Multi-Agent Reinforcement Learning for Decentralized Resilient Secondary Control of Energy Storage Systems Against DoS Attacks
Author:
Affiliation:
1. Department of Electronics, Carleton University, Ottawa, ON, Canada
2. Department of Automation and the Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, China
Funder
NSERC Discovery Grant
National Natural Science Funds of China
Zhejiang Provincial Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Computer Science
Link
http://xplorestaging.ieee.org/ielx7/5165411/9761268/09676705.pdf?arnumber=9676705
Reference40 articles.
1. Risk-Aware Energy Scheduling for Edge Computing With Microgrid: A Multi-Agent Deep Reinforcement Learning Approach
2. Multi-agent actor-critic for mixed cooperative-competitive environments;lowe;Advances in neural information processing systems,2017
3. Distributed State-of-Charge Balance Control With Event-Triggered Signal Transmissions for Multiple Energy Storage Systems in Smart Grid
4. Distributed Secondary and Optimal Active Power Sharing Control for Islanded Microgrids With Communication Delays
5. Distributed Secondary Control for Islanded Microgrids—A Novel Approach
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