District heating networks are considered to be a key element for the decarbonization of Europe. The RELaTED project seeks to contribute to the decarbonization of these infrastructures with the demonstration of low temperature district heating networks. One of the demonstration sites consists of more than 50 substations within a subsection of a larger network in the city of Tartu

(Estonia), where the temperature was lowered by 10 ◦C. To ensure the benefits of this new generation district heating network and the fulfillment of comfort requirements, data have been monitored and analyzed at the substation level in an automatic way to facilitate the inspection of every user.This project has received funding from the European Union’s Horizon 2020 research and

innovation program under grant agreement No. 768567. This publication only reflects the authors’

views, and neither the Agency nor the Commission are responsible for any use that may be made of

the information contained herein

Eguiarte, Olaia

Garay-Martinez, Roberto

Garrido-Marijuan, Antonio

Hagu, Indrek

Raud, Margus

English

District heating networks are considered to be a key element for the decarbonization of Europe. The RELaTED project seeks to contribute to the decarbonization of these infrastructures with the demonstration of low temperature district heating networks. One of the demonstration sites consists of more than 50 substations within a subsection of a larger network in the city of Tartu (Estonia), where the temperature was lowered by 10 °C. To ensure the benefits of this new generation district heating network and the fulfillment of comfort requirements, data have been monitored and analyzed at the substation level in an automatic way to facilitate the inspection of every user

Antonio Garrido-Marijuan

Olaia Eguiarte

Roberto Garay

Margus Raud

Indrek Hagu

NEUROSURGERY ENTHUSIASTIC WOMEN SOCIETY

Lessons Learnt from Substation Inspection on Low Temperature District Heating Networks

