AI 中文总结
提出优化感知机器学习框架，通过生成发电机特定置信度阈值来固定机组组合决策，缩小搜索空间，实现低于0.5%最优性差距和超20倍加速，获2025年EPRI竞赛冠军。
AI 中文摘要
机组组合是一个计算要求高的混合整数线性优化问题，需要在调度时间范围内做出许多二进制承诺决策。为了减少这种计算负担，机器学习方法可以预测这些决策的一个子集，从而缩小优化求解器探索的搜索空间。现有的基于置信度的方法根据用户定义的概率阈值来确定要固定的变量，这些阈值与下游优化问题无关。我们通过引入一个优化感知框架来解决这一限制，该框架基于固定误差对机组组合解质量的影响，生成特定于发电机的置信度阈值。该方法在2025年EPRI AI加速机组组合竞赛中获得第一名。所提出的框架实现了低于0.5%的平均最优性差距，同时提供了超过20倍的平均加速。因此，它为将概率预测转化为混合整数搜索空间的可控缩减提供了一种通用机制，直接将学习决策与其下游计算和经济后果联系起来。
英文摘要
Unit Commitment is a computationally demanding mixed-integer linear optimisation problem requiring many binary commitment decisions across a scheduling horizon. To reduce this computational burden, machine learning approaches can predict a subset of these decisions, thereby shrinking the search space explored by the optimisation solver. Existing confidence-based approaches determine which variables to fix based on user-defined probability thresholds, which are agnostic to the downstream optimisation problem. We address this limitation by introducing an optimisation-aware framework that yields generator-specific confidence thresholds based on the impact of fixing errors on Unit Commitment solution quality. This approach won first place in the 2025 EPRI AI-ccelerating Unit Commitment competition. The proposed framework achieves a mean optimality gap below 0.5% while also delivering an average speed-up of more than 20x. It therefore provides a general mechanism for translating probabilistic predictions into controlled reductions of mixed-integer search spaces, directly linking learning decisions to their downstream computational and economic consequences.
