Formulation and Solution Techniques for Integrated Charging Network Design under Risk of Disruption (FAST-ICNET)

中断风险下的集成充电网络设计的制定和解决方案技术 (FAST-ICNET)

基本信息

批准号：
EP/V061607/1
负责人：
Trung Hieu Tran
金额：
$ 8.11万
依托单位：
CRANFIELD UNIVERSITY
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FV061607%2F1
关键词：
Formulation Solution Techniques Integrated Charging

项目摘要

The project will develop a proof-of-concept planning model for central planners to optimally locate electric vehicles (EVs) charging infrastructure under the risk of disruption to charging points (i.e. unexpected failure of charging points due to technical faults or breakdowns). The aim of the model will be to maximise total expected traffic volume of EVs that can be charged by an unreliable integrated charging network. Both static and dynamic wireless charging systems, as well as railway feeder stations will be considered. A robust mixed-integer non-linear programming (MINLP) model for this problem will be formulated. Queuing theory equations will be incorporated into the model to account for the stochastic nature of demand both spatially and over time (e.g. peak versus off-peak periods). The model will be further generalized to a multi-period planning problem given limited periodic budgets. The model will be linearized so that it can be solved using a general purpose solver. Finally, an efficient metaheuristic algorithm will be developed to solve the large-scale real-world instances within a reasonable computational time.A case study of the road network in the UK will be used to assess the accuracy and performance of the linearized optimization model and the metaheuristic algorithm. Besides the model and the algorithm, other project outputs will be the creation of test datasets and one or more journal articles. Codes of the model and algorithm, and test datasets will also be made available to the community of Operational Research so that other researchers and practitioners (e.g., National Grid) can use them in their own case studies.

该项目将为中央规划人员开发概念验证计划模型，以最佳定位电动汽车（EV），以造成充电点的干扰风险（即由于技术故障或故障导致的充电点意外失败）。该模型的目的是最大化电动汽车的总预期流量量，这可以由不可靠的集成充电网络收取。将考虑静态和动态无线充电系统以及铁路馈线站。将制定一个针对此问题的强大混合企业非线性编程（MINLP）模型。排队理论方程将被纳入模型，以说明在空间和随着时间的时间上（例如峰值与非高峰时期）的随机性质。鉴于有限的定期预算，该模型将进一步概括为多周期计划问题。该模型将线性化，以便可以使用通用求解器来解决。最后，将开发出一种有效的元启发式算法，以在合理的计算时间内解决大规模的现实世界实例。英国道路网络的案例研究将用于评估线性化优化模型和元数据算法的准确性和性能。除模型和算法外，其他项目输出还将创建测试数据集和一份或多个期刊文章。模型和算法的代码以及测试数据集也将提供给运营研究社区，以便其他研究人员和从业人员（例如，国家电网）可以在自己的案例研究中使用它们。