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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.
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:
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.
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:
Identify different representative, typical cases for district heating in the country/region under investigation
Carry out analyses of district heating grid construction/expansion and district heat supply for the identified representative cases
Calculate indicators of decentral heat supply
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:
The following resulting indicators can be retrieved from the Hotmaps Calculation Modules (CMs):
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:
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.
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/country of interest.
The developed heat demand and floor area density maps are further used in the subsequent steps in other Calculation Modules (CMs).
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:
In the next step, the potentials for excess heat and renewable energy in the regions 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 regions 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:
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.
Possible indicators for grouping of typical district heating areas:
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.
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:
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.
For the representative cities/areas, an economic assessment of district heating can be performed 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:
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.
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.
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.
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:
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.
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.
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)
Marcus Hummel, in Hotmaps-Wiki, Guidelines for using the Hotmaps toolbox for analyses at national level (October 2020)
This page is written by Marcus Hummel*.
* 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
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
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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