FREM Article Series: Regionalizations for the Network Development Plan
One of the basic tasks of FREM is to regionalize – i.e. spatially distribute – different input data. Various distribution rates or methodologies can be used for this: from the simple distribution of a national value to the regional level via the inhabitants per region, to complex, high-resolution grids that generate a distribution rate on the basis of several input variables. Through its regionalization work, the FfE repeatedly contributes analyses to the Electric Network Development Plan (NEP – from the German Netzentwicklungsplan), which the transmission system operators (TSOs) must regularly prepare and submit to the Bundesnetzagentur. This forms the basis for decisions on grid expansion in Germany. Selected regionalizations that the FfE has carried out for the NEP are presented in this edition of our series of articles about FREM.
Overview of the topics in the FREM Article Series
- Development of an Energy-related Geo-database
- Regionalizations for the Network Development Plan
- The Wind Scenario Tool WiSTl
- Modeling the Expansion of Ground-Mounted and Roof-Mounted Photovoltaic Systems
- Weather Data
- Open Data
Regionalization of Renewable Energies
Since 2014, the FfE has been working with the TSOs to develop forecasts for the expansion of renewable energies in Germany. The Bundesnetzagentur first approves overarching figures for the development of renewables in various scenarios (Scenario A – C), which are then distributed to municipality or postcode level using the FfE’s regionalization methods. Wind turbines and ground-mounted and roof-mounted photovoltaic units are considered separately.
As an example, the regionalization of the expansion of wind turbines (WT) for the 2019 version of the Electrical Network Development Plan 2030 1 is presented in this section.
In a first step, all exisiting turbines in Germany are mapped. Various sources were processed and combined to create this dataset, which serves as the basis of further analyses. For the modeling of the wind energy expansion, the areas available for expansion must also be determined. For this purpose, different categories of areas for wind turbines are defined, which result from a combination of the wind suitability and wind priority areas designated by the spatial planning authorities and specially surveyed white areas. The latter are the result of a Germany-wide spatial analysis that removes all exclusion areas (protected areas, settlements, etc.) from consideration and divides the remaining areas according to usability (e.g. forest = tougher restriction).
Furthermore, additional restrictions related to required distances from settlements for turbines are considered in order to be reflect current political discussions in the analysis. Figure 1 shows all identified and classified potential areas for wind turbines in Germany.
In the next step, the FfE’s Wind Scenario Tool (WiSTl) is used to model the turbine-specific expansion. WiSTl can be used to determine the optimal configuration for wind farms in designated areas. The tool considers the most suitable turbine type per area and builds the wind farms according to the typical ellipsoidal shape and in alignment with the main wind direction. WiSTl is the focus of the next article in the series and will be discussed there in more detail. After WiSTl has determined the optimal turbine configuration for all potential areas, the next step is the actual regionalization – the distribution of the overarching figures to respective areas.
In order to assign the overarching figures for future installed capacity in each scenario to geographical areas, the existing plant inventory in each considered year must first be subtracted. This value is in turn dependent on the technical lifetime of the turbines. Taking into account the existing turbines in the wind suitability areas, the remaining area per site is determined and converted by WiSTl into an electrical potential evaluated according to wind speeds and wind frequencies. Several site classes are derived from this potential. The remaining future capacity to be distributed is then regionalized via the site classes, sorted from good to poor.
For the scenario framework of the 2021 version of the Electric Network Development Plan 2035 1, the FfE was also able to contribute its expertise in the field of electromobility. Specifically, the aim was to estimate how high the additional regional electric load would be due to the expected rapid increase in battery-electric vehicles (BEVs) for various scenarios. To answer this question, a methodology was first developed to regionalize the expected annual BEV growth through 2050. For this purpose, three ramp-up phases (pioneers, mainstream and standard) were defined. The pioneer phase describes the early-adopters: technology-savvy people who are likely to purchase BEVs first, and tend to be younger and wealthier. The mainstream phase, on the other hand, includes more people from less affluent regions, and in the standard phase it can be assumed that a large proportion of the vehicle pool is electrified. To quantify these qualitative assumptions, various census parameters such as average income and living space, building or age structure, parking and charging options from OpenStreetMap, and other data are combined in a comprehensive geograhpical grid. By combining the parameters, an indicator can be formed for each grid-pixel with an edge length of 100 m in Germany, indicating the probability of acquiring an electric vehicle. The density of BEVs per square kilometer and county per ramp-up phase are displayed in Figure 3.
