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High-Resolution GIS District Heating Source-Load Mapping

Fossil fuels still account for around 7% of input energy used for the production of district heating (DH) in Sweden. With this study, we will develop a high-resolution Geographical Information System (GIS) platform, which can map the potentials of the renewable and recycled heat sources surrounding cities, and analyse cost efficient modalities of matching the sources and seasonal storages with building heat loads.

Funded by:

The Swedish Energy Agency

Energimyndigheten

KTH IRIS Research Initiative

Project partners:

Stockholm Exergi

Stockholm Exergy

ESIEE Paris

ESIEE

Nottingham Trent University

NTU

Universite Gustave Eiffel

UGE

Time period

2020.01 to 2023.06

Background

Eliminating the last percentage of fossil fuels by renewable and recycled heat (e.g. industrial excess heat, geothermal, etc.) is essential for Sweden to become sustainable by 2040. It is therefore at the crux for the DH sector to investigate where the heat sources locate, how much potentials are available and how to effectively match them with building loads. The outcomes of this project will enable the Swedish DH sector to have a revolutionary tool at its disposal, one that is inexpensive and effective in integrating and utilising clean heat sources and storages to decarbonise the DH systems.

In Sweden, district heating consumes 46 TWh final energy use in buildings, which accounts for 32% [1]. Internationally, the United Nations Sustainable Development Goals advocate providing clean energy and encouraging a transformation away from fossil reliant energy systems into renewable systems [4]. To reflect the clean DH urges, the established and newly planned DH systems in Sweden need to maximize their contributions to a global decarbonized energy system, and strive to be the backbone of a sustainable energy future at city level.

In modern days, a real opportunity to better plan new DH constructions and better evaluate expanding DH networks has come, since large amounts of spatial-temporal GIS data are generated from production sites down to end users. The utilization of spatial-temporal data through GIS for DH is not new. As early as 2003 in Sweden, GIS had been used to identify heat load clusters for reusing excess heat in industrial processes [5]. In 2008, the Danish Heat Plan project launched the establishment of GIS-based Denmark Heat Atlas [2]. For example, Nielsen and Möller evaluated the technical and economic potential of expanding DH in Denmark [6]. Lund and Persson estimated various heat sources potential of heat pumps in DH in Denmark [7]. GIS-based analysis had also been used in finding potential spots of using industrial surplus heat in Denmark [8]. Large cities such as London [9] and Berlin [10] had developed their own city-level building heat maps using GIS. In the USA and China, GIS-based spatial analysis has showed its power in evaluating DH potential at national level [11], sub-national level [12] and city level [13]. Advanced over time, publications in recent five years focused more on applications of spatial analysis algorithms for DH system management. For example, a worldwide spatial clustering analysis of residential building heat and cooling demand had been performed in [14]. Mixed-integer-linear-programming had been used to study geothermal potential integration with DH systems in Lausanne Switzerland [15].

Nevertheless, most of the studies focused mainly on the demand side analysis. But the input data quality varies disproportionally. Often, the modelling resolution is coarse, which is at 1km or even lower. Besides, there is a lack of a comprehensive source potential mapping at the heat supply side. An integration is needed for all renewable and recycled heat sources and thermal storages. Therefore, a high-resolution DH mapping platform that can visualize the sources locations and evaluate their potentials, as well as recommend solutions on matching them with loads, is essential for the future Swedish DH sector.

The HiReSoLo platform aims at solving the aforementioned challenges, striving to be the first of its kind in Sweden. The project originates from a pilot study named ‘4th-Generation DH within and beyond Europe’, led by KTH Energy Technology researcher Dr. Chang Su. The project has collected extensive high-resolution data for GIS DH source-load mapping, which is a sound foundation for the HiReSoLo platform development.

Previously, the HiReSoLo proposal had been submitted to IEA District Heating and Cooling Annex XIII Programme [16] in May 2020. Despite not being granted, we received very high score in selection review and was strongly recommended to resubmit to future calls (40+ proposals were submitted and we were among top 15, but only top 7 can be funded). The feedbacks greatly enhance our confidence in proceeding the HiReSoLo platform development.

In this project, the HiReSoLo platform will fill the research blank by providing the Swedish DH sector with a new tool at its disposal, to effectively achieve the zero-carbon targets and contribute to both Swedish national climate mitigation commitments and the United Nations Sustainable Development Goals.

