Technical and Economic Evaluation for Off-Grid Hybrid Renewable Energy System Using Novel Bonobo Optimizer

Hassan M. H. Farh, Abdullrahman A. Al-Shamma’a, Abdullah M. Al-Shaalan, Abdulaziz Alkuhayli, Abdullah M. Noman and Tarek Kandil
Additional contact information
Hassan M. H. Farh: Faculty of Construction and Environment, Department of Building and Real Estate, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Abdullrahman A. Al-Shamma’a: Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Abdullah M. Al-Shaalan: Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Abdulaziz Alkuhayli: Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Abdullah M. Noman: Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia
Tarek Kandil: Department of Electrical and Computer Engineering, College of Engineering and Computing, Georgia Southern University, Statesboro, GA 30460, USA

Sustainability, 2022, vol. 14, issue 3, 1-18

Abstract: In this study, a novel bonobo optimizer (BO) technique is applied to find the optimal design for an off-grid hybrid renewable energy system (HRES) that contains a diesel generator, photovoltaics (PV), a wind turbine (WT), and batteries as a storage system. The proposed HRES aims to electrify a remote region in northern Saudi Arabia based on annualized system cost (ASC) minimization and power system reliability enhancement. To differentiate and evaluate the performance, the BO was compared to four recent metaheuristic algorithms, called big-bang–big-crunch (BBBC), crow search (CS), the genetic algorithm (GA), and the butterfly optimization algorithm (BOA), to find the optimal design for the proposed off-grid HRES in terms of optimal and worst solutions captured, mean, convergence rate, and standard deviation. The obtained results reveal the efficacy of BO compared to the other four metaheuristic algorithms where it achieved the optimal solution of the proposed off-grid HRES with the lowest ASC (USD 149,977.2), quick convergence time, and fewer oscillations, followed by BOA (USD 150,236.4). Both the BBBC and GA algorithms failed to capture the global solution and had high convergence time. In addition, they had high standard deviation, which revealed that their solutions were more dispersed with obvious oscillations. These simulation results proved the supremacy of BO in comparison to the other four metaheuristic algorithms.

Keywords: hybrid renewable energy system; bonobo optimizer; annualized system cost; optimal solution; convergence rate; renewable energy fraction; artificial intelligent algorithms (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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