Abstract

As District Heating (DH) networks are experiencing an evolution towards the so-called 4th generation, there is a need to update the currently used models to take into account the ever-increasing complexity of this technology. Indeed, to further improve the reduction in energy consumption and carbon-dioxide emissions, a wide range of technologies and management strategies are being introduced within district heating, such as a large exploitation of Renewable Energy Sources (RES). As a consequence, thermal transients assume a major importance, posing the need to redefine the relevant physical parameters and to develop a model which accurately describes their behaviour. In this framework, this paper proposes a quantitative analysis of the influence of the pipe heat-capacity on the model. Moreover, an equivalent-model, which is able to take into account the two heat capacities of steel and water in just one equation, is proposed and compared with two commonly used approaches. One of the features of the proposed model is the suitability for application to large networks. To prove its capabilities, an application to the Turin district heating network, which is among the largest systems in Europe, is proposed. Results show significant improvements in terms of accuracy over computational time ratio.

Highlights

  • Nowadays, District Heating (DH) technology is regarded as a core element in the costeffective decarbonization of the European energy system [1]

  • As District Heating (DH) networks are experiencing an evolution towards the so-called 4th generation, there is a need to update the currently used models to take into account the ever-increasing complexity of this technology

  • The aim of this paper was to provide a quantitative analysis of the influence of the steel pipe heat capacity on the thermal transients of district heating networks

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Summary

Introduction

District Heating (DH) technology is regarded as a core element in the costeffective decarbonization of the European energy system [1]. In order to reach the 4GDH, numerical models are needed to simulate and optimize the configuration of existing and planned district heating networks. Two main approaches have been adopted for district heating modelling: black-box models and physical models [5]. The former are statistical models based on standard transfer function models or neural networks. These methods suffer from low accuracy in the time-delay estimation, especially when temperatures are changed abruptly [5].

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