An efficient interior point method for sequential quadratic programming based optimal power flow

This paper presents a new sequential quadratic programming algorithm for solving the optimal power flow problem. The algorithm is structured with an outer linearization loop and an inner optimization loop. The inner loop solves a relaxed reduced quadratic programming problem. Because constraint relaxation keeps the inner loop problem of small dimension, the algorithm is quite efficient. Its outer loop iteration counts are comparable to Newton power flow and the inner loops are efficient interior point iterations. Several IEEE test systems were run. The results indicate that both outer and inner loop iteration counts do not vary greatly with problem size.