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（即由于技术故障或故障导致充电点意外故障）的风险下，优化定位电动汽车（ev）充电基础设施。该模型的目标是最大化可通过不可靠的集成充电网络充电的电动汽车的总预期交通量。将考虑静态和动态无线充电系统以及铁路接驳站。本文将建立一个鲁棒混合整数非线性规划（MINLP）模型。排队理论方程将被纳入模型，以解释需求在空间上和时间上的随机性（例如高峰与非高峰时期）。该模型将进一步推广到给定有限周期预算的多周期规划问题。该模型将被线性化，以便可以使用通用求解器进行求解。最后，将开发一种有效的元启发式算法，在合理的计算时间内解决大规模的现实世界实例。本文将以英国的道路网络为例，评估线性化优化模型和元启发式算法的准确性和性能。除了模型和算法之外，其他项目输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者（例如，国家电网）可以在他们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.