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ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research

Lee, Zachary J. and Sharma, Sunash and Johansson, Daniel and Low, Steven H. (2020) ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20210510-085201964

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Abstract

ACN-Sim is a data-driven, open-source simulation environment designed to accelerate research in the field of smart electric vehicle (EV) charging. It fills the need in this community for a widely available, realistic simulation environment in which researchers can evaluate algorithms and test assumptions. ACN-Sim provides a modular, extensible architecture, which models the complexity of real charging systems, including battery charging behavior and unbalanced three-phase infrastructure. It also integrates with a broader ecosystem of research tools. These include ACN-Data, an open dataset of EV charging sessions, which provides realistic simulation scenarios and ACN-Live, a framework for field-testing charging algorithms. It also integrates with grid simulators like MATPOWER, PandaPower and OpenDSS, and OpenAI Gym for training reinforcement learning agents.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2012.02809arXivDiscussion Paper
https://resolver.caltech.edu/CaltechAUTHORS:20190614-091204247Related ItemConference Paper (ACM)
https://resolver.caltech.edu/CaltechAUTHORS:20191204-131602950Related ItemConference Paper (IEEE)
ORCID:
AuthorORCID
Lee, Zachary J.0000-0002-5358-2388
Low, Steven H.0000-0001-6476-3048
Additional Information:This material is based upon work supported by NSF through grants CCF 1637598, ECCS 1619352, and CPS including grant 1739355, as well fellowship support through the NSF Graduate Research Fellowship Program 1745301 and Resnick Sustainability Institute. We would like to thank the team at PowerFlex, especially Cheng Jin, Ted Lee and George Lee, as well as Rand Lee, James Anderson, and Jorn Reniers for providing data, expertise and ideas to this project.
Group:Resnick Sustainability Institute
Funders:
Funding AgencyGrant Number
NSFCCF-1637598
NSFECCS-1619352
NSFECCS-1739355
NSF Graduate Research FellowshipDGE-1745301
Resnick Sustainability InstituteUNSPECIFIED
Record Number:CaltechAUTHORS:20210510-085201964
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210510-085201964
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109023
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:10 May 2021 17:52
Last Modified:10 May 2021 17:52

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