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Title data
Braun, Philipp ; Grüne, Lars ; Kellett, Christopher M. ; Weller, Steven R. ; Worthmann, Karl:
Predictive control of a smart grid: a distributed optimization algorithm with centralized performance properties.
Department of Mathematics, University of Bayreuth
Bayreuth
,
2015
. - 8 S.
![[thumbnail of pred_cont_of_smart_grid_dits_opt_alg_with_cent_perf_prop2015braun_gruene_kellett_weller_worthmann.pdf]](https://epub.uni-bayreuth.de/style/images/fileicons/application_pdf.png) |
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Project information
| Project title: |
Project's official title Project's id DFG Priority Program 1305 "Control theory for digitally networked dynamical systems", Project "Performance Analysis for Distributed and Multiobjective Model Predictive Control" GR1569/13-1 ARC Future Fellowship FT1101000746 |
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| Project financing: |
Deutsche Forschungsgemeinschaft Australian Research Council |
Abstract
The authors recently proposed several model predictive control (MPC) approaches to managing residential level energy generation and storage, including centralized, distributed, and decentralized schemes. As expected, the distributed and decentralized schemes result in a loss of performance but are scalable and more flexible with regards to network topology. In this paper we present a distributed optimization approach which asymptotically recovers the performance of the centralized optimization problem performed in MPC at each time step. Simulations using data from an Australian electricity distribution company, Ausgrid, are provided showing the benefit of a variable step size in the algorithm and the impact of an increasing number of participating residential energy systems. Furthermore, when used in a receding horizon scheme, simulations indicate that terminating the iterative distributed optimization algorithm before convergence does not result in a significant loss of performance.
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