Multi-agent Deep Reinforcement Learning for Distributed Energy Management and Strategy Optimization of Microgrid Market

Xiaohan Fang; Qiang Zhao; Jinkuan Wang; Yinghua Han; Yuchun Li

doi:10.1016/j.scs.2021.103163

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JOURNAL ARTICLE

Multi-agent Deep Reinforcement Learning for Distributed Energy Management and Strategy Optimization of Microgrid Market

Xiaohan Fang Qiang Zhao Jinkuan Wang Yinghua Han Yuchun Li

Year: 2021 Journal: Sustainable Cities and Society Vol: 74 Pages: 103163-103163 Publisher: Elsevier BV

DOI: 10.1016/j.scs.2021.103163

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Keywords:

Microgrid Reinforcement learning Computer science Randomness Distributed generation Energy management Energy market Independence (probability theory) Operations research Mathematical optimization Distributed computing Risk analysis (engineering) Energy (signal processing) Electricity Engineering Control (management) Artificial intelligence Business Renewable energy

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8.10

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0.98

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Topics

Microgrid Control and Optimization

Physical Sciences → Engineering → Control and Systems Engineering

Smart Grid Energy Management

Physical Sciences → Engineering → Electrical and Electronic Engineering

Electric Vehicles and Infrastructure

Physical Sciences → Engineering → Electrical and Electronic Engineering

Multi-agent Deep Reinforcement Learning for Distributed Energy Management and Strategy Optimization of Microgrid Market

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Citation History

Topics

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