A Quasi oppositional smell agent optimization and its levy flight variant: A PV/Wind/battery system optimization application

Mas'ud, Abdullahi Abubakar; Salawudeen, Ahmed T; Umar, Abubakar Ahmad; Aziz, Ali Saleh; Shaaban, Yusuf A; Muhammad-Sukki, Firdaus; Musa, Umar

Abstract

In this study, two novel algorithms are developed: the quasi oppositional smell agent optimization (QOBL-SAO) and its levy flight variation (LFQOBL-SAO), and their performance is compared to that of the conventional smell agent optimization (SAO). Two investigations were carried out. First, the capabilities of the novel algorithms were tested in solving ten benchmarked functions and five CEC2020 real world optimization problems. Second, they are applied to optimize the hybrid photovoltaic (PV)/wind/battery, PV/battery and wind/battery power system for a healthcare centre in a Nigerian village. Load demand, PV and wind profiles of the aforementioned location were used to developed the hybrid system. All simulations were carried out in MATLAB software and the results show that the novel algorithms are capable of solving both the benchmarked functions and the CEC2020 real world constrained optimization competition. In particular, the performance of the QOBL and the LF-QOBL are as good as the top performing functions like the IUDE, í µí¼–MAgES and the iLSHADε algorithms. However, in terms of convergence time, lowest cost of energy (LCE), and total annualized cost (TAC), the novel algorithms outperformed the SAO for the PV/wind/battery optimization application. The hybrid PV/wind/battery system is the most cost-effective when using LFQOBL-SAO and QOBL-SAO, with a TAC value of $15100. Furthermore, the results demonstrate that the LFQOBL-SAO method is accurate and outperforms the QOBL-SAO and SAO algorithms.

Citation

Mas'ud, A. A., Salawudeen, A. T., Umar, A. A., Aziz, A. S., Shaaban, Y. A., Muhammad-Sukki, F., & Musa, U. (2023). A Quasi oppositional smell agent optimization and its levy flight variant: A PV/Wind/battery system optimization application. Applied Soft Computing, 147, Article 110813. https://doi.org/10.1016/j.asoc.2023.110813

• Journal Article Type: Article
• Acceptance Date: Aug 31, 2023
• Online Publication Date: Sep 9, 2023
• Publication Date: 2023-11
• Deposit Date: Sep 5, 2023
• Publicly Available Date: Sep 10, 2024
• Print ISSN: 1568-4946
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 147
• Article Number: 110813
• DOI: https://doi.org/10.1016/j.asoc.2023.110813
• Keywords: Quasi oppositional smell agent optimization, levy flight quasi oppositional smell agent optimization, smell agent optimization, photovoltaic, wind, renewable energy sources, lowest cost of energy, net present cost and hybrid system.
• Publisher URL: https://www.sciencedirect.com/journal/applied-soft-computing