Probabilistic Security-Constrained Optimal Power Flow (PSCOPF) Considering a Wide Range of Uncertainties Based on Data Clustering

The optimal power flow (OPF) problem plays a critical role in power system operation and planning. However, the optimal solution derived from OPF may lead to operating limit violations under certain credible contingencies. Enhancing OPF by incorporating additional security constraints to ensure system reliability and security is known as security-constrained OPF (SCOPF). Meanwhile, the increasing integration of wind power generation (WPG) has introduced significant uncertainties into power system operations due to its variable nature. As a result, solutions obtained for the SCOPF problem within a deterministic framework may become invalid when WPG output fluctuates. In such cases, employing an appropriate probabilistic method is essential to effectively account for these uncertainties. This paper introduces a probabilistic framework for solving the SCOPF problem using the data clustering method. Compared to Monte Carlo simulation, this approach offers high speed and good accuracy, making it a practical solution. The primary objective of this study is to balance system security—measured by the expected power not served index—and the expected operational cost. The effectiveness of the proposed framework is validated through IEEE 14-bus and IEEE 57-bus test systems.