CM: District Cooling

Introduction

👉 Click here to access the online tutorial for this module

This calculation module assesses the feasibility of district cooling (DC) networks by identifying areas where DC implementation is economically viable. Designed as a planning tool, the module helps policymakers and energy planners determine where DC could be a cost-effective alternative to conventional cooling methods.

By integrating techno-economic analysis with spatial planning, the module provides insights into potential DC zones, supporting preliminary design and investment decisions. It considers economic factors, cooling demand distribution, and infrastructure requirements while maintaining a simplified approach that does not delve into detailed engineering design.

This module is particularly valuable for high-demand zones—such as hospitals, office buildings, and grocery stores—where DC networks can leverage economies of scale. By comparing levelized cooling costs between DC grids and individual cooling systems, the module identifies areas where DC can offer financial and operational advantages.

Additionally, the module incorporates pipe sizing estimates, network cost assessments, and feasibility evaluations, offering a comprehensive yet streamlined approach to DC planning. The tool is open-source and accessible via GitHub, encouraging transparency and community involvement in cooling infrastructure development.

The module operates at Hectare level.

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Method

The module operates at a 100 × 100 meter resolution, allowing for a detailed spatial analysis of DC feasibility across a selected region. The analysis focuses on economic viability while avoiding in-depth design specifics, ensuring it remains a practical tool for early-stage planning and policy guidance.

The key features of the model are:

Techno-economic Feasibility: Identifies potential DC zones based on cost-effectiveness.
Pipe Sizing: Estimates necessary pipe diameters using simplified physical calculations adapted to specific cell layouts.
Open Source: Available on GitHub to promote transparency and community collaboration.

The analysis is based on several simplifying assumptions to maintain computational efficiency:

Cooling Supply Options: The module considers a limited selection of cooling sources for DC supply.
Infrastructure Exclusion: Existing infrastructure is not factored in to simplify cost estimates.
DC Connection Rate: Assumed to be equal to the cooling technology diffusion rate in the region.
Standardized Economic Assumptions: DC is treated as a single technology, with uniform cost assumptions for different network components.

Granularity and technical details of the model include:

Fine Spatial Resolution: The module evaluates feasibility at a high geographic granularity to enhance planning accuracy.
Annual Peak Demand Consideration: The module only considers annual peak cooling demand, simplifying the overall energy demand profile.

The following cost and network design considerations are made:

Pipe Diameter Estimation: Uses a combination of empirical data and theoretical calculations to determine appropriate pipe sizes.
Network Costing: Estimates costs based on average pipe diameter and total projected network length within each cell.
Feasibility Assessment: Uses the levelized cost of cooling (LCOC) to determine economically viable areas for DC implementation.

Further methodological details is available in [2].

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Input

Inputs

Average Electricity Price: This refers to the typical retail electricity prices, which are essential for estimating the operational costs of individual cooling systems. For district cooling (DC), these prices are adjusted downward to reflect the more favorable rates often available to larger-scale, industrial operations.
Estimated Cooling Days in a year: Enter the average number of days in your region that require cooling. Based on this, the model calculates the peak load from the annual total space cooling demand.
COP District Cooling Supply Technology: The average coefficient of performance of large-scale district cooling supply units. The default value is based on literature that represents the average values of such large systems
COP Individual Cooling Supply Technology: The average coefficient of performance of individual/conventional space cooling units like air-conditioners.
Discount Rate: Based on present market values
Lifetime-District Cooling: Total average lifetime of the overall district cooling system.
Lifetime-Individual Supply Technology: Total average lifetime of the individual supply system.
Select the cold demand density layer: Select default layers or external layers if available.
Select the gross floor area density layer: Select default layers or external layers if available.
Select the gross floor area density layer non-residential: Select default layers or external layers if available.

Advanced Inputs

Network Delta T: The difference in the supply and return temperatures of the district cooling grid.
Threshold of Non-Residential GFA Ratio: This parameter sets the threshold for identifying anchor points. Only cells with a non-residential floor area equal to or exceeding this threshold will be classified as anchor cells.
Unit CAPEX for Individual Supply System: The investment costs of the individual supply system are used to calculate the threshold LCOC of individual supply systems for a given electricity price. The average investment costs for different countries are in [Table - Individual System Costs].
Unit OPEX for Individual Supply System (Euros/MW/Year): The fixed operational costs of the individual supply system are used to calculate the threshold LCOC of individual supply systems for a given electricity price. The average investment costs for different countries are in [Table - Individual System Costs]
Unit CAPEX for District Cooling Supply System: The input is used to calculate the supply costs for the district cooling supply technologies. Average values are provided as default. Only change if more reliable local-level values are available.
Unit OPEX for Individual Supply System (Euros/MW/Year) - The input is used to calculate the supply costs for the district cooling supply technologies. Average values are provided as default. Only change if more reliable local-level values are available. Literature shows values ranging from (3,200-16,000 EUR/MWh)

Table - Individual System Costs [1]

Country	CAPEX (1000€/MW)	OPEX (€/MW/Year)
Cyprus	284.55	11382.02
Austria	285.52	11420.67
Denmark	290.57	11622.72
Estonia	291.24	11649.50
France	286.51	11460.47
Germany	287.30	11491.90
Italy	285.52	11420.69
Poland	285.52	11420.67
Romania	285.90	11436.19
Spain	283.30	11332.18
Sweden	290.56	11622.26

