  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.
