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

该项目将为中央规划者开发一个概念验证规划模型，以便在充电点中断的风险下（即由于技术故障或故障导致的充电点意外故障）最佳定位电动汽车（EV）充电基础设施。该模型的目标是最大限度地提高电动汽车的总预期交通量，这些交通量可以通过不可靠的集成充电网络进行充电。静态和动态无线充电系统以及铁路支线站都将被考虑。一个强大的混合整数非线性规划（MINLP）模型，这个问题将制定。模型中将纳入随机理论方程，以说明需求在空间和时间上的随机性（例如高峰期与非高峰期）。该模型将进一步推广到一个多周期规划问题，有限的定期预算。该模型将被线性化，以便可以使用通用求解器对其进行求解。最后，本文提出了一种有效的元启发式算法，并以英国的道路网络为例，对线性化优化模型和元启发式算法的精度和性能进行了评估。除了模型和算法，其他项目产出将是创建测试数据集和一篇或多篇期刊文章。模型和算法的代码以及测试数据集也将提供给运筹学社区，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。

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.