samiei Moghaddam
{"title":"Resilient day-ahead microgrid energy management with uncertain demand, EVs, storage, and renewables","authors":"Ahmad Niknami , Mohammad Tolou Askari , Meysam Amir Ahmadi , Majid Babaei Nik , Mahmoud Samiei Moghaddam","doi":"10.1016/j.clet.2024.100763","DOIUrl":null,"url":null,"abstract":"<div><p>Managing microgrid energy presents a complex challenge due to unpredictable renewable sources, fluctuating demand, and diverse equipment like batteries, distributed generators, and electric vehicles. This paper introduces a novel two-step optimization model, the Robust Day-Ahead Scheduling for Enhanced Resilience, tailored for microgrid operations. The model addresses the integration of electronic generation, uncertain demand patterns, and small-scale renewable resources. Detailed formulations optimize microgrid energy use, including strategic battery usage, efficient electric vehicle charging, balancing device utilization, and distributed generation dispatch. This multi-faceted approach aims to minimize costs over 24 h, including energy loss, power purchases, reduced power usage, generator operation, and battery/EV expenses. Employing a column-and-constraint generation (C&CG) algorithm ensures efficient problem solving. The proposed model achieved a significant reduction in operational costs, outperforming existing methods by at least 8%. Notably, it minimized energy purchases, energy losses, and load shedding while improving voltage stability, showcasing its effectiveness in enhancing microgrid performance and resilience.</p></div>","PeriodicalId":34618,"journal":{"name":"Cleaner Engineering and Technology","volume":"20 ","pages":"Article 100763"},"PeriodicalIF":5.3000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666790824000430/pdfft?md5=078f8a297c6e6e48752c444a4d30b391&pid=1-s2.0-S2666790824000430-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cleaner Engineering and Technology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666790824000430","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Managing microgrid energy presents a complex challenge due to unpredictable renewable sources, fluctuating demand, and diverse equipment like batteries, distributed generators, and electric vehicles. This paper introduces a novel two-step optimization model, the Robust Day-Ahead Scheduling for Enhanced Resilience, tailored for microgrid operations. The model addresses the integration of electronic generation, uncertain demand patterns, and small-scale renewable resources. Detailed formulations optimize microgrid energy use, including strategic battery usage, efficient electric vehicle charging, balancing device utilization, and distributed generation dispatch. This multi-faceted approach aims to minimize costs over 24 h, including energy loss, power purchases, reduced power usage, generator operation, and battery/EV expenses. Employing a column-and-constraint generation (C&CG) algorithm ensures efficient problem solving. The proposed model achieved a significant reduction in operational costs, outperforming existing methods by at least 8%. Notably, it minimized energy purchases, energy losses, and load shedding while improving voltage stability, showcasing its effectiveness in enhancing microgrid performance and resilience.