Globally Convergent Optimal Power Flow by Trust-Region Interior-Point Methods

As power systems becomes heavily loaded there is an increasing need for globally convergent optimal power flow (OPF) algorithms. By global convergence we mean the OPF algorithm being able to find a solution, if one exists, in spite of the choice of the starting point for the iterative process. This paper attempts to develop a globally convergent OPF by incorporating trust region strategies into the computationally efficient and versatile primal- dual interior-point (PDIP) algorithms that have been applied to OPF problems. The performance of the trust region interior- point (TRIP) algorithm, when applied to the IEEE test systems with 30, 57, 118 and 300 bus, is compared with that of the pure PDIP algorithm and its predictor-corrector variant.