newsletter

Identifying Green Heat Sources for District Heating


Dear reader,

Many of our newsletters so far have dealt with topics close to the engineering of district heating systems. In our current issue, we would like to look a little bit further and present an article that shows how large district heating systems can be decarbonized in the future. The article proposes a new method that uses computer-aided analysis to identify renewable heat sources and determine the potential for district heating networks. As an example, the new methodology is applied to the existing district heating system of the city of Stockholm in Sweden. Based on this case study, heat sources are selected that have the theoretical potential to cover 100 % of the heat demand.

The short version

In this issue, we present the paper High-resolution mapping of the clean heat sources for district heating in Stockholm City by Chang Su et al. from the Department of Energy Technology at KTH Royal Institute of Technology (Stockholm, Sweden).

Chang Su et al. address the issue of how to identify and characterize sustainable energy sources for large district heating systems. First, georeferenced maps (GIS) are used to identify the locations of potential energy sources and store them in a database. A high-resolution grid is used and public data sources (OpenStreetMap, GoogleMaps etc.) are utilized. In a second step, specific characteristics of the heat potential are assigned to the locations. Here, literature sources, and simplifications are made. In the last step, clusters are formed, and how the heat sources can synergize with the existing network are discussed.

A total of 324 potential heat sources is identified. The heat sources are categorized into a supermarket, data center, ice rink, sewage treatment plant, subway station, as well as water body and geothermal energy. Cluster analysis is used to identify nine regions that have a particularly high level of heat potentials. The authors highlight the particular importance of data centers and heat from water body. For Stockholm, ~ 45% of the total identified potential is data centers, ~ 48% environmental heat from water bodies. Supermarkets, which have the highest number of locations in absolute terms, contribute only about 4.5 % to the theoretical potential.

Our full summary

The article High-resolution mapping of the clean heat sources for district heating in Stockholm City by Chang Su et al. from the Department of Energy Technology at KTH Royal Institute of Technology (Stockholm, Sweden) characterizes heat sources in the city of Stockholm using a top-down method. The goal is to identify possible clusters for sustainable energy sources and to consider them in the municipal heat planning of the city of Stockholm. Beforehand, possible heat sources are defined (e.g. supermarkets). The locations of these heat sources are selected by means of a computer-aided GIS analysis and stored in a geospatial database. Since vector data is used, the geolocations of the heat sources are defined accurately. To precisely locate the locations of large-scale heat sources (e.g., geothermal fields and water bodies for heat pumps application) polygons are used in addition to point objects. We particularly like the fact that the collected and cleaned data is made freely available to the public. For this, the authors provide a download that can be accessed at the following address: https://doi.org/10.1016/j.enconman.2021.113983

After the locations of each heat source have been located, the authors have attached an empirical model to each heat source. The parameters of the models are based on literature data or approximations. An important limitation of this study is that only annual energy amounts are considered, this was considered by the selection of the heat sources (only those with a full-year potential were chosen), but especially seasonal variations have not been considered.

In all, the theoretical potential of seven different heat sources was investigated. The following overview describes the characterization of the heat sources made by Su et al. In addition to the annual available energy, the temperature level was also characterized.

  • Supermarkets: The refrigeration and freezing of products in supermarkets accounts for about 50% of the electricity demand of supermarkets. In the process of providing refrigeration, waste heat is generated at the condenser of the chillers, which can be used for heating purposes. In the study, supermarkets are divided into small, medium and large markets. It is assumed that a medium supermarket provides 1.6 GWh/a waste heat. For small markets, the value is multiplied by 0.5 for large ones by 1.5. To use the waste heat in the existing district heating network, an additional heat pump must be used to raise the temperature level (estimated between 20 °C - 80 °C).
  • Ice rinks: The waste heat potential of ice rinks is given as 43 % of the electrical demand, which is set at 1 GWh/a. The system is similar to that of supermarkets in that the waste heat in the condenser is used as source energy for a separate heat pump.
  • data centers: The cooling of data centers has a very high waste heat potential (see also our newsletter issue #4 newsletter issue #4). The temperature level provided depends on the type of cooling (air cooling: 45 °C, water cooling: 60 °C or two-phase cooling: 75 °C). For this study, it is assumed that the data centers can provide about 100 GWh/a waste heat.
  • wastewater treatment plants: Both treated and untreated wastewater have waste heat potential. For the larger wastewater treatment plants, waste heat recovery of the treated water is already integrated in Stockholm, these are not considered further. The potential for the untreated water is estimated at 18 GWh per WWTP. The temperature level is only 10 °C and must be raised with heat pumps.
  • subway stations: In subway systems, the air heats up due to the trains and other internal loads. Normally, the heated air is exhausted to the outside air. The study assumes that there is an annual potential of 1.1 GWh for each subway station. The waste heat has a temperature level of 10 - 20 °C, so it must also be raised to the level of the district heating network using a heat pump.
  • water bodies: Stockholm's urban area is located in the archipelago and is therefore surrounded by water. Su et al. assume that locations with a depth of at least 10 meters are suitable for exploitation of the water bodies. In addition, the extraction points must be at least 1 km apart to prevent thermal short-circuits. The temperature is given with an average of 4 °C and the heat potential per extraction point with 100 GWh/a.
  • Geothermal energy: Geothermal energy is the last possible heat source to be investigated. Large open areas in Stockholm are determined and a fixed distance of 10 m of the boreholes is defined. At a drilling depth of 200 m, an output of about 120 kWh per year and meter of the borehole is assumed. Since the temperature is far below the district heating network, a heat pump is used again.

Biomass and industrial waste heat are not considered in the study. Biomass is imported from outside Stockholm, but the ambition is to develop local sources. Waste heat from industrial processes is also not considered, as these plants are mostly located further outside the urban area.

Taken together, the heat sources can cover the entire energy demand of the district heating network. The theoretical potential is 7054 GWh/a, the heat demand of the Stockholm heat network is about 6000 GWh/a. The presented heat sources all use heat pumps to raise the temperature level to that of the heating network. Of course, this has a major influence on the electric demand of cities like Stockholm. The authors highlight the particular importance of data centers and water bodies. For Stockholm, ~ 45% of the total identified potential is data centers, ~ 48% is environmental heat from water bodies. Supermarkets, which have the highest number of locations in absolute terms, contribute only about 4.5 % to the theoretical potential. However, these energy sources are distributed throughout Stockholm's urban area. To account for this, the authors apply clustering and identify 9 clusters that have a denser potential (energy). The clusters almost always contain a data center.

This top-down analysis makes some simplifications. Therefore, Su et al. also emphasize that for each heat source a detailed technical and economic analysis has to be carried out. Also, regarding legal feasibility and responsibilities (e.g. waste heat supermarket) detailed investigations have to follow.

Su et al. show that existing energy sources can be used to meet the heat demand of large district heating systems, in this case the city of Stockholm. We believe that computer-aided municipal heat planning can contribute significantly to the decarbonization of heat supply. The work of Su et al. highlight another trend in district heating supply. Larger heat grids will also be supplied more with decentralized supplies in the future.

At heatbeat, we focus on dynamic simulation tools for thermal network and generators, thus forming a good interface between the top-down approach of Su et al. and the actual planning of energy systems by translating the annual potential into a dynamic simulation and thus being able to make concrete statements about the usability of heat sources.

Further Information

The article by Su et al. is freely available at https://doi.org/10.1016/j.enconman.2021.113983 and, in addition to the results presented here, mainly includes graphical representations of the heat sources and cluster analyses.

The next issue of our newsletter will be published on October 6th, 2021.

Best regards,
Your heatbeat team