  We develop DeepOPF as a Deep Neural Network (DNN) approach for solving direct
current optimal power flow (DC-OPF) problems. DeepOPF is inspired by the
observation that solving DC-OPF for a given power network is equivalent to
characterizing a high-dimensional mapping between the load inputs and the
dispatch and transmission decisions. We construct and train a DNN model to
learn such mapping, then we apply it to obtain optimized operating decisions
upon arbitrary load inputs. We adopt uniform sampling to address the
over-fitting problem common in generic DNN approaches. We leverage on a useful
structure in DC-OPF to significantly reduce the mapping dimension, subsequently
cutting down the size of our DNN model and the amount of training data/time
needed. We also design a post-processing procedure to ensure the feasibility of
the obtained solution. Simulation results of IEEE test cases show that DeepOPF
always generates feasible solutions with negligible optimality loss, while
speeding up the computing time by two orders of magnitude as compared to
conventional approaches implemented in a state-of-the-art solver.
