4.7 Article

Justifying network reconstruction when switching to low temperature district heating

期刊

ENERGY
卷 248, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2022.123618

关键词

Energy; Network; Supply; Flow; Demand; Substation

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The aim of this paper is to propose a method to make low temperature district heating (LTDH) more feasible by utilizing operational data. The total network cost, compared to traditional systems, increases by 28.4% using design data only. However, a new assessment shows that the cost to modernize the DH network is only 18.6 million EUR, with 12.9% savings. Operational data, including network and outdoor temperatures, flow rate, and pressure, leads to more accurate evaluation. Adjusting heat demands and supply/return temperatures closer to operational values brings positive effects, outweighing the negative impact of higher pressure drop.
The aim of the paper is to deduce a way to make low temperature district heating (LTDH) more feasible by involving operational data. Using design data only, the total network cost compared to traditional system increases by 28.4%. New assessment gives only 18.6 million EUR of costs to modernize DH network, which provide 2.4 million EUR (12.9%) as savings. The lower total network cost is achieved when the usage of existing pipes is higher. Only one section of the research brings negative trend to differential pressure head. The reason is a higher pressure drop that derives from the fact that the Colebrook-White formulae expresses the Darcy friction factor as a function of Reynolds number and pipe relative roughness. However, it does not supersede the positive effect provided by adjusting heat demands and supply/return temperatures closer to operational values. To sum up, operational data including network and outdoor temperatures, flow rate and pressure, results into more accurate evaluation. Moreover, costs for network reconstruction are sensitive to errors, when considering the overall costs to switch to low temperature technologies. This method is fast and scalable, able to include any type of a district heating system for which basic demand data is available.(c) 2022 Elsevier Ltd. All rights reserved.

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