The FfE Regionalised Energy System Model “FREM” is a extensive geo-database built on the open source systems PostgreSQL and PostGIS, with spatial and statistical data on the energy system. With the help of SQL queries, this data can be derived in different regional resolutions and the results exported into different data formats for cartographic and statistical visualisations.
FREM, launched in 2007 as a building dataset for modelling heat and electricity consumption, is constantly being developed and expanded by FfE. Since the beginning of development, the database has grown continuously through the constant integration of new data and the focus is constantly being expanded to include new applications (see Figure 1). Today, FREM as a comprehensive geodatabase forms the core of the FfE data landscape. The rapid development in the availability and processing possibilities of spatial data in recent years has made geographical information systems (GIS) and geodata increasingly relevant for energy system modelling as well.
Through the development of interfaces to the other FfE models such as ISAaR and GridSim, FREM has become increasingly interconnected in the FfE model landscape and, as a data management system, is now a core component in almost all FfE projects. Since 2016, data exchange between FREM and external databases has also gained in importance. The increased contribution of baseline data will also be one of FREM’s goals in the near future. In this context, the OpenEnergy Platform (OEP), among others, is of central importance, as open data on the energy system is playing an increasingly important role. For this reason, FfE actively supports the OEP through FREM and is also constantly expanding its own data portal, opendata.ffe.de.
How is the FfE Regionalised Energy System Model FREM structured today?
Today, FREM is a comprehensive geodatabase built on the open source systems PostgreSQL and PostGIS, with spatial and statistical data on the energy system (see Figure 2). These come mainly from open data sources. Primary and derived data are stored in thematic schemas such as renewable energies, weather models, time series, power plants, statistics and geographical data. The consistent structure of the database is key for fast modelling and scenario calculation to solve various energy issues. Through the geo extension PostGIS, FREM can be used like a geographic information system (GIS) that puts energy data into a spatial context. It is possible to derive data in different regional resolutions with SQL queries and export the results in different data formats for cartographic and statistical visualisations.
How is the FREM IT infrastructure structured?
The heart of FREM-IT is the powerful Mercator server, which hosts the geo-database and runs under Ubuntu Linux. Written SQL code is versioned in GitLab and sent directly or in parallel via SQL parser to Mercator, which can thus make full use of its 24 cores. Careful physical separation of the operating system, geo-database, data and logs on different raids further reduces computing time. A comprehensive backup strategy with partial and full dumps protects against devastating data loss in the event of damage. In addition to its function as a weather data store, Herodot, Mercator’s predecessor, acts as a time-delayed mirror of the FREM database to enable short-term data recovery without costly backup and restore operations. The connection to another geo-database hosted on an external server represents the FREM interface “to the outside world” and supplies the FfE Open Data Portal with data that can be freely obtained from there via a PostgREST-API.
What is the FfE Regionalised Energy System Model FREM suitable for?
The combination of the PostGIS geo-database with extensive energy industry data thus results in a flexible energy system model with high temporal and spatial resolution. This model is used as a rich data pool in most of FfE’s projects.
Potential renewable electricity production per country/NUTS-3 and energy source
In order to assess the potential for renewable electricity production in Europe, the eXtremOS project conducted a comprehensive analysis for the energy carriers rooftop and ground-mounted photovoltaics (PV) as well as onshore and offshore wind. This resulted in a Europe-wide potential of 31.5 PWh for all four energy sources (see Figure 4).
Where does the electricity come from if it is to be sourced as regionally as possible? Information on this is provided by the self-consumption and surplus rates of German municipalities, i.e. the share that is generated by renewable energies within a municipality and directly consumed in the same period (self-consumption) or the share that is not directly consumed (surplus).
FREM was used to analyse the potential of wind energy in Bavaria, which is specified by the 10H rule. This potential is significantly influenced by the magnitude of the specified distance to the nearest settlement.