A Neural Column-and-Constraint Generation Method for Solving Two-Stage Stochastic Unit Commitment

TL;DR

This paper presents a Neural Column-and-Constraint Generation method that accelerates solving two-stage stochastic unit commitment problems by replacing traditional subproblem solving with neural network evaluations, enabling faster and scalable power system optimization.

Contribution

The paper introduces a neural network integrated into the CCG framework to significantly speed up 2S-SUC problem solving, maintaining high solution quality.

Findings

Achieves up to 130.1× speedup over traditional methods.

Maintains a mean optimality gap below 0.096%.

Validated on IEEE 118-bus system.

Abstract

Two-stage stochastic unit commitment (2S-SUC) problems have been widely adopted to manage the uncertainties introduced by high penetrations of intermittent renewable energy resources. While decomposition-based algorithms such as column-and-constraint generation has been proposed to solve these problems, they remain computationally prohibitive for large-scale, real-time applications. In this paper, we introduce a Neural Column-and-Constraint Generation (Neural CCG) method to significantly accelerate the solution of 2S-SUC problems. The proposed approach integrates a neural network that approximates the second-stage recourse problem by learning from high-level features of operational scenarios and the first-stage commitment decisions. This neural estimator is embedded within the CCG framework, replacing repeated subproblem solving with rapid neural evaluations. We validate the effectiveness of the proposed method on the IEEE 118-bus system. Compared to the original CCG and a state-of-the-art commercial solver, Neural CCG achieves up to 130.1$\times$ speedup while maintaining a mean optimality gap below 0.096\%, demonstrating its strong potential for scalable stochastic optimization in power system.