Comparative Evaluation of Maximum Power Point Algorithms in Photovoltaic Systems for Renewable Energy Utilization

Abstract

The irregular generation patterns of renewable energy systems lead to undesirable fluctuations in power grids. Integrating energy storage facilities into renewable energy systems is proposed as a solution to this issue. In this study, a photovoltaic energy system with energy storage is designed, and the effects of deterministic and stochastic optimisation-based algorithms on maximum power point tracking are analysed to ensure high-efficiency operation. In the designed system, maximum power point tracking of the photovoltaic system is achieved using the conventional Perturb and Observe, Incremental Conductance, Fuzzy Logic-Based Perturb and Observe, and Particle Swarm Optimization. The algorithms are extensively compared based on performance metrics such as rise time, settling time, and overshoot rate. The Fuzzy Logic-Based Perturb and Observe algorithm exhibits the best performance, with a rise time of 14.28 milliseconds and a settling time of 51.6 milliseconds, achieving the highest efficiency with a battery state of charge level of 69.97%. Detailed simulation analyses conducted in the Matlab/Simulink environment reveal that the fuzzy logicbased method provides faster and more stable results than other methods. Furthermore, a 24-hour real solar irradiance dataset is utilised to test the model under realistic environmental conditions, allowing for a more reliable evaluation of the performance of our storageintegrated photovoltaic.