DeepOPF: A FeasibilityOptimized Deep Neural Network Approach for AC Optimal Power Flow Problems
Abstract
High percentage penetrations of renewable energy generations introduce significant uncertainty into power systems. It requires grid operators to solve alternative current optimal power flow (ACOPF) problems more frequently for economical and reliable operation in both transmission and distribution grids. In this paper, we develop a Deep Neural Network (DNN) approach, called DeepOPF, for solving ACOPF problems in a fraction of the time used by conventional solvers. A key difficulty for applying machine learning techniques for solving ACOPF problems lies in ensuring that the obtained solutions respect the equality and inequality physical and operational constraints. Generalized the 2stage procedure in [1], [2], DeepOPF first trains a DNN model to predict a set of independent operating variables and then directly compute the remaining dependable ones by solving power flow equations. Such an approach not only preserves the powerflow balance equality constraints but also reduces the number of variables to predict by the DNN, cutting down the number of neurons and training data needed. DeepOPF then employs a penalty approach with a zeroorder gradient estimation technique in the training process to preserve the remaining inequality constraints. As another contribution, we drive a condition for tuning the size of the DNN according to the desired approximation accuracy, which measures the DNN generalization capability. It provides theoretical justification for using DNN to solve the ACOPF problem. Simulation results of IEEE 30/118/300bus and a synthetic 2000bus test cases show that DeepOPF speeds up the computing time by up to two orders of magnitude as compared to a stateoftheart solver, at the expense of $<$0.1% cost difference.
 Publication:

arXiv eprints
 Pub Date:
 July 2020
 arXiv:
 arXiv:2007.01002
 Bibcode:
 2020arXiv200701002P
 Keywords:

 Electrical Engineering and Systems Science  Systems and Control;
 Computer Science  Machine Learning
 EPrint:
 13 pages, 2 figures