Grey-Box Modelling of District Heating Networks Using Modified LPV Models

Olamilekan E. Tijani (), Sylvain Serra (), Patrick Lanusse, Rachid Malti, Hugo Viot and Jean-Michel Reneaume
Additional contact information
Olamilekan E. Tijani: LaTEP, Universite de Pau et des Pays de l’Adour, 64075 Pau, France
Sylvain Serra: LaTEP, Universite de Pau et des Pays de l’Adour, 64075 Pau, France
Patrick Lanusse: Universite de Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, 33400 Talence, France
Rachid Malti: Universite de Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, 33400 Talence, France
Hugo Viot: Nobatek, 67 rue de Mirambeau, 64600 Anglet, France
Jean-Michel Reneaume: LaTEP, Universite de Pau et des Pays de l’Adour, 64075 Pau, France

Energies, 2025, vol. 18, issue 7, 1-32

Abstract: The International Energy Agency (IEA) 2023 report highlights that global energy losses have persisted over the years, with 32% of the energy supply lost in 2022 alone. To mitigate this, this research adopts optimisation to enhance the efficiency of district heating networks (DHNs), a key global energy supply technology. Given the dynamic nature of DHNs and the challenges in predicting disturbances, a dynamic real-time optimisation (DRTO) approach is proposed. However, this research does not implement DRTO; instead, it develops a fast grey-box linear parameter varying (LPV) model for future integration into the DRTO algorithm. A high-fidelity physical model replicating theoretical time delays in pipes serves as a reference for model validation. For a single pipe, the grey-box model achieved a 91.5% fit with an R 2 value of 0.993 and operated 5 times faster than the reference model. At the DHN scale, it captured 98.64% of the reference model’s dynamics, corresponding to an R 2 value of 0.9997, while operating 52 times faster. Low-fidelity physical models (LFPMs) were also developed and validated, proving to be more precise and faster than the grey-box models. This research recommends performing dynamic optimisation with both models to determine which better identifies local minima.

Keywords: district heating networks; grey box; linear parameter varying; modelling and simulation (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: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/7/1626/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/7/1626/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:7:p:1626-:d:1619328

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().