Hierarchical distributed ADMM for predictive control with applications in power networks

Publikationstyp
Journal Article
Date Issued
2018-03-30
Sprache
English
Author(s)
Braun, Philipp  orcid-logo
Faulwasser, Timm  
Grüne, Lars  
Kellett, Christopher M.  
Weller, Steven R.
Worthmann, Karl  
TORE-URI
https://hdl.handle.net/11420/46283
Journal
IFAC Journal of Systems and Control  
Volume
3
Start Page
10
End Page
22
Citation
IFAC Journal of Systems and Control 3: 10-22 (2018)
Publisher DOI
10.1016/j.ifacsc.2018.01.001
Scopus ID
2-s2.0-85050092171
Publisher
Elsevier
In this paper, we investigate optimal control and operation of a network of linear, physically decoupled systems with a coupling in the objective function. To deal with the corresponding distributed control problem, we propose a new Model Predictive Control (MPC) scheme based on the Alternating Direction Method of Multipliers (ADMM). In particular, we thoroughly investigate the flexibility of the proposed hierarchical distributed MPC algorithm with respect to both its plug-and-play capability and changes in the (local) system dynamics and objective functions at runtime. Moreover, we show linear scalability in the number of subsystems. The efficacy of the distributed optimization algorithm embedded in MPC is illustrated on three battery scheduling problems arising from the predictive control of residential microgrid electricity networks.
Subjects
Alternating direction method of multipliers (ADMM)
Large-scale systems
Model predictive control (MPC)
Optimal scheduling
Smart grid
DDC Class
004: Computer Sciences
510: Mathematics