> Academic opportunities > PHD positions

Parametric reduction approaches for MPC-type control of innovative decarbonized energy production systems

Technological challenge: Smart Energy grids (learn more)

Department: Département Thermique Conversion et Hydrogène (LITEN)

Laboratory: Laboratoire des systèmes énergétiques pour les territoires

Start Date: 01-10-2022

Location: Grenoble

CEA Code: SL-DRT-22-0673

Contact: nicolas.vasset@cea.fr

In a context of massive decarbonisation, the growing complexity of energy production systems makes advanced management and control issues crucial. The fine control of these systems, ensuring optimality and robustness, is also a central ingredient for the implementation of the flexibility of the production means, guaranteeing their performance and the capacity to function in critical operation. The possibility of using model-predictive control approaches (MPC) via advanced optimisation techniques is particularly relevant in the management of these energy systems. The deployment of these model-based predictive control methods on real systems, whose use is spreading and whose capabilities are established, often comes up against the complexity of implementing large algorithms on physical targets from an operational point of view. This is the main pitfall preventing the democratisation of such approaches on energy systems, and in general in various industrial automation contexts. The aim of this thesis is to design, implement and validate in simulation a mathematical method of parametric reduction for optimal control algorithms of energy systems. In particular, it will build on recent advances in parameter space exploration, which is a current obstacle for industrial applications. The developed method will be evaluated and compared on concrete industrial cases (geothermal, solar, heat networks) with several classical methods called 'explicit MPC', as well as with heuristic methods specific to the considered domains.

See all positions Download the offer (.zip)

Email Bookmark and Share