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     Research Journal of Applied Science, Engineering and Technology


A Fast Algorithm for Large-Scale MDP-Based Systems in Smart Grid

1Hua Xiao, 1Huaizong Shao, 1Fan Yang, 1, 2Yingjie Zhou and 1Qicong Peng
1School of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu, China
2Department of Electrical Engineering, Columbia University, New York, USA
Research Journal of Applied Science, Engineering and Technology  2013  1:213-217
http://dx.doi.org/10.19026/rjaset.5.5107  |  © The Author(s) 2013
Received: May 24, 2012  |  Accepted: June 21, 2012  |  Published: January 01, 2013

Abstract

In this study, we investigate the fast algorithms for the Large-Scale Markov Decision Process (LSMDP) problem in smart gird. Markov decision process is one of the efficient mathematical tools to solve the control and optimization problems in wireless smart grid systems. However, the complexity and the memory requirements exponentially increase when the number of system state grows in. Moreover, the limited computational ability and small size of memory on board constraint the application of wireless smart grid systems. As a result, it is impractical to implement those LSMDP-based approaches in such systems. Therefore, we propose the fast algorithm with low computational overhead and good performance in this study. We first derive the factored MDP representation, which substitutes LSMDP in a compact way. Based on the factored MDP, we propose the fast algorithm, which considerably reduces the size of state space and remains reasonable performance compared to the optimal solution.

Keywords:

Factored MDP, fast algorithm, large-scale MDP, smart grid, wireless communication,


References


Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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