Bridging Data-Driven and Model-Based Methods: A Learn-to-Optimize Architecture for Distributed Optimal Power Flow

Abstract

This letter proposes a learn-to-optimize (LTO) architecture for distributed optimal power flow (D-OPF) as the nexus between data-driven and model-based methods. By unfolding alternating direction method of multipliers (ADMM) into a deep neural network (NN) and embedding differentiable optimization layers, our architecture realizes near-instantaneous interpretable distributed decision-making. For mainstream relaxed formulations of D-OPF, the decisions from our architecture achieve comparable optimality with that of state-of-the-art solvers and excelled feasibility compared with existing data-driven approaches. Comparative case studies underpin the effectiveness of our architecture regarding the optimality and feasibility.