Fairness-Guaranteed Online Power Allocation Policies for EV Fast Charging Stations

TL;DR

This paper presents two online power allocation policies for EV fast charging stations that guarantee fairness without prior charge curve data, ensuring efficient, real-time resource distribution in oversubscribed settings.

Contribution

The authors introduce two novel algorithms, FAIR-OPAP-C and FAIR-OPAP-M, that guarantee fairness in EV charging without needing charge curve information, with proven theoretical guarantees and high scalability.

Findings

Algorithms outperform benchmarks in fairness and efficiency metrics.

Methods are significantly faster than optimization-based approaches, with runtimes below 1 ms for 300 EVs.

Proposed policies are suitable for real-time deployment on hardware-constrained devices.

Abstract

The rapid expansion of electric vehicles (EVs) necessitates scalable and efficient fast charging station (FCS) infrastructure. These stations often operate in oversubscribed configurations where the total port rating exceeds a station-level cap reflecting infrastructure limits, grid constraints or market setpoints. In such settings, ensuring fairness in real-time power allocation is essential to prevent user bias and secure equitable access to limited resources while maximizing infrastructure utilization. This task is further complicated by state-of-charge dependent EV power limits defined by charge curves, for which accurate data is often unavailable. This paper introduces two fairness-guaranteed online power allocation policies: FAIR-OPAP-C for conventional FCSs with continuously adjustable power delivery, and FAIR-OPAP-M for modular FCSs composed of discrete assignable power modules. Unlike existing methods, these algorithms require no prior knowledge of charge curves, utilizing only instantaneous power requests available via standard protocols. We formalize fairness with a unified framework encompassing envy-freeness, Pareto efficiency, and proportionality, and establish theoretical guarantees for both algorithms. The algorithms rely on lightweight operations, achieving near-linear and logarithmic scalability for the conventional and modular cases, respectively. Comprehensive evaluations show the proposed methods achieve superior performance across various metrics among seven benchmarks from EV charging and fair division literature. Furthermore, they are orders of magnitude faster than optimization-based approaches, with runtimes below 1 ms for up to 300 EVs, validating their suitability for real-time deployment on hardware-constrained edge devices.