Electrical Engineering and Systems Science > Systems and Control
arXiv:2207.11403 (eess)
[Submitted on 23 Jul 2022]
Title:A Deployable Online Optimization Framework for EV Smart Charging with Real-World Test Cases
View a PDF of the paper titled A Deployable Online Optimization Framework for EV Smart Charging with Real-World Test Cases, by Nathaniel Tucker and 1 other authors
View PDFAbstract:We present a customizable online optimization framework for real-time EV smart charging to be readily implemented at real large-scale charging facilities. Notably, due to real-world constraints, we designed our framework around 3 main requirements. First, the smart charging strategy is readily deployable and customizable for a wide-array of facilities, infrastructure, objectives, and constraints. Second, the online optimization framework can be easily modified to operate with or without user input for energy request amounts and/or departure time estimates which allows our framework to be implemented on standard chargers with 1-way communication or newer chargers with 2-way communication. Third, our online optimization framework outperforms other real-time strategies (including first-come-first-serve, least-laxity-first, earliest-deadline-first, etc.) in multiple real-world test cases with various objectives. We showcase our framework with two real-world test cases with charging session data sourced from SLAC and Google campuses in the Bay Area.
| Comments: | 7 pages. arXiv admin note: text overlap witharXiv:2203.06847 |
| Subjects: | Systems and Control (eess.SY) |
| Cite as: | arXiv:2207.11403 [eess.SY] |
| (orarXiv:2207.11403v1 [eess.SY] for this version) | |
| https://doi.org/10.48550/arXiv.2207.11403 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled A Deployable Online Optimization Framework for EV Smart Charging with Real-World Test Cases, by Nathaniel Tucker and 1 other authors
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