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

Lee, Zachary J. ; Sharma, Sunash ; Johansson, Daniel LU and Low, Steven H. (2021) In IEEE Transactions on Smart Grid 12(6). p.5113-5123
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... (More)

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.

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Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
charging stations, computer simulation, cyber-physical systems, distributed energy resources, Electric vehicles, open-source software
in
IEEE Transactions on Smart Grid
volume
12
issue
6
pages
11 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85114467953
ISSN
1949-3053
DOI
10.1109/TSG.2021.3103156
language
English
LU publication?
yes
id
0be2beb7-e474-40ea-a261-021252a2b606
date added to LUP
2022-02-28 17:27:10
date last changed
2022-06-27 13:17:55
@article{0be2beb7-e474-40ea-a261-021252a2b606,
  abstract     = {{<p>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. </p>}},
  author       = {{Lee, Zachary J. and Sharma, Sunash and Johansson, Daniel and Low, Steven H.}},
  issn         = {{1949-3053}},
  keywords     = {{charging stations; computer simulation; cyber-physical systems; distributed energy resources; Electric vehicles; open-source software}},
  language     = {{eng}},
  month        = {{11}},
  number       = {{6}},
  pages        = {{5113--5123}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Transactions on Smart Grid}},
  title        = {{ACN-Sim : An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research}},
  url          = {{http://dx.doi.org/10.1109/TSG.2021.3103156}},
  doi          = {{10.1109/TSG.2021.3103156}},
  volume       = {{12}},
  year         = {{2021}},
}