Transmission expansion planning using benders decomposition and local branching

Abstract

This paper studies the application of an accelerated Benders decomposition algorithm using local branching on a transmission expansion planning (TEP) problem. TEP analyses the trade-off between investment cost of adding network capacity and load curtailment cost of existing network capacity. The problem is formulated as a mixed (0-1) programming problem which is tedious to solve through conventional Benders decomposition. Local branching is used to accelerate this algorithm by dividing the feasible region of the problem into some smaller sub-regions and then using a generic solver to find the best solution in each of these sub-regions. By strengthening lower and upper bounds at earlier stages of the decomposition algorithm, local branching helps reduce number of Benders iterations to solve the TEP problem. The efficacy of this technique is demonstrated by comparing with classical Benders decomposition when applied to a 46-bus power transmission network.