A Novel Technique for the Optimization of Energy Cost Management and Operation of Microgrids Inspired from the Behavior of Egyptian Stray Dogs

Abstract

Managing costs in microgrids presents a formidable challenge due to the intricate blend of renewable and non-renewable energy sources that underpin their power generation. Ensuring seamless integration of microgrids with the national grid is pivotal for continuous load demand satisfaction and adherence to liberalized energy market mandates. To address this challenge, this paper introduces a new optimization technique for the Cost Management and Operation System (CMOS) of multi-source microgrids through a smart management unit. The cornerstone of this unit is the Egyptian Stray Dog Optimization (ESDO) algorithm, meticulously designed to optimize operational costs in line with load demands, energy cost dynamics, and generation proficiencies. Rigorous testing of the proposed system was conducted on a multi-resource microgrid using MATLAB, encompassing various operational scenarios. The simulation outcomes consistently highlighted the unit’s capability to achieve optimal cost-efficiency. Comparative analysis with other optimization techniques, particularly Particle Swarm Optimization (PSO), demonstrated the superior performance of the Egyptian Stray Dog algorithm, underscoring its potential as a leading solution in this domain.