Hybrid Grey Wolf and Cuckoo Search Optimization for Optimal Energy Management in Microgrid Systems

N Chinna Alluraiah; Pujari Harish Kumar; Reshma Gopi R; P Vijayapriya

doi:10.5935/jetia.v12i59.3426

Authors

N Chinna Alluraiah Department of Electrical and Electronics Engineering, Annamacharya University, Rajampet, Annamayya, India. http://orcid.org/0000-0003-0722-6523 (unauthenticated)
Pujari Harish Kumar Department of Electrical and Electronics Engineering, Alliance School of Applied Engineering, Alliance University, Bengaluru, Karnataka, India http://orcid.org/0000-0002-6580-9906 (unauthenticated)
Reshma Gopi R Department of Electronics and Communication Engineering, East Point College of Engineering and Technology, Bengaluru, Karnataka, India. http://orcid.org/0000-0003-2061-9161 (unauthenticated)
P Vijayapriya School of Electrical Engineering, Department of Electrical Engineering, VIT University, Vellore-632014, Tamilnadu, India. http://orcid.org/0000-0002-5893-213X (unauthenticated)

DOI:

https://doi.org/10.5935/jetia.v12i59.3426

Abstract

Microgrid (MG) technology has the potential to effectively incorporate a new method to produce renewable energy on a massive scale for the grid-connected system. An Energy Management System is necessary for a microgrid to make the most efficient use of its distributed energy resources in ways that are also intelligent, protected, consistent, and synchronised. In this work, the management of battery energy storage system that is connected to a solar photovoltaic array and wind turbine system in a microgrid, as well as the control of battery charge and discharge operations using a converter based on the demand for the energy has been carried out. The strategy suggested was the daily energy management of a MG system by implementing hybrid grey wolf and cuckoo search optimization (HGWCSO) algorithm for reducing the total variable cost of energy. The output variables of each component of microgrid such as solar PV, wind turbine, grid, energy storage system as well as load power graphs. Energy storage system state of charge graphs is analysed at each instance of time and the variable cost of energy for a day were determined by using proposed algorithm. The proposed HGWCSO algorithm provided the overall system cost of 8.03%, which is less than the value obtained by heuristic approach.