Machine Learning Assisted Stochastic Unit Commitment During Hurricanes With Predictable Line Outages
Machine Learning Assisted Stochastic Unit Commitment During Hurricanes With Predictable Line Outages
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DOI：
10.1109/tpwrs.2021.3069443
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发表时间：
2021-11-01
影响因子：
6.6
通讯作者：
Hatziargyriou, Nikos D.
中科院分区：
文献类型：
--
作者：
Mohammadi, Farshad;Sahraei-Ardakani, Mostafa;Hatziargyriou, Nikos D.
随机单元承诺是一种有效的解决存在显著不确定性的电网运行问题的方法。一个例子是在预报的飓风期间进行的操作，该飓风具有不确定的线路中断。然而，解决方案的质量是以大量的计算负担为代价的，这使得它的采用具有挑战性。本文评估了一些可能的方法，机器学习可以用来减少这种计算负担。首先，进行了一系列可行性研究。结果表明，使用机器学习作为随机单元承诺求解器的助手比使用它作为独立求解器更有利。特别是，机器学习模型通过确定可以从原始问题中去除的不必要的约束而不影响最终精度来促进问题的解决。可以用作机器学习模型的输入特征/预测因子或输出的变量是通过可行性研究确定的。然后，提出了一种训练和利用机器学习模型的算法。该方法在南卡罗来纳州的500总线合成系统上进行了测试。各种测试用例表明，通过使用训练有素的机器学习模型来辅助随机单元承诺求解器，求解时间平均减少了90%以上。
Stochastic unit commitment is an efficient method for grid operation in the presence of significant uncertainties. An example is an operation during a predicted hurricane with uncertain line out-ages. However, the solution quality comes at the cost of substantial computational burden, which makes its adoption challenging. This paper evaluates some possible ways that machine learning can be used to reduce this computational burden. First, a set of feasibility studies is conducted. Results suggest that using machine learning as an assistant to the stochastic unit commitment solver is more advantageous than using it as a standalone solver. In particular, the machine learning model is trained to facilitate solving the problem by determining the unnecessary constraints that can be removed from the original problem without affecting the final accuracy. The variables that can be used as input features/predictors or outputs for the machine learning model are determined through feasibility studies. Then, an algorithm to train and utilize a machine learning model is proposed. The method is tested on a 500-bus synthetic South Carolina system. Various test cases show an average reduction in solution time by more than 90% by using the trained machine learning model to assist the stochastic unit commitment solver.