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Build Network

Build Network#

Rule build_base_network#

Relevant Settings

interconnect:
offshore_shape:
aggregation_zones:
countries:

Inputs

  • data/breakthrough_network/base_grid/{interconnect}/bus.csv

  • data/breakthrough_network/base_grid/{interconnect}/branch.csv

  • data/breakthrough_network/base_grid/{interconnect}/dcline.csv

  • data/breakthrough_network/base_grid/{interconnect}/bus2sub.csv

  • data/breakthrough_network/base_grid/{interconnect}/sub.csv

  • resources/country_shapes.geojson: confer Rule build_shapes

  • resources/offshore_shapes.geojson: confer Rule build_shapes

  • resources/{interconnect}/state_boundaries.geojson: confer Rule build_shapes

Outputs

  • networks/base.nc:

  • data/breakthrough_network/base_grid/{interconnect}/bus2sub.csv

  • data/breakthrough_network/base_grid/{interconnect}/sub.csv

  • resources/{interconnect}/elec_base_network.nc

Description

Reads in Breakthrough Energy/TAMU transmission dataset, and converts it into PyPSA compatible components. A base netowork file (*.nc) is written out. Included in this network are:

  • Geolocated buses

  • Geoloactated AC and DC transmission lines + links

  • Transformers

Rule build_shapes#

Description

The build_shapes rule builds the GIS shape files for the balancing authorities and offshore regions. The regions are only built for the {interconnect} wildcard. Because balancing authorities often overlap- we modify the GIS dataset developed by [Breakthrough Energy Sciences](https://breakthrough-energy.github.io/docs/).

Relevant Settings

interconnect:

Inputs

Outputs

  • resources/country_shapes.geojson:

    # .. image:: ../img/regions_onshore.png # :scale: 33 %

  • resources/onshore_shapes.geojson:

    # .. image:: ../img/regions_offshore.png # :scale: 33 %

  • resources/offshore_shapes.geojson:

    # .. image:: ../img/regions_offshore.png # :scale: 33 %

  • resources/state_boundaries.geojson:

    # .. image:: ../img/regions_offshore.png # :scale: 33 %

Rule build_bus_regions#

Description

Creates Voronoi shapes for each bus representing both onshore and offshore regions.

Relevant Settings

interconnect:
aggregation_zones:

Inputs

Outputs

  • resources/regions_onshore.geojson

  • resources/regions_offshore.geojson

Rule build_demand#

Builds the demand data for the PyPSA network.

Relevant Settings

network_configuration:

snapshots:
    start:
    end:
    inclusive:

scenario:
interconnect:
planning_horizons:

Inputs

  • base_network:

  • eia: (GridEmissions data file)

  • efs: (NREL EFS Load Forecasts)

Outputs

  • demand: Path to the demand CSV file.

Rule build_fuel_prices#

Description

Build_fuel_prices.py is a script that prepares data for dynamic fuel prices to be used in the add_electricity module. Data is input from retrieve_caiso_data and retrieve_eia_data to create a combined dataframe with all dynamic fuel prices available. The prices are modified to be on an hourly basis to match the network snapshots, and converted to $/MWh_thermal. The output is a CSV file containing the hourly fuel prices for each Balancing Authority and State.

Relevant Settings

fuel_year:
snapshots:

Inputs

  • ‘’data/caiso_ng_prices.csv’’: A CSV file containing the daily average fuel prices for each Balancing Authority in the WEIM.

  • ‘’data/eia_ng_prices.csv’’: A CSV file containing the monthly average fuel prices for each State.

Outputs

  • ‘’data/ng_fuel_prices.csv’’: A CSV file containing the hourly fuel prices for each Balancing Authority and State.

Rule build_cutout#

Create cutouts with atlite.

For this rule to work you must have

See also

For details on the weather data read the atlite documentation. If you need help specifically for creating cutouts the corresponding section in the atlite documentation should be helpful.

Relevant Settings

atlite:
    nprocesses:
    cutouts:
        {cutout}:

See also

Documentation of the configuration file config/config.yaml at atlite

Inputs

None

Outputs

  • cutouts/{cutout}: weather data from the ERA5 reanalysis weather dataset satellite-based historic weather data with the following structure:

Description

Using the ERA5 cutout, the following parameters are accessible:

Field

Dimensions

Unit

Description

height

y, x

m

Surface elevation above sea level

wnd100m

time, y, x

ms**-1

Wind speeds at 100 meters (regardless of direction)

wnd_azimuth

time, y, x

ms**-1

100 metre U wind component

roughness

y, x

m

Forecast surface roughness (roughness length)

influx_toa

time, y, x

Wm**-2

Top of Earth’s atmosphere TOA incident solar radiation

influx_direct

time, y, x

Wm**-2

Total sky direct solar radiation at surface

influx_diffuse

time, y, x

Wm**-2

Diffuse solar radiation at surface. Surface solar radiation downwards minus direct solar radiation.

solar_altitude

time, y, x

rad

solar_azimuth

time, y, x

rad

temperature

time, y, x

K

Air temperature 2 meters above the surface.

soil temperature

time, y, x

K

Soil temperature between 1 meters and 3 meters depth (layer 4).

influx_toa

time, y, x

Wm**-2

Top of Earth’s atmosphere TOA incident solar radiation

influx_direct

time, y, x

Wm**-2

Total sky direct solar radiation at surface

runoff

time, y, x

m

Runoff (volume per area)

albedo

time, y, x

–

Albedo measure of diffuse reflection of solar radiation. Calculated from relation between surface solar radiation downwards (Jm**-2) and surface net solar radiation (Jm**-2). Takes values between 0 and 1.

