Computational Enhancement of Sparse Tableau via Block LU Factorization for Power Flow Studies

Recent work has proposed alternatives to avoid the limitations that are inherent in
$Y_{bus}$
-based formulations for power flow problems. Alternatives based on the Sparse Tableau Formulation (STF) provide conceptual benefits relative to
$Y_{bus}$
methods. This letter focuses on specific computational approaches tailored to the features of STF, that allow it to match the computational speed of well-established
$Y_{bus}$
-based methods. In particular, it presents enhanced Newton Raphson (NR) algorithms exploiting the block LU factorization to improve the computational performance of STF. These methods are compared with the classic
$Y_{bus}$
-based solution algorithm using several power system test networks. Computational case studies demonstrate the significant computational improvement for the STF-based power flow solution.