论文标题
大规模电力网络的分层优化体系结构
A Hierarchical Optimization Architecture for Large-Scale Power Networks
论文作者
论文摘要
我们为大规模电力网络提供了层次优化架构，该体系结构克服了完全集中和完全分散的体系结构的局限性。该体系结构利用了多机计算方案的原理，这些原理被广泛用于大规模平行计算机上的部分微分方程的解决方案。体系结构的顶层使用整个网络的粗略表示，而底层则由一个分散的优化剂组成，各个分散的优化代理都以完整分辨率在网络子域上运行。我们使用乘数（ADMM）框架的交替方向方法来驱动分散剂的协调。我们表明，从顶层获得的状态和双重信息可用于加速分散的优化剂的协调，并恢复整个系统的最佳性。我们证明了分层体系结构可用于管理大型微电网集合。
We present a hierarchical optimization architecture for large-scale power networks that overcomes limitations of fully centralized and fully decentralized architectures. The architecture leverages principles of multigrid computing schemes, which are widely used in the solution of partial differential equations on massively parallel computers. The top layer of the architecture uses a coarse representation of the entire network while the bottom layer is composed of a family of decentralized optimization agents each operating on a network subdomain at full resolution. We use an alternating direction method of multipliers (ADMM) framework to drive coordination of the decentralized agents. We show that state and dual information obtained from the top layer can be used to accelerate the coordination of the decentralized optimization agents and to recover optimality for the entire system. We demonstrate that the hierarchical architecture can be used to manage large collections of microgrids.
