Survey on AI and Machine Learning Techniques for Microgrid Energy Management Systems

doi:10.1109/JAS.2023.123657

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > IEEE/CAA Journal of Automatica Sinica

	Survey on AI and Machine Learning Techniques for Microgrid Energy Management Systems
	Aditya Joshi; Skieler Capezza; Ahmad Alhaji; Mo-Yuen Chow
刊名	IEEE/CAA Journal of Automatica Sinica
	2023
卷号	10 期号:7 页码:1513-1529
关键词	Consensus energy management system (EMS) reinforcement learning supervised learning
ISSN号	2329-9266
DOI	10.1109/JAS.2023.123657
英文摘要	In the era of an energy revolution, grid decentralization has emerged as a viable solution to meet the increasing global energy demand by incorporating renewables at the distributed level. Microgrids are considered a driving component for accelerating grid decentralization. To optimally utilize the available resources and address potential challenges, there is a need to have an intelligent and reliable energy management system (EMS) for the microgrid. The artificial intelligence field has the potential to address the problems in EMS and can provide resilient, efficient, reliable, and scalable solutions. This paper presents an overview of existing conventional and AI-based techniques for energy management systems in microgrids. We analyze EMS methods for centralized, decentralized, and distributed microgrids separately. Then, we summarize machine learning techniques such as ANNs, federated learning, LSTMs, RNNs, and reinforcement learning for EMS objectives such as economic dispatch, optimal power flow, and scheduling. With the incorporation of AI, microgrids can achieve greater performance efficiency and more reliability for managing a large number of energy resources. However, challenges such as data privacy, security, scalability, explainability, etc., need to be addressed. To conclude, the authors state the possible future research directions to explore AI-based EMS’s potential in real-world applications.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/51990]
专题	自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Aditya Joshi,Skieler Capezza,Ahmad Alhaji,et al. Survey on AI and Machine Learning Techniques for Microgrid Energy Management Systems[J]. IEEE/CAA Journal of Automatica Sinica,2023,10(7):1513-1529.
APA	Aditya Joshi,Skieler Capezza,Ahmad Alhaji,&Mo-Yuen Chow.(2023).Survey on AI and Machine Learning Techniques for Microgrid Energy Management Systems.IEEE/CAA Journal of Automatica Sinica,10(7),1513-1529.
MLA	Aditya Joshi,et al."Survey on AI and Machine Learning Techniques for Microgrid Energy Management Systems".IEEE/CAA Journal of Automatica Sinica 10.7(2023):1513-1529.