Solving Multi-objective Optimization Problems in Power Systems Based on Extended Goal Programming Method

Abstract

This thesis proposes an approach to solve multi-objective optimization problems in power systems based on the Extended Goal Programming (EGP) method. In the first part, the EGP method is applied to deterministic multi-objective optimal power flow (MOOPF) problem. The results are compared with classical methods and the efficiency of the EGP method is evaluated. A method for ranking the solutions is introduced to help decision makers choose their preferred solution. In the second part, Taguchi's Orthogonal Array Technique (TOAT) and EGP method are jointly applied to solve probabilistic MOOPF problem with load and renewable generation uncertainties. This approach finds a solution that is robust to uncertain variations in load and renewable power generations. An analysis of the significance of generators ramp rate variation with the degree of robustness of the solution is shown. The results are compared with that of deterministic model and the robustness of the solution is evaluated.