Aim and objectives

Principal objective:

Develop a high spatial (10-100m) and temporal (hourly) resolution DH source-load mapping platform, which can contribute to eliminating the last 7% fossil fuel energy input in DH systems in Sweden.

Outcomes

  1. A DH source-load database, containing extensive datasets of heat sources and demands of the DH system.
  2. New mapping methodology on thermal resource potential and user demand.
  3. Source-load mapping atlases, Stockholm Renewable and Recycled Heat Atlas.
  4. Comprehensive DH decarbonization solutions and roadmaps for Swedish DH sector to become zero-carbon.
  5. At least 3 journal papers, targeting high impact factor journals.
  6. Two working package reports and a synthesis project report.
  7. Attending two conference papers.
  8. Annual seminars on project progress with audiences from, among others, industries, public sector and academia.
  9. Enhanced international collaboration with French and British Universities.

Publications

Chang Su*, Johan Dalgren, Björn Palm, “High-resolution mapping of the clean heat sources for district heating in Stockholm City”, Energy Conversion and Management, under review.

Project contact persons

List of References

[1] Swedish Energy Agency, “Energy in Sweden 2019 an overview,” 2019.

[2] A. Dyrelund and H. Lund, “Heat Plan for Denmark,” 2008.

[3] D. Connolly, H. Lund, R. Lund, and S. Werner, “Heat Roadmap Europe - Pre-Study 1,” 2012.

[4] United Nations, “SDG7, Affordable and clean energy.” .

[5] C. Sundlöf, “Svenska Värmenät - Potential för utökat värmeunderlag för kraftvärme och spillvärme genom sammanbyggand av fjärrvärmenät (Swedish Heat Grids - Potential for more aggregated heat loads for higher utilisation of combined heat and power and industrial excess,” 2003.

[6] S. Nielsen and B. Möller, “GIS based analysis of future district heating potential in Denmark,” Energy, vol. 57, pp. 458–468, 2013, doi: https://doi.org/10.1016/j.energy.2013.05.041.

[7] R. Lund and U. Persson, “Mapping of potential heat sources for heat pumps for district heating in Denmark,” Energy, vol. 110, pp. 129–138, 2016, doi: https://doi.org/10.1016/j.energy.2015.12.127.

[8] F. Bühler, S. Petrović, T. Ommen, F. M. Holm, H. Pieper, and B. Elmegaard, “Identification and evaluation of cases for excess heat utilisation using GIS,” Energies, vol. 11, no. 4, 2018, doi: 10.3390/en11040762.

[9] Greater London Authority, “London Heat Map.” .

[10] Institut für angewandte Forschung Berlin, “HeatMap - Visualisierung von Heizenergieverschwendungen in öffentlichen Gebäuden durch eine Heatmap.” [Online]. Available: https://www.ifaf-berlin.de/projekte/heatmap/.

[11] H. C. Gils, J. Cofala, F. Wagner, and W. Schöpp, “GIS-based assessment of the district heating potential in the USA,” Energy, vol. 58, pp. 318–329, 2013, doi: https://doi.org/10.1016/j.energy.2013.06.028.

[12] C. Su, H. Madani, and B. Palm, “Building heating solutions in China: A spatial techno-economic and environmental analysis,” Energy Convers. Manag., vol. 179, pp. 201–218, 2019, doi: https://doi.org/10.1016/j.enconman.2018.10.062.

[13] C. Su, H. Madani, H. Liu, R. Wang, and B. Palm, “Seawater heat pumps in China, a spatial analysis,” Energy Convers. Manag., vol. 203, p. 112240, 2020, doi: https://doi.org/10.1016/j.enconman.2019.112240.

[14] J. Sachs, D. Moya, S. Giarola, and A. Hawkes, “Clustered spatially and temporally resolved global heat and cooling energy demand in the residential sector,” Appl. Energy, vol. 250, pp. 48–62, 2019, doi: https://doi.org/10.1016/j.apenergy.2019.05.011.

[15] J. Unternährer, S. Moret, S. Joost, and F. Maréchal, “Spatial clustering for district heating integration in urban energy systems: Application to geothermal energy,” Appl. Energy, vol. 190, pp. 749–763, 2017, doi: https://doi.org/10.1016/j.apenergy.2016.12.136.

[16] International Energy Agency, “IEA Technology Collaboration Programme on District Heating and Cooling including Combined Heat and Power,” 2020.

Page responsible:Oxana Samoteeva
Belongs to: Energy Technology
Last changed: Aug 25, 2021
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