Optimizing Hybrid Renewable Energy Systems Using Hippopotamus Optimization Meta-Heuristic Algorithm

Solar and wind energy systems have become increasingly important because they provide both environmental advantages and sustainable power generation capabilities. Large-scale integration of renewable energy systems faces difficulties because of their intermittent and variable nature. This research investigates the optimal setup of Hybrid Renewable Energy Systems (HRES) which integrates solar photovoltaic and wind turbines and battery energy storage. A new nature-based optimization method called Hippopotamus Optimization (HO) helps find the best energy source mix to handle power supply reliability and energy cost while managing excess energy production. The study develops an optimization framework which minimizes Excess Energy and Loss of Power Supply Probability and Levelized Cost of Energy. MATLAB/R2022a serves as the platform for extensive simulations where the HO algorithm demonstrates its performance against leading algorithms Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Slime Mould Algorithm (SMA), Harris Hawks optimization (HHO), and Sunflower Optimization (SFO) algorithms. The results reveals that total cost estimated by HO technique is 15,907$ whereas it is 16,639$ by SFO, 17,492$ by HHO, 19,474$ by SMA, 24,762$ by GA and 25,267$ by PSO for the sizing of HRES. The HO algorithm demonstrates its potential as an effective solution for sustainable HRES design by delivering superior performance in energy cost reduction and reliability improvement and energy efficiency enhancement. The research adds value to the field through its novel optimization framework and its practical implementation of energy management strategies in real-world applications.