A Practical Security Assessment Methodology for Power System Operations Considering Uncertainty

Abstract

Today, renewable energy sources (RESs) are increasingly being integrated into power systems. This means adding more sources of uncertainty to the power system. To deal with the uncertainty of input random variables (RVs) in power system calculation and analysis problems, probabilistic power flow (PPF) techniques have been introduced and proven to be effective. Currently, although there are many techniques proposed for solving the PPF problem, the Monte Carlo simulation (MCS) method is still considered as the method with the highest accuracy and its results are used as a reference for the evaluation of other methods. However, MCS often requires very high computational intensity, and this makes practical application difficult, especially with large-scale power systems. In the current paper, an advanced data clustering technique is proposed to process input RV data in order to the decrease computational burden of solving the PPF problem while upholding an acceptable level of accuracy. The proposed method can be effectively applied to solve practical problems in the operating time horizon of power systems. The developed approach is tested on the modified IEEE-300 bus system, indicating good performance in reducing computation time.