Table of Contents

Introduction

These guidelines describe how the Hotmaps toolbox can be used to analyse costs and potentials for efficient and renewable heating and cooling at national level. The guide is especially oriented towards the development of results according to the comprehensive assessment of national heating and cooling potentials referred to in Article 14(1) of the Energy Efficiency Directive (EED) in its current version.

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Part I: Overview of heating and cooling

The Hotmaps database and toolbox provides two different inputs to this part: first, the Hotmaps database provides default data for several of the data needed to include in this part I of the comprehensive assessment. Second, the Hotmaps toolbox is basically a mapping tool that allows a geographical representation of default data in the toolbox but also of other data uploaded to the toolbox. In the following we describe the different default data form Hotmaps that might be of use and we link to the descriptions of how to use the upload function of the Hotmaps toolbox.

The following data relevant for Part I of Annex VIII is available in the Hotmaps database:

  • Point 2: current heating and cooling supply
    • (b) (v) industrial installations:
      • DB – Industrial sites excess heat
      • DB – Industrial sites company names
      • DB – Industrial sites subsector
    • All other:
      • No default data is contained for supply points, but own data can uploaded and displayed
      • How to create an account
      • How to upload own data to the toolbox
  • Point 3: a map covering the entire national territory
    • (a) Heating and cooling demand areas (not for industrial demand)
      • DB – Heat density residential
      • DB – Heat density non-residential
      • DB – Heat density total
    • (b) + (c) Existing and planned supply points
      • No default data is contained for supply points, but own data can uploaded and displayed
      • How to create an account
      • How to upload own data to the toolbox
  • Point 4: forecast of trends in the demand for heating and cooling
    • Default scenarios for all EU28 MS are available from the H2020 project CHEETAH
    • This data is also integrated into the CM – Demand projection and used for the calculations in this module

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Part III: Analysis of the economic potential for efficiency in heating and cooling

The Hotmaps toolbox contains a number of calculation modules (CMs) that can be used to analyse the economic potential for efficiency in heating and cooling. In the following, a possible approach for generating results for the comprehensive assessment with the Hotmaps toolbox is described also linking to the respective default data and calculation modules.

Overview of the Hotmaps approach

To assess the economic potential for efficiency in heating and cooling it is important to distinguish between areas potentially supplied by district heating and areas where decentral supply will most probably be more economically efficient. Thus, the Hotmaps approach strongly builds on the following four steps:

  1. Identify different representative, typical cases for district heating in the country/region under investigation

  2. Carry out analyses of district heating grid construction/expansion and district heat supply for the identified representative cases

  3. Calculate indicators of decentral heat supply

  4. Compare different scenarios of district heating and decentral heat supply and sensitivity calculations

The following figure shows this approach graphically. The different steps will be explained in more detail in the following chapters of these guidelines.

Figure: Hotmaps approach for analysing the economic potential for efficiency in heating and cooling in course of Article 14 of the Energy Efficiency Directive (EED)

In all of these steps various scenarios and sensitivities should be taken into account:

  • Different levels of heat savings (implemented in step 1, 2 and 3)
  • Different levels of district heating shares in total heat supply (implemented in step 2)
  • Different future energy prices (implemented in step 2 and 3)
  • Different depreciation times and discount rates (socio-economic vs. private-economic calculations) (implemented in step 2 and 3)

The following resulting indicators can be retrieved from the Hotmaps Calculation Modules (CMs):

  • Economic potential:
    • Levelised costs of Heat (LCOH) [EUR/MWh]:
      • CM - District heating supply dispatch for costs of heat supply to district heating
      • CM - Decentral heating supply for costs of decentral heat supply
      • CM - Excess heat transport potential for costs of transporting excess heat to potential district heating networks
    • Specific district heating grid costs (expansion and/or new construction) [EUR/MWh]:
      • CM - District heating potential: economic assessment
  • GHG emissions:
    • CO2 emissions [kt]:
      • CM - District heating supply dispatch for CO2 emissions from heat supply to district heating
      • CM - Decentral heating supply for CO2 emissions from decentral heat supply
  • Impact on the share of RES can be calculated based on the results of the following CMs:
    • CM - District heating supply dispatch for share of RES from heat supply to district heating
    • CM - Decentral heating supply for share of RES from decentral heat supply

