Planejamento estocástico da expansão de sistemas de transmissão de energia elétrica baseado em critério de dominância

Abstract

This work presents the developed tools to support multi-objective transmission expansion planning (TEP) in the presence of multiple uncertainties, including: (i) unavailability of generation and transmission equipment; (ii) load behavior; (iii) intermittency of wind renewable sources. The Non-Dominated - Monte Carlo Simulation (ND-MCS) and Improved Non-Dominated - Monte Carlo Simulation (IND-MCS) algorithms developed allow, through reliability estimation, an efficient and stochastic approach to TEP based on Monte Carlo Simulation (MCS) and probabilistic Pareto criteria, resulting in considerable reduction in computational effort when compared to the conventional MCS method. In addition, a Support Vector Machines (SVM) network is incorporated into the developed algorithms to increase the robustness of the method. The methodology is applied to Multi-objective Grey Wolf Optimization (MOGWO), associated with a Fuzzy decision-making criterion to define expansion strategies based on Pareto-optimal solutions. The case studies demonstrate that the representation of uncertainties throughout the search process (a priori) allows a better search in the solution space due to its ability to identify non-dominated and therefore attractive candidate solutions throughout the optimization steps. The results are found with considerable computational efficiency when compared to the conventional probabilistic method, finding multiple solutions under different planning criteria and uncertainties, which can provide comprehensive and realistic options to the system planner.