A novel Hybrid Beetle Swarm Optimization-based MPPT strategy for PV battery-based microgrid with real-time OPAL-RT validation

Use of renewable energy sources in achieving sustainable solutions to reliable and uninterrupted supply of electricity is becoming a dominant trend, and solar photovoltaic (PV) technology is playing a central role with regard to this trend. Nevertheless, the nature of solar energy intermittency due to changes in solar irradiance and temperature, as well as the fact that most solar panels do not convert all the energy incident to the panel, and thus requires the introduction of sophisticated Maximum Power Point Tracking (MPPT) methods. This research proposes an improved, robust, and high-performance Hybrid Beetle Swarm Optimization (BSO)-based MPPT strategy for solar PV systems. A high-fidelity MATLAB/Simulink-based microgrid model integrating a solar PV array with a battery energy storage system is developed to evaluate the proposed control scheme. The performance of the hybrid BSO-MPPT algorithm is strictly tested under various conditions of operation, such as a dynamic change of irradiance and temperature. The comparative performance analysis with the traditional optimization methods, including Particle Swarm Optimization (PSO) and Dung Beetle Optimization (DBO) proves to be more accurate in tracking, faster in reacting to changes, less oscillation in steady-state, and more robust in the proposed method. The detailed graphical and tabular outputs of the results also confirm better efficiency of the algorithm. Besides, the suggested controller is tested and proven on an OPAL-RT OP4510 hardware in the loop (HIL) platform with real-time performance and latency requirements in mind. The similarity between the real-time HIL performance and the actual hardware performance validates the strength, computational performance, and real-time feasibility of the suggested hybrid strategy of BSO with MPPT to microgrids.