A review on the microgrid sizing and performance optimization by metaheuristic algorithms for energy management strategies

Abstract

A growing concern over climate change and the depletion of conventional energy resources have led to the urgent need for sustainable and resilient energy solutions. The optimization of the size of renewable sources is crucial to maximizing their effectiveness. In contrast to conventional single-objective optimization, the multi-objective technique aims to achieve a trade-off between energy cost and power supply reliability. Due to this need, microgrids (MG) have emerged as a promising paradigm, allowing for localized and decentralized energy generation and distribution. Consequently, the conventional techniques for modelling and optimizing exhibit numerous limitations as the power grid continues to produce substantial volumes of high-dimensional and diverse data types. This review paper examines the use of metaheuristic algorithms in the context of multi-objective energy optimization for hybrid renewable energy-integrated microgrids. A comparative analysis of diverse metaheuristic algorithms for microgrid optimization is provided in this paper, which emulates natural phenomena, such as evolutionary processes and swarm dynamics. Based on the findings of case studies, it can be concluded that trade-offs exist between various objectives, eventually leading to the development of both resilient and efficient microgrid designs. By reviewing sustainable energy solutions, and advocating microgrids as viable alternatives to conventional centralized power systems, the review enhances the advancement of sustainable energy solutions.