A Fast Solution Method for Stochastic Transmission Expansion Planning

Abstract

Stochastic programming is a cost-effective approach to model the transmission expansion planning (TEP) considering the uncertainties of wind and load, which is known as stochastic TEP (STEP). The uncertainty can be accurately represented by a large number of scenarios, which need to be reduced to a relatively small number in order to shorten the computational time required by the STEP. The forward selection algorithm (FSA) is an accurate scenario reduction method which, however, is quite time consuming. An improved FSA (IFSA) is proposed in order to shorten the computational time. The STEP is a large-scale mixed-integer programming problem, and, therefore, is difficult to be solved directly. Benders decomposition algorithm is suitable to solve the STEP by decomposing it into master and multiple slave problems. The slave problems are nonlinear and thereby are difficult and time consuming to be solved. In this regard, a linearization method is proposed to solve the slave problems faster and to calculate the Lagrangian multipliers needed by the master problem. Two medium and a large datasets are used to demonstrate the efficiency of the IFSA and a 24-, a 300-, and a 2383-bus test systems are used to verify the efficiency of the linearization method.