Implementation of Modified Grey Wolf Optimizer for Multi-Objective Economic Dispatch of Renewable-Integrated Grid

Aliyu Sabo, Ibrahim Danlami Akabe, Titilayo Oyedele, Mathew Asih, Juliana Emamuyovwi Omatseye

Abstract


The global push towards sustainable energy has accelerated the integration of renewable sources into power grids, fundamentally complicating the Economic Load Dispatch (ELD) problem. Traditional optimization methods are often inadequate for handling the non-convex, non-linear, and multi-objective nature of modern power systems that incorporate wind, solar, and stringent environmental regulations. This study presents a Modified Grey Wolf Optimizer (MGWO) algorithm designed to solve the Multi-Objective Economic Dispatch (MOED) problem for a renewable-integrated grid. The proposed MGWO enhances the standard GWO by introducing adaptive control coefficients and a chaotic randomization factor to improve the balance between global exploration and local exploitation, thereby preventing premature convergence. A comprehensive MATLAB simulation was developed, incorporating valve-point loading effects, emission constraints, and the stochastic nature of solar and wind power. The algorithm's performance was rigorously evaluated on the IEEE 30-bus test system and benchmarked against Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and the conventional Lambda Iteration method. Results demonstrate that the MGWO achieves a superior convergence rate, attaining the lowest total generation cost of $1033.22/h, compared to $1061.59/h for PSO and $1085.48/h for both GA and Lambda Iteration. Furthermore, the MGWO exhibited robust performance in navigating the complex solution space, proving its effectiveness for cost-efficient and environmentally conscious power system operation. This framework presents a viable tool for real-time dispatch applications in future smart grids. 


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