用神经网络加速电力调度中的随机优化，精度高且快十倍以上
Neural Two-Stage Stochastic Optimization for Solving Unit Commitment Problem
- 用深度神经网络近似第二阶段调度成本，替代传统复杂计算
- 在多个电网系统上实现小于1%的误差率，速度提升数量级
- 模型规模不随场景增多而变大，适合大规模电力系统应用
本文提出一种基于神经网络的随机优化方法，用于高效求解高维不确定性下的两阶段随机机组组合（2S-SUC）问题。该方法利用深度神经网络，将启停决策与不确定性特征映射为补救成本，训练后嵌入第一阶段机组组合问题，构建混合整数线性规划（MILP），在保留关键不确定性特性的同时显式满足运行约束。采用情景嵌入网络实现任意情景集的降维与特征聚合，构成数据驱动的情景缩减机制。在IEEE 5-bus、30-bus和118-bus系统上的数值实验表明，所提方法所得解的最优性间隙低于1%，相比传统MILP求解器和分解方法实现数量级加速。此外，模型规模不随情景数量增加而变化，显著提升了大规模随机机组组合问题的可扩展性。
原文摘要 · Abstract (English)
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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