Optimized Energy Management in Hybrid Microgrid Systems using Enhanced Meta-Heuristic Algorithms

Abstract

Energy management in renewable energy systems is a complex, multiobjective challenge that requires the rapid and robust dispatch of electrical power from various generation assets. This critical process of controlling the components within an energy system is essential for maintaining efficiency and operational effectiveness. In addressing these challenges, this paper presents the application of Particle Swarm Optimization (PSO), a biologically inspired direct search method, to achieve real-time optimal energy management solutions in a standalone hybrid system consisting of wind turbines, microturbines, and Photovoltaic (PV) panels. The inherent complexity of managing such a diverse energy system arises from balancing multiple conflicting objectives, including minimizing the cost of generated electricity, maximizing the operational efficiency of Microturbines (MT), and minimizing charges from the utility. The PSO algorithm, known for its efficacy in handling extensive solution spaces and its ability to converge towards optimal solutions efficiently, is adapted to navigate this multiobjective, multiconstraint problem effectively.