A Distributed Optimization Algorithm for the Predictive Control of Smart Grids

In this paper, we present a hierarchical, iterative distributed optimization algorithm and show that the algorithm converges to the global solution of a particular optimization problem. The motivation for the distributed optimization problem is the predictive control of a smart grid, in which the st...

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Veröffentlicht in:IEEE transactions on automatic control Jg. 61; H. 12; S. 3898 - 3911
Hauptverfasser: Braun, Philipp, Grune, Lars, Kellett, Christopher M., Weller, Steven R., Worthmann, Karl
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.12.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Batteries
Communication
Control over communications
Cost function
Electric power distribution
Heuristic algorithms
Iterative methods
Optimization
Optimization algorithms
Prediction algorithms
Predictive control
predictive control for linear systems
Smart grid
System dynamics
ISSN:0018-9286, 1558-2523
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Zusammenfassung:In this paper, we present a hierarchical, iterative distributed optimization algorithm and show that the algorithm converges to the global solution of a particular optimization problem. The motivation for the distributed optimization problem is the predictive control of a smart grid, in which the states of charge of a network of residential-scale batteries are optimally coordinated so as to minimize variability in the aggregated power supplied to/from the grid by the residential network. The distributed algorithm developed in this paper calls for communication between a central entity and an optimizing agent associated with each battery, but does not require communication between agents. The distributed algorithm is shown to achieve the performance of a large-scale centralized optimization algorithm, but with greatly reduced communication overhead and computational burden. A numerical case study using data from an Australian electricity distribution network is presented to demonstrate the performance of the distributed optimization algorithm.
Bibliographie:ObjectType-Article-1
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ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2016.2525808