add minimal description of all scripts
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@ -157,21 +157,6 @@ Documentation
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configuration
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costs
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**Implementation details**
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* :doc:`spatial_resolution`
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* :doc:`supply_demand`
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* :doc:`technology_assumptions`
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.. toctree::
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:hidden:
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:maxdepth: 1
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:caption: Implementation details
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spatial_resolution
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supply_demand
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technology_assumptions
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**Rules Overview**
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* :doc:`preparation`
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@ -204,6 +189,22 @@ Documentation
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myopic
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perfect
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**Implementation details**
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* :doc:`spatial_resolution`
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* :doc:`supply_demand`
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* :doc:`technology_assumptions`
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.. toctree::
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:hidden:
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:maxdepth: 1
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:caption: Implementation details
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spatial_resolution
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supply_demand
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technology_assumptions
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**References**
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* :doc:`release_notes`
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@ -3,6 +3,8 @@
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#
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# SPDX-License-Identifier: MIT
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"""Prepares brownfield data from previous planning horizon."""
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import logging
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logger = logging.getLogger(__name__)
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@ -3,6 +3,8 @@
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#
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# SPDX-License-Identifier: MIT
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"""Adds existing power and heat generation capacities for initial planning horizon."""
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import logging
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logger = logging.getLogger(__name__)
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@ -3,7 +3,7 @@
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#
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# SPDX-License-Identifier: MIT
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"""
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Build ammonia production.
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Build historical annual ammonia production per country in ktonNH3/a.
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"""
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import country_converter as coco
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@ -2,6 +2,10 @@
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# SPDX-FileCopyrightText: : 2021-2023 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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"""
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Compute biogas and solid biomass potentials for each clustered model region
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using data from JRC ENSPRESO.
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"""
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import geopandas as gpd
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import pandas as pd
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@ -4,7 +4,8 @@
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# SPDX-License-Identifier: MIT
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"""
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Build clustered population layouts.
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Build population layouts for all clustered model regions as total as well as
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split by urban and rural population.
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"""
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import atlite
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@ -4,19 +4,20 @@
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# SPDX-License-Identifier: MIT
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"""
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Build COP time series for air- or ground-sourced heat pumps.
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Build coefficient of performance (COP) time series for air- or ground-sourced
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heat pumps.
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The COP is a function of the temperature difference between
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source and sink.
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The quadratic regression used is based on Staffell et al. (2012)
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https://doi.org/10.1039/C2EE22653G.
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"""
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import xarray as xr
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def coefficient_of_performance(delta_T, source="air"):
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"""
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COP is function of temp difference source to sink.
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The quadratic regression is based on Staffell et al. (2012)
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https://doi.org/10.1039/C2EE22653G.
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"""
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if source == "air":
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return 6.81 - 0.121 * delta_T + 0.000630 * delta_T**2
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elif source == "soil":
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@ -3,6 +3,10 @@
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#
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# SPDX-License-Identifier: MIT
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"""
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Build total energy demands per country using JRC IDEES, eurostat, and EEA data.
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"""
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import logging
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logger = logging.getLogger(__name__)
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@ -5,7 +5,7 @@
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"""
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Build import locations for fossil gas from entry-points, LNG terminals and
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production sites.
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production sites with data from SciGRID_gas and Global Energy Monitor.
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"""
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import logging
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@ -4,7 +4,7 @@
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# SPDX-License-Identifier: MIT
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"""
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Preprocess gas network based on data from bthe SciGRID Gas project
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Preprocess gas network based on data from bthe SciGRID_gas project
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(https://www.gas.scigrid.de/).
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"""
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@ -4,7 +4,7 @@
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# SPDX-License-Identifier: MIT
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"""
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Build heat demand time series.
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Build heat demand time series using heating degree day (HDD) approximation.
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"""
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import atlite
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@ -4,7 +4,7 @@
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# SPDX-License-Identifier: MIT
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"""
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Build industrial distribution keys from hotmaps database.
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Build spatial distribution of industries from Hotmaps database.
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"""
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import logging
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@ -4,7 +4,7 @@
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# SPDX-License-Identifier: MIT
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"""
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Build industrial energy demand per node.
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Build industrial energy demand per model region.
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"""
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import pandas as pd
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@ -4,7 +4,7 @@
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# SPDX-License-Identifier: MIT
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"""
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Build industrial energy demand per node.
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Build industrial energy demand per model region.
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"""
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from itertools import product
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@ -4,7 +4,7 @@
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# SPDX-License-Identifier: MIT
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"""
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Build industrial production per node.
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Build industrial production per model region.
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"""
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from itertools import product
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@ -4,7 +4,7 @@
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# SPDX-License-Identifier: MIT
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"""
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Build industry sector ratios.
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Build specific energy consumption by carrier and industries.
