Optimal sizing of hybrid renewable energy systems relying on the black winged kite algorithm for performance evaluation - Sarada Mohapatra; Deepa Kaliyaperumal; T. Porselvi; S. V. Tresa Sangeetha; Roobaea Alroobaea; Ahmed Emara (Scientific Reports)
Abstract The hybrid photovoltaic (PV) and wind turbine (WT) system combined with a battery (BT) have emerged as an effective renewable energy solution. As the adoption of hybrid systems gain popularity, determining the optimal system size plays a vital role for achieving cost-effectiveness. This study employs a recently developed Black-Winged Kite Algorithm (BKA) to optimize the size of a PV/WT/BT hybrid system to continuously distribute the electrical power to an educational institution sited in Puri, Odisha, India, with the goal of minimizing the total annual cost (TAC) while considering least levelized cost of electricity (LCOE). To ensure the system reliability, the maximum loss of power supply probability (LPSP) is considered 5%. To validate the performance, the BKA algorithm is compared against four prominent intelligence algorithms, including Harris Hawks Optimisation (HHO), Sine Cosine Algorithm (SCA), White Shark Optimisation (WSO), Arithmetic Optimisation Algorithm (AOA), and Snake Optimiser (SO). The examination focused on estimating the optimum size of the suggested off-grid hybrid system in the context of statistical outcome scenarios. The experimental outcomes demonstrated that the suggested BKA approach in optimization of renewable energy system provided the best results compared to HHO, SCA, WSO, and AOA with a TAC of $7105.23 and LCOE of $0.1874 per kWh at LPSP of 5%, achieving an optimal configuration of 60.4722 kW PV, 23.8337 kW WT, and 13.6159 kWh BT. Additionally, the convergence study reveals that BKA has better convergence and convergence than the other four intelligence algorithms by achieving the optimum solution. As a result, the PV/WT/BT combination evolved as a more realistic choice in designing a reliable and costless hybrid system for satisfying load demand in regional areas.