A hybrid approach based energy management of hybrid energy systems with efficient economic model

Abstract

ABSTRACT This paper proposes a hybrid AHO-SNN-based energy management (EM) of hybrid energy systems (HES) with an efficient economic model. The proposed hybrid method is combined with the Archerfish Hunting Optimizer (AHO) and Spiking Neural Network (SNN). Commonly, it is named as AHO-SNN technique. The major aim of the AHO-SNN method is the efficient economical modeling of HES and the study of sizing and EM. The comprehensive approach for describing the ideal size EM system (EMS) combination for HES is presented in this paper. The HESs, like fuel cell (FC), electrolyzer (EL), PV, battery energy system (BES), diesel generator (DSL), and hydrogen tank (HT). To determine the ideal size-EMS combination, this new EMS is employed to re-use the analytical as well as economic sizing. The AHO-SNN technique is implemented in the MATLAB software and is compared to various existing techniques. The AHO-SNN method provides an optimum outcome than the existing Particle Swarm optimization (PSO), Heap based optimizer (HBO), and Wild horse optimizer (WHO) methods. The power comparison of the existing technique HBO achieved power is 30 W, WHO is 20 W, WHO is around 10 W. In the proposed technique, the power generated is 40 W, which is way higher than the existing techniques. After implementing the integrated framework, the size of PV is cut in half, from 140 to 60 kW, which reduces the electrolyzer and the size of the hydrogen tank in also cut in half. In addition to a 35% decrease in diesel generator working hours and a 40% reduction in the leveled energy cost, PV degrades at a rate of 0.5%.