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Faculté des Sciences appliquées
Faculté des Sciences appliquées
MASTER THESIS
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Travail de fin d'études et stage[BR]- Travail de fin d'études : Optimization of collective battery storage systems usage in energy communities[BR]- Stage d'insertion professionnelle

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Messens, Martin ULiège
Promotor(s) : Cornélusse, Bertrand ULiège
Date of defense : 26-Jun-2023/27-Jun-2023 • Permalink : http://hdl.handle.net/2268.2/17247
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Title : Travail de fin d'études et stage[BR]- Travail de fin d'études : Optimization of collective battery storage systems usage in energy communities[BR]- Stage d'insertion professionnelle
Translated title : [fr] Optimisation de l'utilisation des systèmes de batteries collectives au sein des communautés d'énergie
Author : Messens, Martin ULiège
Date of defense  : 26-Jun-2023/27-Jun-2023
Advisor(s) : Cornélusse, Bertrand ULiège
Committee's member(s) : Lepièce, Marc 
Quoilin, Sylvain ULiège
Language : English
Number of pages : 66
Keywords : [en] Energy communities
[en] Community battery
[en] Value stacking
[en] Collective storage
Discipline(s) : Engineering, computing & technology > Energy
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en ingénieur civil électromécanicien, à finalité spécialisée en énergétique
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[en] This thesis presents an optimization framework for the utilization of collective
batteries within Energy Communities. Community batteries have various ap-
plications, including maximizing self-consumption, mitigating demand peaks,
offering grid services, and participating in wholesale markets. The paper con-
ducts a comprehensive study on the feasibility and advantages of these appli-
cations in Energy Communities, along with a comparative techno-economic
analysis focused on a specific Energy Community project situated in Brussels.
The objective of this research is to develop optimization algorithms that de-
termine an optimal charging and discharging schedule for the batteries, ulti-
mately maximizing asset profitability based on data forecasts. The algorithms
are implemented as Python modules employing constrained mathematical pro-
gramming techniques.
Each application is individually reviewed and optimized. Subsequently, a com-
prehensive assessment is performed by combining the applications to evaluate
the benefits of employing multi-objective community batteries.


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Author

  • Messens, Martin ULiège Université de Liège > Master ing. civil électro., à fin.

Promotor(s)

Committee's member(s)

  • Lepièce, Marc
  • Quoilin, Sylvain ULiège Université de Liège - ULiège > Département d'aérospatiale et mécanique > Systèmes énergétiques
    ORBi View his publications on ORBi
  • Total number of views 19
  • Total number of downloads 1










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