Point – Line – Polygon: Geodata in energy research

Geodata is essential for both modeling today’s energy systems and those of the future. Renewable energy generation plants will have to be expanded. Energy consumption will change. The number of new consumers such as electric vehicles and heat pumps will increase. Decentralization and prosumers are causing new challenges for grids, with digitalization and flexibility playing a major role.

In addition to “what?”, “how?”, “how much?” and “when?”, all these changes raise the question of “where?” as well. And in the question of “where?” geodata comes into play. We often see energy data represented in maps.

But what is geodata? It is data that has a spatial reference. In the simplest case, this is a point with coordinates (in a defined coordinate system). For example, the location of a wind turbine or a charging station or a node. If points are connected, this results in lines by which, for example, power grids can be mapped. If the lines surround a closed area, the result is a polygon. Examples for polygons are administrative boundaries, but also e.g., buildings. Through these three geometry types (point, line, polygon) all two-dimensional geodata can be described.

Some questions arise frequently: How high is the potential for different technologies of renewable energy production in a defined area, considering exclusion areas, public acceptance and economic viability. This is where so-called GIS analyses come into play, where different geodata are intersected. There is also a great need for the regionalization of data (e.g., disaggregating energy consumption from the country level as realistically as possible to districts using a distribution formula) with the aid of various statistical data such as population or weather data of the regions.

Since 2009, the FfE has steadily expanded its competencies in the field of geodata and developed the FfE Regionalized Energy System Model FREM – a database with many statistical and spatial data. Therefore, we conduct our analyses almost exclusively in the database with PostgreSQL and PostGIS. To visualize the spatial data, we use geoinformation systems like QGIS and OpenJUMP. For interactive web maps we use the JavaScript library OpenLayers.

While the exact boundaries of polygons are important for spatial intersection in GIS analyses, this level of detail is not necessary for map visualization and costs unnecessary computing resources. Therefore, generalized, meaning simplified polygon geometries, are often used for cartographic visualization.

On the FfE Open Data Portal, we provide various (generalized) geodata, which we obtained from different sources and partly enhanced and or simplified, to the community with open licenses. In the following web map you can find selected administrative regions. In order to uniquely identify geodatasets, we have assigned them each a combination of region type (id_regiontype) and region (id_region) in our database. The region type describes the “level” of the regions, e.g., communities, districts, countries, …

Open Layers Open Data FfE Test

Basemap: © OpenStreetMap contributors
Administrative Boundaries: © GeoBasis-DE / BKG 2017 | Generalization: FfE München
Basemap: © OpenStreetMap contributors
Administrative Boundaries: © GeoBasis-DE / BKG 2017 | Generalization: FfE München

Figure 1:  Administrative boundaries of different region types (“levels”). Hover over a polygon to retrieve information about the region.

The geometries are provided as PostGIS-geometries. But you can also retrieve them with the function “map_region_type” in GeoJSON format, e.g., German districts: http://opendata.ffe.de:3000/rpc/map_region_type?idregiontype=4

Geodatasets on the FfE Open Data Portal

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