The USA Interconnect weather data is shown below:

Rule build_renewable_profiles#

Calculates for each network node the (i) installable capacity (based on land- use), (ii) the available generation time series (based on weather data), and (iii) the average distance from the node for onshore wind, AC-connected offshore wind, DC-connected offshore wind and solar PV generators. In addition for offshore wind it calculates the fraction of the grid connection which is under water.

Note

Hydroelectric profiles are built in script build_hydro_profiles.

Relevant settings

snapshots:

atlite:
    nprocesses:

renewable:
    {technology}:
        cutout:
        corine:
        grid_codes:
        distance:
        natura:
        min_depth:
        max_depth:
        max_shore_distance:
        min_shore_distance:
        capacity_per_sqkm:
        correction_factor:
        potential:
        min_p_max_pu:
        clip_p_max_pu:
        resource:

See also

Documentation of the configuration file config/config.yaml at snapshots, atlite, renewable

Inputs

Outputs

  • resources/profile_{technology}.nc with the following structure

    Field

    Dimensions

    Description

    profile

    bus, time

    the per unit hourly availability factors for each node

    weight

    bus

    sum of the layout weighting for each node

    p_nom_max

    bus

    maximal installable capacity at the node (in MW)

    potential

    y, x

    layout of generator units at cutout grid cells inside the Voronoi cell (maximal installable capacity at each grid cell multiplied by capacity factor)

    average_distance

    bus

    average distance of units in the Voronoi cell to the grid node (in km)

    underwater_fraction

    bus

    fraction of the average connection distance which is under water (only for offshore)

    • profile

    # .. image:: img/profile_ts.png # :scale: 33 % # :align: center

    • p_nom_max

    # .. image:: img/p_nom_max_hist.png # :scale: 33 % # :align: center

    • potential

    # .. image:: img/potential_heatmap.png # :scale: 33 % # :align: center

    • average_distance

    # .. image:: img/distance_hist.png # :scale: 33 % # :align: center

    • underwater_fraction

    # .. image:: img/underwater_hist.png # :scale: 33 % # :align: center

Description#

This script functions at two main spatial resolutions: the resolution of the network nodes and their Voronoi cells, and the resolution of the cutout grid cells for the weather data. Typically the weather data grid is finer than the network nodes, so we have to work out the distribution of generators across the grid cells within each Voronoi cell. This is done by taking account of a combination of the available land at each grid cell and the capacity factor there.

First the script computes how much of the technology can be installed at each cutout grid cell and each node using the GLAES library. This uses the CORINE land use data, Natura2000 nature reserves and GEBCO bathymetry data.

To compute the layout of generators in each node’s Voronoi cell, the installable potential in each grid cell is multiplied with the capacity factor at each grid cell. This is done since we assume more generators are installed at cells with a higher capacity factor.

This layout is then used to compute the generation availability time series from the weather data cutout from atlite.

Two methods are available to compute the maximal installable potential for the node (p_nom_max): simple and conservative:

  • simple adds up the installable potentials of the individual grid cells. If the model comes close to this limit, then the time series may slightly overestimate production since it is assumed the geographical distribution is proportional to capacity factor.

  • conservative assertains the nodal limit by increasing capacities proportional to the layout until the limit of an individual grid cell is reached.

Rule add_electricity#

Description

This module integrates data produced by build_renewable_profiles, build_demand, build_cost_data, build_fuel_prices, and build_base_network to create a network model that includes generators, demand, and costs. The module attaches generators, storage units, and loads to the network created by build_base_network. Each generator is assigned regional capital costs, and regional and daily or monthly marginal costs.

Extendable generators are assigned a maximum capacity based on land-use constraints defined in build_renewable_profiles.

Relevant Settings

network_configuration:

snapshots:
    start:
    end:
    inclusive:

electricity:

See also

Documentation of the configuration file config/config.yaml at costs, electricity, renewable, lines

Inputs

  • resources/costs.csv: The database of cost assumptions for all included technologies for specific years from various sources; e.g. discount rate, lifetime, investment (CAPEX), fixed operation and maintenance (FOM), variable operation and maintenance (VOM), fuel costs, efficiency, carbon-dioxide intensity.

  • resources/demand.csv Hourly per-country load profiles.

  • resources/regions_onshore.geojson: confer Rule build_bus_regions

  • resources/profile_{}.nc: all technologies in config["renewables"].keys(), confer Rule build_renewable_profiles.

  • networks/elec_base_network.nc: confer Rule build_base_network

  • resources/ng_fuel_prices.csv: Natural gas fuel prices by state and BA.

Outputs

  • networks/elec_base_network_l_pp.nc

Contents