Journal of Applied Research in Electrical Engineering

Journal of Applied Research in Electrical Engineering

Combined Economic Emission Dispatch in A Grid-Connected Microgrid Using an Improved Mayfly Algorithm

Document Type : Research article

Authors
Nicholas Kwesi Prah II
Elvis Twumasi
Emmanuel Asuming Frimpong
Department of Electrical and Electronic Engineering, College of Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
10.22055/jaree.2023.43803.1070
Abstract
The Combined Economic Emission Dispatch (CEED) is an important consideration in every power system. In this paper, a modified Mayfly Algorithm named Modified Individual Experience Mayfly Algorithm (MIE-MA) is used to solve the CEED optimization problem. The modified algorithm enhances the balance between exploration and exploitation by utilizing a chaotic decreasing gravity coefficient. Additionally, instead of the MA relying solely on the best position, it calculates the experience of a mayfly by averaging its positions. The CEED problem is modeled as a nonlinear optimization problem constrained with four equality and inequality constraints and tested on a grid-connected microgrid that consists of four dispatchable distributed generators and two renewable energy sources. The performance of the MIE-MA on the CEED problem is compared to Particle Swarm Optimisation (PSO), an MA variant that incorporates a levy flight algorithm named IMA and Dragonfly Algorithm (DA) using the MATLAB R2021a software. The MIE-MA achieved the best optimum cost of 11306.6 $/MWh, compared to 12278.0 $, 12875.8$, and 17146.4$ of the DA, IMA, and PSO respectively. The MIE-MA also achieved the best average optimum cost over 20 runs of 12163.48 $, compared to 12555.36 $, 13419.67 $, and 17270.08 $ of the DA, IMA, and PSO respectively. The hourly cost curve of the MIE-MA was also the best compared to the other algorithms. The MIE-MA algorithm thus achieves superior optimal values with fewer iterations.

Highlights

  • Enhancement of the mayfly algorithm by modifying its individual experience. Individual experience is now average of positions instead of individual best position.
  • Adoption of the chaotic random decreasing inertia weight strategy to the Mayfly Algorithm.
  • Application of the enhanced mayfly algorithm to a Combined Economic Emission Dispatch problem in a grid-connected micro grid.
  • Enhanced mayfly algorithm outperformed Particle Swarm Optimisation, Dragonfly Algorithm and a variant of the Mayfly Algorithm in achieving optimum cost and other statistical tests.

Keywords
MIE-MA
Mayfly algorithm
Swarm intelligence
Economic dispatch
Subjects
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Journal of Applied Research in Electrical Engineering
Volume 2, Issue 2
Summer and Autumn 2023
July 2023
Pages 112-119

  • Receive Date 22 May 2023
  • Revise Date 12 July 2023
  • Accept Date 02 August 2023