Energy management of microgrid for off-grid communities: a metaheuristic optimization approach

Abstract

This research, Energy Management of Microgrids for Off-Grid Communities: A Metaheuristic Optimization Approach, advances engineering optimization by developing and applying two novel metaheuristic algorithms. The first, Lévy Arithmetic Algorithm, enhances the Arithmetic Optimization Algorithm by incorporating Lévy random steps. This modification mitigates the limitations of linear search strategies, preventing premature convergence and stagnation while improving global search efficiency. Evaluations using ten IEEE CEC 2019 benchmark functions and applications to real-world problems, such as economic load dispatch in renewable-integrated microgrids, show that the Lévy Arithmetic Algorithm consistently achieves optimal solutions with fewer function evaluations, yielding lower means and standard deviations than the Arithmetic Optimization Algorithm. The second contribution, Jackal Arithmetic Algorithm, is a hybrid method that combines, Arithmetic Optimization Algorithm, arithmetic-based exploration-exploitation strategies, with the adaptive foraging mechanisms of the Golden Jackal Optimization algorithm. This synergy enhances search balance, leading to faster and more accurate convergence. Jackal Arithmetic Algorithm was rigorously tested on ten benchmark functions and applied to diverse engineering problems, including solar photovoltaic model parameter extraction, robot arm positioning, and various design optimizations. The proposed algorithms are further applied to the energy management of a hybrid solar-wind-diesel microgrid for off-grid communities. This study optimizes economic and emission dispatch, minimizing operational costs while reducing carbon emissions. The capacity planning framework evaluates scenarios from diesel-only systems to hybrid solar-wind-diesel configurations. Comparative analyses against established metaheuristic methods, including Arithmetic Optimization Algorithm, Crow Search Algorithm, Hybrid Modified Grey Wolf Algorithm, Interior Search Algorithm, Cuckoo Search Algorithm, Particle Swarm Optimization, and Genetic Algorithm, demonstrate that both Lévy Arithmetic Algorithm and Jackal Arithmetic Algorithm outperform these techniques in convergence speed, robustness, and accuracy. Overall, this research advances metaheuristic optimization for hybrid microgrid energy management, improving solution accuracy, computational efficiency, and sustainability. The findings establish a scalable and adaptable optimization framework, offering practical solutions for off-grid communities and setting a foundation for future developments in renewable energy optimization.