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Unit commitment (UC) are essential tools to transmission system operators forfinding the most economical and feasible generation schedules and dispatchsignals. Constraint screening has been receiving attention as it holds thepromise for reducing a number of inactive or redundant constraints in the UCproblem, so that the solution process of large scale UC problem can beaccelerated by considering the reduced optimization problem. Standardconstraint screening approach relies on optimizing over load and generations tofind binding line flow constraints, yet the screening is conservative with alarge percentage of constraints still reserved for the UC problem. In thispaper, we propose a novel machine learning (ML) model to predict the mosteconomical costs given load inputs. Such ML model bridges the cost perspectivesof UC decisions to the optimization-based constraint screening model, and canscreen out higher proportion of operational constraints. We verify the proposedmethod's performance on both sample-aware and sample-agnostic setting, andillustrate the proposed scheme can further reduce the computation time on avariety of setup for UC problems.

Enabling Fast Unit Commitment Constraint Screening via Learning Cost Model