Security Risk Assessment of Power System Based on Latin Hypercube Sampling and Daily Peak Load Forecasting

With the continued growth of electricity demand, the power system is trending towards extreme operation increasingly. In order to analyze the impact of load changes on power system operating risks, a novel power system security risk assessment algorithm is proposed based on Latin hypercube sampling and daily peak load forecasting. Firstly, the gated recurrent neural network combining dynamic time warping is applied to forecast the daily peak load. Then a Markov-based component failure model is constructed and the component state is determined by Latin hypercube sampling. Finally, the optimal load reduction and security risk of the power system is calculated. The simulation results show that, compared to Monte Carlo sampling, Latin Hypercube sampling requires fewer samples to achieve the same accuracy. Furthermore, it can be found by observing the fluctuation of power system security risk and daily peak load that their trend of changing is basically the similar but the amplitude is different. When the load level is low, there is a “compression” effect on the power system risk changes, and when the load level is high, there is a “stretch” effect. Therefore, accurate load forecasting can determine the risk trend of the system in advance, which is beneficial to the safe and stable operation of the power system.