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◦ 58′ 15.13″N latitude and 40◦ 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.