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Developing a Decision Support Framework for Sustainably Locating Electric Vehicle Charging Station Locations (Advert Reference: RDF22-R/BL/MOS/NOOR)

   Faculty of Business and Law

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  Dr Mahdi Noorizadegan  No more applications being accepted  Competition Funded PhD Project (UK Students Only)

About the Project

In the future power systems, clean energy technologies will supply a considerable share of the energy consumption. Electrical Vehicles (EVs) as a clean version of transportation system are appropriate alternatives for the current transportation system. Given the government’s plan to ban sales of new fossil fuel cars from 2030 (Dep. Transportation, 2020), the move towards EVs have been intensified. This will significantly impact the balance between demand and supply of electricity. 

In addition to environmental impacts, this policy will have substantial impacts on different dimensions from the national and local development plans to social aspects (Pathak et al., 2021, Chen and Fan, 2021, Dwyer et al. 2021). In particular, location of such facilities cannot be treated from a pure economic perspective, due to strong their social effects which include the demographic and population characteristics of the location and the customer as part of the social capital (Anvari and Turkay, 2017). One of the most critical decisions in planning towards the net-zero policy is finding right locations for Charging Stations (CSs). There are three main perspectives to consider for this problem:

·        Customers: long charging time for EVs compared refiling petrol cars. Alternatives to charging at stations are to swap empty batteries with fully charged batteries, or partially charge EVs. These also contribute towards shortening queues.

·        National/local authorities: restriction imposed by transmission line capacity. Unlike patrol stations which can be located anywhere and supplied by fuel trucks, location of CSs depends on the type of distribution lines available at each area. This requires interaction with national/local government regarding rules/policies/regulation.

·        Investors: CSs are profit maximiser agents. In many hours, CSs are consumers, buying electricity from the network to supply EVs. Although, they have obligations to supply electricity to EVs, CSs can sell stored electricity back to the network at a high price during peak hours.

We aim to develop a decision support framework for CS location problem that addresses the above complexities within the triple dimensions of sustainability: environmental e.g., concerning impacts of distribution line expansion, social concerning impacts of CS on local businesses and communities, and customer waiting time, and profitability concerning investors considerations. From supply chain’s perspective, we model a complex supply chain problem with strategic and operational decisions. From optimisation model perspective, this problem will involve developing a novel efficient solution method. Our objectives are

1.      Develop a centralised optimisation model based all players main considerations.

2.      Forecast/project data required.

3.      Design appropriate solution method and solve the problem.

The project will start with a multi-disciplinary review of literature including sustainability, operations, and mathematical (e.g., Noorizadegan & Shokri, 2021, Fattahi et al., 2021, Noorizadegan & Chen 2018, Skeete et al., 2020, Hu et al., 2020). A mathematical model will provide a flexible framework to effectively incorporate considerations and agendas of the three dimensions of sustainability and find an optimal equilibrium which reasonably addresses them. Our result will contribute towards theory of sustainability (Kumar and Alok, 2020) and Game theory (Avci et al., 2015) to find an optimal equilibrium for a multi-player game theory problem with sustainability agenda.

This project is supervised by Dr Mahdi Noorizadegan

Eligibility and How to Apply:

Please note eligibility requirement:

·       Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.

·       Appropriate IELTS score, if required.

For further details of how to apply, entry requirements and the application form, see 

Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. SF21/…) will not be considered.

Deadline for applications: 27th June 2022

Start Date: October 2022

Northumbria University takes pride in, and values, the quality and diversity of our staff and students. We welcome applications from all members of the community.

Funding Notes

Each studentship supports a full stipend, paid for three years at RCUK rates (for 2021/22 full-time study this is £15,609 per year) and full tuition fees.
Studentships are available for applicants who wish to study on a part-time basis over 5 years (0.6 FTE, stipend £9,365 per year and full tuition fees) in combination with work or personal responsibilities.
Please also read the full funding notes which includes advice for part-time applicants.


Anvari & Turkay (2017) The facility location problem from the perspective of triple bottom line accounting of sustainability, International Journal of Production Research, 55:21, 6266-6287
Avci et al., (2015) Electric Vehicles with a Battery Switching Station: Adoption and Environmental Impact, Management Science, 61(4), 707-929
Chen & Fan (2021). Improvement strategies of battery driving range in an electric vehicle supply chain considering subsidy threshold and cost misreporting. Annal Operation Research
Dep. Transportation (2020) Government takes historic step towards net-zero with end of sale of new petrol and diesel cars by 2030, available at
Dwyer et al., (2021) An Australian Perspective on Local Government Investment in Electric Vehicle Charging Infrastructure. Sustainability, 13, 6590.
Fattahi et al., (2021) Sustainable supply chain planning for biomass-based power generation with environmental risk and supply uncertainty considerations: a real-life case study, International Journal of Production Research, 59(10), 3084-3108
Hu et al., (2020) Impact of policies on electric vehicle diffusion: An evolutionary game of small world network analysis, Journal of Cleaner Production, 265.
Noorizadegan & Chen (2018) Vehicle routing with probabilistic capacity constraints, European Journal of Operational Research 270 (2), 544-555
Noorizadegan & Shokri (2021) Optimization Methods on Electricity Generation and Transmission Expansion Planning Problem. In: Dorsman A.B., Atici K.B., Ulucan A., Karan M.B. (eds) Applied Operations Research and Financial Modelling in Energy. Springer, Cham.
Kumar & Alok (2020) Adoption of electric vehicle: A literature review and prospects for sustainability, Journal of Cleaner Production, 253.
Pathak et al., (2021) Impacts of electrification & automation of public bus transportation on sustainability—A case study in Singapore, Forsch Ingenieurwes 85, 431–442
Skeete et al., (2020) Beyond the EVent horizon: Battery waste, recycling, and sustainability in the United Kingdom electric vehicle transition, Energy Research & Social Science, 69
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