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Efficiency and reliability play a crucial role in district heating networks. A potential malfunction or fault in the system can lead to inefficient operation, energy losses and ultimately financial damage. In the past, district heating networks often depended on manual monitoring processes to detect and correct faults. Today however, advanced monitoring in combination such as automatic fault detection enable effective monitoring and diagnosis of faults in real time. In this newsletter, we present an article that takes a closer look at this topic and how this technology can help minimize energy losses and improve the reliability of district heating networks. To counteract future faults in operation, a suitable system design of the handover between the heat network and the building will help. The second article presented discusses how an optimized design of the secondary temperature can contribute to an optimized overall system.
In the article Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities by van Dreven et al., a total of 57 articles from the last 12 years are reviewed. The paper focuses on current trends and identifies research needs as well as limitations of the methods used. It is emphasized that especially in the last five years more attention has been paid to this topic, but the biggest obstacle is not a lack of methods, but the availability of so-called labeled data sets, which link measurement data and actual errors that occur. Additionally, data sets are often not publicly available. It is assumed that up to 43 % - 75 % of the substations used in district heating networks are not operated optimally and have faults in physical or control systems. This often leads to high return temperatures, which limit user comfort and the hydraulic capacity of the network.
The systematic evaluation of the large number of publications in this topic area, is evaluated using a SWOT analysis (Strength - Weaknesses - Opportunities - Threats). Strengths are the increased interest in automated fault detection and the increasing digitalization of district heating systems and especially of substations. System complexity and heterogeneous district heating systems are named as threats. In addition, there is a lack of uniform descriptions and uniform monitoring concepts. This is especially true for the secondary side of district heating networks. Uniform documentation of faults is also lacking at the moment and is thus a threat in the application of automated fault detection. Similar to the threats, the weaknesses are seen in particular in the lack of ground truth, that is, perfectly labeled data for training machine learning models. While this can be partially addressed by simulations, these are not able to generalize every behavior. Future opportunities are seen in advanced machine learning models. This also includes hybrid models, which for example combine machine learning and probabilistic modelling.
The second article by A. Maccarini et al. presents a simulation study with four different temperatures of the secondary side. In particular, the influence on the electricity demand of the decentralized heat pumps as well as the circulating pumps is elaborated. The model has been modeled in Modelica and represents a case study in Denmark. The case study includes a total of 281 building units (residential buildings and office buildings). The substations have been modeled with separate heat pumps for heating and domestic hot water. Office buildings can be cooled directly via a heat exchanger. Four heat transfer systems are investigated. High-temperature radiators (70 °C), low-temperature radiators (55 °C), underfloor heating (35 °C), and a new transfer system that has so far only been prototyped and requires temperatures only slightly above the necessary room temperature (23 °C). A linear flow temperature curve between - 10 °C and 20 °C outside temperature was provided for all systems.
The electrical energy of the decentralized heat pumps, as well as the electrical energy of the central circulation pumps are used to compare the four heat transfer systems. It can be shown that the electrical energy is reduced by 4.2 kWh/m2a on average (from 10.4 to 6.2 kWh/m2a) between the highest and lowest flow temperatures. For underfloor heating, the value is 7.5 kWh/m2*a. Due to the improved efficiency of heat pumps, more ambient heat is absorbed from the network. This increases the pump energy flow used. The pump energy flow is between 1 and 5% of the electrical energy used. An attempt is also made to generalize the results. To this end, it is found that for every 1 Kelvin of temperature reduction, about 1.5 % electrical energy can be saved. However, this also depends to a large extent on the source temperature available in the heating network.
As always, we recommend both articles in full:
The next issue of our newsletter will be published on June 7, 2023. Until then, feel welcome to follow us on LinkedIn where we share smaller use cases and information with you.