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：

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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。