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"""
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import pandas as pd
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@ -4,7 +4,7 @@
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# SPDX-License-Identifier: MIT
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"""
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Build mapping between grid cells and population (total, urban, rural)
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Build mapping between cutout grid cells and population (total, urban, rural).
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"""
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import logging
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@ -4,7 +4,7 @@
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# SPDX-License-Identifier: MIT
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"""
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Build population-weighted energy totals.
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Distribute country-level energy demands by population.
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"""
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import pandas as pd
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@ -5,13 +5,13 @@
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# SPDX-License-Identifier: MIT
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"""
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Created on Fri Jan 22 10:36:39 2021.
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This script should calculate the space heating savings through better
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This script calculates the space heating savings through better
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insulation of the thermal envelope of a building and corresponding costs for
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different building types in different countries.
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-----------------METHODOLOGY ------------------------------------------------
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Methodology
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-----------
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The energy savings calculations are based on the
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EN ISO 13790 / seasonal method https://www.iso.org/obp/ui/#iso:std:iso:13790:ed-2:v1:en:
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@ -29,7 +29,9 @@ The energy savings calculations are based on the
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- tabula https://episcope.eu/fileadmin/tabula/public/calc/tabula-calculator.xlsx
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---------------------BASIC EQUAIONS -------------------------------------------
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Basic Equations
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---------------
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The basic equations:
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The Energy needed for space heating E_space [W/m²] are calculated as the
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@ -53,7 +55,8 @@ The basic equations:
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H_gains = nu * (H_solar + H_int)
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---------------- STRUCTURE OF THE SCRIPT --------------------------------------
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Structure
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---------
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The script has the following structure:
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@ -64,9 +67,6 @@ The script has the following structure:
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(3) calculate costs for corresponding additional insulation material
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(4) get cost savings per retrofitting measures for each sector by weighting
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with heated floor area
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-------------------------------------------------------------------------------
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@author: Lisa
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"""
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import pandas as pd
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import xarray as xr
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#
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# SPDX-License-Identifier: MIT
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"""
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Build regionalised geological sequestration potential for carbon dioxide using
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data from `CO2Stop
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<https://setis.ec.europa.eu/european-co2-storage-database_en>`_.
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"""
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import geopandas as gpd
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import pandas as pd
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# SPDX-License-Identifier: MIT
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"""
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Build temperature profiles.
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Build time series for air and soil temperatures per clustered model region.
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"""
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import atlite
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@ -4,7 +4,9 @@
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# SPDX-License-Identifier: MIT
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"""
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Build transport demand.
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Build land transport demand per clustered model region including efficiency
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improvements due to drivetrain changes, time series for electric vehicle
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availability and demand-side management constraints.
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"""
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import numpy as np
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# SPDX-License-Identifier: MIT
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"""
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Cluster gas network.
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Cluster gas transmission network to clustered model regions.
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"""
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import logging
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#
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# SPDX-License-Identifier: MIT
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"""
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Copy used configuration files and important scripts for archiving.
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"""
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from pathlib import Path
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from shutil import copy
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#
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# SPDX-License-Identifier: MIT
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"""
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Create summary CSV files for all scenario runs including costs, capacities,
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capacity factors, curtailment, energy balances, prices and other metrics.
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"""
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import logging
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logger = logging.getLogger(__name__)
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#
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# SPDX-License-Identifier: MIT
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"""
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Creates plots for optimised network topologies, including electricity, gas and
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hydrogen networks, and regional generation, storage and conversion capacities
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built.
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"""
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import logging
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logger = logging.getLogger(__name__)
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#
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# SPDX-License-Identifier: MIT
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"""
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Creates plots from summary CSV files.
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"""
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import logging
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logger = logging.getLogger(__name__)
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#
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# SPDX-License-Identifier: MIT
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"""
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Adds all sector-coupling components to the network, including demand and supply
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technologies for the buildings, transport and industry sectors.
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"""
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import logging
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import os
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import re
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#
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# SPDX-License-Identifier: MIT
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"""
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Retrieve and extract sector data bundle.
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Retrieve and extract data bundle for sector-coupled studies.
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"""
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import logging
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#
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# SPDX-License-Identifier: MIT
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"""
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Solve network.
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Solves sector-coupled network.
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"""
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import logging
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@ -17,7 +17,7 @@ scenario:
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clusters:
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- 5
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sector_opts:
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- 40H-T-H-B-I-A-solar+p3-dist1
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- 24H-T-H-B-I-A-solar+p3-dist1
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planning_horizons:
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- 2030
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- 2040
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clusters:
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- 5
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sector_opts:
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- CO2L0-40H-T-H-B-I-A-solar+p3-dist1
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- CO2L0-24H-T-H-B-I-A-solar+p3-dist1
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planning_horizons:
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- 2030
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clusters:
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- 5
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opts:
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- Co2L-40H
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- Co2L-24H
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countries: ['BE']
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