Optimal multi-objective design of a Photovoltaic/Battery/Wind hybrid system by implementing an innovative meta-heuristic algorithm

• Caracal Optimization: Inspired by caracal hunting for precision and adaptability. • Combines chaotic exploration and adaptive leaps for efficient search and refinement. • Optimizes Wind/PV/Battery systems, minimizing costs and ensuring reliability. • Outperforms GWO, WOA, ZOA, and PSO in quality, speed, and computational cost. • Ideal for high-dimensional hybrid energy system optimization challenges. Advancing optimization methodologies is crucial for addressing the complex challenges of real-world energy systems, particularly those involving high-dimensional search spaces. This work introduces the Caracal Optimization Algorithm (CAO), a novel metaheuristic inspired by the hunting behavior of caracals, known for their precision, agility, and adaptability. By mimicking the caracal's stealthy stalking, explosive leaps, and dynamic adjustments to prey movement, the algorithm incorporates chaotic exploration mechanisms and adaptive leap strategies, effectively balancing global search diversity and local solution refinement. This innovation enables the CAO to navigate intricate solution landscapes, avoid local optima, and achieve rapid convergence. The CAO was applied to optimize the sizing of off-grid hybrid energy systems, particularly Wind/Photovoltaic/Battery configurations, focusing on key metrics such as loss of power supply probability (LPSP), net present cost (NPC), and levelized cost of energy (LCOE). The algorithm was benchmarked against four established metaheuristic methods, Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Zebra Optimization Algorithm (ZOA), and Particle Swarm Optimization (PSO). Comparative analyses showed that CAO outperforms these benchmarks, achieving the best solution quality, faster convergence, and significantly reduced computational time. Notably, CAO reduced the LCOE to 0.1069 US$/kWh, the NPC to approximately US$ 50,874, and demonstrated superior energy cost optimization with faster convergence compared to other algorithms. The findings also highlighted significant variability in photovoltaic power output, peaking at 20 kW during high solar radiation, reflecting the intermittent nature of solar energy. Wind turbine power showed more consistency, peaking at 12 kW. Battery charging and discharging exhibited fluctuations based on weather, time of day, and seasonal changes. The analysis revealed that lower LCOE values occur under favorable financial conditions, such as low inflation and interest rates. Conversely, higher LCOE values were observed with increased inflation and interest rates, emphasizing the need for minimizing these financial factors for cost-effective energy generation. These results underline the Caracal Optimization Algorithm's potential to enhance hybrid renewable energy systems, offering a cleaner, more cost-effective solution. This study not only demonstrates the effectiveness of CAO in optimizing energy systems but also highlights its adaptability in addressing complex, multi-objective optimization problems, proving its capability to navigate high-dimensional spaces efficiently.