3 research outputs found
Inducing Human Behavior to Alleviate Overstay at PEV Charging Station
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.Comment: Submitted to 2020 American Control Conferenc
Robo-Chargers: Optimal Operation and Planning of a Robotic Charging System to Alleviate Overstay
Charging infrastructure availability is a major concern for plug-in electric
vehicle users. Nowadays, the limited public chargers are commonly occupied by
vehicles which have already been fully charged. Such phenomenon, known as
overstay, hinders other vehicles' accessibility to charging resources. In this
paper, we analyze a charging facility innovation to tackle the challenge of
overstay, leveraging the idea of Robo-chargers - automated chargers that can
rotate in a charging station and proactively plug or unplug plug-in electric
vehicles. We formalize an operation model for stations incorporating
Fixed-chargers and Robo-chargers. Optimal scheduling can be solved with the
recognition of the combinatorial nature of vehicle-charger assignments,
charging dynamics, and customer waiting behaviors. Then, with operation model
nested, we develop a planning model to guide economical investment on both
types of chargers so that the total cost of ownership is minimized. In the
planning phase, it further considers charging demand variances and service
capacity requirements. In this paper, we provide systematic techno-economical
methods to evaluate if introducing Robo-chargers is beneficial given a specific
application scenario. Comprehensive sensitivity analysis based on real-world
data highlights the advantages of Robo-chargers, especially in a scenario where
overstay is severe. Validations also suggest the tractability of operation
model and robustness of planning results for real-time application under
reasonable model mismatches, uncertainties and disturbances