标题： 基于神经两阶段随机优化的机组组合问题求解方法 显示英文标题

标题： Neural Two-Stage Stochastic Optimization for Solving Unit Commitment Problem

摘要： 本文提出了一种神经随机优化方法，以高效解决高维不确定性场景下的两阶段随机机组组合（2S-SUC）问题。 所提出的方法使用一个深度神经网络来近似第二阶段的应对问题，该网络经过训练，能够将机组组合决策和不确定性特征映射到应对成本。 训练后的网络随后被嵌入到第一阶段的机组组合问题中作为混合整数线性规划（MILP），从而在保留关键不确定性特征的同时显式执行操作约束。 采用了一种场景嵌入网络，以实现任意场景集上的维度约简和特征聚合，作为数据驱动的场景约减机制。 在IEEE 5母线、30母线和118母线系统上的数值实验表明，所提出的神经两阶段随机优化方法获得的解的最优性间隙小于1%，同时相比传统MILP求解器和基于分解的方法实现了数量级的速度提升。 此外，模型的大小不随场景数量的变化而变化，为大规模随机机组组合问题提供了显著的可扩展性。 显示英文摘要

摘要： 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.