Deep Reinforcement Learning for Hybrid Energy Storage Systems: Balancing Lead and Hydrogen Storage

Louis Desportes, Inbar Fijalkow and Pierre Andry
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Louis Desportes: Equipes Traitement de l’Information et Systèmes, UMR 8051, National Center for Scientific Research, ENSEA, CY Cergy Paris University, 95000 Cergy-Pontoise, France
Inbar Fijalkow: Equipes Traitement de l’Information et Systèmes, UMR 8051, National Center for Scientific Research, ENSEA, CY Cergy Paris University, 95000 Cergy-Pontoise, France
Pierre Andry: Equipes Traitement de l’Information et Systèmes, UMR 8051, National Center for Scientific Research, ENSEA, CY Cergy Paris University, 95000 Cergy-Pontoise, France

Energies, 2021, vol. 14, issue 15, 1-22

Abstract: We address the control of a hybrid energy storage system composed of a lead battery and hydrogen storage. Powered by photovoltaic panels, it feeds a partially islanded building. We aim to minimize building carbon emissions over a long-term period while ensuring that 35% of the building consumption is powered using energy produced on site. To achieve this long-term goal, we propose to learn a control policy as a function of the building and of the storage state using a Deep Reinforcement Learning approach. We reformulate the problem to reduce the action space dimension to one. This highly improves the proposed approach performance. Given the reformulation, we propose a new algorithm, DDPG α rep , using a Deep Deterministic Policy Gradient (DDPG) to learn the policy. Once learned, the storage control is performed using this policy. Simulations show that the higher the hydrogen storage efficiency, the more effective the learning.

Keywords: deep reinforcement learning; hybrid energy storage system; smart building (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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