Using the number of BEVs per region, driving profiles from the “Mobility in Germany 2017” dataset, and synthetic charging profiles, it is finally possible to determine the regional load caused by charging electric vehicles.
Regionalization of the Demand for Local and District Heating in Germany
The building sector plays a central role in achieving the national climate targets. In order to achieve the German government’s sector targets for 2030, the building sector must save an additional 50 million tons of emissions per year.
The FfE is therefore contributing spatially resolved time series of local and district heating demand up to 2050 for the upcoming version of the Electric Network Development Plan. In doing so, it builds on the results of the AGORA-Energiewende study “Climate Neutral Germany 2045” (“KNDE2045”) 1 . This study describes sectoral transformation paths for a climate-neutral German energy system by 2045. In the building sector, for example, this means a refurbishment rate of 1.6% per year and a strong expansion of heating networks. However, the results are not regionally differentiated – with the help of FREM, the FfE is undertaking a retrospective regionaization of these results for the NEP.
First, by evaluating the main reports of the AGFW and regional statistics, the current regional feed-in to heat networks is determined, separated by public supply (here additionally differentiated by energy source and technology, hot water and steam network, CHP and heating plants) and industry. Thus, the majority of the German heating networks can be mapped – a complete coverage, however, is not achieved.
For the regional assignment of the feed-in quantities, the district heating potential grid, which was created in the course of studies 2 and 3, is used. To determine this potential, the statistically determined heat demand of the private household, trade, commerce, services and industry sectors for each municipality is first regionalized on a small scale based on census data (living space of private households), data on the building stock and the sealed surface area. Potentials for district heating are only reported for suitable areas, defined as areas able to meet a minimum sales volume for district heating in order to ensure the economic operation of the heating network. This minimum sales volume is quantified as 400 MWh/a*ha according to 4.
For this study, the district heating potential is aggregated at the municipality level (see Figure 4) and superimposed with the regional feed-in quantities according to AGFW (as columns) for comparison. In doing so, a good correlation between the data sets becomes apparent. Where, a district heating potential is identified but no feed-in quantities can be taken from the AGFW main reports, the regionalization consequently places heat networks in order to make up the feed-in quantities still missing from the totality of feed-in quantities determined from the main reports.
Development of the heating demand
For the development of the regional heating demand, three scenarios are formed, which describe different ambition levels and heating network situations and are based on the “KNDE2045”. This achieves climate neutrality in 2045 in public supply through an annual building renovation rate of 1.6%, 6 million installed heat pumps, and strong expansion of heating grids. These framework parameters correspond to the medium FfE scenario, which depicts a “centralized heat supply”. Accordingly, another scenario is formed with a lower (“trend”) and one with a higher ambition level (“decentralized”). The latter, for example, envisages a renovation rate of up to 2%. The regionalization of these national scenarios is done taking into account the regional district heating potential. For reasons of space, the industrial scenarios are not discussed in detail here.
For the regionalization of the updated heat sales of the public supply, the difference between heating demand and expansion target of the scenario is first calculated for each support year. This difference represents the necessary national heating network expansion. The regionalization of the expansion is then performed using the district heating potential raster described above. Finally, the resulting regional heating grid feed-in per energy source can be calculated (see Figure 5).
The spatial resolution of the status quo and the development of local heat supply consequently allows detailed statements to be made about how the sector targets can be achieved not only nationally, but specifically locally. For example, the analysis shows that an expansion of the heating grid in Flensburg is not possible because the full district heating potential here has already been tapped. In Hamburg, on the other hand, the heating grid is undergoing a major transformation. The declining feed-in from hard coal will initially be replaced by natural gas and, in the long term, by large-scale heat pumps, geothermal energy and hydrogen. Another advantage of the high geographic resolution is the integration of special conditions such as the local suitability for geothermal energy.