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resolve merge conflict

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This commit is contained in:
Fabian Neumann 2023-03-06 09:39:10 +01:00
commit ad6a490f57
57 changed files with 4683 additions and 3214 deletions
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98
.github/workflows/ci.yaml vendored
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@ -16,7 +16,7 @@ on:
branches:
- master
schedule:
- cron: "0 5 * * TUE"
- cron: "0 5 * * TUE"
env:
CONDA_CACHE_NUMBER: 1 # Change this value to manually reset the environment cache
@ -29,9 +29,9 @@ jobs:
matrix:
include:
# Matrix required to handle caching with Mambaforge
- os: ubuntu-latest
label: ubuntu-latest
prefix: /usr/share/miniconda3/envs/pypsa-eur
- os: ubuntu-latest
label: ubuntu-latest
prefix: /usr/share/miniconda3/envs/pypsa-eur
# - os: macos-latest
# label: macos-latest
@ -50,57 +50,57 @@ jobs:
shell: bash -l {0}
steps:
- uses: actions/checkout@v2
- uses: actions/checkout@v2
- name: Clone pypsa-eur subworkflow
run: |
git clone https://github.com/pypsa/pypsa-eur ../pypsa-eur
cp ../pypsa-eur/test/config.test1.yaml ../pypsa-eur/config.yaml
- name: Clone pypsa-eur subworkflow
run: |
git clone https://github.com/pypsa/pypsa-eur ../pypsa-eur
cp ../pypsa-eur/test/config.test1.yaml ../pypsa-eur/config.yaml
- name: Setup secrets
run: |
echo -ne "url: ${CDSAPI_URL}\nkey: ${CDSAPI_TOKEN}\n" > ~/.cdsapirc
- name: Setup secrets
run: |
echo -ne "url: ${CDSAPI_URL}\nkey: ${CDSAPI_TOKEN}\n" > ~/.cdsapirc
- name: Add solver to environment
run: |
echo -e "- coincbc\n- ipopt<3.13.3" >> ../pypsa-eur/envs/environment.yaml
- name: Add solver to environment
run: |
echo -e "- coincbc\n- ipopt<3.13.3" >> ../pypsa-eur/envs/environment.yaml
- name: Setup Mambaforge
uses: conda-incubator/setup-miniconda@v2
with:
miniforge-variant: Mambaforge
miniforge-version: latest
activate-environment: pypsa-eur
use-mamba: true
- name: Setup Mambaforge
uses: conda-incubator/setup-miniconda@v2
with:
miniforge-variant: Mambaforge
miniforge-version: latest
activate-environment: pypsa-eur
use-mamba: true
- name: Set cache dates
run: |
echo "DATE=$(date +'%Y%m%d')" >> $GITHUB_ENV
echo "WEEK=$(date +'%Y%U')" >> $GITHUB_ENV
- name: Set cache dates
run: |
echo "DATE=$(date +'%Y%m%d')" >> $GITHUB_ENV
echo "WEEK=$(date +'%Y%U')" >> $GITHUB_ENV
- name: Cache data and cutouts folders
uses: actions/cache@v3
with:
path: |
data
../pypsa-eur/cutouts
../pypsa-eur/data
key: data-cutouts-${{ env.WEEK }}-${{ env.DATA_CACHE_NUMBER }}
- name: Cache data and cutouts folders
uses: actions/cache@v3
with:
path: |
data
../pypsa-eur/cutouts
../pypsa-eur/data
key: data-cutouts-${{ env.WEEK }}-${{ env.DATA_CACHE_NUMBER }}
- name: Create environment cache
uses: actions/cache@v2
id: cache
with:
path: ${{ matrix.prefix }}
key: ${{ matrix.label }}-conda-${{ env.DATE }}-${{ env.CONDA_CACHE_NUMBER }}
- name: Create environment cache
uses: actions/cache@v2
id: cache
with:
path: ${{ matrix.prefix }}
key: ${{ matrix.label }}-conda-${{ env.DATE }}-${{ env.CONDA_CACHE_NUMBER }}
- name: Update environment due to outdated or unavailable cache
run: mamba env update -n pypsa-eur -f ../pypsa-eur/envs/environment.yaml
if: steps.cache.outputs.cache-hit != 'true'
- name: Update environment due to outdated or unavailable cache
run: mamba env update -n pypsa-eur -f ../pypsa-eur/envs/environment.yaml
if: steps.cache.outputs.cache-hit != 'true'
- name: Test snakemake workflow
run: |
conda activate pypsa-eur
conda list
snakemake -call --configfile test/config.overnight.yaml
snakemake -call --configfile test/config.myopic.yaml
- name: Test snakemake workflow
run: |
conda activate pypsa-eur
conda list
snakemake -call --configfile test/config.overnight.yaml
snakemake -call --configfile test/config.myopic.yaml

