Description

Context and research challenge

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility and increasing the penetration of renewable resources in the power system. Aggregators are being lauded as critical in enabling DR to provide these valuable electricity services at scale.  At the aggregator level,  where there is a multitude and variety of devices and appliances across the aggregator’s portfolio (building HVAC, PVs, EVs, water tanks etc), the process of control and scheduling is rendered infeasible without automating a big part, or the whole process.

The bigger challenges of the whole control and scheduling problem for the aggregator are:

  • real-life DR systems are increasingly an aggregation of a large number of heterogeneous systems, users and devices. Modelling such heterogeneous system is infeasible for three main reasons:  (i) the lack of complete information and data from the different components, (ii) the traditional approaches (mainly from the mathematical optimization and control theory) results in complex and intractable problems as the number of devices and therefore the variables involved increase, and (iii) there are many sources of uncertainty (e.g. dynamic prices, user behavior,  availability of renewables etc.) which introduce additional complexities to the ones described to the previous point;
  • the increasing integration of renewable resources in the electric power grid has increased the need for producers and virtual producers (i.e. aggregators) to offer/trade or correct their offer close to real-time in order to better deal with the high uncertainty due to the availability of renewables. However, the  current approaches (MILP, MPC etc.) cannot guarantee a good ‘anytime’ solution for real-time control and scheduling due to the aforementioned computational issues.

Approach

In this project, innovative methods need to be researched and initiated to design to optimally schedule and control aggregator’s portfolio with a numbers of heterogeneous prosumers for DR schemas.

Particularly, the successful candidate will have the aim to research and design new hybrid approaches that combine modern machine learning methods (e.g. deep reinforcement learning) and optimal control approaches (e.g. model predictive control) to address the challenges faced by the uncertainty and  the complexity of coordinating a large number of prosumers.

With this call, we invite researchers to submit their resumé (including track-record) and a one-page project description, that will be the basis for selecting candidates with whom we will collaborate for developing a competitive MSCA-PF proposal. 

Collaborations

This research is performed within the framework of EnergyVille, a research collaboration on sustainable energy between VITO, KU Leuven, Imec and UHasselt.

Deadline application to VITO

Interested candidates should submit their resume (incl. track record) and a one-page note describing the project for which a Marie Curie grant will be applied, as soon as possible and no later than Friday 2 April 2021 17h Brussels time.

Supervisor

Dr. Carlo Manna is a researcher in applied artificial intelligence for energy systems. He  had experience as senior researcher in academia (University College Cork 2011-2017) and as R&D scientist in industry (ZF-Automotive AG 2017-2019). Hi is co-authored of more than 30  journals/peer-review proceeding publications listed in Scopus and 7 German/US patents applications.

Successful candidates will be supervised by Dr. Carlo MannaFor any inquiries please contact Dr. Carlo Manna at carlo.manna@vito.be.

Deadline MSCA-PF 2021

Wednesday 15 September 2021 17h Brussels time.

Target start date

The EU informs the results on the MSCA-PF applications in February 2022. Successful candidates are expected to be available to start within the following two months and no later than summer 2022.

Qualification

The successful candidate should have the following qualifications/skills:

  • A PhD in a relevant field as (but not limited to):  engineering, computer science, applied maths, physics etc.;
  • A strong background in one or more of the following areas: machine learning and data science, mathematical optimization,  control theory, operations research, artificial intelligence;
  • Good background/interest in energy, and especially in energy system operation and planning, demand response, renewables integration, energy policy and modelling;
  • Experience with python/matlab or similar packages used for machine learning optimization/control and/or simulation and modelling will be highly beneficial;

The following assets will be advantageous:

  • An excellent track record in research, necessary for being able to develop a competitive Marie Curie Fellowship application;
  • Already published relevant research work in prestigious scientific journals;
  • An open and cooperation-oriented nature, but with strong abilities for independent research work;
  • highly proficient in spoken and written English.
Offer

Initially, we offer assistance in developing competitive Marie Curie Individual Fellowship proposals.

Then, to successful applicants to the Marie Curie programme, we offer;

  • An exciting opportunity at VITO, the independent Flemish research organisation driven by the major global challenges. Our goal? To accelerate the transition to a sustainable world;
  • Participation in a dynamic professional research & innovation community;
  • Flexible working conditions;
  • An inclusive and friendly work environment;
  • On-boarding assistance and other services.

 

Requisition

Location: 
Genk
Jobfield: 
Postdoc
ID: 
32483