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Details of Grant 

EPSRC Reference: EP/V061607/1
Title: Formulation and Solution Techniques for Integrated Charging Network Design under Risk of Disruption (FAST-ICNET)
Principal Investigator: Tran, Dr T
Other Investigators:
O'Hanley, Professor JR
Researcher Co-Investigators:
Project Partners:
National Grid ESO
Department: School of Water, Energy and Environment
Organisation: Cranfield University
Scheme: Standard Research - NR1
Starts: 01 July 2021 Ends: 30 June 2022 Value (£): 63,614
EPSRC Research Topic Classifications:
Mathematical Aspects of OR
EPSRC Industrial Sector Classifications:
Energy Transport Systems and Vehicles
Related Grants:
Panel History:
Panel DatePanel NameOutcome
22 Mar 2021 EPSRC Mathematical Sciences Small Grants Panel March 2021 Announced
Summary on Grant Application Form
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.

Key Findings
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Further Information:  
Organisation Website: http://www.cranfield.ac.uk