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Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning

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  • Harrold, Daniel J.B.
  • Cao, Jun
  • Fan, Zhong

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.

Suggested Citation

  • Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pc:s0360544221022064
    DOI: 10.1016/j.energy.2021.121958
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    1. Andresen, Gorm B. & Rodriguez, Rolando A. & Becker, Sarah & Greiner, Martin, 2014. "The potential for arbitrage of wind and solar surplus power in Denmark," Energy, Elsevier, vol. 76(C), pages 49-58.
    2. Díaz, Guzmán & Gómez-Aleixandre, Javier & Coto, José & Conejero, Olga, 2018. "Maximum income resulting from energy arbitrage by battery systems subject to cycle aging and price uncertainty from a dynamic programming perspective," Energy, Elsevier, vol. 156(C), pages 647-660.
    3. Richard Bellman, 1954. "Some Applications of the Theory of Dynamic Programming---A Review," Operations Research, INFORMS, vol. 2(3), pages 275-288, August.
    4. Kofinas, P. & Dounis, A.I. & Vouros, G.A., 2018. "Fuzzy Q-Learning for multi-agent decentralized energy management in microgrids," Applied Energy, Elsevier, vol. 219(C), pages 53-67.
    5. Pinto, Giuseppe & Piscitelli, Marco Savino & Vázquez-Canteli, José Ramón & Nagy, Zoltán & Capozzoli, Alfonso, 2021. "Coordinated energy management for a cluster of buildings through deep reinforcement learning," Energy, Elsevier, vol. 229(C).
    6. Totaro, Simone & Boukas, Ioannis & Jonsson, Anders & Cornélusse, Bertrand, 2021. "Lifelong control of off-grid microgrid with model-based reinforcement learning," Energy, Elsevier, vol. 232(C).
    7. Richard Bellman, 1954. "On some applications of the theory of dynamic programming to logistics," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 1(2), pages 141-153, June.
    8. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    9. Hansen, Kenneth & Breyer, Christian & Lund, Henrik, 2019. "Status and perspectives on 100% renewable energy systems," Energy, Elsevier, vol. 175(C), pages 471-480.
    10. Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    11. Vázquez-Canteli, José R. & Nagy, Zoltán, 2019. "Reinforcement learning for demand response: A review of algorithms and modeling techniques," Applied Energy, Elsevier, vol. 235(C), pages 1072-1089.
    12. Kuznetsova, Elizaveta & Li, Yan-Fu & Ruiz, Carlos & Zio, Enrico & Ault, Graham & Bell, Keith, 2013. "Reinforcement learning for microgrid energy management," Energy, Elsevier, vol. 59(C), pages 133-146.
    13. Carrillo, C. & Obando Montaño, A.F. & Cidrás, J. & Díaz-Dorado, E., 2013. "Review of power curve modelling for wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 572-581.
    14. Bogdanov, Dmitrii & Ram, Manish & Aghahosseini, Arman & Gulagi, Ashish & Oyewo, Ayobami Solomon & Child, Michael & Caldera, Upeksha & Sadovskaia, Kristina & Farfan, Javier & De Souza Noel Simas Barbos, 2021. "Low-cost renewable electricity as the key driver of the global energy transition towards sustainability," Energy, Elsevier, vol. 227(C).
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    7. Ruisheng Wang & Zhong Chen & Qiang Xing & Ziqi Zhang & Tian Zhang, 2022. "A Modified Rainbow-Based Deep Reinforcement Learning Method for Optimal Scheduling of Charging Station," Sustainability, MDPI, vol. 14(3), pages 1-14, February.
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