# Neural Two-Stage Stochastic Optimization for Solving Unit Commitment Problem

arXiv: https://arxiv.org/abs/2507.09503 · HTML: https://mlsanity.com/paper/2507.09503/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 13 Jul 2025.

## Builds on (most cited first, 9 of 9)

- [Strong mixed-integer programming formulations for trained neural networks](https://mlsanity.com/paper/1811.08359/) (2018)
- [Maximum Resilience of Artificial Neural Networks](https://mlsanity.com/paper/1705.01040/) (2017)
- [OMLT: Optimization & Machine Learning Toolkit](https://mlsanity.com/paper/2202.02414/) (2022)
- [ReLU Networks as Surrogate Models in Mixed-Integer Linear Programs](https://mlsanity.com/paper/1907.03140/) (2019)
- [Neur2SP: Neural Two-Stage Stochastic Programming](https://mlsanity.com/paper/2205.12006/) (2022)
- [Optimal Energy System Scheduling Using A Constraint-Aware Reinforcement Learning Algorithm](https://mlsanity.com/paper/2305.05484/) (2023)
- [An Efficient Robust Solution to the Two-Stage Stochastic Unit Commitment Problem](https://mlsanity.com/paper/1606.06014/) (2016)
- [Towards Improving Unit Commitment Economics: An Add-On Tailor for Renewable Energy and Reserve Predictions](https://mlsanity.com/paper/2208.13065/) (2022)
- [Chance-Constrained Two-Stage Unit Commitment under Uncertain Load and Wind Power Output Using Bilinear Benders Decomposition](https://mlsanity.com/paper/1611.08775/) (2016)

## Abstract (arXiv)

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.