PHD position Risk-aware quantification and aggregation techniques for optimal valorization of energy flexibility.

Job description

Recent years have seen an increasing interest on Smart Grids and particularly on Demand Response (DR) as a means to provide energy flexibility and increasing the penetration of renewable resources in the power system. Real-life DR systems are extremely complex, as they require to schedule and control a large number of heterogeneous systems, users and devices.
Modelling such heterogeneous system is challenging for three main reasons: (i) traditional optimization approaches often results in complex and intractable problems as the number of devices and therefore the variables involved increase, (ii) the lack of complete information and data from the different components and (iii) the presence of different sources of uncertainty (e.g. dynamic prices, user behaviour, the use of energy consuming/producing devices, availability of renewables etc.).

As a PhD candidate, your role will be engage the above challenges, with a particular focus on uncertainty and unpredictability of the variables involved. In the state-of-the-art of flexibility research, emerging uncertainty is explored and sampled when modelling and optimizing flexibility, but precise definitions and quantifications are lacking.
This uncertainty will be quantified using appropriate data science techniques (e.g.: data analytics techniques, generative models, applying machine learning, etc.), enabling us to calculate the risk-of-unexpected-unavailability of the flexibility that was offered earlier, and -- more importantly -- enabling us to take this risk into account when valorising the energy flexibility in different applications and electricity markets.
Finally, you will investigate and implement novel Demand-Response optimization algorithms to take into account the quantified risks that are (will be) relevant for existing and future energy- and flexibility-markets.

The PhD fellowship is granted within the collaboration between the University of Ghent (UGhent) and VITO.
The successful candidate will be supervised by Prof. Chris Develder (UGhent) and co-promoted by Dr. Carlo Manna (VITO).

For more information, please contact Dr. Carlo Manna:
You can apply for this PhD vacancy no later than September 5, 2022.

Job requirements

  • You hold a M.Sc. degree relevant to the position (e.g., electrical engineering, computer science, etc.);
  • You are fluent in English, both oral and written.
  • You have strong analytical skills, and a strong sense of responsibility, you are an excellent communicator and you are also able to autonomously plan and perform research tasks.
  • Experience with software development using Python, Java, Matlab etc., is a big plus.