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.

未来能源系统的决策范式日益变得分散化和多实体/主体化。交替方向乘子法（ADMM）已被广泛用于解决分散决策问题的计算需求，例如最优潮流（OPF）。在本文中，我们提出了一种新的数据驱动方法来加速用于分散式直流最优潮流（DC - OPF）的交替方向乘子法的收敛，我们的优化器将学习主体的迭代行为以产生高质量的可行解。所提出的方法利用规范映射在迭代惩罚违反共享约束的同时，确保主体局部约束的可行性。我们使用IEEE 57节点系统展示了所提方法的性能。我们的实验结果表明，使用交替方向乘子法解决直流最优潮流问题时，运行时间显著减少。