Collaborative optimization of power system load control measures for disaster scenarios

Extreme natural disasters and multi-point disturbances, such as earthquakes, typhoons, and equipment failures, significantly affect the stability and reliability of power systems. Traditional control measures often lack efficiency in addressing complex fault scenarios, leading to widespread load loss and prolonged recovery times. This study proposes a multilevel cooperative optimization method for load control in power systems, aiming to minimize load loss and enhance system stability. The method optimizes several control measures—stability-control load shedding, low-frequency and low-voltage load reduction, low-voltage tripping, and zonal operation—thereby improving system resilience to large-scale disturbances. It dynamically adjusts control strategies based on real-time fault conditions, improving recovery speed and anti-disturbance capability, and thus enhancing overall stability and recovery efficiency.

The proposed method integrates multiple strategies to strengthen power system stability under complex fault scenarios. First, a regionally differentiated load-shedding strategy identifies high-risk areas and applies small-scale, multiple rounds of load shedding to avoid instability caused by large-scale load shedding. Second, dynamic threshold adjustment and cross-regional coordination mechanisms optimize load control delays in real time to respond quickly to frequency and voltage fluctuations. Wide-area measurements coordinate shedding and recovery across multiple regions, ensuring grid stability. Finally, a hybrid optimization mechanism combining genetic algorithms and model predictive control (MPC) is employed for real-time optimization. The genetic algorithm addresses nonlinear and multi-constrained problems, whereas MPC dynamically optimizes strategies based on real-time and predictive system states, enhancing response flexibility. Fault-scenario simulations using sequential Monte Carlo methods validate the adaptability of the proposed method under diverse conditions.

The simulation results showed that the proposed method significantly improved system performance under extreme fault conditions. The key results included: (1) Reduced overall load loss and accelerated recovery, enhancing system resilience during large-scale faults. (2) Improved real-time optimization through the combination of genetic algorithms and MPC, balancing load shedding across nodes and preventing localized losses. (3) Markedly better load-shedding effectiveness and recovery speed than traditional measures did, particularly in complex fault scenarios. (4) Consistent improvements in recovery speed and balanced load-shedding distribution in 100 simulated fault scenarios.

The multilevel cooperative optimization method provides a systematic solution to enhance the resilience and stability of power systems under large-scale fault conditions. Combining multiple load control measures and optimizing them with genetic algorithms and MPC, it significantly strengthens the system's ability to withstand complex faults. The method not only reduces load loss but also accelerates recovery, thereby improving overall operational reliability. Future research may focus on advancing real-time adaptability and cross-regional coordination to further enhance the global optimization of power grids under increasingly frequent and severe fault scenarios.