Clustering-based chance-constrained transmission expansion planning using an improved benders decomposition algorithm

This study presents a chance-constrained transmission expansion planning (TEP) approach considering the uncertainty of renewable generation and load. On the basis of the underlying idea of density-based clustering techniques, a novel scenario generation method is presented to characterise the uncertainty sources in the form of representative scenarios. Then, the chance constraints imposed on the sampling scenarios are incorporated into the TEP model to avoid uneconomical transmission investment. The authors further develop an improved Benders decomposition (BD) algorithm with specialised Benders cuts to solve the chance-constrained TEP problem. Numerical examples are given to verify the validity of the proposed TEP approach in simulating uncertainties and providing reasonable planning schemes. Their results on two test systems also demonstrate that the proposed BD algorithm is computationally efficient in solving this kind of chance-constrained TEP problem.