Accelerating Power Flow Calculations via Constant Jacobian Structure

In computational modeling, nonlinear systems are often solved through iterative linearization around estimated states until convergence. A key optimization opportunity exists in these iterative processes: the structure of the linearized system typically remains constant, while the numerical values change. We demonstrate how leveraging this property can accelerate power flow solutions across two case studies by reusing sparsity-preserving orderings for LU decompositions. Although the underlying method is well-established, its practical application within specific power flow tools yields significant performance benefits. The implementation reduces factorization time by approximately 80% and overall computation time by up to 42% for large networks. Since this pattern, nonlinear problems with fixed matrix structures, appears across many engineering and scientific domains, the presented approach can help reduce simulation costs in a variety of fields beyond power systems.