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Projects: Projects for Investigator
Reference Number EP/V061607/1
Title Formulation and Solution Techniques for Integrated Charging Network Design under Risk of Disruption (FAST-ICNET)
Status Completed
Energy Categories Other Cross-Cutting Technologies or Research(Energy Models) 90%;
Energy Efficiency(Transport) 10%;
Research Types Basic and strategic applied research 100%
Science and Technology Fields PHYSICAL SCIENCES AND MATHEMATICS (Statistics and Operational Research) 90%;
PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 10%;
UKERC Cross Cutting Characterisation Systems Analysis related to energy R&D (Other Systems Analysis) 100%
Principal Investigator Dr T Tran

School of Water, Energy and Environmen
Cranfield University
Award Type Standard
Funding Source EPSRC
Start Date 01 October 2021
End Date 31 December 2022
Duration 15 months
Total Grant Value £63,614
Industrial Sectors Energy; Transport Systems and Vehicles
Region East of England
Programme Mathematical Sciences – Additional Funding Programme
Investigators Principal Investigator Dr T Tran , School of Water, Energy and Environmen, Cranfield University (99.999%)
  Other Investigator Professor JR O'Hanley , Kent Business School, University of Kent (0.001%)
  Industrial Collaborator Project Contact , National Grid plc (0.000%)
Web Site
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.
Publications (none)
Final Report (none)
Added to Database 22/11/21