162
config.default.yaml
View File

@ -13,17 +13,17 @@ foresight: overnight # options are overnight, myopic, perfect (perfect is not ye
scenario:
simpl: # only relevant for PyPSA-Eur
- ''
- ''
lv: # allowed transmission line volume expansion, can be any float >= 1.0 (today) or "opt"
- 1.0
- 1.5
- 1.0
- 1.5
clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
- 45
- 50
- 45
- 50
opts: # only relevant for PyPSA-Eur
- ''
- ''
sector_opts: # this is where the main scenario settings are
- Co2L0-3H-T-H-B-I-A-solar+p3-dist1
- Co2L0-3H-T-H-B-I-A-solar+p3-dist1
# to really understand the options here, look in scripts/prepare_sector_network.py
# Co2Lx specifies the CO2 target in x% of the 1990 values; default will give default (5%);
# Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
@ -41,7 +41,7 @@ scenario:
# cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
# decay with initial growth rate 0
planning_horizons: # investment years for myopic and perfect; for overnight, year of cost assumptions can be different and is defined under 'costs'
- 2050
- 2050
# for example, set to
# - 2020
# - 2030
@ -84,18 +84,18 @@ electricity:
# in PyPSA-Eur-Sec
pypsa_eur:
Bus:
- AC
- AC
Link:
- DC
- DC
Generator:
- onwind
- offwind-ac
- offwind-dc
- solar
- ror
- onwind
- offwind-ac
- offwind-dc
- solar
- ror
StorageUnit:
- PHS
- hydro
- PHS
- hydro
Store: []
@ -110,25 +110,25 @@ biomass:
scenario: ENS_Med
classes:
solid biomass:
- Agricultural waste
- Fuelwood residues
- Secondary Forestry residues - woodchips
- Sawdust
- Residues from landscape care
- Municipal waste
- Agricultural waste
- Fuelwood residues
- Secondary Forestry residues - woodchips
- Sawdust
- Residues from landscape care
- Municipal waste
not included:
- Sugar from sugar beet
- Rape seed
- "Sunflower, soya seed "
- Bioethanol barley, wheat, grain maize, oats, other cereals and rye
- Miscanthus, switchgrass, RCG
- Willow
- Poplar
- FuelwoodRW
- C&P_RW
- Sugar from sugar beet
- Rape seed
- "Sunflower, soya seed "
- Bioethanol barley, wheat, grain maize, oats, other cereals and rye
- Miscanthus, switchgrass, RCG
- Willow
- Poplar
- FuelwoodRW
- C&P_RW
biogas:
- Manure solid, liquid
- Sludge
- Manure solid, liquid
- Sludge
solar_thermal:
@ -143,10 +143,10 @@ existing_capacities:
grouping_years_heat: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019] # these should not extend 2020
threshold_capacity: 10
conventional_carriers:
- lignite
- coal
- oil
- uranium
- lignite
- coal
- oil
- uranium
sector:
@ -238,7 +238,7 @@ sector:
2040: 0.16
2045: 0.21
2050: 0.29
retrofitting : # co-optimises building renovation to reduce space heat demand
retrofitting: # co-optimises building renovation to reduce space heat demand
retro_endogen: false # co-optimise space heat savings
cost_factor: 1.0 # weight costs for building renovation
interest_rate: 0.04 # for investment in building components
@ -279,7 +279,7 @@ sector:
hydrogen_underground_storage: true
hydrogen_underground_storage_locations:
# - onshore # more than 50 km from sea
- nearshore # within 50 km of sea
- nearshore # within 50 km of sea
# - offshore
ammonia: false # can be false (no NH3 carrier), true (copperplated NH3), "regional" (regionalised NH3 without network)
min_part_load_fischer_tropsch: 0.9 # p_min_pu
@ -401,14 +401,14 @@ solving:
min_iterations: 4
max_iterations: 6
keep_shadowprices:
- Bus
- Line
- Link
- Transformer
- GlobalConstraint
- Generator
- Store
- StorageUnit
- Bus
- Line
- Link
- Transformer
- GlobalConstraint
- Generator
- Store
- StorageUnit
solver:
name: gurobi
@ -486,47 +486,47 @@ plotting:
energy_min: -20000
energy_threshold: 50
vre_techs:
- onwind
- offwind-ac
- offwind-dc
- solar
- ror
- onwind
- offwind-ac
- offwind-dc
- solar
- ror
renewable_storage_techs:
- PHS
- hydro
- PHS
- hydro
conv_techs:
- OCGT
- CCGT
- Nuclear
- Coal
- OCGT
- CCGT
- Nuclear
- Coal
storage_techs:
- hydro+PHS
- battery
- H2
- hydro+PHS
- battery
- H2
load_carriers:
- AC load
- AC load
AC_carriers:
- AC line
- AC transformer
- AC line
- AC transformer
link_carriers:
- DC line
- Converter AC-DC
- DC line
- Converter AC-DC
heat_links:
- heat pump
- resistive heater
- CHP heat
- CHP electric
- gas boiler
- central heat pump
- central resistive heater
- central CHP heat
- central CHP electric
- central gas boiler
- heat pump
- resistive heater
- CHP heat
- CHP electric
- gas boiler
- central heat pump
- central resistive heater
- central CHP heat
- central CHP electric
- central gas boiler
heat_generators:
- gas boiler
- central gas boiler
- solar thermal collector
- central solar thermal collector
- gas boiler
- central gas boiler
- solar thermal collector
- central solar thermal collector
tech_colors:
# wind
onwind: "#235ebc"

