[AllUsers-ISR] PhD position Behavioral Operations Research and multi objective optimization in residential energy management systems

[Ana Soares]

https://jobs.vito.be/o/phd-position-behavioral-operations-research-and-multi-objective-optimization-in-residential-energy-management-systems


PhD position Behavioral Operations Research and multi objective
optimization in residential energy management systemsJob 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 March 29, 2023.*

*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.

Kind regards,
Ana Soares
INESC Coimbra
Edifício DEEC
Rua Sílvio Lima, Pólo II
3030-790 Coimbra
Portugal

skype: araquelgs
+351 239 796 339
internal extension DEEC 42 1325
https://www.cienciavitae.pt//en/5E10-F4A3-6215
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