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

微电网 可扩展性 计算机科学 能源管理 分布式计算 强化学习 能源管理系统 智能电网 分布式发电 调度(生产过程) 网格 权力下放 可再生能源 风险分析(工程) 人工智能 工程类 能量(信号处理) 数据库 控制(管理) 几何学 数学 法学 政治学 电气工程 统计 运营管理 医学
作者
Aditya Joshi,Skieler Capezza,Ahmad Alhaji,Mo–Yuen Chow
出处
期刊:IEEE/CAA Journal of Automatica Sinica [Institute of Electrical and Electronics Engineers]
卷期号:10 (7): 1513-1529 被引量:21
标识
DOI:10.1109/jas.2023.123657
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.
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