District heating networks (DHNs) play a vital role in energy transition strategies due to their ability to integrate renewable and waste heat effectively. In France, the national low-carbon strategy emphasizes expanding and optimizing DHNs, including smaller networks with multiple heat sources like solar thermal and storage. Smart control systems, such as model-predictive control (MPC), aim to replace manual, expert-based practices to enhance efficiency. However, deploying advanced control systems on small DHNs remains challenging due to the cost and complexity of hardware and maintenance requirements.

Current industrial solutions for large DHNs leverage mixed-integer linear programming (MILP) for real-time optimization, while smaller networks often rely on rule-based systems. Research efforts focus on simplifying MPC models, utilizing offline pre-calculations, or incorporating machine learning to reduce complexity. Comparative studies assess various control strategies for adaptability, interpretability, and operational performance.

This postdoctoral project aims to advance DHN control strategies by developing, testing, and deploying innovative approaches on a real DHN experimental site. It involves creating and comparing control models, implementing them in a physical simulator, and deploying the most promising solutions. Objectives include optimizing operational costs, improving system robustness, and simplifying deployment while disseminating findings through conferences, publications, and potential patents. The researcher will have access to cutting-edge tools, computational resources, and experimental facilities.