Impact of network modelling in the analysis of district heating systems

Elisa Guelpa

Energy, 2020, vol. 213, issue C

Abstract: Network modelling is crucial for the simulation of district heating system responses to changes in operating conditions. Various applications, aimed at finding optimal district heating design and operations, neglect or strongly simplify the network dynamics. In this paper, the effect of including network dynamics in district heating system modelling has been analyzed. Different physical contributions have been considered separately: thermal losses, thermal transients and delay time due to the various costumer distances. This allows estimating the significance of the various phenomena in the estimation of the thermal request, in particular during demand peaks. Results shows that the thermal power required by the thermal plant is significantly different if evaluated relying on a network model or not; in case of thermal peak this is under-estimated up to 20% if the network dynamic is not taken into account. In particular, the inclusion of the thermal transient in the model is found to be crucial for considerably improving the result accuracy in the peak estimation. Effects for inclusion of thermal losses calculation have been quantified; errors reaches 4% in case of not perfectly insulated pipelines. The effect of neglecting network dynamics have also been analyzed in the context of demand side management (DSM) district heating systems. In particular, the effects are tested on a model for the best rescheduling of on-off time of the building heating device to optimally shave the thermal peak. Results show that the benefits achieved by the demand response model that include the thermal dynamics contribution increase from 1 to 18%; this is because the contribution of the different times the water trains takes to reach the plants (from the buildings) and of the water in the pipelines cooled down during night are relevant. Furthermore, different options are discussed to take into account compactly the network dynamic.

Keywords: District heating simulation; Smart operation; Thermal network model; Thermal peak; Demand side management; Demand response (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:213:y:2020:i:c:s0360544220315000

DOI: 10.1016/j.energy.2020.118393

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