Learning to Optimize Power Distribution Grids using Sensitivity-Informed Deep Neural Networks

Understand

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.