Computational Impacts of SVCs on Optimal Power Flow using Approximated Active-Set Interior Point Algorithm

Abstract

Interior point methods (IPMs) are popular and powerful methods for solving large-scale nonlinear and nonconvex optimization problems, such as AC optimal power flow (ACOPF). There are various ways to model ACOPF, depending on the objective and the physical components that need to be optimized. This paper models shunt flexible AC transmission systems (FACTS). Shunt FACTS devices such as static VAR compensators (SVCs) are sources for reactive power compensations and addressing voltage stability issues. The co-optimization of SV Cs with power dispatch can impact the computational performance of ACOPF. In this paper, we evaluate the performance of different ACOPF formulations with approximated active-set interior point (AASIP) algorithm and co-optimization of SV C set points alongside other decision variables. Our numerical results suggest that both AASIP and SVCs alone improves the computation performance of almost all formulations. The gain in performance, however, depends on the sparsity of the formulation. The most spares formulation, such as branch power flow rectangular voltages (BPFRV), shows the highest gain in performance. In the event of co-optimizing SVCs with power dispatch using AASIP, the performance gain is minimal. Finally, the results are verified using various test cases ranging from 500-bus systems to 9591-bus systems.