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 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 R2 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 R2 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.