Learning-Accelerated ADMM for Distributed Optimal Power Flow

Abstract

We propose a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared between independent network partitions. Using previous observations of ADMM trajectories for a given system under varying load, the method trains a recurrent neural network (RNN) to predict the converged values of dual and consensus variables. Given a new realization of system load, a small number of initial ADMM iterations is taken as input to infer the converged values and directly inject them into the iteration. We empirically demonstrate that the online injection of these values into the ADMM iteration accelerates convergence by a significant factor for partitioned 14-, 118- and 2848-bus test systems under differing load scenarios. The proposed method has several advantages: it maintains the security of private decision variables inherent in consensus ADMM; inference is fast and so may be used in online settings; RNN-generated predictions can dramatically improve time to convergence but, by construction, can never result in infeasible ADMM subproblems; it can be easily integrated into existing software implementations. While we focus on the ADMM formulation of distributed DC-OPF in this paper, the ideas presented are naturally extended to other distributed optimization problems.

References in corpus (11)

• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• Learning Combinatorial Optimization Algorithms over Graphs
• Multi-Agent Distributed Optimization via Inexact Consensus ADMM
• The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms
• Data-driven Local Control Design using Optimization and Machine Learning Techniques
• AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks
• ADMM and Accelerated ADMM as Continuous Dynamical Systems
• High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow
• DeepOPF+: A Deep Neural Network Approach for DC Optimal Power Flow for Ensuring Feasibility
• Gauss-Newton Unrolled Neural Networks and Data-driven Priors for Regularized PSSE with Robustness
• DeepOPF: Deep Neural Network for DC Optimal Power Flow

Cited by in corpus (1)

• DeepOPF+: A Deep Neural Network Approach for DC Optimal Power Flow for Ensuring Feasibility