Efficient framework for energy management of microgrid installed in Aljouf region considering renewable energy and electric vehicles

This paper proposes an efficient one-to-one-based optimizer as a new energy management method for a gridconnected microgrid in order to address both environmental and economic concerns. The suggested approach is distinguished by its robust exploration capabilities that allow the technique to reach the global solution and avoid local ones, along with its ease of deployment. The microgrid under consideration consists of conventional resources, microturbine, fuel cell, storage batteries, and electric vehicles, as well as renewable energy sources like photovoltaic and wind turbine. Real-time 24-hour solar irradiance, wind speed, and air temperature data of Sakaka, Aljouf region in Saudi Arabia located at 29 degrees 58 ' 15.13 '' N latitude and 40 degrees 12 ' 18.03 '' E longitude are utilized while the stochastic natures of renewable resources have been modeled using Beta and Weibull probability distribution functions. Various scenarios of renewable resources' generations as well as electric vehicle's charging states are analyzed. A thorough comparison is made with the published krill herd optimizer, in addition to other programmed algorithms such as grey wolf optimizer, Runge Kutta optimization, salp swarm algorithm, hippopotamus optimization algorithm, and Newton Raphson based optimizer. Also, the suggested approach is validated statistically through the use of Kruskal Wallis, Friedman, ANOVA, and Wilcoxon rank tests. With renewable resources working normally, the recommended strategy outperformed the published krill herd optimizer in terms of operating cost savings and emission reductions, which were 53.85 % and 46.62 %, respectively. While during the rated operation of renewable resources, the net savings and emission reductions were 10.14 % and 38.91 %, respectively. Additionally, the greatest cost savings during connecting electric vehicles at smart charging mode was 55.69 % as compared to the published approach. The suggested strategy can be recommended as an effective method for managing microgrid energy.