Load Frequency Control of a PV-Thermal Power System with Integrated EV Battery Using the INFO Algorithm

Load frequency control is crucial for keeping the stability of power systems by balancing energy supply and demand in electrical grids, essential for an accurate and fast-acting controller to address sizeable parametric uncertainties. In particular, the growing population of electric vehicles (EVs) requires advanced control and optimization strategies to ensure secure and stable operation. In this paper, the effect of the INFO algorithm on load frequency control is investigated as a new method in the system with renewable energy sources and EV Battery. The novelty of the proposed study lies in the INFO-based optimal tuning of the controller for a PV-thermal system with integrated EV battery and its detailed comparison with several traditional and recent optimization algorithms such as genetic algorithm (GA), firefly algorithm (FA), chess algorithm (CA), flood algorithm (FLA), sinhcosh algorithm (SCHO), artificial rabbits optimization (ARO) and black widow optimization algorithm (BWOA). The obtained results show that the controller optimized with the INFO algorithm provides a robust dynamic response with fast settling time and the lowest overshoot and undershoot values. Compared to the results of other controllers, an average improvement of approximately 30% in system frequency overshoot and approximately 40% in settling time was achieved.