Improved gray wolf optimization algorithm for solving placement and sizing of electrical energy storage system in micro-grids

Abstract

Micro-grids consist of distributed power generation systems (DGs), distributed energy storage devices (DSs), and loads. Micro-grids are small-scale networks at low voltage levels that are use to provide thermal and electrical loads of small locations where there is no access to the main electrical grid. Given the environmental and economic issues for these areas, micro-grids can be a good solution for energy production. In this paper, determining the size and location of optimal electrical energy storage systems is presented. In other side, a new method based on the cost benefit analysis for optimal sizing of an energy storage system in a microgrid (MG) is proposed. The uncertainties associated with renewable energy sources and the occurrence of defects in the grid connection network and the effect of the contribution of load responses in a micro-grid are taken into account. The combined system consists of wind turbines and fuel cells. Basically, wind power is not definitively available. The new proposed method is based on two-stage randomization design (TSRD) for modeling the effect of wind power uncertainty so that the predicted wind energy error is considered as the main random parameter in the model. A standard probability distribution function is used to represent the error variations. Given the continuity of the mentioned function, the probability error function is extracted using the new discrete method and a certain number of scenarios with a certain probability. Finally, the problem has been transformed into an optimization problem, and a gray wolf optimization (GWO) algorithm has been used to solve it. In the proposed developed model based on local and global search, the algorithm tries to reach the final result in the shortest possible time and with the most precision. The results of the simulation show the efficiency of the proposed method in solving the micro-grid problem.