TECNALIA Publications

Citation: Garrido-Marijuan, A.;Eguiarte, O.; Garay-Martinez, R.;Raud, M.; Hagu, I. Lessons Learntfrom Substation Inspection on LowTemperature District HeatingNetworks. Environ. Sci. Proc. 2021, 11,33. https://doi.org/10.3390/environsciproc2021011033Academic Editor: Zia LennardPublished: 24 December 2021Publisher’s Note: MDPI stays neutralwith regard to jurisdictional claims inpublished maps and institutional affil-iations.Copyright: © 2021 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Proceeding PaperLessons Learnt from Substation Inspection on Low TemperatureDistrict Heating Networks †Antonio Garrido-Marijuan 1,* , Olaia Eguiarte 1,2 , Roberto Garay-Martinez 1 , Margus Raud 3and Indrek Hagu 31 Tecnalia, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain;olaia.eguiarte@tecnalia.com (O.E.); roberto.garay@tecnalia.com (R.G.-M.)2 ENEDI Research Group, Department of Energy Engineering, University of the Basque Country UPV/EHU,48013 Bilbao, Spain3 Gren Tartu AS, 51014 Tartu, Estonia; margus.raud@gren.com (M.R.); indrek.hagu@gren.com (I.H.)* Correspondence: antonio.garridomarijuan@tecnalia.com† Presented at the Sustainable Places 2021, 29 September–1 October 2021.Abstract: District heating networks are considered to be a key element for the decarbonization ofEurope. The RELaTED project seeks to contribute to the decarbonization of these infrastructureswith the demonstration of low temperature district heating networks. One of the demonstrationsites consists of more than 50 substations within a subsection of a larger network in the city of Tartu(Estonia), where the temperature was lowered by 10 ◦C. To ensure the benefits of this new generationdistrict heating network and the fulfillment of comfort requirements, data have been monitored andanalyzed at the substation level in an automatic way to facilitate the inspection of every user.Keywords: district heating; low temperature; automatic inspection1. IntroductionLimiting global warming below 2 ◦C implies critical actions to reduce GreenhouseGas (GHG) emissions [1]. The European Union (EU) seeks to mitigate GHG emissions by atleast 80% by 2050 relative to 1990 levels. To do so, it is mandatory to transform the energysystem from a fossil-based system to a renewable-based system. At the urban scale, whereapproximately 80% of the global energy consumption takes place [2], it is essential to fosterthe development of District Heating (DH) systems. Moreover, the need to evolve into smartenergy systems with a low carbon footprint is also a crucial element [3].In recent years, the concept of Low Temperature District Heating Systems (LTDH)has emerged [4,5]. These heating systems allow for the incorporation of low-grade heatsources, the reduction of operational costs and losses, and improved performance levels inheat production plants.This paper reports on the activities conducted for the LTDH conversion of an existingDH network in Tartu, Estonia. Operation temperature levels were reduced in a subset ofthe network corresponding to the Tarkon area (marked area in Figure 1). By 2021, the demoarea’s length was 5.34 km, with a peak heat load of 4.3 MW and comprising 54 consumers,including buildings with different uses: residential, commercial, educational, and offices.The supply temperature curve was reduced by 10–15 ◦C after the installation of amixing station equipped with the mixing and booster pumps and valves required to mixthe return flow with the supply flow.Environ. Sci. Proc. 2021, 11, 33. https://doi.org/10.3390/environsciproc2021011033 https://www.mdpi.com/journal/environsciprocEnviron. Sci. Proc. 2021, 11, 33 2 of 4Environ. Sci. Proc. 2021, 11, 33 2 of 4   every user. Commonly, this large set of collected data, with more than 6 different data types (mainly inlet, outlet temperature, and flows for both the primary and secondary side of the substation) from over 50 substations collected hourly, makes the inspection of the data a resource-intensive task [6], and normally, only the most important substations are carefully analyzed (bigger ones or those at the end of the line, where normally the problems are encountered). To ease the process, automatization was set up using Python. The present document presents the methodology used and the main lessons learnt from this process.  Figure 1. Tartu DH network (Estonia), with the demo area highlighted in blue. 2. Materials and Methods A dedicated monitoring system was deployed at the mixing chamber and substation level, as shown in Figure 2. The data are recorded hourly and are stored in a database owned by GREN Tartu (formerly AS Fortum Tartu), the DH operator.  Figure 2. Substation configuration and meters of the Tartu DH network subset. For this purpose, an automatic inspection of substations based on data visualization was developed in Python. Threshold levels were also defined so that the substations with improper performance were tagged for further analysis by the engineering team. The system provides the following outputs 2.1. Primary Side Temperatures (T1 and T2) Figure 3 shows the relationship between the supply temperature (red line of the fig-ure) and the outside air temperature. Additionally, the return temperature (blue line of the figure) is also represented to determine whether there have been any issues. Figure 1. Tartu DH network (Estonia), with the demo area highlighted in blue.In o der to ensure both the benefits of temperature reduction (economic and environ-mental) and the fulfillment of comfort requirements for every costumer, data have beenmonitored at the substation level in an automatic way to facilitate the inspection of everyuser. Commonly, this large set of collected data, with more than 6 