Power
Nicholas Kwesi Prah II; Elvis Twumasi; Emmanuel Asuming Frimpong
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 ...
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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.