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Benders decomposition is widely used to solve large mixed-integer problems.This paper takes advantage of machine learning and proposes enhanced variantsof Benders decomposition for solving two-stage stochastic security-constrainedunit commitment (SCUC). The problem is decomposed into a master problem andsubproblems corresponding to a load scenario. The goal is to reduce thecomputational costs and memory usage of Benders decomposition by creatingtighter cuts and reducing the size of the master problem. Three approaches areproposed, namely regression Benders, classification Benders, andregression-classification Benders. A regressor reads load profile scenarios andpredicts subproblem objective function proxy variables to form tighter cuts forthe master problem. A criterion is defined to measure the level of usefulnessof cuts with respect to their contribution to lower bound improvement. Usefulcuts that contain the necessary information to form the feasible region areidentified with and without a classification learner. Useful cuts areiteratively added to the master problem, and non-useful cuts are discarded toreduce the computational burden of each Benders iteration. Simulation studieson multiple test systems show the effectiveness of the proposed learning-aidedBenders decomposition for solving two-stage SCUC as compared to conventionalmulti-cut Benders decomposition.

Accelerating L-shaped Two-stage Stochastic SCUC with Learning Integrated Benders Decomposition