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

Camille Pajot, Nils Artiges, Benoit Delinchant, Simon Rouchier, Frédéric Wurtz and Yves Maréchal
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Camille Pajot: Univ. Grenoble Alpes, CNRS, Grenoble INP (Institute of Engineering Univ. Grenoble Alpes), G2Elab, F-38000 Grenoble, France
Nils Artiges: Univ. Grenoble Alpes, CNRS, Grenoble INP (Institute of Engineering Univ. Grenoble Alpes), G2Elab, F-38000 Grenoble, France
Benoit Delinchant: Univ. Grenoble Alpes, CNRS, Grenoble INP (Institute of Engineering Univ. Grenoble Alpes), G2Elab, F-38000 Grenoble, France
Simon Rouchier: University Savoie Mont-Blanc, LOCIE UMR CNRS 5271, Campus Scientifique SavoieTechnolac, F-73376 Le Bourget-du-Lac, France
Frédéric Wurtz: Univ. Grenoble Alpes, CNRS, Grenoble INP (Institute of Engineering Univ. Grenoble Alpes), G2Elab, F-38000 Grenoble, France
Yves Maréchal: Univ. Grenoble Alpes, CNRS, Grenoble INP (Institute of Engineering Univ. Grenoble Alpes), G2Elab, F-38000 Grenoble, France

Energies, 2019, vol. 12, issue 19, 1-22

Abstract: 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 C O 2 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.

Keywords: district scale; demand-side management; flexibility; MILP; CO 2 emissions; heat pump; ETL; data management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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