Enhancing Frequency Stability in a Real Power System for the Energy Transition: A Data‐Driven LFC/AGC Optimization Study of the El Bayadh, Algeria Grid

The large‐scale integration of variable renewable energy sources (VRESs) challenges the frequency stability of modern power systems due to reduced inertia and increased power fluctuations. This study presents the first data‐driven dynamic model and optimization framework for the regional grid of El Bayadh, Algeria, a real‐world system facing high renewable penetration. A detailed two‐area dynamic model was developed, representing the KEF wind farm (WF) and the ESC photovoltaic (PV) park, using actual operational data from March 27, 2024, to ensure a realistic analysis. The primary objective was to enhance small‐signal frequency stability by systematically tuning the parameters of the load frequency control (LFC), automatic generation control (AGC), and power system stabilizer (PSS) schemes using three meta‐heuristic algorithms: particle swarm optimization (PSO), gray wolf optimizer (GWO), and whale optimization algorithm (WOA). The results demonstrate a profound improvement in system performance. The baseline, unoptimized system was found to be dynamically unstable, exhibiting severe frequency oscillations with a nadir reaching −1.88 Hz and tie‐line power saturating at 40 MW. In contrast, the optimized controllers successfully stabilized the grid, with the GWO–tuned controller reducing the maximum frequency deviation to −0.69 Hz and containing the tie line power flow to 12.15 MW. Furthermore, the optimized control strategy led to a significant co‐benefit by reducing network power losses by up to 25.1%. This study validates the efficacy of using a data‐driven optimization framework to enhance the stability and efficiency of real‐world power systems with high VRES penetration.