Hybrid modeling of coupled multi-energy power systems

3 May 2024
Job Information

Organisation/Company

centrale lille

Research Field

Engineering
Technology » Energy technology

Researcher Profile

Recognised Researcher (R2)
Leading Researcher (R4)
First Stage Researcher (R1)
Established Researcher (R3)

Country

France

Application Deadline

29 May 2024 - 22:00 (UTC)

Type of Contract

Temporary

Job Status

Full-time

Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

Scientific context:

Digital twins enable real-time diagnosis of the state of systems as well as fast and accurate projections of the future behavior of real assets, enabling near real-time decision making. To make a prognosis, embedded models are required to evaluate and anticipate the responses of the system. Based on physical knowledge, a mathematical model of the system can be derived. A strong constraint is that results must be calculated in real time. Model order reduction techniques are classically implemented but are altering the accuracy and limit the development of an immersive digital twin [1]. Another solution is to learn a model from the collected data using machine learning. The choice of the most suitable machine learning technique for such modeling depends on the quantity of data, its quality and the time available to build the model. Hybrid modelling approaches, at the heart of the hybrid twin concept, propose to mix both approaches: a data based model uses collected data to enrich the existing physics based model.

In the goal to balance generation with load demands, the massive integration of renewable energy sources introduces uncertainty in the generation forecasting and can only be massively jointly deployed with alternative sources that can be quickly mobilized and associated with storage systems. While many studies exist on the optimal energy management of multi-energy networks, relatively few studies consider the inherent capacity of certain types of networks to store energy (natural gas and district heating networks, for example). This lack of work on this topic can be explained by the increased complexity in the physical modeling of gas networks and by the too often used restrictive simplifications masking the storage potential of such networks.

PhD objectives:

The research goal is the implementation of a hybrid digital twin (physical and real-time data-based modeling) of a multi-energy power network allowing optimal operating and dispatching of intermittent renewable energy based sources along with using the intrinsic storage capacity of gas networks. The digital twin will focus on the real-time modeling of a residential district equipped with PV generation. The main scientific barriers concern the choice of the level of complexity of the physical modeling of the networks, particularly those of gas, and the data to be measured allowing the enrichment of the associated physical model (hybrid modelling). These obstacles will require a bibliographic work on the state of the art of smart grid architecture modeling tools, the formalization of methods allowing the sizing and design of digital twins for multi-energy systems and the determination of needs for the implementation of real-time multi-energy digital twins for multi-objective test, control, training and optimization applications.

Funding category: Autre financement public
Programme prioritaire de recherche - PEPR
PHD title: Doctorat en Génie electrique
PHD Country: France

Requirements
Specific Requirements

Ideal profile:

A MSc in Computing Science or Electrical Engineering with a focus on energy and process engineering
Sound knowledge in energy technology, control engineering, artificial intelligence, data science
Ability of coding in Matlab or one higher programming language (Python, Julia, ..)
Experience in simulation tools and power system software as for examples: Simulink, Opal RT, Spherea, RTDS, SCADA, DERMS and ADMS
Interest in applied research work in the fields of intelligent energy systems, a past experience related to research activities will be appreciated.
The candidate must have the ability to work independently
Good analytical, synthesis and innovation skills
Good communication and writing skills in English.

Additional Information
Work Location(s)

Number of offers available

1

Company/Institute

centrale lille

Country

France

City

villeneuve d'Ascq

Geofield

Where to apply

Website

https://www.abg.asso.fr/fr/candidatOffres/show/id_offre/123614

Contact

Website

https://l2ep.univ-lille.fr/

STATUS: EXPIRED