Unsupervised Deep Learning for AC Optimal Power Flow via Lagrangian Duality
This work addresses real-time operation challenges in large-scale power networks, offering an incremental improvement over supervised methods by removing dependency on solver-generated datasets.
The paper tackles the computational complexity of AC optimal power flow in power systems by proposing an unsupervised deep learning framework that eliminates the need for conventional solvers in training, achieving a feasible solution with reduced computational time.
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.