Smart microgrid energy management using a novel artificial shark optimization
type: Item
instance of: scholarly article
title: Smart microgrid energy management using a novel artificial shark optimization
zbMATH Open document ID: 1377.93119
DOI: 10.1155/2017/2158926
author: Peng Zhang
published in: Complexity
publication date: 2018-01-05T00:00:00Z
review text: Summary: 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 proposes 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.
Mathematics Subject Classification ID: 93C95
Mathematics Subject Classification ID: 49N90
Mathematics Subject Classification ID: 91B76
Mathematics Subject Classification ID: 93B40
zbMATH DE Number: 6822303
zbMATH Keywords: renewable energy sources (RESs) integration
zbMATH Keywords: microgrid (MG) technology
zbMATH Keywords: microgrid optimization
zbMATH Keywords: artificial shark optimization (ASO)
zbMATH Keywords: battery energy storage (BES)
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/2158926
OpenAlex ID: W2761507194
cites work: Self-adaptive learning based particle swarm optimization
cites work: A study of particle swarm optimization particle trajectories
description: scientific article; zbMATH DE number 6822303