Nicholas Prah II - Department of Electrical and Electronic Engineering, College of Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
Elvis Twumasi - Department of Electrical and Electronic Engineering, College of Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
Emmanuel Frimpong - Department of Electrical and Electronic Engineering, College of Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

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 was modelled 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 was compared to Particle Swarm Optimisation (PSO), an MA variant that incorporates levy flight algorithm named IMA and Dragonfly Algorithm (DA) using the MATLAB R۲۰۲۱a software. The MIE-MA achieved the best optimum cost of ۱۱۳۰۶.۶ /MWh, compared to ۱۲۲۷۸.۰ , ۱۲۸۷۵.۸, and ۱۷۱۴۶.۴ of the DA, IMA and PSO respectively. The MIE-MA also achieved the best average optimum cost over ۲۰ runs of ۱۲۱۶۳.۴۸ , compared to ۱۲۵۵۵.۳۶ , ۱۳۴۱۹.۶۷ and ۱۷۲۷۰.۰۸ 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.

MIE-MA, mayfly algorithm, Swarm Intelligence, economic dispatch