The CEA develops a software to optimize dimensioning and control of energy systems, in order to conduct tec-eco studies, including an environmental part, for industry and territories. The optimization is run by a MILP solver.

We want to go further by optimizing the deployment of infrastructure over time and space. Indeed, changes in demand, economic environment, and technological performance need to be taken into account from the beginning of an energy system deployment. The spatial dimension is also important, to make the good choice between centralizing production to make economies of scale, or dispatch the production resources across a territory and ensuring transportation.

Addressing these broader issues leads to more complex calculation with higher times of resolution.

The goals of the PHD will therefore be as follows:
- Establish a generic formalism to describe this type of problem and make it easily modelable, taking into account economic and environmental aspects, as well as the associated uncertainties.
- Compare, select and improve methods of optimization and artificial intelligence allowing to deal with the complexity of the problem.
- Apply this algorithm on concrete case studies.
We are looking for a candidate with a background in applied mathematics. They should be interested in the energy transition.