Enhancing a standalone hybrid green energy system based on the starfish optimization algorithm

In this work, a starfish optimization algorithm (SFOA) is employed to enhance the component sizing of a grid-independent hybrid green energy system (HGES) integrating multiple sources, focusing on operational consistency and economic performance. Lately, the significant penetration of green energy has drawn increasing attention. Because of the stochastic and discontinuous characteristics of wind and solar resources, the adoption of energy storage alongside green energy has become an essential direction. A comparative assessment is carried out among three HGES models, where a wind turbine unit (WTU) and photovoltaic panel (PVP) serve as the main sources, integrated with a battery unit (BU), a pumped-storage integrated hydro station (PSIHS), or a dual storage option. The widely recognized whale optimization algorithm (WOA) and grasshopper optimization algorithm (GOA) are utilized to assess the effectiveness of the SFOA. Among the assessed cases, the dual-storage HGES system demonstrates the most well-rounded trade-off across the three objectives—reliability, expenses, and emission mitigation. It was validated through outcomes that SFOA provides the most favorable outperforms across all considered cases in comparison to alternative optimizers. Simulation results show that SFOA outperforms WOA and GOA by 0.7 % and 1.1 %, respectively, highlighting its effectiveness in solving the HGES sizing optimization problem.