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| | 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 [http://openmod-initiative.org/manifesto.html openmod manifesto]). | | 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 [http://openmod-initiative.org/manifesto.html openmod manifesto]). |
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| | + | See also [[Data|Data]]. |
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| | = Electricity demand = | | = Electricity demand = |
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| | == Bottom-up approach == | | == Bottom-up approach == |
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| | 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. [https://en.wikipedia.org/wiki/Nomenclature_of_Territorial_Units_for_Statistics 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. | | 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. [https://en.wikipedia.org/wiki/Nomenclature_of_Territorial_Units_for_Statistics 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. |
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| | + | The [[DESSTinEE|DESSTinEE model]] performs all these taks at the national level for Europe, and could be used as a framework to base this from? |
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| | == Simple top-down approach == | | == Simple top-down approach == |
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| | <br/> | | <br/> |
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| | = Transport demand = | | = Transport demand = |
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| | People (passenger-km) versus cargo (tonne-km) transport demand. | | People (passenger-km) versus cargo (tonne-km) transport demand. |
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| | + | Passengers and freight transport demand '''for Italy''' can be found, for the years 2005 and 2008-2014, on the annual report ''Conto Nazionale delle Infrastrutture e dei Trasporti - 2013-2014'' (pp 46-49) available at: http://www.mit.gov.it/mit/mop_all.php?p_id=24349 |
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| | == Work To Do == | | == Work To Do == |
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| | Literature review. | | Literature review. |
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| | + | = Energy Efficiency = |
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| | + | Many modelling efforts target reduced emissions from greenhouse gases. Promoting energy efficiency can be one of the most effective ways of reducing emissions. |
| | + | |
| | + | The goal would be collect information on the costs of increasing efficiency and lowering energy demand, e.g. by retrofitting buildings with better heat insulation, replacing inefficient electric devices, etc. |
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| | + | The [http://e3p-beta.jrc.nl/ European Energy Efficiency Platform] may be useful. |
| | + | |
| | + | |
| | + | == Work To Do == |
| | + | |
| | + | Literature review. |
| | + | |
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| | = Weather data for availability of wind/water/solar power plants = | | = Weather data for availability of wind/water/solar power plants = |
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| | == Projects to turn weather data into power availability == | | == Projects to turn weather data into power availability == |
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| − | Various projects exist that transform weather data into power availability time series for different solar/wind power plant model types, such as the [http://arxiv.org/abs/1409.3353 Aarhus University RE Atlas] or the [https://github.com/oemof/feedinlib oemof feedinlib]. | + | Various projects exist that transform weather data into power availability time series for different solar/wind power plant model types, such as the [http://arxiv.org/abs/1409.3353 Aarhus University RE Atlas] or the [https://github.com/oemof/feedinlib oemof feedinlib] or [http://renewables.ninja/ renewables.ninja]. |
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| | <br/> | | <br/> |
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| | The [[Transmission network datasets#GridKit European Dataset|GridKit European Dataset]] already provides an extract of this information, but it needs post-processing/cleaning to turn it into an electrical network model. | | The [[Transmission network datasets#GridKit European Dataset|GridKit European Dataset]] already provides an extract of this information, but it needs post-processing/cleaning to turn it into an electrical network model. |
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| − | Advantage: Open data, no licencing issues, transparency, no worries about deliberate distortion. | + | Advantage: Open data, no licencing issues, transparency, no worries about deliberate distortion, methodology easily extends to any part of the planet |
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| | 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. | | 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. |
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| | Advantage: Public domain licence, model has electrical properties | | Advantage: Public domain licence, model has electrical properties |
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| − | Disadvantage: Non-algorithmic origin based on manual matching with ENTSO-E map | + | Disadvantage: Non-algorithmic origin based on manual matching with ENTSO-E map; only covers continental Europe (old UCTE area) |
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| | <br/> | | <br/> |
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| | Still needs lots of work. | | Still needs lots of work. |
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| | = Gas network = | | = Gas network = |
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| | Read ENTSO-G map? | | Read ENTSO-G map? |
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| | == Work To Do == | | == Work To Do == |
