With the increasing effects of climate change, the urgency to step away from
fossil fuels is greater than ever before. Electric vehicles (EVs) are one way
to diminish these effects, but their widespread adoption is often limited by
the insufficient availability of charging stations. In this work, our goal is
to expand the infrastructure of EV charging stations, in order to provide a
better quality of service in terms of user satisfaction (and availability of
charging stations). Specifically, our focus is directed towards urban areas. We
first propose a model for the assignment of EV charging demand to stations,
framing it as a maximum flow problem. This model is the basis for the
evaluation of user satisfaction with a given charging infrastructure. Secondly,
we incorporate the maximum flow model into a mixed-integer linear program,
where decisions on the opening of new stations and on the expansion of their
capacity through additional outlets is accounted for. We showcase our
methodology for the city of Montreal, demonstrating the scalability of our
approach to handle real-world scenarios. We conclude that considering both
spacial and temporal variations in charging demand is meaningful when solving
realistic instances.

气候变化、电动汽车、充电站、用户满意度和最大流问题是这篇研究论文的主要关键词和主题。

电动汽车充电站最优位置的最大流量模型

Maximum flow-based formulation for the optimal location of electric  vehicle charging stations

This work presents an investigation and assessment framework, which,
supported by realistic data, aims at provisioning operators with in-depth
insights into the consumer-perceived Quality-of-Experience (QoE) at public
Electric Vehicle (EV) charging infrastructures. Motivated by the unprecedented
EV market growth, it is suspected that the existing charging infrastructure
will soon be no longer capable of sustaining the rapidly growing charging
demands; let alone that the currently adopted ad hoc infrastructure expansion
strategies seem to be far from contributing any quality service sustainability
solutions that tangibly reduce (ultimately mitigate) the severity of this
problem. Without suitable QoE metrics, operators, today, face remarkable
difficulty in assessing the performance of EV Charging Stations (EVCSs) in this
regard. This paper aims at filling this gap through the formulation of novel
and original critical QoE performance metrics that provide operators with
visibility into the per-EVCS operational dynamics and allow for the
optimization of these stations' respective utilization. Such metrics shall then
be used as inputs to a Machine Learning model finely tailored and trained using
recent real-world data sets for the purpose of forecasting future long-term
EVCS loads. This will, in turn, allow for making informed optimal EV charging
infrastructure expansions that will be capable of reliably coping with the
rising EV charging demands and maintaining acceptable QoE levels. The model's
accuracy has been tested and extensive simulations are conducted to evaluate
the achieved performance in terms of the above listed metrics and show the
suitability of the recommended infrastructure expansions.

开发一种基于 QoE 指标的评估框架，通过机器学习模型和数据集预测未来长期的电动汽车充电需求，以实现可靠的可持续充电基础设施扩展。