@article{Wang:2023:10.1109/tste.2022.3194728,
author = {Wang, J and Pinson, P and Chatzivasileiadis, S and Panteli, M and Strbac, G and Terzija, V},
doi = {10.1109/tste.2022.3194728},
journal = {IEEE Transactions on Sustainable Energy},
pages = {1230--1243},
title = {On machine learning-based techniques for future sustainable and resilient energy systems},
url = {http://dx.doi.org/10.1109/tste.2022.3194728},
volume = {14},
year = {2023}
}

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TY  - JOUR
AB  - 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.
AU  - Wang,J
AU  - Pinson,P
AU  - Chatzivasileiadis,S
AU  - Panteli,M
AU  - Strbac,G
AU  - Terzija,V
DO  - 10.1109/tste.2022.3194728
EP  - 1243
PY  - 2023///
SN  - 1949-3029
SP  - 1230
TI  - On machine learning-based techniques for future sustainable and resilient energy systems
T2  - IEEE Transactions on Sustainable Energy
UR  - http://dx.doi.org/10.1109/tste.2022.3194728
UR  - http://hdl.handle.net/10044/1/104817
VL  - 14
ER  - 

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