Building Sector-Coupled Networks#
This part of the documentation is under development.
Prepares brownfield data from previous planning horizon.
Adds existing power and heat generation capacities for initial planning horizon.
Build historical annual ammonia production per country in ktonNH3/a.
Compute biogas and solid biomass potentials for each clustered model region using data from JRC ENSPRESO.
Reads biomass transport costs for different countries of the JRC report.
“The JRC-EU-TIMES model. Bioenergy potentials for EU and neighbouring countries.” (2015)
converts them from units ‘EUR per km/ton’ -> ‘EUR/ (km MWh)’
assuming as an approximation energy content of wood pellets
Build population layouts for all clustered model regions as total as well as split by urban and rural population.
Build coefficient of performance (COP) time series for air- or ground-sourced heat pumps.
The COP is a function of the temperature difference between source and sink.
The quadratic regression used is based on Staffell et al. (2012) https://doi.org/10.1039/C2EE22653G.
Build total energy demands per country using JRC IDEES, eurostat, and EEA data.
Build import locations for fossil gas from entry-points, LNG terminals and production sites with data from SciGRID_gas and Global Energy Monitor.
Preprocess gas network based on data from bthe SciGRID_gas project (https://www.gas.scigrid.de/).
Build heat demand time series using heating degree day (HDD) approximation.
Build spatial distribution of industries from Hotmaps database.
Build industrial energy demand per country.
Build industrial energy demand per model region.
Build industrial energy demand per model region.
Build future industrial production per country.
Build industrial production per country.
Build industrial production per model region.
Build specific energy consumption by carrier and industries.
Build mapping between cutout grid cells and population (total, urban, rural).
Distribute country-level energy demands by population.
This script calculates the space heating savings through better insulation of the thermal envelope of a building and corresponding costs for different building types in different countries.
The energy savings calculations are based on the
EN ISO 13790 / seasonal method https://www.iso.org/obp/ui/#iso:std:iso:13790:ed-2:v1:en:
calculations heavily oriented on the TABULAWebTool
http://webtool.building-typology.eu/ http://www.episcope.eu/fileadmin/tabula/public/docs/report/TABULA_CommonCalculationMethod.pdf which is following the EN ISO 13790 / seasonal method
- building stock data:
mainly: hotmaps project hotmaps/building-stock missing: EU building observatory https://ec.europa.eu/energy/en/eu-buildings-database
- building types with typical surfaces/ standard values:
The basic equations:
The Energy needed for space heating E_space [W/m²] are calculated as the sum of heat losses and heat gains:
E_space = H_losses - H_gains
Heat losses constitute from the losses through heat transmission (H_tr [W/m²K]) (this includes heat transfer through building elements and thermal bridges) and losses by ventilation (H_ve [W/m²K]):
H_losses = (H_tr + H_ve) * F_red * (T_threshold - T_averaged_d_heat) * d_heat * 1/365
F_red : reduction factor, considering non-uniform heating [°C], p.16 chapter 2.6 [-] T_threshold : heating temperature threshold, assumed 15 C d_heat : Length of heating season, number of days with daily averaged temperature below T_threshold T_averaged_d_heat : mean daily averaged temperature of the days within heating season d_heat
Heat gains constitute from the gains by solar radiation (H_solar) and internal heat gains (H_int) weighted by a gain utilisation factor nu:
H_gains = nu * (H_solar + H_int)
The script has the following structure:
fixed parameters are set
prepare data, bring to same format
calculate space heat demand depending on additional insulation material
calculate costs for corresponding additional insulation material
get cost savings per retrofitting measures for each sector by weighting with heated floor area
Build salt cavern potentials for hydrogen storage.
Technical Potential of Salt Caverns for Hydrogen Storage in Europe CC-BY 4.0 https://doi.org/10.20944/preprints201910.0187.v1 https://doi.org/10.1016/j.ijhydene.2019.12.161
Figure 6. Distribution of potential salt cavern sites across Europe with their corresponding energy densities (cavern storage potential divided by the volume).
Figure 7. Total cavern storage potential in European countries classified as onshore, offshore and within 50 km of shore.
The regional distribution is taken from the map (Figure 6) and scaled to the capacities from the bar chart split by nearshore (<50km from sea), onshore (>50km from sea), offshore (Figure 7).
Build regionalised geological sequestration potential for carbon dioxide using data from CO2Stop.
Build regional demand for international navigation based on outflow volume of ports.
Build solar thermal collector time series.
Build time series for air and soil temperatures per clustered model region.
Build land transport demand per clustered model region including efficiency improvements due to drivetrain changes, time series for electric vehicle availability and demand-side management constraints.
Cluster gas transmission network to clustered model regions.
Copy used configuration files and important scripts for archiving.
Adds all sector-coupling components to the network, including demand and supply technologies for the buildings, transport and industry sectors.