On Machine Learning-Based Techniques for Future Sustainable and Resilient Energy Systems

被引：36

作者：

Wang, Jiawei ^{[1
]}

Pinson, Pierre ^{[2
]}

Chatzivasileiadis, Spyros ^{[1
]}

Panteli, Mathaios ^{[3
]}

Strbac, Goran ^{[4
]}

Terzija, Vladimir ^{[5
]}

机构：

[1] Tech Univ Denmark, Dept Wind & Energy Syst, DK-2800 Lyngby, Denmark

[2] Imperial Coll London, Dyson Sch Design Engn, London SW7 2AZ, England

[3] Univ Cyprus, Dept Elect & Comp Engn, CY-1678 Nicosia, Cyprus

[4] Imperial Coll London, Dept Elect & Elect Engn, London SW7 2AZ, England

[5] Skolkovo Inst Sci & Technol, Ctr Energy Sci & Technol, Moscow 121205, Russia

来源：

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY | 2023年 / 14卷 / 02期

基金：

英国工程与自然科学研究理事会; 欧盟地平线“2020”; 欧洲研究理事会;

关键词：

Resilience; Power system stability; Stability analysis; Security; Power systems; Power system reliability; Power system dynamics; Extreme events; machine learning; multi-energy systems; resilience; sustainable energy systems; FREQUENCY STABILITY ASSESSMENT; POWER; SECURITY; EXTREME; RELIABILITY; OPERATION; INERTIA; RISK;

D O I：

10.1109/TSTE.2022.3194728

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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.

引用

页码：1230 / 1243

页数：14

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