 Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learningDaniel J.B. Harrold, Jun Cao and Zhong Fan Energy, 2022, vol. 238, issue PC Abstract: As the world seeks to become more sustainable, intelligent solutions are needed to increase the penetration of renewable energy. In this paper, the model-free deep reinforcement learning algorithm Rainbow Deep Q-Networks is used to control a battery in a microgrid to perform energy arbitrage and more efficiently utilise solar and wind energy sources. The grid operates with its own demand and renewable generation, as well as dynamic energy pricing from a real wholesale energy market. Four scenarios are tested including using demand and price forecasting produced with local weather data. The algorithm and its subcomponents are evaluated against an actor-critic method and a linear programming model with Rainbow able to outperform all other methods. This research shows the importance of using the distributional approach for reinforcement learning for complex environments, as well as how it can visualise and contextualise the agents’ behaviour for real-world applications. Keywords: Actor-critic methods; Deep Q-Networks; Demand response; Microgrids; Renewable energy (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:238:y:2022:i:pc:s0360544221022064 DOI: 10.1016/j.energy.2021.121958 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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