Paper Info
Title
Smart Microgrid Energy Management Using a Novel Artificial Shark Optimization.
Abstract
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.
Year
DOI
Venue
2017
10.1155/2017/2158926
COMPLEXITY
Field
DocType
Volume
Battery energy storage,Energy management,Renewable energy,Electric vehicle,Control theory,Simulation,Smart microgrid,Optimization problem,Grid,Mathematics,Microgrid,Distributed computing
Journal
2017
ISSN
Citations 
PageRank 
1076-2787
4
0.46
References 
Authors
6
2
Name
Order
Citations
PageRank
Pawan Singh151.49
Baseem Khan284.93