ACN-Sim : An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research
(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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- author
- Lee, Zachary J. ; Sharma, Sunash ; Johansson, Daniel LU and Low, Steven H.
- organization
- publishing date
- 2021-11-01
- 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}}, }