Feasibility analysis and optimal sizing of islanded hybrid energy system by using bio-inspired algorithms

• The most profitable system is PV- Wind turbine- Li-Ion battery- Diesel Generator system with a NPC of 1.79 M$. • Optimal sizing of three cases of green, red, and pink communities with varying load and distance parameters. • Ant Colony Optimization, Whale Optimization and Flying Foxes Optimization were used in optimal sizing. • Novel Flying Foxes Optimization proved to be effective in optimal power flow and optimal sizing. • Higher load demand and distance increased system size to minimize losses in the related area. Industrialization and technological advancement have led to a rapid increase in energy demand to support the social and economic development of human societies. Although fossil fuels such as oil, coal, and natural gas have historically met this demand, their environmental impacts, resource depletion, and rising extraction costs necessitate a transition to renewable energy sources. This study aims to design an off-grid hybrid energy system to meet the energy needs of 300 households in Turkey and to evaluate its technical and economic performance across varying renewable penetration levels. System design, simulation, and techno-economic analysis were conducted using HOMER Pro, with four configurations corresponding to 25 %, 50 %, 75 %, and 100 % penetration levels. Li-Ion and Lead-Acid batteries were employed separately to compare performance, and three scenarios differing in load demand and inter-community distances were modeled using Python and PYPSA. Optimal configurations were determined using Ant Colony Optimization (ACO), Whale Optimization (WO), and Flying Foxes Optimization (FFO). Results indicate that the lowest net present cost (NPC) system corresponds to 100 % penetration with Li-Ion batteries, comprising 28 wind turbines, 364 kW photovoltaic arrays, a 233 kW generator, a 387 kW inverter, and 1000 batteries, with a NPC of 1.79 million $. Li-Ion batteries demonstrated superior economic performance compared to Lead-Acid batteries, and systems with 75 % and 100 % penetration were the most cost-effective. The FFO method responded most effectively to load variations, adjusting system sizing in high-demand regions to minimize transmission losses. FFO was the fastest responding optimization to the changing parameters. Overall, the findings demonstrate that FFO offers high responsiveness in energy system optimization and that simulation-based, region-specific hybrid system design provides actionable insights for sustainable energy policy and planning.