A Two-Level ADMM Algorithm for AC OPF With Global Convergence Guarantees
A Two-Level ADMM Algorithm for AC OPF With Global Convergence Guarantees
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发表时间：
2020
影响因子：
6.6
通讯作者：
Kaizhao Sun;X. Sun
中科院分区：
文献类型：
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作者：
Kaizhao Sun;X. Sun
本文提出了一种两级分布式算法框架，用于解决具有收敛保证的交流最优潮流（OPF）问题。 OPF 中高度非凸约束的存在对基于乘法器交替方向法 (ADMM) 的分布式算法提出了重大挑战。特别是，对于像 AC OPF 这样的非凸网络优化问题，收敛性无法得到证明。为了克服这个困难，我们提出了一种新的AC OPF分布式重构和超越ADMM标准框架的两级ADMM算法。我们在温和的假设下建立了所提出算法的全局收敛性和迭代复杂度。对 NESTA 和 PGLib-OPF（最多 30 000 总线系统）的一些最大测试用例进行的广泛数值实验证明了所提出的算法相对于现有 ADMM 变体在收敛性、可扩展性和鲁棒性方面的优势。此外，在适当的并行实现下，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性。
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.