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The Adaptive Charging Network Research Portal: Systems, Tools, and Algorithms

Citation

Lee, Zachary Jordan (2021) The Adaptive Charging Network Research Portal: Systems, Tools, and Algorithms. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/8eqg-e110. https://resolver.caltech.edu/CaltechTHESIS:05282021-174411678

Abstract

Millions of electric vehicles (EVs) will enter service in the next decade, generating gigawatt-hours of additional energy demand. Charging these EVs cleanly, affordably, and without excessive stress on the grid will require advances in charging system design, hardware, monitoring, and control. Collectively, we refer to these advances as smart charging. While researchers have explored smart charging for over a decade, very few smart charging systems have been deployed in practice, leaving a sizeable gap between the research literature and the real world. In particular, we find that research is often based on simplified theoretical models. These simple models make analysis tractable but do not account for the complexities of physical systems. Moreover, researchers often lack the data needed to evaluate the performance of their algorithms on real workloads or apply techniques like machine learning. Even when promising algorithms are developed, they are rarely deployed since field tests can be costly and time-consuming.

The goal of this thesis is to develop systems, tools, and algorithms to bridge these gaps between theory and practice.

First, we describe the architecture of a first-of-its-kind smart charging system we call the Adaptive Charging Network (ACN). Next, we use data and models from the ACN to develop a suite of tools to help researchers. These tools include ACN-Data, a public dataset of over 80,000 charging sessions; ACN-Sim, an open-source simulator based on realistic models; and ACN-Live, a platform for field testing algorithms on the ACN. Finally, we describe the algorithms we have developed using these tools. For example, we propose a practical and robust algorithm based on model predictive control, which can reduce infrastructure requirements by over 75%, increase operator profits by up to 3.4 times, and significantly reduce strain on the electric power grid. Other examples include a pricing scheme that fairly allocates costs to users considering time-of-use tariffs and demand charges and a data-driven approach to optimally size on-site solar generation with smart EV charging systems.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:electric vehicle charging; smart charging; scheduling; distributed energy resources; open-source software; cyber-physical systems
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Electrical Engineering
Awards:Bhansali Family Doctoral Prize in Computer Science, 2021. Demetriades-Tsafka-Kokkalis Prize in Environmentally Benign Renewable Energy Sources or Related Fields, 2021.
Thesis Availability:Not set
Research Advisor(s):
  • Low, Steven H.
Group:Resnick Sustainability Institute
Thesis Committee:
  • Wierman, Adam C. (chair)
  • Bouman, Katherine L.
  • Chandrasekaran, Venkat
  • Low, Steven H.
Defense Date:18 May 2021
Funders:
Funding AgencyGrant Number
NSFCCF 1637598
NSFECCS 1619352
NSFCPS 1739355
NSF Graduate Research Fellowship1745301
Resnick Sustainability Institute Graduate Research FellowshipUNSPECIFIED
Schmidt Academy for Software EngineeringUNSPECIFIED
Projects:Adaptive Charging Network Research Portal, Schmidt Academy for Software Engineering
Record Number:CaltechTHESIS:05282021-174411678
Persistent URL:https://resolver.caltech.edu/CaltechTHESIS:05282021-174411678
DOI:10.7907/8eqg-e110
Related URLs:
URLURL TypeDescription
https://ev.caltech.eduRelated DocumentHomepage for EV Charging Research at Caltech
https://doi.org/10.1109/TSG.2021.3074437DOIArticle adapted for Ch. 2, Ch. 6
https://doi.org/10.1109/SmartGridComm.2018.8587550DOIArticle adapted for Ch. 2
https://doi.org/10.1145/3307772.3328313DOIArticle adapted for Ch. 3, 8
https://doi.org/10.1109/SmartGridComm.2019.8909765DOIArticle adapted for Ch. 4
https://arxiv.org/abs/2012.02809arXivArticle adapted for Ch. 4, 6
https://doi.org/10.1016/j.epsr.2020.106694DOIArticle adapted for Ch. 7
https://github.com/zach401/acnportalRelated ItemGithub repository for open-source ACN Portal project
https://github.com/caltech-netlab/acnportal-experimentsRelated ItemGithub repository for experiments in Ch. 6, Ch. 8
https://github.com/caltech-netlab/pricing_ev_charging_serviceRelated ItemGithub repository for experiments in Ch. 7
https://ev.caltech.edu/datasetRelated ItemLink to ACN-Data
ORCID:
AuthorORCID
Lee, Zachary Jordan0000-0002-5358-2388
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:14191
Collection:CaltechTHESIS
Deposited By: Zachary Lee
Deposited On:03 Jun 2021 17:56
Last Modified:01 Dec 2021 17:23

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