Reference-point modified honey badger algorithm: Intelligent optimization for off-grid photovoltaic system / Nur Atharah Kamarzaman ... [et al.]

To address the intricate balance between technical reliability and economic viability in off-grid photovoltaic (PV)-battery-diesel generator systems, particularly in regions without grid access, this study introduces the Reference-Point Modified Honey Badger Algorithm (RP-MHBA) as an innovative optimization methodology. RP-MHBA enhances the conventional Honey Badger Algorithm (HBA) by offering a more comprehensive range of alternative solutions through a Pareto front that trades off between conflicting technical reliability and economic viability, facilitating the discovery of balanced solutions. Consequently, the resulting optimized off-grid PV-battery-diesel generator systems provide a reliable energy supply and yield economic and environmental benefits. Through increased adoption of renewable energy and cost-effective energy provision, this innovation supports entities like TNB, SEDA, and MOSTI in advancing national electricity generation and harnessing artificial intelligence for PV system optimization. Furthermore, this aligns with the United Nations Sustainable Development Goal (SDG) 7, which aims to ensure access to affordable, reliable, sustainable, and modern energy for all.