Optimizing Microgrid Management with Intelligent Planning: A Chaos Theory-Based Salp Swarm Algorithm for Renewable Energy Integration and Demand Response

Abstract

This paper presents a novel intelligent planning approach to optimize microgrid management with multiple random renewable energy sources. The key contribution is a developed slap algorithm enhanced with chaos theory to prevent local optima and premature convergence. The system incorporates various components—photovoltaic units, wind turbines, fuel cells, micro-turbines, energy storage, electrolysis—and accounts for smart home participation in energy demand response. Using a scenario-based method, it models uncertainties like wind speed, solar radiation, electricity demand, and price. The paper compares batteries and hydrogen storage tanks as energy storage options and validates the algorithm's effectiveness through four cases evaluating hydrogen storage and demand response. Findings demonstrate significant economic benefits and performance improvements in microgrid management by integrating hydrogen storage and load response programs. The study evaluates four cases, comparing systems with and without demand response (DR) and hydrogen storage. The results show that integrating DR and hydrogen storage reduces costs by 12.4% and 23.4%, respectively, compared to the reference model. The paper also presents a comparative analysis of battery and hydrogen storage, highlighting the efficiency and economic benefits of hybrid storage systems. By incorporating stochastic modeling and multi-objective optimization, the proposed approach enhances energy efficiency, reliability, and cost-effectiveness.