Tackling Renewable Energy Intermittency in Hydrogen-Based Solar Energy System Control Using DRL-Reference-Cited by-同舟云学术

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Tackling Renewable Energy Intermittency in Hydrogen-Based Solar Energy System Control Using DRL

Published:2023-11-13 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Green Energy and Smart Systems Conference (IGESSC)
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Author:

Dos Santos Luiz F. G.¹,Zhao Tianxia²,Xiao Fu³,Pappas Iosif³,Demirhan C. Doga⁴,Michler Christian⁵,Gao Hao¹,Ling Meng¹

Affiliation:

1. Shell Global Solutions (US) Inc,Deep Learning & AI,Houston,USA

2. Shell International Exploration and Production Inc,Deep Learning & AI,Houston,USA

3. Shell Global Solutions International B.V,Energy Systems & Pathfinding,Amsterdam,Netherlands

4. Shell International Exploration and Production Inc,Energy Systems & Pathfinding,Houston,USA

5. Shell Global Solutions International B.V,Deep Reinforcement Learning,Amsterdam,Netherlands

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10321700/10321745/10321749.pdf?arnumber=10321749

Reference19 articles.

1. A coordinated control method for hybrid energy storage system in microgrid based on deep reinforcement learning;zhang;Power System Technology,2019

2. Fuzzy Q-Learning for multi-agent decentralized energy management in microgrids

3. Mean Field Game Guided Deep Reinforcement Learning for Task Placement in Cooperative Multiaccess Edge Computing

4. Policy gradient methods for reinforcement learning with function approximation;sutton;Advances in neural information processing systems,1999

5. Deep reinforcement learning for energy management in a microgrid with flexible demand

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