论文标题
在智能网格中使用Aladin进行MPC的分布式优化
Distributed Optimization using ALADIN for MPC in Smart Grids
论文作者
论文摘要
本文介绍了一种分布式优化算法，该算法是针对解决智能电网引起的优化问题的分布式优化算法。详细介绍，我们提出了增强的基于拉格朗日的交替方向不可推动的牛顿（Aladin）方法的变体，该方法随附了全局收敛的保证，可保证一类经过的线性界限 - 偏端优化问题。我们建立了拟议方案的局部二次收敛，并与乘数交替方向方法（ADMM）相比阐述了其优势。特别是，我们表明，以更多的交流为代价，阿拉丁需要更少的迭代才能达到所需的准确性。此外，从数值上证明迭代次数与子系统的数量无关。通过在基准案例研究上同时运行Aladin和基于ADMM的模型预测控制器来说明所提出的方案的有效性。
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.
