PhD position Behavioral Operations Research and multi objective optimization in residential energy management systems

Job description

Energy management strategies for residential buildings are gaining substantial traction within the energy transition community, since they can systematically reduce costs, energy, and emissions to varying degrees without sacrificing thermal comforts. As the dependence on conventional energy decrease and renewable energy increase, the ability to adapt demand to fluctuating availability of cheap and green power will outweigh efficiency in energy use. This ability is generally referred to as flexibility in the literature and the implementation of this flexibility for energy management is known as Demand Side Management (DSM) or Demand Response (DR). This contrasts with supply side management which lends itself to conventional energies, and this shift towards DR is at core of energy transition. The outcome of DR is broadly categorized in to three: economic (reduction in costs of energy consumption), environmental (moving towards sustainable energy use thereby reducing emissions), technical (avoid imbalance: scarce/excess power).

The residential building sector is responsible for a significant fraction in EU’s energy expenditure, currently it is at a massive 40% of the total energy consumption. The amount of usable flexibility, particularly in energy efficient buildings with strict comfort constraints, is limited. It is possible to gain wider flexibility and exploit it to increase value of DR by relaxing comfort constraints. This widening of flexibility comes with its own unique challenge, i.e. the Goldilocks Zone in the trade-off between user comfort and flexibility will be highly dynamic. In ideal times, i.e. relatively stable balance between supply and demand and high availability of power from renewables, stringent comfort constraints can be accommodated easily, while impeding critical situations in the energy system would warrant violation of stringent comfort constraints if trying to exploit flexibility.

Currently, comfort constraints are static – flexibility is activated in between these constraints with no consideration of specific and temporary circumstances in which comfort constraints might change rapidly. However, a user-centric approach that considers user behavior and preferences towards the energy system conditions (availability of green energy, etc.) has a higher potential towards increasing flexibility. End-users can have different motivations to change energy consumption behaviors and to be more relaxed in terms of comfort requirements (constraints) in some situations. Dynamic tariffs are one such motivation where users can consume more during the hours when cheap and green power is available. We also foresee that current energy crisis awareness might induce people to lean in towards relaxing comfort constraints.

With the preceding context and background, we expect to tackle some novel challenges in this Ph.D. research. These novel challenges will broadly include costs and emission reduction leading to a multi-objective optimization challenge. Added novelty will also be in the form of dynamic comfort constraints owing to duration, timing, and frequency of comfort-level interventions related to user-behavior. Appropriate mathematical models to mimic buildings, DR devices (heat pump, cooling heat pump, air conditioning, and EVs etc.), and user behavior are also an important piece of the puzzle. The modeling and simulation of real environments can be done through different techniques. Depending on goals, availability of information, and level of difficulty generic white box, grey box or black box models will be developed for buildings and DR devices. State-of-the art and current user behavior models will be used to mimic comfort constraints. Use of forecasting tools is also foreseen to account for demand, production, energy prices, grid congestion, etc. The approach to optimization will be two pronged, where we use model-based optimization methods or machine learning based methods based on modeling techniques and quality of models.

Our offer
VITO and INESC Coimbra offer a PhD scholarship to the candidate for 4 years. The student will be enrolled at the University of Coimbra and the university promotor for this PhD will be Prof. dr. Carlos Henggeler - INESC Coimbra/University of Coimbra.
At VITO, the PhD candidate will work within the AMO (Algorithms Modelling and Optimisation) team under the supervision of Dr. ir. Sarnavi Mahesh.

How to apply?

Applications should be submitted online and include a copy of your CV, diploma transcripts and a cover letter. You can apply for this PhD vacancy no later than January 7, 2024.
For more information, please contact Dr. ir. Sarnavi Mahesh (

Job requirements

  • You hold a Master's degree cum laude in electrical engineering, software engineering, computer science, mathematical engineering, data science.
  • You can work independently, as well as in a team.
  • You are fluent in English, both oral and written.
  • You are eager to disseminate your research results by scientific publications or communications at conferences.