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| − | Work out what data is out there and who has the rights to it. | + | <span style="background-color: rgb(255, 255, 255);">Work out what data is out there and who has the rights to it.</span><br/> |
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| | + | *OpenGridEurope: |
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| | + | <span style="font-family: Arial, Helvetica, sans-serif; font-size: 13px;">Open Grid Europe GmbH designs, engineers, installs, and operates natural gas transmission pipeline network in Germany. On the webpage of OpenGridEurope there is a so called: [https://www.open-grid-europe.com/cps/rde/oge-internet/hs.xsl/Netzdaten-233.htm Network Data] , where one can navigate an i[https://transparencybo.open-grid-europe.com/BOE/OpenDocument/1608031852/OpenDocument/opendoc/openDocument.faces?logonSuccessful=true&shareId=0 nteractive map of the gas network] in Germany and read samples of csv files of the network topology. However, the data is not public nor available to third parties.</span><br/> |
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| | + | *National Grid Network: |
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| | + | On the Webpage of the National Grid (UK) one can download the [http://www2.nationalgrid.com/uk/services/land-and-development/planning-authority/gas-network/ topology of the transmission gas netwrok] as shapefiles for UK. One can also obtain [http://www2.nationalgrid.com/uk/industry-information/gas-transmission-operational-data/ transmission operational data] for the gas network. |
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| | == Current Status == | | == Current Status == |
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| | Still needs lots of work. | | Still needs lots of work. |
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| | = Heating network = | | = Heating network = |
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| | = Power plant database = | | = Power plant database = |
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| | + | There are several open databases of power plants: [http://globalenergyobservatory.org/ GEO], Enipedia, CAMRA, Wikipedia, OpenStreetMap, some of which, e.g. Enipedia, already link nicely to other databases. |
| | + | |
| | + | See also [[Power plant portfolios|Power plant portfolios]]. |
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| − | There are several open databases of power plants: GeOS, Enipedia, CAMRA, Wikipedia, OpenStreetMap
| + | Many network operators/regulators have online datasets, see the [http://open-power-system-data.org/data-sources#Other_European_countries OPSD list of European countries with power plant databases] |
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| − | Many network operators/regulators have online datasets, see the [http://open-power-system-data.org/data-sources#Other_Europan_countries OPSD list of European countries with power plant databases]
| + | Enipedia has a nice [http://enipedia.tudelft.nl/wiki/Energy_and_Industry_Data_Sets list of energy and industry data sets] |
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| | And some commercial ones: Platts | | And some commercial ones: Platts |
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| | Work out which datasets have what coverage and identify holes. | | Work out which datasets have what coverage and identify holes. |
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| − | <br/> | + | Work to link the datasets, e.g. using Enipedia's [http://enipedia.tudelft.nl/Elasticsearch.html Elastic Search] so that datasets can be cross-fertilised and cross-checked. Enipedia could act as a repository of links to the various open databases, i.e. for each power plant (defined as a collection of blocks) you can link/reference each openly available database and choose your preferred source. |
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| | + | <br/><br/> |
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| | == Current Status == | | == Current Status == |
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| | Lots of work to do. | | Lots of work to do. |
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| | + | = Costs and Learning Curves = |
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| | + | Build a database of different cost assumptions and learning curves for all generation technologies (capital, fuel and operating and maintenance costs), network technologies (transmission lines, transformers, gas, pipelines) and other technologies (Demand-Side Management, Dynamic Line Rating, etc.). |
| | + | |
| | + | == Work To Do == |
| | + | |
| | + | Literature review. |
| | + | |
| | + | == Current status == |
| | + | |
| | + | Lots of research papers and studies document their cost assumptions; this needs to be collated into consistent datasets. |
| | | | |
| | = Software for modelling = | | = Software for modelling = |
| | | | |
| | See [[Open Models|Open Models]]. | | See [[Open Models|Open Models]]. |
Latest revision as of 09:54, 9 October 2016
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).
See also Data.
[edit] Electricity demand
[edit] 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.
The DESSTinEE model performs all these taks at the national level for Europe, and could be used as a framework to base this from?
[edit] 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.
[edit] 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.
[edit] Current status
The information for the simplest alternative already exists online in an open form.
[edit] Heating demand
Heating demand here means low temperature heating demand (below 100 degrees Celsius) for water and space heating.
[edit] 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.
[edit] Work To Do
Build a standard reference dataset for heat demand at high resolution.
[edit] Current status
The information for the simplest alternative (i.e. temperature time series) already exists online in an open form.
[edit] Cooling demand
Since almost all cooling is powered using electricity, cooling is already included in the electricity demand.
