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

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 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.

Combined Economic Emission Dispatch in A Grid -Connected Microgrid Using An Improved Mayfly Algorithm. Keywords:

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