ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research
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.
Additional Information
© 2021 IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. Manuscript received November 4, 2020; revised March 28, 2021 and July 3, 2021; accepted July 6, 2021. Date of publication August 9, 2021; date of current version October 21, 2021. This work was supported in part by the National Science Foundation under the Graduate Research Fellowship Program under Grant 1745301, in part by NSF ECCS under Grant 1932611 and Grant 1619352, in part by NSF CCF under Grant 1637598, and in part by NSF CPS under Grant 1739355. It also received support under the Resnick Sustainability Institute Graduate Fellowship. The authors 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.Attached Files
Published - ACN-Sim_An_Open-Source_Simulator_for_Data-Driven_Electric_Vehicle_Charging_Research.pdf
Submitted - 2012.02809.pdf
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Additional details
- Eprint ID
- 109023
- Resolver ID
- CaltechAUTHORS:20210510-085201964
- DGE-1745301
- NSF Graduate Research Fellowship
- ECCS-1932611
- NSF
- ECCS-1619352
- NSF
- CCF-1637598
- NSF
- ECCS-1739355
- NSF
- Resnick Sustainability Institute
- Created
-
2021-05-10Created from EPrint's datestamp field
- Updated
-
2021-11-12Created from EPrint's last_modified field
- Caltech groups
- Resnick Sustainability Institute