An Approach to Study District Thermal Flexibility Using Generative Modeling from Existing Data

Camille Pajot (1, 2) , Nils Artiges (1, 2) , Benoit Delinchant (1, 2) , Simon Rouchier (3) , Frederic Wurtz (1, 2) , Yves Marechal (1, 2)

1 G2ELab - Laboratoire de Génie Electrique de Grenoble
2 G2Elab-MAGE - G2Elab-Modèles, Méthodes et Méthodologies Appliqués au Génie Electrique
3 LOCIE - LabOratoire proCédés énergIe bâtimEnt

Energy planning at the neighborhood level is a major development axis for the energy transition. This scale allows the pooling of production and storage equipment, as well as new possibilities for demand-side management such as flexibility. To manage this growing complexity, one needs two tools. The first concerns modeling, allowing exhaustive simulation analyses of buildings and their energy systems. The second concerns optimization, making it possible to decide on the sizing or control of energy systems. In this article, we analyze, in the case of an existing residential neighborhood, the ability to study by modeling and optimization tools two scenarios of energy flexibility of indoor heating. We propose in particular a method allowing to rely on a varied set of data available to build the various models necessary for optimization tools or dynamic simulation. A study was conducted to identify the neighborhood’s flexibility potential in minimizing CO2 emissions, through shared physical storage, or storage in the building envelope. The results of this optimization study were then compared to their application to the virtual neighborhood by simulation.

Mots clés: district scale; demand-side management; flexibility; MILP; CO 2 emissions; heat pump; ETL; data management

Domaines: Energie électrique

Camille Pajot, Nils Artiges, Benoit Delinchant, Simon Rouchier, Frederic Wurtz, et al.. An Approach to Study District Thermal Flexibility Using Generative Modeling from Existing Data. Energies, 2019, 12 (19), pp.3632. ⟨10.3390/en12193632⟩. ⟨hal-02509491⟩