Grey-Box Modelling of District Heating Networks Using Modified LPV Models
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 ener...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/7/1626 |
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| Summary: | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> 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. |
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| ISSN: | 1996-1073 |