Efficient Unit Commitment Constraint Screening under Uncertainty

TL;DR

This paper introduces a novel constraint screening method for day-ahead unit commitment under uncertainty, significantly reducing problem size and computation time while improving feasibility and efficiency in power system optimization.

Contribution

The paper develops a new screening approach that handles uncertainty in UC, utilizing multi-parametric programming and multi-area techniques for large-scale problems.

Findings

Screening time reduced by up to 131.3 times.

Improved feasibility with robust screening.

More efficient models with chance-constrained screening.

Abstract

Day-ahead unit commitment (UC) is a fundamental task for power system operators, where generator statuses and power dispatch are determined based on the forecasted nodal net demands. The uncertainty inherent in renewables and load forecasting requires the use of techniques in optimization under uncertainty to find more resilient and reliable UC solutions. However, the solution procedure of such specialized optimization may differ from the deterministic UC. The original constraint screening approach can be unreliable and inefficient for them. Thus, in this work we design a novel screening approach under the forecasting uncertainty. Our approach accommodates such uncertainties in both chance-constrained and robust forms, and can greatly reduce the UC instance size by screening out non-binding constraints. To further improve the screening efficiency, we utilize the multi-parametric programming theory to convert the underlying optimization problem of the screening model to a piecewise affine function. A multi-area screening approach is further developed to handle the computational intractability issues for large-scale problems. We verify the proposed method's performance on a variety of UC setups and uncertainty situations. Experimental results show that our robust screening procedure can guarantee better feasibility, while the CC screening can produce more efficient reduced models. The average screening time for a single line flow constraint can be accelerated by 71.2X to 131.3X using our proposed method.