论文标题
全球融合保证的AC OPF的两级ADMM算法
A Two-level ADMM Algorithm for AC OPF with Global Convergence Guarantees
论文作者
论文摘要
本文提出了一个两级分布式算法框架，用于解决AC最佳功率流（OPF）问题，并提供收敛保证。 OPF中高度非凸约限制的存在对分布式算法提出了重大挑战，该算法基于乘数的交替方向方法（ADMM）。特别是，对于非convex网络优化问题（例如AC OPF），无法保证收敛性。为了克服这一困难，我们为AC OPF和一种两级ADMM算法提出了新的分布式重新制定，该算法超出了ADMM的标准框架。我们在轻度假设下建立了所提出算法的全球收敛性和迭代复杂性。在NESTA和PGLIB-OPF（多达30,000个总线系统）的一些最大测试用例上进行了广泛的数值实验，证明了所提出的算法在收敛性，可伸缩性和鲁棒性方面比现有ADMM变体的优势。此外，在适当的并行实施下，所提出的算法表现出与最先进的集中式求解器相当甚至更好的快速收敛。
This paper proposes a two-level distributed algorithmic framework for solving the AC optimal power flow (OPF) problem with convergence guarantees. The presence of highly nonconvex constraints in OPF poses significant challenges to distributed algorithms based on the alternating direction method of multipliers (ADMM). In particular, convergence is not provably guaranteed for nonconvex network optimization problems like AC OPF. In order to overcome this difficulty, we propose a new distributed reformulation for AC OPF and a two-level ADMM algorithm that goes beyond the standard framework of ADMM. We establish the global convergence and iteration complexity of the proposed algorithm under mild assumptions. Extensive numerical experiments over some largest test cases from NESTA and PGLib-OPF (up to 30,000-bus systems) demonstrate advantages of the proposed algorithm over existing ADMM variants in terms of convergence, scalability, and robustness. Moreover, under appropriate parallel implementation, the proposed algorithm exhibits fast convergence comparable to or even better than the state-of-the-art centralized solver.
