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Distributed Optimization using ALADIN for MPC in Smart Grids
arXiv - CS - Systems and Control Pub Date : 2020-04-03 , DOI: arxiv-2004.01522 Yuning Jiang, Philipp Sauerteig, Boris Houska, Karl Worthmann
arXiv - CS - Systems and Control Pub Date : 2020-04-03 , DOI: arxiv-2004.01522 Yuning Jiang, Philipp Sauerteig, Boris Houska, Karl Worthmann
This paper presents a distributed optimization algorithm tailored to solve
optimization problems arising in smart grids. In detail, we propose a variant
of the Augmented Lagrangian based Alternating Direction Inexact Newton (ALADIN)
method, which comes along with global convergence guarantees for the considered
class of linear-quadratic optimization problems. We establish local quadratic
convergence of the proposed scheme and elaborate its advantages compared to the
Alternating Direction Method of Multipliers (ADMM). In particular, we show
that, at the cost of more communication, ALADIN requires fewer iterations to
achieve the desired accuracy. Furthermore, it is numerically demonstrated that
the number of iterations is independent of the number of subsystems. The
effectiveness of the proposed scheme is illustrated by running both an ALADIN
and an ADMM based model predictive controller on a benchmark case study.
中文翻译:
在智能电网中使用 ALADIN 进行分布式优化 MPC
本文提出了一种分布式优化算法,专门用于解决智能电网中出现的优化问题。详细地说,我们提出了一种基于增广拉格朗日的交替方向不精确牛顿 (ALADIN) 方法的变体,它为所考虑的线性二次优化问题类提供了全局收敛保证。我们建立了所提出方案的局部二次收敛,并阐述了与乘法器交替方向法(ADMM)相比的优势。特别是,我们表明,以更多的通信为代价,ALADIN 需要更少的迭代来达到所需的精度。此外,数值证明了迭代次数与子系统的数量无关。
更新日期:2020-04-06
中文翻译:
在智能电网中使用 ALADIN 进行分布式优化 MPC
本文提出了一种分布式优化算法,专门用于解决智能电网中出现的优化问题。详细地说,我们提出了一种基于增广拉格朗日的交替方向不精确牛顿 (ALADIN) 方法的变体,它为所考虑的线性二次优化问题类提供了全局收敛保证。我们建立了所提出方案的局部二次收敛,并阐述了与乘法器交替方向法(ADMM)相比的优势。特别是,我们表明,以更多的通信为代价,ALADIN 需要更少的迭代来达到所需的精度。此外,数值证明了迭代次数与子系统的数量无关。