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Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging

Lee, Zachary J. and Lee, George and Lee, Ted and Jin, Cheng and Lee, Rand and Low, Zhi and Chang, Daniel and Ortega, Christine and Low, Steven H. (2021) Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging. IEEE Transactions on Smart Grid . ISSN 1949-3053. (In Press) https://resolver.caltech.edu/CaltechAUTHORS:20210503-115705210

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Abstract

We describe the architecture and algorithms of the Adaptive Charging Network (ACN), which was first deployed on the Caltech campus in early 2016 and is currently operating at over 100 other sites in the United States. The architecture enables real-time monitoring and control and supports electric vehicle (EV) charging at scale. The ACN adopts a flexible Adaptive Scheduling Algorithm based on convex optimization and model predictive control and allows for significant over-subscription of electrical infrastructure. We describe some of the practical challenges in real-world charging systems, including unbalanced three-phase infrastructure, non-ideal battery charging behavior, and quantized control signals. We demonstrate how the Adaptive Scheduling Algorithm handles these challenges, and compare its performance against baseline algorithms from the deadline scheduling literature using real workloads recorded from the Caltech ACN and accurate system models. We find that in these realistic settings, our scheduling algorithm can improve operator profit by 3.4 times over uncontrolled charging and consistently outperforms baseline algorithms when delivering energy in highly congested systems.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/tsg.2021.3074437DOIArticle
https://arxiv.org/abs/2012.02636arXivDiscussion Paper
ORCID:
AuthorORCID
Lee, Zachary J.0000-0002-5358-2388
Low, Steven H.0000-0001-6476-3048
Additional Information:© 2021 IEEE. This work was supported in part by the National Science Foundation under the Graduate Research Fellowship Program (Grant No. 1745301), NSF AIR-TT (Grant No. 1602119), NSF ECCS (Grant No. 1932611), and NSF CCF (Grant No. 1637598). It also received support under the Resnick Sustainability Institute Graduate Fellowship, Caltech RocketFund, Caltech CI2 Grant, the Emerging Technologies Coordinating Council of Utilities, and Well Fargo/NREL IN2. Large-scale infrastructure projects like the ACN require herculean effort and coordination between researchers, administrators, private companies, and funding agencies. In addition to the authors, the ACN project would not have been possible without the efforts of John Onderdonk from Caltech Facilities, Stephanie Yankchinski and Neil Fromer from the Resnick Sustainability Institute, Jennifer Shockroo and Fred Farina from Caltech Office of Technology Transfer, and Roger Klemm from the JPL Green Club. Technology development was aided by data provided by Roff Schreiber of Google and generations of Caltech and international students since 2016, especially the work of Karl Fredrik Erliksson of Lund University. We would also like to thank PowerFlex Systems and EDF Renewables North America for their continued support, development, and commercialization of the ACN project.
Group:Resnick Sustainability Institute
Funders:
Funding AgencyGrant Number
NSF Graduate Research FellowshipDGE-1745301
NSFIIP-1602119
NSFECCS-1932611
NSFCCF-1637598
Resnick Sustainability InstituteUNSPECIFIED
Caltech Innovation Initiative (CI2)UNSPECIFIED
Caltech RocketFundUNSPECIFIED
Emerging Technologies Coordinating Council of UtilitiesUNSPECIFIED
Wells Fargo Innovation Incubator (IN2)UNSPECIFIED
Record Number:CaltechAUTHORS:20210503-115705210
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210503-115705210
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108942
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:03 May 2021 19:48
Last Modified:03 May 2021 19:48

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