使用灵敏度信息深度神经网络学习优化配电网
Learning to Optimize Power Distribution Grids using Sensitivity-Informed Deep Neural Networks
摘要：
配电网优化的深度学习可以被认为是接近最优但及时的逆变器调度的有前途的解决方案。其原理是训练深度神经网络（DNN）来预测最优潮流（OPF）的解，从而将计算工作从实时转移到离线。尽管如此，在训练这个DNN之前，人们必须解决大量的OPF来创建一个标记的数据集。假设后一步在时间关键的应用中仍然是禁止的，这项工作提出了一种通过考虑OPF最小化器相对于OPF参数的灵敏度来提高DNN预测精度的原创技术。通过扩展多参数规划，它表明，虽然逆变器控制问题可能会表现出双重退化，所需的灵敏度确实存在一般，可以很容易地计算使用任何标准的二次规划（QP）求解器的输出。数值测试表明，敏感性深度学习可以以最小的计算开销将预测精度（均方误差（MSE））提高2-3个数量级。在小数据领域，改进更为重要，DNN必须学习使用一些示例进行优化。除了多参数QP，该方法目前正在推广到参数（非）凸优化问题。
英文摘要：
Deep learning for distribution grid optimization can be advocated as a promising solution for near-optimal yet timely inverter dispatch. The principle is to train a deep neural network (DNN) to predict the solutions of an optimal power flow (OPF), thus shifting the computational effort from real-time to offline. Nonetheless, before training this DNN, one has to solve a large number of OPFs to create a labeled dataset. Granted the latter step can still be prohibitive in time-critical applications, this work puts forth an original technique for improving the prediction accuracy of DNNs by taking into account the sensitivities of the OPF minimizers with respect to the OPF parameters. By expanding on multiparametric programming, it is shown that although inverter control problems may exhibit dual degeneracy, the required sensitivities do exist in general and can be computed readily using the output of any standard quadratic program (QP) solver. Numerical tests showcase that sensitivity-informed deep learning can enhance prediction accuracy in terms of mean square error (MSE) by 2-3 orders of magnitude at minimal computational overhead. Improvements are more significant in the small-data regime, where a DNN has to learn to optimize using a few examples. Beyond multiparametric QPs, the approach is currently being generalized to parametric (non)-convex optimization problems.
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