4 research outputs found
Competitive Charging Station Pricing for Plug-in Electric Vehicles
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.Comment: 15 pages, 21 figure
Dynamic Modeling and Real-time Management of a System of EV Fast-charging Stations
Demand for electric vehicles (EVs), and thus EV charging, has steadily
increased over the last decade. However, there is limited fast-charging
infrastructure in most parts of the world to support EV travel, especially
long-distance trips. The goal of this study is to develop a stochastic dynamic
simulation modeling framework of a regional system of EV fast-charging stations
for real-time management and strategic planning (i.e., capacity allocation)
purposes. To model EV user behavior, specifically fast-charging station
choices, the framework incorporates a multinomial logit station choice model
that considers charging prices, expected wait times, and detour distances. To
capture the dynamics of supply and demand at each fast-charging station, the
framework incorporates a multi-server queueing model in the simulation. The
study assumes that multiple fast-charging stations are managed by a single
entity and that the demand for these stations are interrelated. To manage the
system of stations, the study proposes and tests dynamic demand-responsive
price adjustment (DDRPA) schemes based on station queue lengths. The study
applies the modeling framework to a system of EV fast-charging stations in
Southern California. The results indicate that DDRPA strategies are an
effective mechanism to balance charging demand across fast-charging stations.
Specifically, compared to the no DDRPA scheme case, the quadratic DDRPA scheme
reduces average wait time by 26%, increases charging station revenue (and user
costs) by 5.8%, while, most importantly, increasing social welfare by 2.7% in
the base scenario. Moreover, the study also illustrates that the modeling
framework can evaluate the allocation of EV fast-charging station capacity, to
identify stations that require additional chargers and areas that would benefit
from additional fast-charging stations
5Gâenhanced smart grid services
This chapter focuses on the 5G key concepts and how they can be extremely beneficial in supporting the advanced smart grid services. It introduces the smart grid environment and discusses some of the future services that will be supported in the future smart grids. These services are broadly classified into two categories, namely data collection and management services that target enhanced grid monitoring capabilities, and control and operation services that deal with demand side management and electric vehicle charging and discharging coordination. The chapter illustrates how the 5G novel concepts such as softwareâdefined networking, network virtualization, and cloud computing offer enhanced services for grid monitoring, data processing, demandâside management, and electric vehicle charging and discharging coordination. It also illustrates a summary of the application of these concepts in supporting the smart grid services. Future research directions are discussed to deal with the open challenging issues