201
doc/conf.py
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@ -12,19 +12,19 @@
# All configuration values have a default; values that are commented out
# serve to show the default.
import sys
import os
import shlex
import sys
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
sys.path.insert(0, os.path.abspath('../scripts'))
sys.path.insert(0, os.path.abspath("../scripts"))
# -- General configuration ------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#needs_sphinx = '1.0'
# needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
@ -32,48 +32,48 @@ sys.path.insert(0, os.path.abspath('../scripts'))
extensions = [
#'sphinx.ext.autodoc',
#'sphinx.ext.autosummary',
'sphinx.ext.autosectionlabel',
'sphinx.ext.intersphinx',
'sphinx.ext.todo',
'sphinx.ext.mathjax',
'sphinx.ext.napoleon',
'sphinx.ext.graphviz',
"sphinx.ext.autosectionlabel",
"sphinx.ext.intersphinx",
"sphinx.ext.todo",
"sphinx.ext.mathjax",
"sphinx.ext.napoleon",
"sphinx.ext.graphviz",
#'sphinx.ext.pngmath',
#'sphinxcontrib.tikz',
#'rinoh.frontend.sphinx',
'sphinx.ext.imgconverter', # for SVG conversion
"sphinx.ext.imgconverter", # for SVG conversion
]
autodoc_default_flags = ['members']
autodoc_default_flags = ["members"]
autosummary_generate = True
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
templates_path = ["_templates"]
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
# source_suffix = ['.rst', '.md']
source_suffix = '.rst'
source_suffix = ".rst"
# The encoding of source files.
#source_encoding = 'utf-8-sig'
# source_encoding = 'utf-8-sig'
# The master toctree document.
master_doc = 'index'
master_doc = "index"
# General information about the project.
project = u'PyPSA-Eur-Sec'
copyright = u'2019-2023 Tom Brown (KIT, TUB), Marta Victoria (Aarhus University), Lisa Zeyen (KIT, TUB), Fabian Neumann (TUB)'
author = u'2019-2023 Tom Brown (KIT, TUB), Marta Victoria (Aarhus University), Lisa Zeyen (KIT, TUB), Fabian Neumann (TUB)'
project = "PyPSA-Eur-Sec"
copyright = "2019-2023 Tom Brown (KIT, TUB), Marta Victoria (Aarhus University), Lisa Zeyen (KIT, TUB), Fabian Neumann (TUB)"
author = "2019-2023 Tom Brown (KIT, TUB), Marta Victoria (Aarhus University), Lisa Zeyen (KIT, TUB), Fabian Neumann (TUB)"
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = u'0.7'
version = "0.7"
# The full version, including alpha/beta/rc tags.
release = u'0.7.0'
release = "0.7.0"
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
@ -84,37 +84,37 @@ language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = '%B %d, %Y'
# today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = ['_build']
exclude_patterns = ["_build"]
# The reST default role (used for this markup: `text`) to use for all
# documents.
#default_role = None
# default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
#add_function_parentheses = True
# add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True
# add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False
# show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
pygments_style = "sphinx"
# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []
# modindex_common_prefix = []
# If true, keep warnings as "system message" paragraphs in the built documents.
#keep_warnings = False
# keep_warnings = False
# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = True
@ -124,174 +124,177 @@ todo_include_todos = True
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
html_theme = 'sphinx_rtd_theme'
html_theme = "sphinx_rtd_theme"
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
html_theme_options = {
'display_version': True,
'sticky_navigation': True,
"display_version": True,
"sticky_navigation": True,
}
# Add any paths that contain custom themes here, relative to this directory.
#html_theme_path = []
# html_theme_path = []
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
#html_title = None
# html_title = None
# A shorter title for the navigation bar. Default is the same as html_title.
#html_short_title = None
# html_short_title = None
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
#html_logo = None
# html_logo = None
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
#html_favicon = None
# html_favicon = None
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
html_static_path = ["_static"]
html_context = {
'css_files': [
'_static/theme_overrides.css', # override wide tables in RTD theme
],
"css_files": [
"_static/theme_overrides.css", # override wide tables in RTD theme
],
}
# Add any extra paths that contain custom files (such as robots.txt or
# .htaccess) here, relative to this directory. These files are copied
# directly to the root of the documentation.
#html_extra_path = []
# html_extra_path = []
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
#html_last_updated_fmt = '%b %d, %Y'
# html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
#html_use_smartypants = True
# html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
#html_sidebars = {}
