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Volume 2017, Article ID 2158926, 22 pages
Research Article

Smart Microgrid Energy Management Using a Novel Artificial Shark Optimization

1School of Informatics, Hawassa University Institute of Technology, Hawassa, Ethiopia
2School of Electrical & Computer Engineering, Hawassa University Institute of Technology, Hawassa, Ethiopia

Correspondence should be addressed to Baseem Khan; moc.liamg@40nahk.meesab

Received 2 April 2017; Revised 17 June 2017; Accepted 27 June 2017; Published 8 October 2017

Academic Editor: Roberto Natella

Copyright © 2017 Pawan Singh and Baseem Khan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


At present, renewable energy sources (RESs) integration using microgrid (MG) technology is of great importance for demand side management. Optimization of MG provides enhanced generation from RES at minimum operation cost. The microgrid optimization problem involves a large number of variables and constraints; therefore, it is complex in nature and various existing algorithms are unable to handle them efficiently. This paper proposed an artificial shark optimization (ASO) method to remove the limitation of existing algorithms for solving the economical operation problem of MG. The ASO algorithm is motivated by the sound sensing capability of sharks, which they use for hunting. Further, the intermittent nature of renewable energy sources is managed by utilizing battery energy storage (BES). BES has several benefits. However, all these benefits are limited to a certain fixed area due to the stationary nature of the BES system. The latest technologies, such as electric vehicle technologies (EVTs), provide all benefits of BES along with mobility to support the variable system demands. Therefore, in this work, EVTs incorporated grid connected smart microgrid (SMG) system is introduced. Additionally, a comparative study is provided, which shows that the ASO performs relatively better than the existing techniques.