FfE heat transformation tool
The Python-based stock-and-flow model “FfE heat transformation tool” is used to model scenarios for the regionalised development of the heat supply and the associated systemic effects as well as the resulting costs for the area under consideration. A simulation of heating networks with different operating strategies (heat driven, cost-optimised and emission-optimised) can also be simulated intrayear (heating network transformation tool). The results of the tool can be used to derive targeted measures that promote the transformation towards a climate-neutral heat supply. In addition, the variability of the tool makes it possible to visualise the effect of different assumptions regarding renovation rates and depths as well as heating system changes on the area as a whole. The tool works at building or land parcel level and the results can be output in cartographic (GIS-based) and differentiated aggregated forms. The core results of the model include
- Heat supply per technology
- Final energy demand
- Greenhouse gas emissions
- Fixed and variable costs and
- subsidies.
The expansion of heating networks and the spread of heat pumps, for example, can be derived directly from these. In turn, this can also be used to identify repercussions on the load on the electricity grid.
Background
The climate-neutral supply of heat for the building sector plays a fundamental role in achieving Germany’s climate targets and the overall transformation of the energy system. The heating sector (space heating, hot water, other process heat, air conditioning cooling and other process cooling) accounts for more than half of final energy consumption in Germany [1]. At the same time, the share of climate-neutral heat sources in generation is less than 20 % and has barely increased in recent years [2]. This contrasts with the target of a 50 % share of renewable energies in the heat supply by 2030 [3]. It is therefore necessary to increase the proportion of climate-neutral heat sources at the centralised and decentralised level of heat generation in the short term, but also in a sustainable and systemically sensible way. In order to provide cities and municipal utilities with a planning tool, the FfE heat transformation tool was developed as part of the “District heating strategy for the future” research project and other practical projects.
The law on municipal heat planning, which entry into force of an act nationwide on 1 January 2024 (see FfE article on municipal heat planning), provides a standardised legal framework to promote efficient, sustainable and future-oriented solutions in the area of heat supply in the coming years. The municipal heating plan is a cornerstone of the energy transition and therefore crucial to achieving climate neutrality. As part of this law, the modelling of a target scenario and various maps are also required. This is where the FfE heat transformation tool comes in. Suitable and project-specific parameterisation is essential here.
Among other things, the municipal heating plan is used to determine which areas are suitable for being supplied with new heating networks and which areas will probably still have to be equipped with their own heating systems. The heating plan is therefore an important tool for all building owners when switching to a sustainable heating supply.
The FfE heat transformation tool is not intended to provide a final decision on the type of heat supply and refurbishment measures for each building. This is the task of an on-site energy consultation. Instead, the aim is to examine the effects that result when the heat supply types are applied in accordance with the defined regionalised prioritisation.
Example result of a scenario calculation
The model can output the results cartographically or, as in Figure 1, aggregated in a graph. Figure 1 shows two exemplary scenario paths for the energy development of heat supply for the same area. The area itself consists of almost 70,000 individual land parcels, which in turn have been divided into 56 clusters. The graphs in Figure 1 show the aggregated heat supply in GWh per technology per year. The comparison of the target scenario shown on the left with the trend scenario shown on the right makes it clear that the development and final state can differ significantly due to the different definition of the implementation parameters in the tool, such as the refurbishment rates and depths as well as the heating system replacement rates. All results can also be visualised cartographically, as shown in Figure 2.
How does the FfE heat transformation tool work?
Figure 3 shows the schematic structure of the FfE heat transformation tool, including the input and output data. Figure 4 also shows a detailed schematic view of the main simulation. The tool processes the input data prepared from the status quo and potential analysis of a municipal heat planning. It also takes into account the project-specific categorisation of the area into different clusters. The data set prepared at parcel or building level is then used in the main simulation for each year within a specified time period, for example 2024 to 2035 or to 2045. For each building and year, the system updates whether the current heating system will be retained, renewed or replaced. In addition, selection logic is used to identify the buildings that are prioritised for refurbishment. Finally, a comprehensive data set is available in which the development of heat demand, final energy, greenhouse gas emissions, costs and subsidies are automatically output for various aggregation levels.
During the development of the FfE heat transformation tool, consideration was given to also setting up the model as an optimisation. However, it was noted that a complex and computationally intensive optimisation would, on the one hand, make it possible to determine the sensitivities of individual parameters more slowly or not at all and, on the other hand, would simulate a fictitious accuracy of the optimum result, which could potentially not be supported with suitable measures. For this reason, the FfE heat transformation tool was not formulated as an optimisation and could already be actively used in workshops to compare or repeat simulations that had already been carried out with new parameter settings. This was very helpful in the discussion and, above all, can also shorten discussion processes on the relevance of individual parameters.
A detailed description of the simulation logic and its assumptions is given in the article “Municipial heating plan for Stuttgart” Further descriptions of the challenges of creating the model and its solution approaches, an outlook for further development and findings for the heat transformation gained from the modelling are presented in the published final report of the research project “District heating strategy for the future”.
Extension: Heating network transformation tool
Heating networks play a decisive role in the heating transition. In order to analyse this in more detail, an extension of the model was made, which can also be used independently in the form of the “heating network transformation tool”. It is used to compare the effect of two heating grid transformation measures (reduction of heating grid temperatures and predictive or system-optimised control of a heat storage facility and the corresponding generation fleet) on individual generators, combined generators and grid operation.
Figure 5 shows an example comparison of different runs for the same heating network area. The result shown on the left „ohne FW-Tool“ shows the result of the calculation for a heating network when only the FfE heat transformation tool is used. It can be seen that the final energy demand for district heating is not further broken down into the individual central generation plants. The other results are taken from the analyses in the heat network transformation model for different compositions: Temperature in the flow of 95 °C or after reduction to 75 °C, with a corresponding reduction in the return temperatures and in each case for a heat-led or cost-optimised control of the system. In summary, this analysis makes it clear that an hour-by-hour mapping of the provision of district heating is necessary in order to visualise the effect of individual measures on the use of the technologies.
In practical application
The model has already been used for the creation of the and for the municipal heat planning of the city of Stuttgart. The model was extended and used to calculate different scenarios, particularly in the context of modelling for the city of Stuttgart.
Literature
[1] Umweltbundesamt (2024): Energieverbrauch für fossile und erneuerbare Wärme https://www.umweltbundesamt.de/daten/energie/energieverbrauch-fuer-fossile-erneuerbare-waerme (abgerufen am 04.01.2024)
[2] Umweltbundesamt (2023), Fachgebiet V 1.8 – Geschäftsstelle der Arbeitsgruppe Erneuerbare Energien-Statistik (AGEE- Stat): Zeitreihen zur Entwicklung der erneuerbaren Energien in Deutschland – unter Verwendung von Daten der Arbeitsgruppe Erneuerbare Energien-Statistik (AGEE-Stat). Dessau-Roßlau: Bundesministerium für Wirtschaft und Klimaschutz (BMWK)
[3] Wissenschaftliche Dienste 5: Wirtschaft und Verkehr, Ernährung und Landwirtschaft (2023), Sachstand: Die Wärmewende in Deutschland – Bedeutung, Ziele und Umsetzbarkeit. WD 5 – 3000 – 010/23 Berlin. Deutscher Bundestag