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Step 1: Identification of different representative cases for district heating

In order to identify different, representative, typical cases for further analysis of the costs and potentials for district heating in the country/region of interest Hotmaps provides various default data layers in the Hotmaps database as well as different Calculation Modules (CMs). Also, own data can be uploaded and used. This identification procedure can consist of the following steps:

  • Calculate scenarios of future heat demand and building floor area density maps for the entire country/region of interest
  • Identify areas potentially interesting for district heating based on user-defined threshold values
  • Analyse/collect potentials for excess heat and renewable energy in the identified country/region potentially interesting for district heating
  • Group/cluster similar regions and select representative cities/areas for further analysis

The following figure shows this procedure graphically and shows the various data sources and calculation modules that can be used.

Figure: Identification of different representative, typical cases for district heating (Step 1)

In the following subchapters the different steps in this procedure are described in more detail.

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Calculation of future heat demand and building floor area density maps

The first step in the analysis is to generate future heat demand and floor area density maps for your region/country of interest. You can use data developed in the course of the Hotmaps project for all EU-28 countries (Hotmaps default data, available in the Hotmaps database), or you can use other heat demand density maps for your region/contry of interest.

  • Use heat demand and floor area density maps developed in the course of the Hotmaps project - default data on heat demand density from the Hotmaps database:
    • For all EU 28 Member States (MS) heat demand density maps reflecting the heat demand from space heating and hot water generation in buildings have been developed. They are available as the total demand in residential and non-residential buildings but also split between residential and non-residential buildings. All maps are all available at hectare level, i.e. with a resolution of 100x100m. The heat demand density maps can be accessed in the layers section of the Hotmaps database and more information on how to select the country/region of your interest can be found here.
    • It is possible to adapt the heat demand density maps according to assumptions regarding the future development of the heat demand in the buildings. Two different Calculation Modules (CMs) can be used:
      • The CM - Scale heat and cold density maps can be used to recalculate the heat demand in each hectare using the same factor for all hectare elements.
      • The CM - Demand projection can be used to generate future heat demand and floor area density maps based on default development scenarios of the building stock in the EU (link to further info on the default scenarios. It is also possible to adapt several parameters compared to the default calculations like a reduction of energy demand or a reduction of floor area.
  • Use own data on heat demand density in your country/region of analysis:
    • It is possible to upload heat density maps in a raster file format (.tif) to the Hotmaps toolbox when creating a user account and logging in to the private section. Uploaded heat demand density layers can reflect the current situation of heat demand densities in the country/region of interest, or also a possible future scenario of heat demand densities, depending on the input data used for generating the respective layers. More information on how to create a user account and how to upload your own data can be found here.
    • It is also possible to further adapt the own heat density maps with the CM - Scale heat and cold density maps or via the CM - Demand projection like for the default layer.

The developed heat demand and floor area density maps are further used in the subsequent steps in other Calculation Modules (CMs).

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Identification of city/area potentially interesting for district heating

After developing possible future scenarios of heat demand and floor area density maps for the city/area of interest, potential district heating cities/areas can be identified. This can be done using the CM - District heating potential areas: user-defined thresholds according to the following steps:

  • The CM is possible to use at NUTS3 - NUTS0 level and also on hectare level (=own selection of an area). However, for larger areas calculation might take a long time, or the module may find too many feasible areas and may not be able to display the results. In such a case the area of interest can be split up, e.g. in the different NUTS3 or Hectare areas, and for each of these the CM can be started.
  • The CM identifies potential district heating areas based on the following two threshold values: a heat demand threshold for the heat demand in each cell of the heat demand density map and a heat demand threshold for groups of connected cells with heat demand above the previous threshold (=coherent area). These two threshold values have to be defined by the user.
  • Besides several other indicators the module generates a shapefile of potential district heating areas that is displayed and stored in the toolbox in the layers section. Especially of interest are the following indicators: total heat demand in the coherent area, average heat demand density in the area.
  • After having used the CM for the entire region/country of interest an overall map of potential district heating areas can be generated out of the single maps.

