Examination of Learning-Augmented Approaches for Initializing Distributed Optimal Power Flow With Consensus ADMM

The rapid integration of distributed energy resources requires optimization and control of power systems with many controllable devices, driving a growing interest in efficient distributed optimization algorithms. To this end, this paper proposes and examines learning-augmented initialization to enhance the convergence speed of Alternating Direction Method of Multipliers (ADMM) for solving the distributed DC and AC optimal power flow (OPF) problems. The core concept is to leverage deep learning techniques, designed with the feedforward neural network and recurrent neural network, as auxiliary tools to accelerate the convergence of ADMM.We perform comprehensive numerical case studies and empirically validate the benefits of the proposed methods with the IEEE 14, 118, and 1888-bus test networks in the DC model and IEEE 14, 118, 2746-bus test networks in the AC model under different loading scenarios. In particular, the proposed method has achieved up to 78% reduction in the average number of ADMM iterations for the DC-OPF problem and 48% reduction for the AC-OPF problem. These findings illustrate a significant potential of combining deep learning frameworks with the ADMM and possibly other distributed optimization algorithms to enhance the efficiency and reliability of future power system and energy market operations.