PhD Student

Last application date
Feb 12, 2023 00:00
TW08 - Department of Electromechanical, Systems and Metal Engineering
Limited duration
Master’s degree in Sciences: Electrical or Electromechanical engineering or Applied Physics or Master in industrial sciences electrical engineering or electronics, masters in math with affiliation to renewable energy and modelling.
Occupancy rate
Vacancy type
Research staff

Job description

The research group EELAB / Lemcko is affiliated with Ghent University and located on the UGent campus in Kortrijk. EELAB/Lemcko is specialized in Power Quality, Smart µGrids, Renewable energy, Hosting capacity and Grid Flexibility. You can find more info on

Objective of the research
Photovoltaic Installations (PV), Electric Vehicles (EV), Energy Storage (ES) and Heat Pumps (HP), combined “Stochastic Distribution Grid Exchanges” (SDGE) can result in additional stress on the distribution system due to their fundamentally different behavior from traditional households and building behavior. Due to the increased use of Stochastic Distribution Grid Exchanges (SDGE), the available grid capacity becomes more unpredictable. The grid capacity, further noted as “ampacity” is important to the Distribution System Operator (DSO). Users need to be aware to what extent they can exchange power with the grid under different circumstances, and to which extent they can participate in the energy services and markets. By doing so, we take into account: (i) the impact of decentralized generation, (ii) the integration of storage and (iii) integration of intelligent systems for flexible grid use. The objective of the project can be summarized as the development of innovative concept to avoid hosting capacity problems on distribution grids, taking into maximum grid use, with the goal to improve the performance and reliability.
Developing a categorical synthetic load profiles including probabilistic load duration curves, in order to develop a definition of the dynamic grid hosting capacity. This must be based on the construction of Categorical Synthetic Load Profiles.
Given the high diversity of the household and appliance stock, customer behavior and lifestyle, as well as number of people per household, it is nearly impossible to determine one generic synthetic load profile that is able to sufficiently model the complex temporal dynamics that are gaining importance for distribution network planning. Consequently, data mining techniques based on (un)supervised machine learning has to be used to generate a set of novel categorical synthetic load profiles (CSLPs) that can (i) distinguish between major types of residential consumers, and (ii) sufficiently capture the diversity in load profile features. For this, several performance indices has to be evaluated to determine suitable categories and representative load profiles per residential user category, making abstraction of the underlying grid and the spatial allocation of individual consumers. However, the LDC does not specify the exact timing of peak demands to occur. Consequently, a forecasting model has to be developed to predict the occurrence of peak demands in the point of common coupling (PCC), leading to the introduction of a stochastic charging capacity that can be dynamically adjusted based on different forecasting models and results.

Research Outcomes/Tasks:

  • A set of categorical synthetic load profiles
  • Prediction of tolerable number of electrical units for a given feeder model
  • Forecasting of dynamic load duration curves of end users and in PCC

Additional Tasks:

  • Project reporting and follow up
  • Publish the gathered research results in high ranked journals
  • Conduct and follow up of Ma thesis students during their projects related to your research.
  • Support the Lemcko staff in teaching and lab sessions.

Job profile

  • You have one of the following Master degrees:
    • Master of Science in Electromechanical, Power Engineering, Electrical Engineering or Applied Physics/Math with affiliation to renewable energy
    • Master in Engineering technology in the domains of Electrical engineering, Electromechanical engineering, Control technics
    • Equivalent Master in Electrotechnics or Electromechanics.
  • You are highly motivated, self-critical, you work with rigor and attention to detail, and how to work accurately
  • A good knowledge of Matlab, Simulink, Python and Latex is an asset
  • You have a strong interest in renewable energy, grid flexibility, power grid distribution and power quality with both scientific and practical skills
  • You combine being an active team player with a strong sense of autonomy and responsibility.
  • You have good communication and writing skills and have an excellent knowledge of written and spoken English.
  • You are willing to absorb notions of the Dutch language

How to apply

You send an e-mail to
The following documents must be included with your application:

  • CV
  • motivation letter
  • a copy of the required diploma (if already in your possession)
  • NARIC recognition of your diploma for candidates out of Western Europe
  • other documents such as a reference letters, study results, etc.

UGent has equal opportunities and diversity policy and therefore encourages everyone to apply.
Candidates are first screened based on the application. Those who are qualified, will be invited to a selection interview. Candidates with a doctoral degree cannot apply.

What we can offer you?
We offer you a PhD scholarship as a contractual recruitment of 1 year, with another 3 years after positive evaluation. During the first period, you will also apply (when eligible, and in collaboration with your promoters) for additional scholarship funding. Your appointment will start by 01/06/2023 at the earliest. The aim is to obtain a doctoral degree.
You will receive a PhD scholarship according to the general conditions at Ghent University. The tax-free scholarship includes full social security coverage.
You will work in a dynamic, multidisciplinary and international research environment.
All Ghent University staff members enjoy a number of benefits, such as 36 days of paid leave, a wide range of training and education opportunities, bicycle commuting reimbursement, etc. A complete overview of all our fringe benefits can be found on our website.
For more information about this vacancy, please contact Prof. Jan Desmet (