An Integrated Energy Storage System Based on Hydrogen Storage

Author(s):  
Dan Gao ◽  
Dongfang Jiang ◽  
Naiqiang Zhang
Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4706
Author(s):  
Louis Desportes ◽  
Inbar Fijalkow ◽  
Pierre Andry

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.


Energy ◽  
2014 ◽  
Vol 66 ◽  
pp. 332-341 ◽  
Author(s):  
Dan Gao ◽  
Dongfang Jiang ◽  
Pei Liu ◽  
Zheng Li ◽  
Sangao Hu ◽  
...  

2016 ◽  
Vol 136 (11) ◽  
pp. 824-832 ◽  
Author(s):  
Mami Mizutani ◽  
Takenori Kobayashi ◽  
Katsunori Watabe ◽  
Tomoki Wada

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