different data types(mainly inlet, outlet temperature, and flows for both the primary and secondary side of thesubstation) from over 50 substations collected hourly, makes the inspection of the data aresource-intensive task [6], and normally, only the most important substations are carefullyanalyzed (bigger ones or those at the end of the line, where normally the problems areencountered). To ease the process, automatization was set up using Python.The present document presents the methodology used and the main lessons learntfrom this process.2. Materials and MethodsA dedicated monitoring system was deployed at the mixing chamber and substationlevel, as shown in Figure 2. The data are recorded hourly and are stored in a databaseowned by GREN Tartu (formerly AS Fortum Tartu), the DH operator.Environ. Sci. Proc. 2021, 11, 33 2 of 4   every user. Commonly, this large set of collected data, with more than 6 different data types (mainly inlet, outlet temperature, and flows for both the primary and secondary side of the substation) from over 50 substations collected hourly, makes the inspection of the data a resource-intensive task [6], and normally, only the most important substations are carefully analyzed (bigger ones or those at the end of the line, where normally the problems are encountered). To ease the process, automatization was set up using Python. The present document presents the methodology used and the main lessons learnt from this process.  Figure 1. Tartu DH network (Estonia), with the demo area highlighted in blue. 2. aterials and ethods A dedicated monitoring system was deployed at the ixing chamber and substation level, as shown i Figure 2. e ata a e recorded hourly and are tored in a database owned by GREN Tartu (formerly AS Fortum Tartu), the DH operator.  Figure 2. Substation configuration and meters of the Tartu DH network subset. For this purpose, an automatic inspection of substations based on data visualization was developed in Python. Threshold levels were also defined so that the substations with improper performance were tagged for further analysis by the engineering team. The system provides the following outputs 2.1. Primary Side Temperatures (T1 and T2) Figure 3 shows the relationship between the supply temperature (red line of the fig-ure) and the outside air temperature. Additionally, the return temperature (blue line of the figure) is also represented to determine whether there have been any issues. Figure 2. Substation configuration and meters of the Tartu DH network subset.For this purpose, an automatic inspection of substations based on data visualizationwas developed in Python. Threshold levels were also defined so that the substations withimproper performance were tagged for further analysis by the engineering team.The system provides the following outputs.2.1. Primary Side Temperatures (T1 and T2)Figure 3 shows the relationship between the supply temperature (red line of the figure)and the outside air temperature. Additionally, the return temperature (blue line of thefigure) is also represented to determine whether there have been any issues.Environ. Sci. Proc. 2021, 11, 33 3 of 4Environ. Sci. Proc. 2021, 11, 33  of 4      (a) (b) (c) Figure 3. Primary side temperatures of properly working substations (a) and substations with issues (b,c). 2.2. Secondary Side Temperatures (T3 and T4) Figure 4 shows the relationship between the DH system supply temperature and the outside air temperature. Additionally, the return temperature is also represented to de-termine whether there have been any issues.   (a) (b) Figure 4. Primary side temperatures of a properly working substation (a) and substations with is-sues (b). 2.3. Domestic Hot Water (T5) Figure 5 shows the average temperature of the DHW on the customer side at the different hours of the day for those temperatures belonging to the horizontal part of the heating curve (any given outdoor temperature above 10 °C).   (a) (b) Figure 5. DHW temperature of a properly working substation (a) and a substation with an issue (b). 3. Lessons Learnt The automatic analysis of the continuously monitored data from over 50 substations draws the following conclusions: • Data monitoring is an essential tool for DH optimization, but it is time consuming to analyze large sets of data in a traditional way. • The automatization of data analysis is beneficial to improve the operation of the DH, allowing issues to be identified in real time. Figure 3. Primary side temperatures of properly working substations (a) and substations withissues (b,c).2.2. Secondary Side Temperatures (T3 and T4)Figure 4 shows th relationship betwe n the DH sys em supply temp rature andthe outside air temperatur . Additionally, the return temperature is also represented todetermine whether there have been any issues.Environ. Sci. Proc. 2021, 11, 33 3 of 4      (a) (b) (c) Figure 3. Primary side temperatures of properly working substations (a) and substations with issues (b,c). 2.2. Secondary Si e Temp ratures (T3 and T4) Figure 4 shows the rela ionship between the DH system supply temperature and the outside air temp r ur . Addi ionally, the return emperatur  is also represented to d -termine whether there ave be n ny issues.   (a) (b) Figure 4. Primary side temperatures of a properly working substation (a) and substations with is-sues (b). 2.3. Domestic Hot Water (T5) Figure 5 shows the av rage temperature of the DHW  the customer side t the different hours of the da for those temperatures belonging to the horizontal part of the heating curve (any given outdoor temperature above 10 °C).   (a) (b) Figure 5. DHW temperature of a properly working substation (a) and a substation with an issue (b). 