Publiée 19 juin 2026
PhD Position F/M [Allocation Région 2026] Computation of stochastic Nash equilibria for production-consumption in energy networks with storage and flexible demand
Inria
Villeneuve-d'Ascq, Hauts-de-France 59491, France
CDI
A propos du centre ou de la direction fonctionnelle
Created in 2008, the Inria center at the University of Lille employs 360 people, including 305 scientists in 16 research teams. Recognized for its strong involvement in the socio-economic development of the Hauts-De-France region, the Inria center at the University of Lille maintains a close relationship with large companies and SMEs. By fostering synergies between researchers and industry, Inria contributes to the transfer of skills and expertise in the field of digital technologies, and provides access to the best of European and international research for the benefit of innovation and businesses, particularly in the region.
For over 10 years, the Inria center at the University of Lille has been at the heart of Lille's university and scientific ecosystem, as well as at the heart of Frenchtech, with a technology showroom based on avenue de Bretagne in Lille, on the EuraTechnologies site of economic excellence dedicated to information and communication technologies (ICT).
Mission confiée
This project develops a modeling framework to assess the impact of large-scale deployment of battery storage (BESS) on electricity markets. It aims to quantify how storage affects price formation, production-consumption patterns, and carbon intensity in systems with high shares of renewable energy.
The approach combines optimization and equilibrium modeling to capture the interaction between a system operator and consumers who adapt their demand in response to price signals. This feedback loop between operational decisions and demand behavior remains only partially addressed in existing models.
To represent adaptive demand, data-driven methods are introduced to learn consumers' responses over time, with reinforcement learning used as a modeling tool. The framework provides quantitative insights for decision-making related to grid congestion, affordability, and decarbonization.
Principales activités
The goal is to propose a new stochastic optimization model and an effcient solution method based on the structure of the problem.
Compétences
Good knowledge in optimization and algorithmic
Langages : Python, C++
Software : Gurobi, Cplex
Avantages
Rémunération
Monthly Gross Salary: 2 300 €
Created in 2008, the Inria center at the University of Lille employs 360 people, including 305 scientists in 16 research teams. Recognized for its strong involvement in the socio-economic development of the Hauts-De-France region, the Inria center at the University of Lille maintains a close relationship with large companies and SMEs. By fostering synergies between researchers and industry, Inria contributes to the transfer of skills and expertise in the field of digital technologies, and provides access to the best of European and international research for the benefit of innovation and businesses, particularly in the region.
For over 10 years, the Inria center at the University of Lille has been at the heart of Lille's university and scientific ecosystem, as well as at the heart of Frenchtech, with a technology showroom based on avenue de Bretagne in Lille, on the EuraTechnologies site of economic excellence dedicated to information and communication technologies (ICT).
Mission confiée
This project develops a modeling framework to assess the impact of large-scale deployment of battery storage (BESS) on electricity markets. It aims to quantify how storage affects price formation, production-consumption patterns, and carbon intensity in systems with high shares of renewable energy.
The approach combines optimization and equilibrium modeling to capture the interaction between a system operator and consumers who adapt their demand in response to price signals. This feedback loop between operational decisions and demand behavior remains only partially addressed in existing models.
To represent adaptive demand, data-driven methods are introduced to learn consumers' responses over time, with reinforcement learning used as a modeling tool. The framework provides quantitative insights for decision-making related to grid congestion, affordability, and decarbonization.
Principales activités
The goal is to propose a new stochastic optimization model and an effcient solution method based on the structure of the problem.
Compétences
Good knowledge in optimization and algorithmic
Langages : Python, C++
Software : Gurobi, Cplex
Avantages
- Subsidized meals
- Partial reimbursement of public transport costs
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
- Possibility of teleworking and flexible organization of working hours
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities
- Access to vocational training
- Social security coverage
Rémunération
Monthly Gross Salary: 2 300 €
