Smoothed Least-Laxity-First Algorithm for EV Charging

TL;DR

This paper introduces the smoothed least-laxity-first (sLLF) algorithm for adaptive EV charging, which efficiently manages uncertain EV arrivals and demands, ensuring high feasibility with minimal resource augmentation.

Contribution

The paper proposes the sLLF algorithm for EV charging, providing analytical and numerical performance analysis, and demonstrating its superior feasibility with minimal resource increase.

Findings

sLLF achieves higher feasibility rates than existing algorithms

Numerical experiments confirm effectiveness with real-world data

Resource augmentation needed is only 0.07 for perfect feasibility

Abstract

Adaptive charging can charge electric vehicles (EVs) at scale cost effectively, despite the uncertainty in EV arrivals. We formulate adaptive EV charging as a feasibility problem that meets all EVs' energy demands before their deadlines while satisfying constraints in charging rate and total charging power. We propose an online algorithm, smoothed least-laxity-first (sLLF), that decides the current charging rates without the knowledge of future arrivals and demands. We characterize the performance of the sLLF algorithm analytically and numerically. Numerical experiments with real-world data show that it has a significantly higher rate of feasible EV charging than several other existing EV charging algorithms. Resource augmentation framework is employed to assess the feasibility condition of the algorithm. The assessment shows that the sLLF algorithm achieves perfect feasibility with only a 0.07 increase in resources.