A Neural Column-and-Constraint Generation Method for Solving Two-Stage Stochastic Unit Commitment
This addresses the challenge of real-time optimization in power systems with renewable energy uncertainties, offering a scalable solution for industry applications.
The paper tackles the computational inefficiency of solving two-stage stochastic unit commitment problems for power systems by introducing a Neural Column-and-Constraint Generation method, achieving up to 130.1x speedup with a mean optimality gap below 0.096% on the IEEE 118-bus system.
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.