On machine learning-based techniques for future sustainable and resilient energy systems
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
Permanently increasing penetration of converter-interfaced generation and renewable energy sources (RESs) makes modern electrical power systems more vulnerable to low probability and high impact events, such as extreme weather, which could lead to severe contingencies, even blackouts. These contingencies can be further propagated to neighboring energy systems over coupling components/technologies and consequently negatively influence the entire multi-energy system (MES) (such as gas, heating and electricity) operation and its resilience. In recent years, machine learning-based techniques (MLBTs) have been intensively applied to solve various power system problems, including system planning, or security and reliability assessment. This paper aims to review MES resilience quantification methods and the application of MLBTs to assess the resilience level of future sustainable energy systems. The open research questions are identified and discussed, whereas the future research directions are identified.
Date Issued
2023-04
Date Acceptance
2022-07-15
Citation
IEEE Transactions on Sustainable Energy, 2023, 14 (2), pp.1230-1243
ISSN
1949-3029
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Start Page
1230
End Page
1243
Journal / Book Title
IEEE Transactions on Sustainable Energy
Volume
14
Issue
2
Copyright Statement
Copyright © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
http://dx.doi.org/10.1109/tste.2022.3194728
Publication Status
Published
Date Publish Online
2022-07-28