Voltage optimization using augmented Lagrangian functions and quasi-Newton techniques

The authors shows how the application of augmented Lagrangian functions and quasi-Newton techniques can be utilized for power system voltage optimization. The developed algorithm is attractive for three reasons: it can accommodate power system constraints in a straightforward manner, it is capable of reaching a solution even from infeasible starting-points, and it converges in a few iterations. The proposed algorithm offers substantial improvements in computational efficiency due to: reduction in the dimensionality of the formulation by exploiting variable reduction and active-reactive decoupling in the AC-network, sparse matrix techniques to generate selectively the required sensitivities, and an active set strategy that relaxes all inactive constraints. Computer runs have been performed, and the results prove the efficiency of the algorithm