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Distributed Optimization Using ALADIN for MPC in Smart Grids
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DOI：10.1109/TCST.2020.3033010.png)
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Abstract
En 中文
This article 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 with 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.
Keywords:
Optimization
Batteries
Convergence
Power demand
Smart grids
Benchmark testing
Distributed optimization
model predictive control (MPC)
smart grid
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