# Enabling Fast Unit Commitment Constraint Screening via Learning Cost Model

arXiv: https://arxiv.org/abs/2212.00483 · HTML: https://mlsanity.com/paper/2212.00483/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 1 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems](https://mlsanity.com/paper/1902.01697/) (2019)
- [Data-Driven Screening of Network Constraints for Unit Commitment](https://mlsanity.com/paper/1907.04694/) (2019)
- [Implied Constraint Satisfaction in Power System Optimization: The Impacts of Load Variations](https://mlsanity.com/paper/1904.01757/) (2019)
- [Cost-driven Screening of Network Constraints for the Unit Commitment Problem](https://mlsanity.com/paper/2104.05746/) (2021)
- [Influence of Stochastic Dependence on Network Constraints Screening for Unit Commitment](https://mlsanity.com/paper/2208.12139/) (2022)

## Built on by (most cited first, 1 of 1)

- [Learning Optimal Power Flow Value Functions with Input-Convex Neural Networks](https://mlsanity.com/paper/2310.04605/) (2023)

## Abstract (arXiv)

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.