A systematic review of metaheuristic algorithms in electric power systems optimization

Electric power system applications are intricate optimization problems. Most literature reviews focus on study-ing an electrical paradigm through different optimization techniques. Since no review targets Metaheuristics (MHs) in electric power system applications, our work provides a general panorama of the paradigms that underlay such applications: Renewable Energies, Load Forecasting, Power Flow, Microgrids and Smart grids, and Power Quality. Our analysis revealed that the most employed MHs are Particle Swarm Optimization, Gray Wolf Optimizer, Genetic Algorithms, Cuckoo Search, and Differential Evolution. Historically, MHs have been classified into metaphor-based and non-metaphor-based. However, in some cases, this categorization does not correspond to pure MH procedures. Therefore, we also analyze MHs from a more formal perspective: their search operators. Moreover, we detected that the Renewable Energies paradigm presents a strong synergy with the remaining ones. Plus, there is a significant interest in topics related to Load-Forecasting optimization problems. Based on these data, we provide helpful recommendations for the current challenges and potential research paths. In doing so, our insights can support researchers and practitioners interested in furthering this field.