Multi-objective Transmission Expansion Planning Using an Elitist Non-dominated Sorting Genetic Algorithm with Fuzzy Decision Analysis

The main objective of transmission expansion planning in deregulated power systems is to provide a non-discriminatory competitive environment for all stakeholders, while maintaining power system reliability. In this paper, a static transmission expansion methodology is proposed using a multi-objective optimization framework that is able to handle different incommensurable objectives with conflicting or supporting relations. Investment cost, reliability, and congestion cost are considered in the optimization as three objectives. An elitist non-dominated sorting genetic algorithm is used followed by a fuzzy decision making analysis to obtain the final optimal solution. The crowding distance method is used to determine shared fitness values in the genetic algorithm. The proposed transmission planning method allows more flexibility by producing a set of Pareto-optimal solutions instead of the one optimal solution of the single-objective formulation. The proposed method is tested on the Luzon grid and the results are found to be superior to results obtained for the single-objective formulation and to historical plans by the grid operator.