Optimization of Multi-actor Energy Systems Enhanced

il y a 1 semaine

Sophia Antipolis, Provence-Alpes-Côte d'Azur, France Mines Paris - PSL, Centre PERSEE Temps plein

Optimization of multi-actor energy systems enhanced with AI and privacy-preserving data sharing
:

  • Réf
    ABG122602
  • Sujet de Thèse 10/04/2024
  • Autre financement privé
  • Mines Paris
  • PSL, Centre PERSEE
  • Lieu de travail
  • Sophia-Antipolis
  • Provence-AlpesCôte d'Azur
  • France
  • Intitulé du sujet
  • Optimization of multiactor energy systems enhanced with AI and privacypreserving data sharing.
  • Champs scientifiques
  • Sciences de l'ingénieur
  • Energie
  • Mathématiques


 Mots clés
  • Artificial Intelligence, Data science, Privacypreserving data sharing,Energy digitalisation, Energy transition, Optimisation, Decarbonization, Smart grids, Renewable energies
    Description du sujet:

Title:

"Optimization of multi-actor energy systems enhanced with AI and privacy-preserving data sharing".**
Context and background:
Scientific objectives and Methodology:

  • The development of distributed optimization methods, automated transactive peertopeer and privacy preserving algorithms offer technical solutions in the field of artificial intelligence that can resolve the abovementioned bottlenecks and promote collaborative strategies towards common goals like the reduction of the global carbon footprint in highly integrated basins.
  • The
    overarching objective of this thesis is double fold:
  • to develop methods that permit data sharing, while respecting confidentiality constraints and for different types of data.
- to develop AI-based agents that can support decision-making in multi-actor and collaborative energy schemes and are compatible with privacy-preserving data sharing.

  • Priority will be given to the use case of an
    industrial basin. Examples of energy intensive industrial basins in Europe exist, where a single basin represents up to 20% of the country's CO2 emissions. In the process of decarbonization of the involved industries, data sharing can be critical.
Key themes and questions proposed in this topic are along the following lines:

  • How can automated negotiation technology provided by peer to peer exchange design can support coordination between industrial agents in a basin?
  • Can decomposition methods be used to coordinate heterogeneous optimization problems to reach a common goal without sharing of the underlying data and models.
  • How can privacypreserving learning provide a collaborative modelling framework for the actors of an industrial basin to improve the global modelling accuracy with no or limited data sharing?

Expected results:

  • Although the applicative field in this PhD project is energy systems, the techniques developed have a replication potential to other fields like health or other

The expected results contain:

  • Algorithms for data sharing, while preserving confidentiality constraints.
  • Distributed optimization methods that implement data sharing and AI techniques.
  • Evaluation on real data with focus to use cases of industrial basins.
  • Recommendations to regulatory authorities and policy makers.

International collaboration:

Prise de fonction:

  • 01/10/2024
    Nature du financement:
  • Autre financement privé

Précisions sur le financement:

  • Air-Liquide
    Présentation établissement et labo d'accueil:
  • Mines Paris
  • PSL, Centre PERSEE
The
PERSEE Center is one of the 18 research centers of MINES Paris. Its field of expertise concerns New Energy Technologies and Renewable Energy Sources (RES). Its research strategy is based on a "micro/macro" approach ranging from (nano)materials to energy systems.

It is built around three structuring themes:
i) materials and components for energy, ii) sustainable energy conversion and storage processes and technologies, and iii) renewable energies and smart energy systems.


The PERSEE Center is located within the scientific parc of Sophia-Antipolis, near the cities of Nice, Cannes and Antibes in the south of France.

Its workforce is around 55 persons.

Site web:

Intitulé du doctorat:

  • Doctorat en Énergétique et Procédés
    Pays d'obtention du doctorat:
  • France

Etablissement délivrant le doctorat:

  • Mines Paris
  • PSL (Ecole Nationale Supérieure des Mines de Paris)
    Ecole doctorale:
  • Ingénierie des Systèmes, Matériaux, Mécanique, Energétique
    REQUESTED PROFILE:
  • Skills in computer programming (i.e. Python, R, Julia) are required together with knowledge of optimization tools (e.g, Gurobi, CPLEX).

Expected level in French:

Good level
Expected level in English:
Proficiency 31/05/2024

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