<p>Commercial solar cells currently exhibit lower efficiency in converting solar radiation into electricity. A considerable amount of the absorbed energy is lost to the environment during the conversion process into electric power. Significant efforts have been focused on researching and developing hybrid photovoltaic and thermal collector systems to improve energy efficiency. A photovoltaic thermal (PV/T) system produces both electric power and thermal energy simultaneously, potentially enhancing the overall efficiency of the system. This paper discusses the methodology for power energy management and control when utilizing PV/T collectors in industrial processes. A PI controller is employed to regulate the water temperature according to the needs of the industrial load. The aim of tuning the PI controller is to improve the system’s performance, including rise time, settling time, and overshoot. This paper concentrates on optimizing the parameters of the PI controller using various optimization techniques. The optimization utilizes two bio-inspired meta-heuristic soft computing methods: the gray wolf optimizer (GWO) algorithm and the ant lion optimizer (ALO) algorithm. The objective function aims to optimize the k_p and ki gains using the GWO and ant lion algorithms to achieve more efficient PI controllers for the system. The system is implemented and simulated in MATLAB/Simulink, with the simulation results displayed graphically to evaluate the controller’s performance. The results show that the optimal values for kp and ki using the GWO algorithm are 100 and 0.0139, respectively. In comparison, the ALO algorithm yields optimal values of 205.6437 for kp and 0.07 for ki. These values represent the tuned PI controller parameters that produce the most efficient flow rate for the load. Simulation studies demonstrate the effectiveness of this approach in enhancing system efficiency and energy output.</p>