NDLI: A reinforcement learning algorithm based technique for thermal energy management of a PEM fuel cell power plant

Content Provider	IEEE Xplore Digital Library
Author	Chowdhury, S. El-sharkh, M.Y.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of South Alabama, Mobile, AL, USA (Chowdhury, S.; El-sharkh, M.Y.)
Abstract	In this paper, a reinforcement learning dynamic programming algorithm (RLDP) has been developed to manage the cogenerated thermal energy and the operation of a PEM fuel cell power plant. The solution methodology is based on selecting the optimal operational schedule of a fuel cell to minimize the total expected cost of generation incurred during a specified schedule period. The optimal operation schedule of the PEM fuel cell power plant is based on estimating the hourly generated electrical power, electrical energy trade with a local utility, and the recovered thermal energy utilization and storage based on electrical and thermal demand. The proposed technique is based on the Q-learning algorithm. At the training stage, the reinforcement learning (RL) is scheduled to explore and learn the Q values using the epsilon greedy policy, and after that, decision making is done using the learned Q values. The proposed algorithm is tested using a 24-hour based electrical and thermal load, the obtained results indicate the viability of the proposed approach.
Starting Page	1
Ending Page	8
File Size	756533
Page Count	8
File Format	PDF
e-ISBN	9781479983940
DOI	10.1109/IAS.2015.7356834
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Protons Learning systems Fuel cell cost-optimized approach Dynamic Programming Fuel cells Reinforcement learning Proton exchange membrane fuel cell Dynamic programming Energy management
Content Type	Text
Resource Type	Article

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