[edit] Industry demand
Here is meant non-electrical industrial energy usage, particularly high temperature heating demand for industrial processes.
[edit] Transport demand
People (passenger-km) versus cargo (tonne-km) transport demand.
Passengers and freight transport demand for Italy can be found, for the years 2005 and 2008-2014, on the annual report Conto Nazionale delle Infrastrutture e dei Trasporti - 2013-2014 (pp 46-49) available at: http://www.mit.gov.it/mit/mop_all.php?p_id=24349
[edit] Work To Do
Literature review.
[edit] Energy Efficiency
Many modelling efforts target reduced emissions from greenhouse gases. Promoting energy efficiency can be one of the most effective ways of reducing emissions.
The goal would be collect information on the costs of increasing efficiency and lowering energy demand, e.g. by retrofitting buildings with better heat insulation, replacing inefficient electric devices, etc.
The European Energy Efficiency Platform may be useful.
[edit] Work To Do
Literature review.
[edit] Weather data for availability of wind/water/solar power plants
[edit] Reanalysis weather data
For sources of reanalysis data, see the OPSD weather data list.
[edit] 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 or renewables.ninja.
[edit] Hydroelectricity inflow data
See Hydroelectricity data#Hydroelectric inflow time series data.
[edit] 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.
[edit] Current status
Data is openly available, accessing and processing it still requires specialist knowledge.
[edit] Electricity network
[edit] 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, methodology easily extends to any part of the planet
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.
[edit] 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.
[edit] 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; only covers continental Europe (old UCTE area)
[edit] Work To Do
Turn GridKit European model into an electrical network model.
Turn the ENTSO-E interactive map into an electrical network model.
[edit] Current Status
Still needs lots of work.
[edit] Gas network
Read ENTSO-G map?
[edit] Work To Do
Work out what data is out there and who has the rights to it.
Open Grid Europe GmbH designs, engineers, installs, and operates natural gas transmission pipeline network in Germany. On the webpage of OpenGridEurope there is a so called: Network Data , where one can navigate an interactive map of the gas network in Germany and read samples of csv files of the network topology. However, the data is not public nor available to third parties.
On the Webpage of the National Grid (UK) one can download the topology of the transmission gas netwrok as shapefiles for UK. One can also obtain transmission operational data for the gas network.
[edit] Current Status
Still needs lots of work.
[edit] Heating network
District heating network, location of CHP plants, etc.
[edit] Power plant database
There are several open databases of power plants: GEO, Enipedia, CAMRA, Wikipedia, OpenStreetMap, some of which, e.g. Enipedia, already link nicely to other databases.
See also Power plant portfolios.
Many network operators/regulators have online datasets, see the OPSD list of European countries with power plant databases
Enipedia has a nice list of energy and industry data sets
And some commercial ones: Platts
Enipedia's Elastic Search can search most of the open databases - the challenge is to match the different databases, combine their information and identify missing data (e.g. in the Balkans).
[edit] Large conventional power plants
Replicate BNetzA list for whole of Europe.
[edit] Small renewable power plants
Should approach completeness of German old EEG-Stammdaten.
[edit] Hydroelectric power plants
Requires additional information on storage dam / pondage volumes, etc.
See Hydroelectricity data#European datasets
[edit] Combined-Heat-and-Power plants
Requires information on heat output, whether the dispatch is electricity- or heat-driven.
[edit] Work To Do
Work out which datasets have what coverage and identify holes.
Work to link the datasets, e.g. using Enipedia's Elastic Search so that datasets can be cross-fertilised and cross-checked. Enipedia could act as a repository of links to the various open databases, i.e. for each power plant (defined as a collection of blocks) you can link/reference each openly available database and choose your preferred source.
[edit] Current Status
Still needs lots of work.
[edit] 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.
[edit] 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.
[edit] 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.
[edit] Work To Do
Build software for CORINE/nature reserves overlaps.
[edit] Current status
Lots of work to do.
[edit] Costs and Learning Curves
Build a database of different cost assumptions and learning curves for all generation technologies (capital, fuel and operating and maintenance costs), network technologies (transmission lines, transformers, gas, pipelines) and other technologies (Demand-Side Management, Dynamic Line Rating, etc.).
[edit] Work To Do
Literature review.
[edit] Current status
Lots of research papers and studies document their cost assumptions; this needs to be collated into consistent datasets.
[edit] Software for modelling
See Open Models.
