Learning-Assisted Variables Reduction Method for Large-Scale MILP Unit Commitment

Abstract

The security-constrained unit commitment (SCUC) challenge is solved repeatedly several times every day, for operations in a limited time. Typical mixed-integer linear programming (MILP) formulations are intertemporal in nature and have complex and discrete solution spaces that exponentially increase with system size. Improvements in the SCUC formulation and/or solution method that yield a faster solution hold immense economic value, as less time can be spent finding the best-known solution. Most machine learning (ML) methods in the literature either provide a warm start or convert the MILP-SCUC formulation to a continuous formulation, possibly leading to sub-optimality and/or infeasibility. In this paper, we propose a novel ML-based variables reduction method that accurately determines the optimal schedule for a subset of trusted generators, shrinking the MILP-SCUC formulation and dramatically reducing the search space. ML indicators sets are created to shrink the MILP-SCUC model, leading to improvement in the solution quality. Test results on IEEE systems with 14, 118, and 300 busses, the Ontario system, and Polish systems with 2383 and 3012 busses report significant reductions in solution times in the range of 48% to 98%. This is a promising tool for system operators to solve the MILP-SCUC with a lower optimality gap in a limited-time operation, leading to economic benefits.