Neural Two-Stage Stochastic Optimization for Solving Unit Commitment Problem

• URL: http://arxiv.org/abs/2507.09503v1
• Date: Sun, 13 Jul 2025 05:55:25 GMT
• Title: Neural Two-Stage Stochastic Optimization for Solving Unit Commitment Problem
• Authors: Zhentong Shao, Jingtao Qin, Nanpeng Yu,
• Abstract summary: This paper proposes a neural optimization method for efficiently solving the two-stage unit commitment (2S-SUC) problem under high-dimensional uncertainty scenarios.<n>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.
• Score: 0.8848340429852071
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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