# html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
#html_additional_pages = {}
# html_additional_pages = {}
# If false, no module index is generated.
#html_domain_indices = True
# html_domain_indices = True
# If false, no index is generated.
#html_use_index = True
# html_use_index = True
# If true, the index is split into individual pages for each letter.
#html_split_index = False
# html_split_index = False
# If true, links to the reST sources are added to the pages.
#html_show_sourcelink = True
# html_show_sourcelink = True
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
#html_show_sphinx = True
# html_show_sphinx = True
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
#html_show_copyright = True
# html_show_copyright = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
#html_use_opensearch = ''
# html_use_opensearch = ''
# This is the file name suffix for HTML files (e.g. ".xhtml").
#html_file_suffix = None
# html_file_suffix = None
# Language to be used for generating the HTML full-text search index.
# Sphinx supports the following languages:
# 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja'
# 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr'
#html_search_language = 'en'
# html_search_language = 'en'
# A dictionary with options for the search language support, empty by default.
# Now only 'ja' uses this config value
#html_search_options = {'type': 'default'}
# html_search_options = {'type': 'default'}
# The name of a javascript file (relative to the configuration directory) that
# implements a search results scorer. If empty, the default will be used.
#html_search_scorer = 'scorer.js'
# html_search_scorer = 'scorer.js'
# Output file base name for HTML help builder.
htmlhelp_basename = 'PyPSAEurSecdoc'
htmlhelp_basename = "PyPSAEurSecdoc"
# -- Options for LaTeX output ---------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#'preamble': '',
# Latex figure (float) alignment
#'figure_align': 'htbp',
# The paper size ('letterpaper' or 'a4paper').
#'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#'preamble': '',
# Latex figure (float) alignment
#'figure_align': 'htbp',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, 'PyPSA-Eur-Sec.tex', u'PyPSA-Eur-Sec Documentation',
u'author', 'manual'),
(
master_doc,
"PyPSA-Eur-Sec.tex",
"PyPSA-Eur-Sec Documentation",
"author",
"manual",
),
]
#Added for rinoh http://www.mos6581.org/rinohtype/quickstart.html
rinoh_documents = [(master_doc, # top-level file (index.rst)
'PyPSA-Eur-Sec', # output (target.pdf)
'PyPSA-Eur-Sec Documentation', # document title
'author')] # document author
# Added for rinoh http://www.mos6581.org/rinohtype/quickstart.html
rinoh_documents = [
(
master_doc, # top-level file (index.rst)
"PyPSA-Eur-Sec", # output (target.pdf)
"PyPSA-Eur-Sec Documentation", # document title
"author",
)
] # document author
# The name of an image file (relative to this directory) to place at the top of
# the title page.
#latex_logo = None
# latex_logo = None
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
#latex_use_parts = False
# latex_use_parts = False
# If true, show page references after internal links.
#latex_show_pagerefs = False
# latex_show_pagerefs = False
# If true, show URL addresses after external links.
#latex_show_urls = False
# latex_show_urls = False
# Documents to append as an appendix to all manuals.
#latex_appendices = []
# latex_appendices = []
# If false, no module index is generated.
#latex_domain_indices = True
# latex_domain_indices = True
# -- Options for manual page output ---------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
(master_doc, 'pypsa-eur-sec', u'PyPSA-Eur-Sec Documentation',
[author], 1)
]
man_pages = [(master_doc, "pypsa-eur-sec", "PyPSA-Eur-Sec Documentation", [author], 1)]
# If true, show URL addresses after external links.
#man_show_urls = False
# man_show_urls = False
# -- Options for Texinfo output -------------------------------------------
@ -300,23 +303,29 @@ man_pages = [
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(master_doc, 'PyPSA-Eur-Sec', u'PyPSA-Eur-Sec Documentation',
author, 'PyPSA-Eur-Sec', 'One line description of project.',
'Miscellaneous'),
(
master_doc,
"PyPSA-Eur-Sec",
"PyPSA-Eur-Sec Documentation",
author,
"PyPSA-Eur-Sec",
"One line description of project.",
"Miscellaneous",
),
]
# Documents to append as an appendix to all manuals.
#texinfo_appendices = []
# texinfo_appendices = []
# If false, no module index is generated.
#texinfo_domain_indices = True
# texinfo_domain_indices = True
# How to display URL addresses: 'footnote', 'no', or 'inline'.
#texinfo_show_urls = 'footnote'
# texinfo_show_urls = 'footnote'
# If true, do not generate a @detailmenu in the "Top" node's menu.
#texinfo_no_detailmenu = False
# texinfo_no_detailmenu = False
# Example configuration for intersphinx: refer to the Python standard library.
intersphinx_mapping = {'https://docs.python.org/': None}
intersphinx_mapping = {"https://docs.python.org/": None}

