Skip to Main Content
This paper concentrates on the solution of the nonlinear optimal power flow. We describe an interior-point algorithm for nonlinear programming which is an extension of interior-point methods for linear and quadratic programming. Our major contribution is the inclusion of a merit function, which is used to measure, at each iteration, the progress along a search direction. Ensuring that the search directions are good descent directions for the merit function provides global convergence for the algorithm. We describe two kinds of merit functions: a classical approach based on a penalty function and a penalty parameter which has to be updated, and a new merit function with no penalties. Numerical tests for a set of power networks are made to compare the performances of the new merit function and the penalized function with two different parameter updating strategies.