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Analysis of potentials for excess heat and renewable energy in the identified country/region with potential interest for district heating

In the next step the potentials for excess heat and renewable energy in the country/region that have been identified as potentially interesting for district heating can be analysed. These data together with the data on heat demand and heat demand density in the cities/areas collected in the previous step can then be used to characterise representative district heating areas for further analysis steps. The following list gives an overview of the heat sources that should be taken into account and links to the default data for the respective energy source, which is available in the Hotmaps database:

  • Renewable energy sources:
    • Waste water treatment plants:
      • DB - Waste water treatment plants power
      • DB - Waste water treatment plants capacity
    • Agricultural biomass:
      • DB - Agricultural residues
      • DB - Livestock effluents
    • Forestal biomass:
      • DB - Forest residues
    • Waste:
      • DB - Municipal solid waste
    • Geothermal energy:
      • DB - Geothermal potential heat conductivity
    • Solar thermal energy:
      • DB - Potential solar thermal collectors - rooftop
      • DB - Potential solar thermal collectors open - field
  • Excess heat:
    • Large industrial sites:
      • DB - Industrial sites excess heat
    • Other excess heat sources:
      • Information on other excess heat sources like power plants, further industrial plants, low temperature heat sources like river water, data centers, etc. can be uploaded into the toolbox. A guide how to do this can be found here.

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Identification of representative, typical district heating areas for further analysis

The data collected in the two previous steps can be used to define different types of representative, typical district heating (DH) areas in the region/country of interest. Regions/cities with similar dimensions and combinations of total heat demand, average heat demand density, and potentials for renewable energy and excess heat can be grouped together.

Possible indicators for grouping of typical district heating areas:

  • overall heat demand in the area [GWh/yr]
  • average heat demand density in the area [MWh/(ha*yr)]
  • Available potential of renewable energy sources:
    • waste water treatment plants power
    • agricultural residues
    • livestock effluents
    • forest residues
    • municipal solid waste
    • geothermal potential heat conductivity
    • potential solar thermal collectors - rooftop
    • potential solar thermal collectors open - field
  • excess heat potentials:
    • large industrial sites
    • other

For each of the developed groups of typical DH areas then one or several representative cities/regions can be selected and further analysed. These can serve as representative case studies.

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Step 2: Costs and potentials for district heating in representative cities/regions

For the identified representative cities/areas analyses on the costs and potentials for the heat supply with district heating can be performed. For these analyses Hotmaps provides various default data layers in the Hotmaps database as well as different Calculation Modules (CMs). Also, own data can be uploaded and used. These analyses can consist of the following steps:

  • Assess the economic potential for district heating networks
  • Estimate the costs for the transport of excess heat to district heating areas
  • Develop future heat load profiles
  • Calculate costs and emissions of heat supply in district heating

The following figure shows this procedure graphically and shows the various data sources and calculation modules that can be used.

Figure: Analysis of costs and potentials for district heating in representative cities/regions (Step 2)

In the following subchapters the different steps in this procedure are described in more detail.

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Economic assessment of the potential for district heating

For the representative cities/areas an economic assessment of district heating can be performed in order to gain more detailed insights on the costs and economic feasibility of district heating and the amount of heat potentially supplied by district heating in the areas. For this purpose the CM - District heating potential: economic assessment can be used. This module generates a map of potential district heating areas based on an assessment of the heat distribution costs. An analysis of the feasibility of district heating in the analysed areas can be assessed in the following way:

  • Adapt network construction costs according to experiences in your region/country of interest
  • Calculate the average heat distribution costs and district heating demands for different input parameters
  • Vary e.g. the following important influencing factors:
    • Heat savings over the analysis period
    • Market shares of district heating
    • The threshold for acceptable heat distribution costs
    • Network construction costs
    • Depreciation time and interest rate

The scenarios can be used to analyse the influence of the different factors on the heat distribution costs in district heating systems in the different representative cities/areas. For different settings of depreciation time and interest rate one scenario of district heating expansion per representative city/area should be selected for further analysis.

The outcomes of this step are the heat demand for district heating [GWh/yr] and the heat distribution costs [EUR/MWh] in each of the representative cities/areas. These results will then be used in the overall scenario comparison in step 4.

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Estimation of costs for the transport of excess heat to district heating areas

In order to estimate the costs of transporting excess heat from potential sources outside of district heating areas to potential district heating areas, the CM - Excess heat transport potential can be used. The module yields levelised costs of excess heat transported to the district heating grid [EUR/MWh]. This can further be used in the next step of calculating heat supply costs in district heating.

