NDLI: Optimal management of energy storage system based on reinforcement learning

Content Provider	IEEE Xplore Digital Library
Author	Liu Jing Tang Hao Matsui, M. Takanokura, M. Zhou Lei Gao Xueying
Copyright Year	2014
Description	Author affiliation: Kanagawa Univ., Yokohama, Japan (Matsui, M.; Takanokura, M.) \|\| Sch. of Electr. Eng. & Autom., Hefei Univ. of Technol., Hefei, China (Tang Hao; Gao Xueying) \|\| Sch. of Comput. & Inf., Hefei Univ. of Technol., Hefei, China (Liu Jing; Zhou Lei)
Abstract	Energy storage system consists of distributed generation, storage device, loads and some intelligent control devices in the smart grid. It enables energy flow from the storage device to the grid. An amount of balancing energy is procured to meet the load demand when there is a deficit in power generation. The excessive distributed generation power of storage device can either be sold to the grid or be used to provide frequency regulation service. Real-time pricing techniques would greatly influence the system control center in deciding when to sell power, buy power or provide regulation service. The power of distributed generation, load demand, electricity price and the frequency regulation price are independent of each other. Each of the four stochastic processes is modeled as a Markov process to reflect the dynamic characteristics. The optimal control problem of deciding when to sell power, buy power or provide regulation service is formulated as a semi-Markov decision process. The Sarsa algorithm is used to adapt the control operation in order to maximize the long-term rewards on the basis of meeting the load demand. Simulation results show a significant increase of total rewards, a faster convergence speed and good effect with the proposed algorithm.
Starting Page	8216
Ending Page	8221
File Size	324059
Page Count	6
File Format	PDF
ISBN	9789881563873
ISSN	19341768
DOI	10.1109/ChiCC.2014.6896376
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	TCCT, CAA
Subject Keyword	smart grid distributed generation Electricity semi-Markov decision process Markov processes Educational institutions Batteries Distributed power generation Sarsa Frequency control
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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