Optimization of Microgrid Operations Using Metaheuristic Algorithms

Abstract

The optimization of the work of microgrids (MGs) has become one of the most important issues of sustainable, cost-effective, and reliable power supply in the context of proliferation of distributed energy resources (DERs) and development of energy storage. The choice of approaches to the nonconvex, stochastic, multi-objective nature of the issues at hand in the day-ahead scheduling, real-time dispatch, economic dispatch, unit commitment, energy storage management, and resilience optimization related to micro grids has been to adopt metaheuristic algorithms (population-based and nature-inspired search strategies such as the Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), Ant Colony Optimization (ACO) and even The paper presents the literature review and synthesis of research on metaheuristic optimization of microgrid operations, considers comparative trends in performance, provides an insight into the advantages and limitations of existing popular algorithms, and suggests the directions of future research (hybridization, surrogate-assisted search, multi-objective formulations, and integration of machine learning in forecasting). The comparison of the algorithms is conducted on recent surveys and studies on the domain (since 2019 to 2024), and representative case studies, which provides the analysis in terms of minimization of cost, reduction of emissions, reliability, and efficiency of the computation. Suggestions to investigators and practitioners regarding the application of both metaheuristic-based energy management systems (EMS) in grid-connected and isolated microgrids.