  Non-convex AC optimal power flow (AC-OPF) is a fundamental optimization
problem in power system analysis. The computational complexity of conventional
solvers is typically high and not suitable for large-scale networks in
real-time operation. Hence, deep learning based approaches have gained
intensive attention to conduct the time-consuming training process offline.
Supervised learning methods may yield a feasible AC-OPF solution with a small
optimality gap. However, they often need conventional solvers to generate the
training dataset. This paper proposes an end-to-end unsupervised learning based
framework for AC-OPF. We develop a deep neural network to output a partial set
of decision variables while the remaining variables are recovered by solving AC
power flow equations. The fast decoupled power flow solver is adopted to
further reduce the computational time. In addition, we propose using a modified
augmented Lagrangian function as the training loss. The multipliers are
adjusted dynamically based on the degree of constraint violation. Extensive
numerical test results corroborate the advantages of our proposed approach over
some existing methods.
