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

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, DDPGαrep , 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
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https://hal.archives-ouvertes.fr/hal-03438233
Contributor : Inbar Fijalkow Connect in order to contact the contributor
Submitted on : Sunday, November 21, 2021 - 10:50:34 AM
Last modification on : Friday, April 1, 2022 - 3:47:33 AM
Long-term archiving on: : Tuesday, February 22, 2022 - 6:56:59 PM

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Louis Desportes, Inbar Fijalkow, Pierre Andry. Deep Reinforcement Learning for Hybrid Energy Storage Systems: Balancing Lead and Hydrogen Storage. Energies, MDPI, 2021, 14, ⟨10.3390/en14154706⟩. ⟨hal-03438233⟩

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