The system operators usually need to solve large-scale unit commitment problems within limited time frame for computation. This paper provides a pragmatic solution, showing how by learning and predicting the on/off commitment decisions of conventional units, there is a potential for system operators to warm start their solver and speed up their computation significantly. For the prediction, we train linear and kernelized support vector machine classifiers, providing an out-of-sample performance guarantee if properly regularized, converting to distributionally robust classifiers. For the unit commitment problem, we solve a mixed-integer second-order cone problem. Our results based on the IEEE 6- and 118-bus test systems show that the kernelized SVM with proper regularization outperforms other classifiers, reducing the computational time by a factor of 1.7. In addition, if there is a tight computational limit, while the unit commitment problem without warm start is far away from the optimal solution, its warmly-started version can be solved to (near) optimality within the time limit.

翻译：系统运营商通常需要在有限的计算时限内解决大规模机组组合问题。本文提供了一种实用方案，展示了通过学习并预测常规机组的开/关组合决策，系统运营商可借此对求解器进行热启动，从而显著加速计算过程。在预测方面，我们训练了线性与核化支持向量机分类器，通过适当正则化可提供样本外性能保证，并将其转化为分布鲁棒分类器。针对机组组合问题，我们求解了一个混合整数二阶锥规划问题。基于IEEE 6节点和118节点测试系统的结果表明，经适当正则化的核化支持向量机分类器优于其他分类器，可将计算时间缩短1.7倍。此外，在存在严格计算时限的情况下，虽然未采用热启动的机组组合问题远未达到最优解，但其热启动版本可在时限内求解至（近）最优性。