Grass Hopper Optimization Algorithm for Off-Grid Rural Electrification of an Integrated Renewable Energy System

Abstract

A suitable alternative to grid expansion has been found in renewable energy sources like wind, solar, and biomass. To put it another way, relying solely on one of the major renewable sources is both inefficient and expensive. As a result, an integrated renewable energy system is a viable option. The purpose of this article is to discuss the use of the Grasshopper Optimization Algorithm (GOA) for renewable energy sizing in the current study area. For an autonomous microgrid network, the proposed technique finds the optimum system size on the basis of Loss of Power Supply Probability (LPSP). The proposed microgrid consists of PV panels, wind turbines, biomass generator and a battery storage system. The proposed GOA algorithm’s convergence efficiency in resolving the current optimization problem is investigated and compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in the MATLAB software environment. The simulation results show that the GOA algorithm outperforms its counterparts, GA and PSO, in terms of system sizing.