Multiobjective combinatorial optimization techniques applied to electrical performance estimation of distribution networks

This paper aims at contributing for the estimation of electrical performance in the distribution of electric energy. Electrical performance is assumed to be the evaluation of network congestion parameters, losses and voltage level. The electrical performance estimation is formulated according to an optimization problem where the objective functions correspond to an evaluation of occurrence probability, and also correspond to a proximity evaluation of calculated parameters with values obtained by measurement. Load values are discretized according to occurrence probabilities within each interval, so that formulation results in a multiobjective combinatorial optimization of exponential dimension. Network reduction procedures to substantially reduce decision domain and network expansion procedures to rebuild it are proposed. Specific heuristics are also proposed to get solutions with load diversity and unbalanced. In order to adequately apply these heuristics, a metaheuristic evolutionary method to build feasible solutions is proposed and applied, and ranked according to Pareto's concept. The mathematical formulation of optimization is flexible enough to be effectively applied taking into account different levels of supervisory systems developed in the utilities. The metaheuristic evolutionary model proposed was applied to a representative case with main potentialities and weak points to be improved.