Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review

By Di Cao, Weihao Hu, Junbo Zhao, Zhang Guozhou, Bin Zhang, Zhou Liu, Zhe Chen, Frede Blaabjerg. Journal of Modern Power Systems and Clean Energy, 2020

356 citations recorded by the source corpus.

With the growing integration of distributed energy resources (DERs), flexible loads, and other emerging technologies, there are increasing complexities and uncertainties for modern power and energy systems. This brings great challenges to the operation and control. Besides, with the deployment of advanced sensor and smart meters, a large number of data are generated, which brings opportunities for novel data-driven methods to deal with complicated operation and control issues. Among them, reinforcement learning (RL) is one of the most widely promoted methods for control and optimization problems. This paper provides a comprehensive literature review of RL in terms of basic ideas, various types of algorithms, and their applications in power and energy systems. The challenges and further works are also discussed.

Subfields: Artificial Intelligence, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/9a8b32c211666e4614a1d35e8e6cd7bf6025db89

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In Artificial Intelligence

1. B. Bak‐Jensen
2. Yuelong Wang
3. Jayakrishnan Radhakrishna Pillai
4. Yusen Wang
5. Wenlong Liao

Citations in the corpus

• A Review of Graph Neural Networks and Their Applications in Power Systems (2021, background): “The recent combinations of RL and DNNs in Eu‐ clidean domains and their application in power systems are critically reviewed in [15], [16].”