We describe a security constraint evaluation (SCE) Python package developed in the SCY 0 project. The SCE package is a simplified version of the simultaneous feasibility test (SFT) developed in the HIPPO project on security constrained unit commitment (SCUC). The HIPPO SFT achieved speedups over a typical commercial SCE product used in large scale electricity markets by using a mathematical technique based on the Sherman-Morrison-Woodbury (SMW) formula for the inverse of a matrix with a low rank update to handle contingencies where some branches are opened or closed. We use the SCE package to study the computational kernels of the SMW approach to understand and improve on the performance of the HIPPO SFT. We describe novel methods for these kernels using Python acceleration techniques including just in time (JIT) compilation and graphical processing unit (GPU) deployment. Run time and memory performance results for these methods on large scale test problems demonstrate scaling properties. Some of these methods achieve speedup factors of 30 to 80, relative to a baseline implementation and to the fastest HIPPO method. After the HIPPO results, these results further support the use of the SMW approach, and they show acceleration techniques can improve SCUC solver performance.

This paper presents a novel fast security analysis method to perform simultaneous feasibility test (SFT) for security constrained unit commitment (SCUC). The method uses the Sherman-Morrison-Woodbury formula to calculate DC power flow with the incremental topology changes across time intervals and with the contingencies for each interval. The extremely fast SFT allows the SCUC Mixed Integer Programming (MIP) solver to check network security constraints in a callback, leading to a significant performance improvement over the traditional MIP-SFT iteration method.