Inducing Human Behavior to Alleviate Overstay at PEV Charging Station

TL;DR

This paper presents a mathematical framework that models human behavior to optimize PEV charging station operations, reducing overstaying and increasing profitability by offering tailored pricing options.

Contribution

It introduces a novel behavioral model and optimization approach for charging station management, addressing overstaying issues through pricing strategies.

Findings

Overstay reduced significantly in simulations.

Profitability increased through optimized pricing.

Enhanced quality-of-service observed with the proposed method.

Abstract

As the plug-in electric vehicle (PEV) market expands worldwide, PEV penetration has out-paced public PEV charging accessibility. In addition to charging infrastructure deployment, charging station operation is another key factor for improving charging service accessibility. In this paper, we propose a mathematical framework to optimally operate a PEV charging station, whose service capability is constrained by the number of available chargers. This mathematical framework specifically exploits human behavioral modeling to alleviate the "overstaying" issue that occurs when a vehicle is fully charged. Our behavioral model effectively captures human decision-making when humans are exposed to multiple charging product options, which differ in both price and quality-of-service. We reformulate the associated non-convex problem to a multi-convex problem via the Young-Fenchel transform. We then apply the Block Coordinate Descent algorithm to efficiently solve the optimization problem. Numerical experiments illustrate the performance of the proposed method. Simulation results show that a station operator who leverages optimally priced charging options could realize benefits in three ways: (i) net profits gains, (ii) overstay reduction, and (iii) increased quality-of-service.