Обзорная статья

Economic Model Predictive Control for Microgrid Optimization: A Review

Jiefeng HuCentre for New Energy Transition Research, Federation University Australia, Mount Helen, VIC, AustraliaYinghao ShanCollege of Information Science and Technology, Donghua University, Shanghai, ChinaYong YangSchool of Rail Transportation, Soochow University, Suzhou, ChinaAlessandra ParisioSchool of Electrical and Electronic Engineering, University of Manchester, Manchester, U.KYong LiSchool of Electrical Engineering, Southwest Jiaotong University, Chengdu, ChinaNima AmjadyCentre for New Energy Transition Research, Federation University Australia, Mount Helen, VIC, AustraliaSyed IslamCentre for New Energy Transition Research, Federation University Australia, Mount Helen, VIC, AustraliaK.W.E. ChengDepartment of Electrical Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong KongJosep M. GuerreroInstitute of Energy Technology, Aalborg University, Aalborg, DenmarkJosé RodríguezFaculty of Engineering, Universidad San Sebastian, Santiago, Chile

2023en

ABI

Аннотация

Microgrids have emerged as a promising solution to integrate distributed energy resources (DERs) and supply reliable and efficient electricity. The operation of a microgrid involves the coordination of different DERs and loads. To date, various control methods have been developed to maximize the overall benefit while satisfying various constraints. Now it is urgently needed to understand and comprehend these approaches to further stimulate the deployment of microgrids. This paper presents an overview for researchers on economic model predictive control (EMPC) methods of microgrids to achieve a variety of objectives such as cost minimization and benefit maximization. The fundamental principle of the EMPC theory is explained in detail. The most popular and important strategies applied to stand-alone microgrids, grid-connected microgrids, residential smart homes, as well as networked microgrids are discussed. Future trends are also highlighted.

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Идентификаторы

DOI: 10.1109/tsg.2023.3266253

Цитирования и источники

Цитирований: 2Использованных источников: 0

Показатели — AkademScholar