Within the scope of the INES2 program, research is conducted on active solar thermal systems with the objective to reduce energy cost by 50 %. Using solar thermal systems as a source of energy for district heating systems is one of the directions to achieve this goal. In this context, algorithms and command-and-control systems designed to maximize the use of available solar energy are of tremendous interest. Specifically, uncertainty about the forecasted consumption as well as uncertainty about the forecasted solar production must be taken into account. In this context, model predictive control seems a relevant approach. The aim of this postdoctoral position is to develop a model predictive control system for the energy optimization of the INES2 experimental district heating system. This system will integrate various aspects of consumption forecasting, solar production forecasting and electricity price forecasting, in order to optimize the use of backup heating generators and thermal storage units. The envisioned optimization approach will be based on mixed linear programming (MILP) techniques, with an additional management of forecast uncertainties.

This position is open until it is filled.

Département: Département Thermique Biomasse et Hydrogène (LITEN)
Laboratory:
Start Date: 01-06-2015
ECA Code: PsD-DRT-15-0027
Contact: mathieu.vallee<στο>cea.fr