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Development of future heat load profiles

Renovation of buildings leads to reductions in energy demand for space heating. This also affects the load profiles of heat demand in the district heating systems: the peak demands in winter decrease and the full load hours increase due to higher shares of hot water generation on the overall heat demand. With the CM - Heat load profiles future heat load profiles can be developed according to different heat-saving levels. This can be done based on load profiles provided in the Hotmaps database (default profiles for all NUTS2 regions in Europe) or based on your own profiles uploaded into the toolbox. The resulting load profiles are then used in the next step, the calculation of costs and emission of heat supply in district heating with the dispatch module.

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Calculation of costs and emissions of heat supply in district heating

The costs and emissions of heat supply in district heating system depend on the interaction of the different installed supply capacities. Hereby the least-cost combination of capacities and their operation over time is of interest. In order to analyse the so-called hourly dispatch of different supply technology combinations and the effect on the overall costs and emissions of heat supply in district heating the CM - District heating supply dispatch can be used. With the module several scenarios with the following input data combinations can be calculated in order to derive costs and benefits:

  • Combinations of different technologies in supply portfolios:
    • Excess heat from industry (with or without heat pump)
    • Waste incineration
    • High-efficiency cogeneration
    • Solar thermal
    • Geothermal
    • Biomass
    • Heat pumps with different heat sources as e.g.
      • wastewater treatment plants
      • river water
      • excess heat from data centers
  • Prices scenarios:
    • for prices of different energy carriers
    • for prices CO2 emissions

The calculations can be used to identify beneficial supply portfolios in the different representative cities/areas and their sensitivity to important influencing parameters like energy carrier and CO2 prices or interest rate and depreciation time.

The outcomes of this step are the heat supply costs to the district heating system [EUR/MWh] in each of the representative cities/areas and the related CO2 emissions [kt/yr]. These results will then be used in the overall scenario comparison in step 4.

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Step 3: Calculation of decentral heat supply

In the third step the costs and emissions of heat supply via decentral technologies are calculated. This calculation should be performed for different representative buildings in the country/region of interest. While for district heating representative cities/areas have been developed in step 1 of the approach, typical buildings in each EU Member State data can be found in the Hotmaps default database. Also, for many EU countries detailed building typologies (building archetypes) with data on heat demand before and after renovation can be found in statistics and literature.

The CM - Decentral heating supply can be used to calculate the costs and emissions of heat supply via different decentral technologies. The module uses data on heat demand as well as data on costs of technologies and prices for energy carriers to calculate the levelised costs of heat supply [EUR/MWh] for the different technologies in the different typical buildings and renovation states. The following figure shows this procedure graphically and shows the various data sources feeding into the CM - Decentral heating supply.

Figure: Calculation of decentral heat supply (Step 3)

The calculations can be used to identify costs and benefits of various supply technologies in different representative buildings and their sensitivity to important influencing parameters like energy carrier and CO2 prices or interest rate and depreciation time.

The outcomes of this step are the costs of heat supply via decentral technologies [EUR/MWh] in each of the representative buildings and the related CO2 emissions [kt/yr]. These results will then be used in the overall scenario comparison in step 4.

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Step 4: Comparison of results for different scenarios

The final step in the analysis is the comparison of the results for the different scenarios and sensitivities. For this all results calculated in the previous steps are collected both from the calculations of district heating as well as from the calculations of decentral supply and compared against each other for main indicators. This can be done in the CM - Scenario Assessment. The following figure shows this approach.

Figure: Comparison of results for different scenarios (Step 4)

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References

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How to cite

Marcus Hummel, in Hotmaps-Wiki, Guidelines for using the Hotmaps toolbox for analyses at national level (October 2020)

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Authors and reviewers

This page is written by Marcus Hummel*.

  • [x] This page was reviewed by Lukas Kranzl**.

* e-think, Zentrum f. Energiewirtschaft und Umwelt, Argentinierstrasse 18/10, 1040 Wien

** Energy Economics Group - TU Wien, Institute of Energy Systems and Electrical Drives, Gusshausstrasse 27-29/370, 1040 Wien

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License

Copyright © 2016-2019: Marcus Hummel

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 Horizon 2020 Hotmaps Project (Grant Agreement number 723677), which provided the funding to carry out the present investigation.

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* machine translated