Robust Transmission Network Expansion Planning With Uncertain Renewable Generation and Loads

Abstract

This paper presents a robust optimization approach for transmission network expansion planning (TNEP) under uncertainties of renewable generation and load. Unlike conventional stochastic programming, the proposed approach does not require knowledge of the probability distribution of the uncertain net injections; rather the uncertainties of the net injections are specified by a simple uncertainty set. The solution algorithm is exact and produces expansion plans that are robust against all possible realizations of the net injections defined in the uncertainty set; it is based on a Benders decomposition scheme that iterates between a master problem that minimizes the cost of the expansion plan and a slave problem that minimizes the maximum curtailment of load and renewable generation. The paper demonstrates that when adopting the dc load flow model, both the master and the dual slave can be formulated as mixed-integer linear programs for which commercial solvers exist. Numerical results on several networks with uncertainties in their loads and renewable generation show that the proposed approach produces solutions that are superior to those from two recent techniques for robust TNEP design.