Neural Two-Stage Stochastic Optimization for Solving Unit Commitment Problem

TL;DR

This paper introduces a neural stochastic optimization approach for the two-stage stochastic unit commitment problem, achieving high accuracy and scalability in complex power system scenarios.

Contribution

It develops a neural network-based method that approximates recourse costs and embeds into MILP, enabling efficient and scalable solutions for large-scale stochastic unit commitment.

Findings

Achieves less than 1% optimality gap in experiments.

Provides orders-of-magnitude speedup over traditional methods.

Model size remains constant regardless of scenario number.

Abstract

This paper proposes a neural stochastic optimization method for efficiently solving the two-stage stochastic unit commitment (2S-SUC) problem under high-dimensional uncertainty scenarios. The proposed method approximates the second-stage recourse problem using a deep neural network trained to map commitment decisions and uncertainty features to recourse costs. The trained network is subsequently embedded into the first-stage UC problem as a mixed-integer linear program (MILP), allowing for explicit enforcement of operational constraints while preserving the key uncertainty characteristics. A scenario-embedding network is employed to enable dimensionality reduction and feature aggregation across arbitrary scenario sets, serving as a data-driven scenario reduction mechanism. Numerical experiments on IEEE 5-bus, 30-bus, and 118-bus systems demonstrate that the proposed neural two-stage stochastic optimization method achieves solutions with an optimality gap of less than 1%, while enabling orders-of-magnitude speedup compared to conventional MILP solvers and decomposition-based methods. Moreover, the model's size remains constant regardless of the number of scenarios, offering significant scalability for large-scale stochastic unit commitment problems.