Dr K Goh
No more applications being accepted
Self-Funded PhD Students Only
About the Project
The demand of Electric Vehicle (EV) has seen a rapid growth. The increase in EV means the grid must fulfil the electricity demand in charging the EV. The demand of electricity will peak at specific time of the day and hence putting huge pressure to the electric grid. The use of sustainable energy technology such as harnessing solar energy using Photovoltaic (PV) panel and wind energy using turbine may alleviate the grid pressure. However the variability of the renewable energy and stability can be an issue. The flexible Vehicle to Grid/Home (V2G/V2H) technology means that EV has a potential to support the electric grid by using its battery as a distributed energy resources (DER). This project will look to develop an integrated system with V2G/V2H, PV system, wind turbine, battery energy storage system and with grid connection with capability of simulating electricity load conditions. The study will contribute to the understanding of energy management for the sophisticated integrated energy system. The focus will be given to the Scottish electricity grid network study.
Academic qualifications
A first degree (at least a 2.1) ideally in an engineering discipline with a good fundamental knowledge of electrical & electronic and sustainable energy technology.
English language requirement
IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components.) Other, equivalent qualifications will be accepted. Full details can be found here https://www.napier.ac.uk/research-and-innovation/research-degrees/application-process
Essential attributes:
• Experience of fundamental mathematical modelling skills.
• Competent in energy conversion.
• Knowledge of electrical power.
• Good written and oral communication skills.
• Strong motivation, with evidence of independent research skills relevant to the project.
• Good time management.
Desirable attributes:
Competency in system modelling and simulation using Matlab Simulink.
When applying for this position, please quote Project ID SEBE0009
Funding Notes
This is a self-funded position. Home/EU and Overseas students are welcome to apply.
References
Irshad W, Goh K and Kubie J, Wind resource assessment in the Edinburgh region, WNWEC Sep 2009.
Goh, K, Spurgeon S and Jones B, Higher order sliding model control of diesel generator set, Journal of Systems and Control Engineering, May 2003
Stephen, B., Galloway, S. & Burt, G. Self-learning load characteristic models for smart appliances.
IEEE Transactions on Smart Grid. Jul 2014.
Nikolaos G. Paterakis; Elena Mocanu; Madeleine Gibescu; Bart Stappers; Walter van Alst. Deep learning versus traditional machine learning methods for aggregated energy demand prediction. 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe).
Karol Lina López; Christian Gagné; Marc-André Gardner. Demand-Side Management using Deep Learning for Smart Charging of Electric Vehicles. IEEE Transactions on Smart Grid. Year: 2018, Volume: PP, Issue: 99.
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