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Asynchronous consensus for optimal power flow control in smart grid with zero power mismatch
Citation
Millar, BS and Jiang, D, Asynchronous consensus for optimal power flow control in smart grid with zero power mismatch, Journal of Modern Power Systems and Clean Energy, 6, (3) pp. 412-422. ISSN 2196-5625 (2018) [Refereed Article]
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Copyright Statement
Copyright The Author(s) 2018. Licensed under Creative Commons Attribution 4.0 International (http://creativecommons.org/licenses/by/4.0/)
DOI: doi:10.1007/s40565-018-0378-4
Abstract
The heterogeneous nature of smart grid components and the desire for smart grids to be scalable, stable and respect customer privacy have led to the need for more distributed control paradigms. In this paper we provide a distributed optimal power flow solution for a smart distribution network with separable global costs, separable non-convex constraints, and inseparable linear constraints, while considering important aspects of network operation such as distributed generation and load mismatch, and nodal voltage constraints. An asynchronous averaging consensus protocol is developed to estimate the values of inseparable global information. The consensus protocol is then combined with a fully distributed primal dual optimization utilizing an augmented Lagrange function to ensure convergence to a feasible solution with respect to power flow and power mismatch constraints. The presented algorithm uses only local and neighbourhood communication to simultaneously find the mismatch between power generation, line loss and loads, to calculate nodal voltages, and to minimize distributed costs, leading to a completely distributed solution of the global problem. An IEEE test feeder system with a reasonable number of nodes is used to illustrate the proposed method and efficiency.
Item Details
Item Type: | Refereed Article |
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Keywords: | consensus protocol, cooperative system, distributed algorithm, distributed control, optimization, smart grid |
Research Division: | Engineering |
Research Group: | Communications engineering |
Research Field: | Signal processing |
Objective Division: | Energy |
Objective Group: | Energy storage, distribution and supply |
Objective Field: | Energy storage, distribution and supply not elsewhere classified |
UTAS Author: | Millar, BS (Dr Benjamin Millar) |
UTAS Author: | Jiang, D (Dr Danchi Jiang) |
ID Code: | 124863 |
Year Published: | 2018 |
Web of Science® Times Cited: | 3 |
Deposited By: | Engineering |
Deposited On: | 2018-03-15 |
Last Modified: | 2022-07-05 |
Downloads: | 120 View Download Statistics |
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