District heating system analysis and design optimization

doi:10.1016/j.energy.2025.136349

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District heating system analysis and design optimization

2025-07-01

Panteleimon Tzouganakis

Maria Fotopoulou

Dimitrios Rakopoulos *

Dmytro Romanchenko

Nikolaos Nikolopoulos

DOI：10.1016/j.energy.2025.136349

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Abstract

En 中文

District heating systems have a crucial role in promoting energy efficiency and reducing greenhouse gas emissions. The purpose of this research paper is to present an algorithm for the analysis of district heating systems as well as its expansion, which aims to optimize the design of a district heating system, thus providing a useful tool to the system operator in terms of future investments and network expansion. The core of the developed algorithm is generalized and therefore applicable to any system, is based on the Newton-Raphson method in order to calculate the pressure drops, the flow velocities and, as a result, the heat losses at the pipes. The proposed district heating analysis algorithm is applied on a model of the system of Kungsbacka, Sweden, which is a demonstration site of ENFLATE, a Horizon Europe project related to the decarbonisation and flexibility enhancement of the energy sector. According to the simulation results of the developed algorithm, the model has a good coherence with the measurements obtained from the demo with a deviation of +3.4 % in the thermal efficiency of the network. Finally, the significance of insulation material and thickness of the network, is demonstrated through an optimization method based on the genetic algorithm that aims to minimize the lifecycle costs. It was demonstrated that polyurethane foam yielded the lowest lifecycle costs when its thickness was optimized, while reducing the heat losses per length to 15.1 W/m. The developed methodology could be applied to any district heating system, for determining the optimal insulation material and thickness based on a lifecycle cost analysis.

Keywords:

District heating network

Numerical modelling

Optimization

Lifecycle cost analysis

Genetic algorithm

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