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High-resolution mapping of the clean heat sources for district heating in Stockholm City
KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration. KTH Royal Inst Technol, Dept Ind Econ & Management INDEK, Lindstedsvagen 30, S-10044 Stockholm, Sweden..ORCID iD: 0000-0002-9362-7421
KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Heat and Power Technology. Stockholm Exergi, Jagmastargatan 2, S-11542 Stockholm, Sweden..ORCID iD: 0000-0003-2233-5162
KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.ORCID iD: 0000-0002-9902-2087
2021 (English)In: Energy Conversion and Management, ISSN 0196-8904, E-ISSN 1879-2227, Vol. 235, article id 113983Article in journal (Refereed) Published
Abstract [en]

Decarbonizing the district heating sector is a key measure to achieve the 2040 net-zero emissions target for Stockholm City. One significant question to answer is to find out the locations of all the clean non-fossil fuel heat sources that could be used for district heating within the city?s administrative boundary, and to evaluate how much heat could be extracted from these heat sources. This paper maps out both the geolocations and the technical potentials of the clean non-fossil fuel heat sources for densely populated Stockholm City region, using Geographical Information System based integrative-analysis method. The mapping achieves 1-meter highresolution and provides integrated open datasets to overcome the data availability issue. The mapping results show that a great number of clean and non-fossil fuel heat sources are available for district heating in Stockholm City. By fully unlocking the potentials of these heat sources, around 7054 GWh heat energy is estimated to be possibly exploited per year, which could cover 100% of the existing district heating energy requirement in Stockholm City. The potential share of each mapped heat source is: water bodies 48.3%, data centers 45.4%, supermarkets 4.5%, underground subway stations 0.8%, sewage plants 0.5%, shallow geothermal 0.3% and ice rinks 0.2%. A total of 9 heat sources clusters are identified, which could be prioritized for clean heating energy exploitation. By using the high-resolution mapping, the district heating utilities could plan the capacity in a forward looking way according to the local heat source availability. The method pipeline developed in this study could be recommended to other cities with district heating needs and assist their clean district heating transition roadmap design.

Place, publisher, year, edition, pages
Elsevier BV , 2021. Vol. 235, article id 113983
Keywords [en]
District heating, Heat source, Geographical Information System (GIS), High-resolution
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:kth:diva-293554DOI: 10.1016/j.enconman.2021.113983ISI: 000636289300002Scopus ID: 2-s2.0-85102389567OAI: oai:DiVA.org:kth-293554DiVA, id: diva2:1555137
Note

QC 20210517

Available from: 2021-05-17 Created: 2021-05-17 Last updated: 2022-06-25Bibliographically approved

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Su, ChangDalgren, JohanPalm, Björn

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