Enabling Fast Unit Commitment Constraint Screening via Learning Cost Model
This work addresses the computational bottleneck for transmission system operators in power systems by improving constraint screening efficiency, though it is incremental as it builds on existing optimization-based methods.
The paper tackles the slow solution process of large-scale unit commitment (UC) problems by proposing a machine learning model to predict economical costs from load inputs, enabling more effective constraint screening and reducing computation time in various setups.
Unit commitment (UC) are essential tools to transmission system operators for finding the most economical and feasible generation schedules and dispatch signals. Constraint screening has been receiving attention as it holds the promise for reducing a number of inactive or redundant constraints in the UC problem, so that the solution process of large scale UC problem can be accelerated by considering the reduced optimization problem. Standard constraint screening approach relies on optimizing over load and generations to find binding line flow constraints, yet the screening is conservative with a large percentage of constraints still reserved for the UC problem. In this paper, we propose a novel machine learning (ML) model to predict the most economical costs given load inputs. Such ML model bridges the cost perspectives of UC decisions to the optimization-based constraint screening model, and can screen out higher proportion of operational constraints. We verify the proposed method's performance on both sample-aware and sample-agnostic setting, and illustrate the proposed scheme can further reduce the computation time on a variety of setup for UC problems.