Locating Charging Stations of Various Sizes with Different Numbers of Chargers for Battery Electric Vehicles
Authored by Shaohua Cui, Hui Zhao, Cuiping Zhang
Date Published: 2018
DOI: 10.3390/en11113056
Sponsors:
Chinese National Natural Science Foundation
Platforms:
No platforms listed
Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
Model code not found
Abstract
Compared with traditional fuel vehicles, battery electric vehicles
(BEVs) as a sustainable transportation form can reduce carbon dioxide
emissions and save energy, so its market share has great potential.
However, there are some problems, such as: Their limited range, long
recharging time, and scarce charging facilities, hindering improvement
in the market potential of BEVs. Therefore, perfect and efficient
charging facility deployment for BEVs is very important. For this
reason, the optimal locations for charging stations for BEVs are
investigated in this paper. Instead of flow-based formulation, this
paper is based on agents under strictly imposed link capacity
constraints, where all agents can select their routes and decide on the
battery recharging plan without running out of charge. In our study, not
only the locations of charging stations, but also the size of charging
stations with the different number of chargers, would be taken into
consideration. Then, this problem is formulated as a location problem
for BEV charging stations of multiple sizes based on agents under link
capacity constraints. This problem is referred to as the
agent-refueling, multiple-size location problem with capacitated network
(ARMSLP-CN). We formulate the ARMSLP-CN as a 0-1 mixed-integer linear
program (MILP) with the aim to minimize the total trip time for all
agents, including four parts, namely, the travel time, queue time, fixed
time for recharging, and variable recharging time depending on the type
of charger and the amount of power recharged, in which commercial
solvers can solve the linearized model directly. To demonstrate this
model, two different numerical instances are designed, and sensitivity
analyses are also presented.
Tags
Agent-based model
Infrastructure
time
Hybrid
Facilities
Fuel
Battery electric vehicles
Charging station location
Charging station
size
Recharging
Multiple types
Routing problem
Buses
Car