NDLI: Reinforcement learning-based control of residential energy storage systems for electric bill minimization

Content Provider	IEEE Xplore Digital Library
Author	Chenxiao Guan Wang, Y. Xue Lin Nazarian, S. Pedram, M.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA (Chenxiao Guan; Wang, Y.; Xue Lin; Nazarian, S.; Pedram, M.)
Abstract	Incorporating residential-level photovoltaic energy generation and energy storage systems have proved useful in utilizing renewable power and reducing electric bills for the residential energy consumer. This is particular true under dynamic energy prices, where consumers can use PV-based generation and controllable storage modules for peak shaving on their power demand profile from the grid. In general, accurate PV power generation and load power consumption predictions and accurate system modeling are required for the storage control algorithm in most previous works. In this work, the reinforcement learning technique is adopted for deriving the optimal control policy for the residential energy storage module, which does not depend on accurate predictions of future PV power generation and/or load power consumption results and only requires partial knowledge of system modeling. In order to achieve higher convergence rate and higher performance in non-Markovian environment, we employ the TD(Λ)-learning algorithm to derive the optimal energy storage system control policy, and carefully define the state and action spaces, and reward function in the TD(Λ)-learning algorithm such that the objective of the reinforcement learning algorithm coincides with our goal of electric bill minimization for the residential consumer. Simulation results over real-world PV power generation and load power consumption profiles demonstrate that the proposed reinforcement learning-based storage control algorithm can achieve up to 59.8% improvement in energy cost reduction.
Starting Page	637
Ending Page	642
File Size	975040
Page Count	6
File Format	PDF
ISSN	23319860
e-ISBN	9781479963904
DOI	10.1109/CCNC.2015.7158054
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Batteries Power demand Power generation Learning (artificial intelligence) Smart grids Partial discharges
Content Type	Text
Resource Type	Article

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