Formulation and Solution Techniques for Integrated Charging Network Design under Risk of Disruption (FAST-ICNET)
Lead Research Organisation:
CRANFIELD UNIVERSITY
Department Name: School of Water, Energy and Environment
Abstract
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.
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.
Publications
| Description | The project developed successfully a proof-of-concept planning model for Electric Vehicles (EVs) charging network design 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 is to maximise total expected traffic volume of EVs that can be charged by an unreliable integrated charging network. A mathematical model for this problem was formulated. A metaheuristic algorithm was developed to efficiently solve the large-scale instances in practice. |
| Exploitation Route | The model serves as a prototype for other areas internationally for the design of integrated charging infrastructure network. The research contributes to the literature of robust mathematical programming and metaheuristic algorithm and the wider field of decarbonising transport through electrification. The project's scientific contribution and impact forms the basis for future work on advanced mathematical programming models and algorithms for designing integrated charging infrastructure networks. |
| Sectors | Transport |
| Description | The 2020 Department for Transport (DfT) report "Decarbonising Transport: Setting the Challenge" identifies six strategic priorities for developing a decarbonisation transport plan. Along with a focus on new technology development for decarbonising the transport sector, a top priority is the need to build and extend charging infrastructure to meet the dramatic increase in Electric Vehicles (EVs) on the UK's roads. Since DfT nor any other national agency or private sector business has sufficient budget to rapidly build a large infrastructure network, consideration of a multi-period planning framework is critical. Additionally, allowing flexibility in the design of an integrated charging infrastructure by considering the range of emerging technologies available (e.g. static and dynamic wireless charging systems and railway feeder stations) and the need to hedge against uncertainty (e.g. unexpected failures of charging stations/lanes) when locating new infrastructure would radically improve the effectiveness and robustness of the UK's future charging infrastructure network. This helps towards the achievement of UK net zero target by 2050. |
| Sector | Transport |
| Impact Types | Societal |