This page describes the datasets which are required and/or already exist to build an open model the European energy system (electricity, heating, cooling, industry and transport demand) at a high spatial and temporal resolution.
The idea is to list the basic requirements for open modelling, so that researchers can collaborate and share the burden of data collection/curation. Once the dataset meets basic requirements, further detail can be added to the datasets.
The level of detail which is initially aimed at is comparable to that found for example in the DIW Germany electricity sector model ELMOD-DE or the unofficial SciGRID-based Germany electricity sector model, i.e. hourly temporal resolution and a spatial resolution at the level of NUTS 3 regions / electricity substations with voltages above 200 kV.
Once this level of detail is achieved, higher resolution data (e.g. detailed ramp rates, start-up/shut-down costs, lower voltage networks, etc.) can be included.
The geographical scope is flexible, but in principle it should cover the ENTSO-E countries and possibly also neighbours. The general data requirements also apply for any other region of the planet.
The temporal scope should include several sample years, so that typical as well as extreme load/weather events are included.
The intention is to build a dataset that can be used for diverse research questions, including analysis of current energy usage and future energy system development.
The dataset should be completely transparent from raw input data (e.g. weather data, national curated datasets on power plants) to model input data, with free software used whenever data is processed (see the nice picture in the openmod manifesto).
Electricity demand
Bottom-up approach
In an ideal world, one would build a bottom-up model of industrial and residential demand profiles to build time series for each and every region (e.g. NUTS 3 regions), which could then be validated against historical data. This bottom-up model could then be re-run to include future changes to electrical demand, such as increased presence of heat pumps, electric vehicles and other changes to consumption.
Simple top-down approach
The simplest alternative is to use the historical hourly time series from ENTSO-E for each country and model the geographical distribution of each country's demand based on GDP and population in each NUTS 3 region. This approach excludes the possibility that different regions within a country have different load profiles.
Work To Do
Build a validated bottom-up model of electrical demand.
Identify where the NUTS 3 GDP and population data is for all European countries.
Current status
The information for the simplest alternative already exists online in an open form.
Heating demand
Heating demand here means low temperature heating demand (below 100 degrees Celsius) for water and space heating.
Degree-day approach
As a simplest assumption, this can be modelled based on temperature and population/GDP distribution using the degree-day heating demand approximation.
Work To Do
Build a standard reference dataset for heat demand at high resolution.
Current status
The information for the simplest alternative (i.e. temperature time series) already exists online in an open form.
Cooling demand
Since almost all cooling is powered using electricity, cooling is already included in the electricity demand.
Industry demand
Here is meant non-electrical industrial energy usage, particularly high temperature heating demand for industrial processes.
Transport demand
People (passenger-km) versus cargo (tonne-km) transport demand.
Work To Do
Literature review.
Weather data for availability of wind/water/solar power plants
Reanalysis weather data
For sources of reanalysis data, see the OPSD weather data list.
Projects to turn weather data into power availability
Various projects exist that transform weather data into power availability time series for different solar/wind power plant model types, such as the Aarhus University RE Atlas or the oemof feedinlib.
Hydroelectricity inflow data
See Hydroelectricity data#Hydroelectric inflow time series data.
Work To Do
There is no big database / interfaced website for generating big sets of time series, which all runs on free software.
Too much work requires downloading/writing specialised software.
Current status
Data is openly available, accessing and processing it still requires specialist knowledge.
Electricity network
SciGRID / GridKit / OpenStreetMap datasets
OpenStreetMap could be used to extract information on where transmission lines are in Europe and what their voltage/circuit/wire configuration is.
The GridKit European Dataset already provides an extract of this information, but it needs post-processing/cleaning to turn it into an electrical network model.
Advantage: Open data, no licencing issues, transparency, no worries about deliberate distortion.
Disadvantage: Data quality/completeness: OSM doesn't yet always list numbers of circuits/wires/voltages accurately, so data quality needs to be improved; currently heuristics must be used to infer the grid topology, i.e. how everything connects together.
ENTSO-E interactive map
ENTSO-E Interactive Grid Map
Extract grid topology, geodata and numbers of circuits from the ENTSO-E interactive map.
Advantage: Source ENTSO-E is eminently citable.
Disadvantage: Copyright clearly belongs to ENTSO-E, so dataset is not open, although the code to extract and process it could be; the data is deliberately distorted.
Bialek model
Bialek Continental Europe Model
This model already exists and their are geo-referenced versions of varying quality.
Advantage: Public domain licence, model has electrical properties
Disadvantage: Non-algorithmic origin based on manual matching with ENTSO-E map
Work To Do
Turn GridKit European model into an electrical network model.
Turn the ENTSO-E interactive map into an electrical network model.
Current Status
Still needs lots of work.
Gas network
Read ENTSO-G map?
Work To Do
Work out what data is out there and who has the rights to it.
Current Status
Still needs lots of work.
Heating network
District heating network, location of CHP plants, etc.
Power plant database
Replicate BNetzA list for whole of Europe.
GeOS, Enipedia, CAMRA, Platts
Large conventional power plants
Small renewable power plants
Should approach completeness of German old EEG-Stammdaten.
Hydroelectric power plants
Requires additional information on storage dam / pondage volumes, etc.
See Hydroelectricity data#European datasets
Combined-Heat-and-Power plants
Requires information on heat output, whether the dispatch is electricity- or heat-driven.
Work To Do
Work out which datasets have what coverage and identify holes.
Current Status
Still needs lots of work.
Geographical potentials for new power plants
To model investment in the future power system, one must know where there is land/resources available to build new power plants.
Onshore wind: must respect current land usage, nature reserves and minimum distances from properties.
Offshore wind: must respect nature reserves, sea depth, shipping lanes.
Solar Photovoltaics: cannot build in e.g. forest.
Hydroelectricity: restricted by various land usages.
Gas power plants: may be restricted by gas network avaiability.
Bottom-up approach
Example for wind turbines: look at each country's rules / minimum distance regulations and look in detail at where buildings are and compute where each and every possible wind turbine could go.
Approximate approach
Use CORINE landcover and other databases of nature reserves to get a coarse-grained potential.
See Wind Potentials Breakout Group Report from the Open Energy Modelling Workshop - London 2015.
Work To Do
Build software for CORINE/nature reserves overlaps.
Current status
Lots of work to do.
Software for modelling
See Open Models.