Output

Indicators

Were feasible locations for District Cooling identified?: Shows if feasible locations for District Cooling were identified by the CM. If no, other indicators will not be shown.
Total theoretical cooling demand in GWh within the selected zone: Total demand in the region under evaluation.
Estimated actual cooling demand in GWh within the selected zone: Considering the diffusion of space cooling technologies, the total demand that is actually ready to be connected to the district cooling network.
DC cooling potential in GWh within the selected zone: Based on the model results, the total demand in locations that are techno-economically feasible for further assessment of district cooling under pre-defined assumptions.
Total Peak Covered by District Cooling: Total peak covered by the identified feasible locations.
Potential share of district cooling from total actual demand in the selected zone: Percentage of the total demand in the region that has the potential of DC supply.
Number of Clusters Identified: Total number of identified locations feasible for district cooling under given assumptions.
Annualized Total Costs: Estimates on the total required investment for the development of the identified areas into district cooling networks. This is the sum of grid, pumping, and supply costs.
Annualized Total Grid Costs: Estimates on the grid investment required for the development of the identified areas into district cooling networks.
Total Annual Pumping Costs: Estimates on the investment in pumps required for the development of the identified areas into district cooling networks.
Total Supply Costs: Estimates on the supply investment required for the development of the identified areas into district cooling networks.
Average levelized cost of cooling for Individual Supply: Average levelized cost of cooling for supply via individual solutions. This is used as the threshold for the identification of the district cooling potential area. (It is to be noted that this value is used as a threshold to identify potential district cooling feasible areas. Areas where the cost for construction of the grid is cheaper than individual supply are characterized as potentially feasible. However, since supply and pumping costs are further added to the grid costs of the identified areas, there may be cases where the overall levelized cost of district cooling supply could be higher than the individual costs)
Average levelized cost of distribution grid in the potential feasible area: Average costs for the distribution network.
Average levelized cost of district cooling supply in the potential feasible area (network + pumping + simplified supply): Overall average levelized cost of district cooling grid for all identified potential areas.
Total pipe trench length: Total trench length to be dug, if all identified potential areas, are to be realized as a district cooling grid.

Layers

District Cooling areas and their Potential -Shapefile: Identified areas with all aggregated above parameters per DC area.

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Sample Run

Step 1: Select the region

The disctrict cooling calculation module only works on a LAU2 region as this a local energy supply system. Make sure you are on the correct level of area. Select Vienna for this sample run. Figure CM - District Cooling Selection of working region (LAU2)

Step 2: Select the basic input parameters.

These include the following:

Electricity Price: With this value, the CM calculates the cost of supply from individual systems and then uses it as a threshold to cut off cells where the development of a DC grid is more expensive than individual supply. Use the default value of 150 EUR/MWh.
Estimated cooling days in the Year: With this value, the annual cooling need per cell is converted into peak cooling load. Use the default value of 60 days
COP District Cooling Supply Technology: This is used in calculating the operational costs of the district cooling system. Use the default value as this is the average value for Austria.
COP Individual Cooling Supply Technology: This is used in calculating the operational costs of the individual cooling system. Use the default value as this is the average value for Austria.
For the sample run we take the default values for the discount rate, lifetime district cooling and lifetime individual supply technology

Figure CM - District Cooling Selection of basic input parameters

Step 3: Enter the advanced set of parameters.

We use the default values here. The technical values of network delta T and Threshold of Non-residential GFA are standard values from the literature. The cost values represent the average values for Austria.

Figure CM - District Cooling Selection of advanced input parameters

Step 4: Select the input layers.

The CM depends on 3 different raster layers as input. For the sample run we take default values. Figure CM - District Cooling Selection of layers

Step 5: Run the CM.

Since the calculation happens on a hectare level, the CM run may take some time depending on the size of the region as well as the defined scenario. Based on our sample Run Scenario the run should take about 15- 20 secs.

Figure CM - District Cooling Run CM

Step 6: View and Download the Results

Once the run is completed, you will be able to visualize the results on the map. In the results pane on the right, you will see the results on the total potential coverage, identified number of regions for district cooling as well as different cost components for implementation. Under the defined scenario for Vienna, almost 12% of the cooling need can be supplied from district cooling, fulfilling a peak load of over 440 MW at average supply costs of 192 EUR/MWh.

On clicking the regions in the map you can visualize the different parameters of the individual identified feasible area. For advanced-level users, you can go to the layers tab and scroll to the bottom to find a downloadable shape file with detailed results for each identified cluster of cells.

Figure CM - District Cooling Results

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References

[1] Mitterrutzner B, Callegher CZ, Fraboni R, Wilczynski E, Pezzutto S. Review of heating and cooling technologies for buildings: A techno-economic case study of eleven European countries. Energy 2023;284:129252. DOI: 10.1016/j.energy.2023.129252

[2] Malla, Aadit; Kranzl, Lukas. Strategic planning and viability assessment for implementing district cooling networks. Energy 2025;319:134846. DOI: 10.1016/j.energy.2025.134846

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Repository of the calculation module

You can access the open-source code for this calculation module here.

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How To Cite

Aadit Malla, in CoolLIFE-Wiki, CM District Cooling

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Authors And Reviewers

This page was written by Aadit Malla EEG-TU WIEN.

This page was reviewed by Ardak Akhatova e-think.

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License

Creative Commons Attribution 4.0 International License

This work is licensed under a Creative Commons CC BY 4.0 International License.

SPDX-License-Identifier: CC-BY-4.0

License-Text: https://spdx.org/licenses/CC-BY-4.0.html

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Acknowledgement

We would like to convey our deepest appreciation to the LIFE Programme CoolLIFE Project (Grant Agreement number 101075405), which co-funded the present investigation.

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