Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning

To reduce global greenhouse gas emissions, the world must find intelligent solutions to maximise the utilisation of carbon-free renewable energy sources. In this paper, multi-agent reinforcement learning is used to control a microgrid in a mixed cooperative and competitive setting. The agents observ...

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Bibliographic Details
Published in:	Applied energy Vol. 318; p. 119151
Main Authors:	Harrold, Daniel J.B., Cao, Jun, Fan, Zhong
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 15.07.2022
Subjects:	algorithms Deep deterministic policy gradients energy Energy arbitrage game theory greenhouse gases Hybrid energy storage systems issues and policy Multi-agent systems Renewable energy renewable energy sources Deep deterministic policy gradients Renewable energy Energy arbitrage Hybrid energy storage systems Multi-agent systems
ISSN:	0306-2619, 1872-9118
Online Access:	Get full text
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