Multi-Agent Deep Reinforcement Learning for Distributed Operation and Control of Microgrids

Dear Colleagues,

It is our pleasure to invite submissions to the Special Issue on “Multi-agent Deep Reinforcement Learning for Distributed Operation and Control of Microgrids”.

With the increasing global concern over the crisis of energy and the environment, microgrids are gaining popularity in modern power systems due to allowing extensive utilization of distributed energy resources (DERs). However, due to the variety of owners of DERs, it is impossible to directly control and operate those DERs from a central authority. Distributed energy management and strategy-making frameworks are more appropriate for future microgrids. Besides, the integration of various DERs with high randomness also introduces challenges to traditional model-based management approaches. Recently, the applications of the multi-agent system and deep reinforcement learning have attracted much attention for developing the distributed operation and control frameworks as well as handling uncertainty factors. In this Special Issue, we are looking for novel methods, algorithms, and technologies using multi-agent deep reinforcement learning to enhance energy efficiency for distributed operation and control of microgrids. Review and survey articles on the following topics are also encouraging for submission.

Topics of interest for publication include, but are not limited to:

• Applications of artificial intelligence in distributed operation and control of microgrids
• Decentralized, and distributed operation and control of microgrids
• Energy management systems for microgrids
• Integration of renewables and EVs in microgrids
• Multiagent systems for microgrids
• Operation and control strategies with distributed energy storage systems
• Peer-to-Peer energy trading in a microgrids
• Power quality enhanced operation of distributed microgrids
• Resilience enhancement through/for microgrids

Dr. Van-Hai Bui
Dr. Akhtar Hussain
Dr. Wencong Su
Guest Editors

Manuscript Submission Information

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