Learning to Optimize Distributed Optimization: ADMM-based DC-OPF Case Study

The decision-making paradigms of future energy systems are increasingly becoming decentralized and multi-entity/agent. The Alternating Direction Method of Multipliers (ADMM) has been widely used to address the computational needs of decentralized decision-making problems, e.g., optimal power flow (OPF). In this paper, we propose a novel data-driven method to accelerate the convergence of ADMM for decentralized DC-OPF, where our optimizer will learn the iterative behavior of agents to produce a high-quality feasible solution. The proposed method utilizes the gauge maps to enforce feasibility with respect to agents’ local constraints while iteratively penalizing violations of the shared constraints. We used the IEEE 57-bus system to showcase the performance of the proposed method. Our experimental results demonstrate significant run-time reduction for using ADMM to solve the DC-OPF problem.