期刊论文详细信息
Energies | |
Deep Reinforcement Learning for Hybrid Energy Storage Systems: Balancing Lead and Hydrogen Storage | |
Inbar Fijalkow1  Louis Desportes1  Pierre Andry1  | |
[1]Equipes Traitement de l’Information et Systèmes, UMR 8051, National Center for Scientific Research, ENSEA, CY Cergy Paris University, 95000 Cergy-Pontoise, France | |
关键词: deep reinforcement learning; hybrid energy storage system; smart building; | |
DOI : 10.3390/en14154706 | |
来源: DOAJ |
【 摘 要 】
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【 授权许可】
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