Optimal Allocation of Solar-Wind based DG Considering Uncertainty Using Improved Grasshopper Algorithm

Abstract

Grasshopper optimization algorithm (GOA) is an efficient technique which mimics the movement orientation and lifestyle of grasshopper in natural. However, the GOA is applied for solve successfully numerous optimizations problem, it failed to solve other cases efficiently and it prone to stuck in local optima. Thus, an improved version of the GOA is proposed to enhance the performance the conventional GOA. The improved GOA (IGOA) is based on improving the exploration and the exploitation process of the GOA. The exploration is enhanced using a mutation operator to enable the algorithm to a new area to avoid the stagnation of this technique while the exploitation process is enhanced using an adaptive operator to update the positions of the grasshopper around the best so far solution. In this paper, the IGOA is applied to address the allocation problem of renewable energy resources in radial distribution grid (RDG). The renewable resources include hybrid solar wind based Distributed Generator (DG) units for the expected power loss minimization to consider the uncertainty in the electric system. The IGOA is tested on 85-bus distribution grid and the captured results are compared with the traditional GOA to verify its applicability and efficiency. Numerous scenarios are generated using Monte-Carlo simulation to take into consideration the uncertainties of system which include load demands, solar irradiation, and wind speed variations. The simulation results demonstrate that the IGOA is superior for addressing the allocation problem of DG compared with traditional GOA in terms of objective function.