Control-oriented Hybrid Modeling of Coupled

Il y a 2 mois

Nantes, France IMT Atlantique Temps plein

**Control-oriented hybrid modeling of coupled multi-carrier energy systems**:

- Réf
- **ABG-115207**
- Sujet de Thèse- 13/06/2023- Autre financement public- IMT Atlantique- Lieu de travail- Nantes - Pays de la Loire - France- Intitulé du sujet- Control-oriented hybrid modeling of coupled multi-carrier energy systems- Champs scientifiques- Energie
- Informatique
- Mathématiques
- Mots clés- Machine learning, deep learning, physics informed machine learning, mechanistic models, data-driven models, Energy systems**Description du sujet**:
**Context**:
Multi-carrier Energy Systems (MES) play an important role in the energy transition as they enable the integration and coupling of diverse energy sources and carriers. These systems contribute to the decarbonization and sustainability of the energy system by fostering flexibility and sector coupling. The flip side of the coin is that MES make the entire energy system more complex and, as a result, difficult to control. Due to high complexity, the development of accurate and computationally lightweight models is a hot research topic.

**Position**:
The main objective of this PhD project is to propose new control-oriented hybrid modeling strategies for small-scale energy systems that combine energy conversion and storage technologies and involve different energy carriers. This will entail combining simplified mechanistic models with artificial intelligence (AI) techniques to enhance the accuracy and the predictive capabilities of the models while preserving their explicability. The specific objectives are:

- Define and model a benchmark case using a pure mechanistic approach and identify the limits of physical modeling for the control of energy conversion and storage systems.
- Investigate different hybrid modeling strategies, including substituting all or parts of physical models with ML, parameters identification, coupling ML and physical models, or using ML for model simplification and reduction.
- Develop hybrid modeling strategies for reducing and simplifying the mechanistic models.
- Develop hybrid modeling strategies for improving the robustness and reliability of energy conversion and storage system models.
- Address the challenges of combining systems modeled by heterogeneous approaches with different degrees of hybridization. Identify the optimal degree of hybridization for energy conversion and storage system models maximizing performance (generalization), while managing complexity.
- Use hybrid modeling to handle different temporal scales (e.g., electrical and thermal temporal scales).
- Investigate data selection and preprocessing methods for hybrid modeling.
- Evaluate the performance of the hybrid models using synthetic data extracted from the physical models. Such an evaluation will rely on the assessment of KPIs dealing with accuracy, computational cost, and data requirement.

**Nature du financement**:

- Autre financement public**Précisions sur le financement**:

- PEPR TASE - HyMES project**Présentation établissement et labo d'accueil**:

- IMT AtlantiqueA technological university and international research center affiliated with “Institut Mines Telecom”. IMT Atlantique conducts research in digital technology, energy, and environment. The Department of Energy Systems and Environment (DSEE, **GEPEA UMR CNRS 6144**) conducts research in process engineering related to bio-resources and environmental technologies through three research teams: Water Treatment Air Metrology (TEAM), Energy / waste Recycling and Emissions Processing (VERTE), and Optimization - System - Energy (OSE).

**PROMES** (“Processes, Materials and Solar Energy”), a CNRS own research unit (UPR 8521) under agreement with University Perpignan Via Domitia (UPVD), rallies about 170 people around a unifying topic: solar energy and its promotion. Its multidisciplinary research activities are structured along three thematic axes: Materials for Energy and Space (MEE), Next Generation Solar Power Plants (CSPG) and Storage and Solar Chemistry (SCS). In the CSPG thematic axis, both the modeling and advanced automatic control of solar energy-related systems, in particular using artificial intelligence, are addressed.

**HyMES** is a 5-year research project within the PEPR TASE framework that brings together seven academic partners (GEPEA, LAPLACE, ISIR, IES, G2ELAB, PROMES, CEA-LITEN). The main objective of the project is to investigate hybrid modeling solutions as a mean to tackle the growing spatial and temporal complexity of multi-energy systems and networks.

**Site web**:
**Intitulé du doctorat**:

- Énergétique, thermique, combustion**Pays d'obtention du doctorat**:

- France**Etablissement délivrant le doctorat**:

- IMT Atlantique**Ecole doctorale**:

- Science pour l'ingénieur & numérique (SPIN) - IMT AtlantiqueThe applicant should have either a background in mechanical or energy engineering with proven experience in developing machine learning models or a backg

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