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

TL;DR

This paper introduces a flexible, real-time online optimization framework for EV smart charging that can be deployed at large-scale facilities, adaptable to various constraints, and validated through real-world test cases.

Contribution

The paper presents a novel, customizable online optimization framework for EV charging that is easily deployable and adaptable to different infrastructure and communication capabilities.

Findings

Outperforms existing real-time strategies in multiple test cases.

Successfully implemented at large-scale facilities with real-world data.

Compatible with chargers with varying communication features.

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.