Automatic Under-Frequency Load Shedding with Sensitivity to Associated Load Type

The increasing penetration of low-inertia renewable energy sources and distributed generation has significantly reduced system inertia, making frequency stability a critical challenge in modern power systems. Traditional Under-Frequency Load Shedding (UFLS) schemes often fail to adapt to varying operating conditions and load behaviors, leading to either insufficient or excessive disconnections. This paper presents an optimization-based UFLS scheme that integrates dynamic simulations in DIgSILENT PowerFactory with Python programming through the Particle Swarm Optimization (PSO) algorithm. The proposed methodology optimizes key UFLS parameters—frequency thresholds, intentional delays, and load-shedding percentages—under different ZIP load model configurations (constant power, constant current, and constant impedance). Simulation results on the IEEE 39-bus test system demonstrate that the type of load model has a significant impact on frequency recovery performance and the total amount of load shed. The constant power model achieved system stability with the lowest load disconnection, whereas the constant impedance model required a greater amount of shedding to restore nominal frequency. The results validate the effectiveness of the proposed optimization tool and highlight the importance of considering load characteristics in UFLS design to enhance operational reliability and resilience in modern power systems.