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arXiv · 2207.11403

A Deployable Online Optimization Framework for EV Smart Charging with Real-World Test Cases

Nathaniel Tucker·Mahnoosh Alizadeh
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Abstract

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.

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BibTeXRIS

Nathaniel Tucker, Mahnoosh Alizadeh. 2022-07-23. A Deployable Online Optimization Framework for EV Smart Charging with Real-World Test Cases. https://arxiv.org/abs/2207.11403

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