Abstarct: With the new structure of power systems and the increasing influence of renewable energy resources with complex dynamic behaviors and high uncertainties, the issue of frequency control has become more significant in power system operations. Although the inertia of generating units can partially cover power imbalances, controlling frequency variations within an acceptable range remains challenging. The primary objective of the frequency control is to minimize the steady-state frequency error. As the grid frequency oscillates with changes in load demand, the frequency control system is responsible for mitigating these oscillations, as the grid frequency oscillates with changes in the load demand. In single-area power systems, frequency control is relatively straightforward and requires only the balancing of generation with the load demand. However, in multi-area power systems, frequency control encounters significant challenges. This thesis focuses on the frequency stability issue for a two-area interconnected power system. Each area of this system comprises conventional generating units (thermal, gas, and hydroelectric units). To assess the performance of this network, a fractional-order frequency control structure (TID) is proposed, considering nonlinear factors such as the deadband governor (GDB), generation rate constraint (GRC), and integration of renewable energy resources, including wind and solar power plants. To assess the performance of the proposed controller, various scenarios were examined, and the simulation results showed this controller could effectively handle uncertainties in generation resources and load variations. It is suitable for two-area power systems with different generating units under different operating conditions. The performance of the proposed controller was evaluated baxsed on criteria such as the maximum frequency deviation (overshoot), settling time, rise time, and the integral of the time-weighted absolute error (ITAE). This controller minimizes the frequency deviation and improves the frequency oscillation damping. The controller coefficients are optimized using a genetic algorithm to minimize the overshoot and error and to enhance the speed of error damping. The effectiveness of the algorithm is shown through a simulation of the power system using the Simulixnk tool in Matlab software.