Optimization of a hybrid microgrid for a small hotel using renewable energy and EV charging with a quadratic interpolation beluga whale algorithm

This study explores the optimization of a hybrid microgrid designed to meet the energy needs of a small hotel and four electric vehicle (EV) charging stations. In light of growing EV adoption, the research highlights the importance of vehicle-to-grid and grid-to-vehicle integration in enhancing grid stability and supporting EV infrastructure. Numerical results demonstrate that the quadratic interpolation beluga whale optimization (QIBWO) algorithm performs exceptionally well in terms of accuracy and robustness across seven benchmark functions. The study also employs advanced stochastic metaheuristic optimization algorithms to maximize system efficiency and sustainability. At the core of this optimized energy system is a hybrid microgrid tailored to the hotel’s specific load demands while powering four strategically placed EV charging stations. The system integrates photovoltaic (PV) panels, wind turbines (WT), and a battery energy storage system (BESS), carefully calibrated to balance energy production, storage, and consumption. The research prioritizes renewable energy and uses various metaheuristic algorithms to find the most cost-effective and technically feasible configuration. Notably, the QIBWO algorithm led to optimal capacities for PV (139.8515 kW), WT (175.0837 kW), and BESS (88.38023 kW), resulting in an annual system cost of $41,867.04, a total net present cost of $679,983.97, a loss of power supply probability (LPSP) of 0.001119%, and a levelized cost of energy (LCOE) of $0.05333/kWh. The system’s renewable sources covered 95.81% of the hotel’s energy needs, significantly reducing dependence on conventional energy, with a grid contribution factor (GCF) of just 4.19%. The results emphasize the effectiveness of integrating renewable energy to meet substantial energy demands, including powering the EV charging stations. A comprehensive sensitivity analysis further confirmed the robustness of the system, laying the groundwork for future advancements in microgrid optimization. Simulations conducted in MATLAB 2022b validate the findings and provide a foundation for future studies on sustainable energy solutions in the hospitality sector.