Online Probabilistic Static Security Assessment for Power Systems Considering High Renewable Penetration

Abstract

With the rapid development of renewable energy, a large number of renewable energy stations are connected to the power system, which leads to a decrease in the inertia of the power system and an increase in safety risks suffered. Thus, online static security assessment (SSA) is increasingly necessary. However, because of the uncertainty of renewable energy, it is not feasible to check all possible scenarios in online SSA. To reduce the number of calculations and achieve SSA in a short time, a new online SSA method based on scenario clustering for future ultra-short-term security assessment is proposed in this paper. In the offline stage, a key scenario set is constructed by Markov Chain Monte Carlo and K-means with historical data. In the online application, the initial probability distribution of renewable energy outputs is calculated by joint distribution with the output of the previous interval and corrected by weather data. Then load flow calculation with N-1 criteria is executed, and the probability for the safe operation of the system is calculated. The effectiveness of the proposed online SSA scheme has been verified in the IEEE-300 system, where one of the generators is replaced by a renewable energy station.