Competitive charging station pricing for plug-in electric vehicles

Abstract

This paper considers the problem of charging station pricing and plug-in electric vehicles (PEVs) station selection. When a PEV needs to be charged, it selects a charging station by considering the charging prices, waiting times, and travel distances. Each charging station optimizes its charging price based on the prediction of the PEVs' charging station selection decisions and the other station's pricing decision, in order to maximize its profit. To obtain insights of such a highly coupled system, we consider a one-dimensional system with two competing charging stations and Poisson arriving PEVs. We propose a multi-leader-multi-follower Stackelberg game model, in which the charging stations (leaders) announce their charging prices in Stage I, and the PEVs (followers) make their charging station selections in Stage II. We show that there always exists a unique charging station selection equilibrium in Stage II, and such equilibrium depends on the charging stations' service capacities and the price difference between them. We then characterize the sufficient conditions for the existence and uniqueness of the pricing equilibrium in Stage I. We also develop a low complexity algorithm that efficiently computes the pricing equilibrium and the subgame perfect equilibrium of the two-stage Stackelberg game.

Compensation of Charging Station Overload via On-Road Mobile Energy Storage Scheduling

“…Thus, ∂ 2 U P (p, Γ m )/∂p 2 < 0, making the utility function concave and differential. Moreover, by substituting equation (13) to constraints (8), (9), both constraints are convex (half-space). Therefore, problem (10) within the sub-domain Γ m is a convex optimization problem.…”

“…This price-service interaction can be formulated by a sequential game model, such as the Stackelberg game for the interaction analysis. The Stackelberg game model has been applied in PEV charging scheduling [7]- [9]. In the work [7], the CS is considered as the leader to maximize its charging revenue while PEVs are considered as followers to maximize their charging energy fairly.…”

“…In the work [7], the CS is considered as the leader to maximize its charging revenue while PEVs are considered as followers to maximize their charging energy fairly. When scheduling PEV charging among a GCS, the GCS can be managed together by the PSO to maximize the overall energy utilization and revenue as in the work [8], or CSs can compete with each other to form a multi-leader multifollower game as in the work [9]. Stackelberg game can also be applied for PEV discharging as in the work [10], where the CS adjusts its price to maximize its charging and vehicleto-grid service revenue.…”

Electrical Vehicle Charging Station Profit Maximization: Admission, Pricing, and Online Scheduling

“…In light of this, several recent studies focused on improving the operation efficiency of EV charging stations (e.g., [3]- [7]) by carefully designing the charging scheduling and pricing mechanisms. In particular, You and Yang in [3] characterized an optimal offline charging scheduling scheme, where "offline" means that the scheduling decision relies on the noncausal information of future EV charging profiles.…”

“…In [5], Tang et al designed an online charging scheduling algorithm that does not require any future information, not even the distribution information. Ghavami and Kar in [6] and Yuan et al in [7] further proposed charging scheduling and pricing schemes to incentivize EV users to achieve social optimality (i.e., minimizing the network-wide charging cost or maximizing the total economic surplus). In brief, various pricing schemes have also been proposed to maximize the charging station's profit through time-scale decomposition, peak valley decomposition and Lagrangian relaxation, and dual decomposition [3]- [7].…”