Enhancing optimal sizing of stand-alone hybrid systems with energy storage considering techno-economic criteria based on a modified artificial rabbits optimizer

Abstract

This paper examines and analyses a novel developed algorithm named Modified Artificial Rabbits Optimization (mARO), which is based on the modification of a bio-inspired meta-heuristic algorithm called Artificial Rabbits Optimization (ARO) combined with dimension learning-based hunting technique. In order to prove the efficiency and evaluate the constrained optimization of this modified algorithm mARO, it is applied to one of an engineering application. This engineering application is the study of the optimal sizing of a stand-alone hybrid system based on techno-economic criteria; this hybrid system consists of the PV, WT, Biomass system, and Battery units. The simulation of hybrid power systems must be carried out with the least amount of expense and harm to the environment so appropriate performance may be ensured using an efficient and optimal sizing strategy. In order to prove the superiority of this modified algorithm mARO, the results of this algorithm were compared with other new algorithms, and these algorithms are the original ARO, Dandelion Optimizer (DO), and Driving Training-Based Optimization (DTOB).