3. Lessons Learnt The automatic analysis of the continuously monitored data from over 50 substations draws the following conclusions: • Data mo itoring is an ssenti l tool fo  DH ptimization, but it is time consuming to analyze large sets of d ta in a tr ditional way. • The automatization of data analys s is beneficial to improve the operation of the DH, allowing issues to be identified in real time. Figure 4. Primary side temperatures of a properly working substation (a) and substations withissues (b).2.3. Domestic Hot Water (T5)Figure 5 s ws the av rag t mperatur of th DHW on t customer sid at thedifferent hours of the day for th s temperatures belonging t the horizontal part of theheating curve (any given outdoor temperature above 10 ◦C).Environ. Sci. Proc. 2021, 11, 33 3 of 4      (a) (b) (c) Figu e 3. Primary side temp ratures of prope ly working substations (a) and substations with issues (b,c). 2.2. Secondary Side Temp ratures (T3 and T4) F gure 4 hows the relat onship betwe n the DH syst m supply temp r ture and the outsid  a r temperatu . Additio ally, he return emperature is also r present d to e-t rmine whet er ther  have been a y issue .   (a) (b) Figu e 4. Primary side temp ratures f a properly working subst tio  (a) and substations with is-sues (b). 2.3. Domes ic Hot Water (T5) Figure 5 hows the verage temperatu e of the DHW on the custom r side at the differe t hours of the day f r those te erat res elonging to the horizontal part of the heating curve (any given utdo r temper ture above 10 °C).   (a) (b) Figure 5. DHW temp rature f a prope ly working subst tio  (a) and a substation w th an issue (b). 3. Lessons Learnt The automatic analysis of the continuously monitored data from over 50 substations draws the followi g conclusions: • D ta mo itoring i  a  essential ool for DH optimization, but it is time consuming to analyz large sets of data in a tr dit onal way. • The automatization of d ta analysis is benefic al to improve the operation of the DH, allowing issues to be identified in real time. Figure 5. DHW emper tu e of a properly working subst tion (a) and a subs ation with an issue (b).3. Lessons LearntThe automatic analysis of the continuously monitored data from over 50 substationsdraws the following conclusions:• Data monitoring is an essential tool for DH optimization, but it is time consuming toanalyze large sets of data in a traditional way.Environ. Sci. Proc. 2021, 11, 33 4 of 4• The automatization of data analysis is beneficial to improve the operation of the DH,allowing issues to be identified in real time.• Automatic data analysis can be performed at a low computational cost. One year ofhourly data from 54 substations were analyzed, along with the consequent productionof warnings generated upon sub-optimal behavior, in less than 10 min with a laptop.• Large DH operators often lack this kind of service, instead focusing on the analysis ofthe biggest consumers to optimize the service or being reactive to consumer complaints.A proactive approach to further optimize DH operation has yet to be exploited.Author Contributions: Conceptualization, R.G.-M.; methodology, O.E., A.G.-M. and R.G.-M.; soft-ware, O.E.; formal analysis, O.E.; investigation, O.E. and A.G.-M.; field data collection: M.R. and I.H.;data curation, O.E. and A.G.-M.; writing—original draft preparation, A.G.-M. and O.E.; writing—review and editing, R.G.-M.; visualization, O.E.; supervision, A.G.-M. and R.G.-M.; project adminis-tration, R.G.-M. All authors have read and agreed to the published version of the manuscript.Funding: This project has received funding from the European Union’s Horizon 2020 research andinnovation program under grant agreement No. 768567. This publication only reflects the authors’views, and neither the Agency nor the Commission are responsible for any use that may be made ofthe information contained herein.Data Availability Statement: Due to privacy issues, data should not be shared.Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the designof the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; orin the decision to publish the results.References1. The Paris Agreement, UNFCCC. Available online: https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement (accessed on 18 December 2019).2. European Commission Cities. Available online: https://ec.europa.eu/clima/policies/international/paris_protocol/cities_en(accessed on 19 December 2019).3. Lund, H.; Werner, S.; Wiltshire, R.; Svendsen, S.; Thorsen, J.E.; Hvelplund, F.; Mathiesen, B.V. 4th Generation District Heating(4GDH): Integrating smart thermal grids into future sustainable energy systems. Energy 2014, 68, 1–11. [CrossRef]4. Garrido Marijuan, A.; Garay, R.; Lumbreras, M.; Sánchez, V.; Macias, O.; Rozas, J.P.S.D. RELaTED Project: New Developments onUltra-Low Temperature District Heating Networks. Proceedings 2020, 65, 8. [CrossRef]5. Garay-Martinez, R.; Sanchez Zabala, V. RELATED, A Flexible Approach to the Deployment and Conversion of DH Networksto Low Temperature, with Increased Use of Local Solar Systems. Solar District Heating, Graz, 2018. Available online: https://www.solar-district-heating.eu/wp-content/uploads/2018/06/Proceedings-SDH-2018.pdf (accessed on 20 December 2021).6. Lumbreras, M.; Garay-Martinez, R.; Garrido Marijuan, A. Energy Meters in District-Heating Substations for Heat ConsumptionCharacterization and Prediction Using Machine-Learning Techniques. In IOP Conference Series: Earth and Environmental Science;IOP Publishing: Bristol, UK, 2020. [CrossRef]

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Lessons Learnt from Substation Inspection on Low Temperature District Heating Networks

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