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  4. Distributed control of charging for electric vehicle fleets under dynamic transformer ratings
 
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Distributed control of charging for electric vehicle fleets under dynamic transformer ratings

Citation Link: https://doi.org/10.15480/882.9189
Publikationstyp
Journal Article
Date Issued
2022-07
Sprache
English
Author(s)
Botkin-Levy, Micah
Engelmann, Alexander
Mühlpfordt, Tillmann
Faulwasser, Timm  
Almassalkhi, Mads R.
TORE-DOI
10.15480/882.9189
TORE-URI
https://hdl.handle.net/11420/45657
Journal
IEEE Transactions on Control Systems Technology  
Volume
30
Issue
4
Start Page
1578
End Page
1594
Citation
IEEE Transactions on Control Systems Technology 30 (4): 1578-1594 (2022-07)
Publisher DOI
10.1109/TCST.2021.3120494
Scopus ID
2-s2.0-85118624488
Publisher
IEEE
Due to their large power draws and increasing adoption rates, electric vehicles (EVs) will become a significant challenge for electric distribution grids. However, with proper charging control strategies, the challenge can be mitigated without the need for expensive grid reinforcements. This article presents and analyzes new distributed charging control methods to coordinate EV charging under nonlinear transformer temperature ratings. Specifically, we assess the tradeoffs between required data communications, computational efficiency, and optimality guarantees for different control strategies based on a convex relaxation of the underlying nonlinear transformer temperature dynamics. Classical distributed control methods, such as those based on dual decomposition and alternating direction method of multipliers (ADMM), are compared against the new augmented Lagrangian-based alternating direction inexact Newton (ALADIN) method and a novel low-information, look-ahead version of packetized energy management (PEM). These algorithms are implemented and analyzed for two case studies on residential and commercial EV fleets with fixed and variable populations. The latter motivates a novel EV hub charging model that captures arrivals and departures. Simulation results validate the new methods and provide insights into key tradeoffs.
Subjects
Alternating direction method of multipliers (ADMM)
augmented Lagrangian-based alternating direction inexact Newton (ALADIN)
distributed optimization
dual decomposition
electric vehicle (EV) charging
fleet
packet-based coordination
DDC Class
621: Applied Physics
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by/4.0/
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