Bridging Data-Driven and Model-Based Methods: A Learn-to-Optimize Architecture for Distributed Optimal Power Flow
It addresses the need for fast, interpretable distributed optimization in power systems, offering a hybrid approach that balances speed and accuracy.
This paper proposes a learn-to-optimize architecture for distributed optimal power flow that achieves near-instantaneous decision-making with comparable optimality to state-of-the-art solvers and better feasibility than existing data-driven methods.
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.