Research Article Smart Microgrid Energy Management Using a Novel Artificial Shark Optimization Pawan Singh 1 and Baseem Khan 2 1 School of Informatics, Hawassa University Institute of Technology, Hawassa, Ethiopia 2 School of Electrical & Computer Engineering, Hawassa University Institute of Technology, Hawassa, Ethiopia Correspondence should be addressed to Baseem Khan; baseem.khan04@gmail.com 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. his 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. he 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 eiciently. his paper proposed an artiicial shark optimization (ASO) method to remove the limitation of existing algorithms for solving the economical operation problem of MG. he 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 beneits. However, all these beneits are limited to a certain ixed area due to the stationary nature of the BES system. he latest technologies, such as electric vehicle technologies (EVTs), provide all beneits of BES along with mobility to support the variable system demands. herefore, 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. 1. Introduction As the world is transforming from the conventional grid system to the smart grid system, renewable energy sources’ integration has become a vital issue in the current situation. he intermittency and climate dependency of RESs make their integration more complex and diicult. A microgrid (MG) ofers an eicient way to incorporate distributed RESs in large electric power systems for supplying the continually growing demand. he smart microgrid (SMG) consist of RESs (especially wind turbine (WT) and solar photovoltaic (SPV)), BESs, electric vehicle technologies (EVTs), and elec- trical demands. BESs and EVTs have a bidirectional battery charging system as well as automatic sensors for detecting over- or undergeneration. SMG coalesced with RESs, BESs, and EVTs is a preferable alternative to manage the increased power demand and is a supplement to the centralized smart power grids [1]. Recently, there are rising issues and concerns regarding the instability and discontinuity of RESs in the MG. herefore, the MG central operator (MGCO) recommends the incorporation of BES in the MG for accumulating surplus power and feedback to the MG during the peak load. Further, the latest EVTs (battery electric vehicle (BEV) and plug-in hybrid vehicle (PHEV)) along with BES play a vital role to store excess power during high availability. he advantages of EVTs are their mobility and ability to supply the stored power intheenergydeicientareasduringpeakhours. herefore, the computation of the suitable capacity of BES, BEV, and PHEV is highly essential for an economized operation of SMG. Currently, the attention is shited towards more eicient fuel cell technologies (FCTs), such as automotive fuel cell electric vehicle (FCEV) and stationary FC power generator (FCPG) [2, 3], due to their numerous advantages (less CO2 emissions, extremely less noise and vibrations). FCEV and FCPG have a lower maintenance cost, as they consist of fewer rotating parts. Additionally, FCTs do not require recharging, Hindawi Complexity Volume 2017, Article ID 2158926, 22 pages https://doi.org/10.1155/2017/2158926