1
doc/data.csv
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@ -27,4 +27,3 @@ Building topologies and corresponding standard values,tabula-calculator-calcsetb
Retrofitting thermal envelope costs for Germany,retro_cost_germany.csv,unknown,https://www.iwu.de/forschung/handlungslogiken/kosten-energierelevanter-bau-und-anlagenteile-bei-modernisierung/
District heating most countries,jrc-idees-2015/,CC BY 4.0,https://ec.europa.eu/jrc/en/potencia/jrc-idees,,
District heating missing countries,district_heat_share.csv,unknown,https://www.euroheat.org/knowledge-hub/country-profiles,,

Can't render this file because it has a wrong number of fields in line 28.

3
doc/supply_demand.rst
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@ -613,6 +613,3 @@ Captured :math:`CO_2` can also be sequestered underground up to an annual seques
*Carbon dioxide transport*
Carbon dioxide can be modelled as a single node for Europe (in this case, :math:`CO_2` transport constraints are neglected). A network for modelling the transport of :math:`CO_2` among the different nodes can also be created if selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L248>`_.

106
scripts/add_brownfield.py
View File

@ -1,70 +1,70 @@
# coding: utf-8
# -*- coding: utf-8 -*-
import logging
logger = logging.getLogger(__name__)
import pandas as pd
idx = pd.IndexSlice
import numpy as np
import pypsa
import yaml
import numpy as np
from add_existing_baseyear import add_build_year_to_new_assets
from helper import override_component_attrs, update_config_with_sector_opts
def add_brownfield(n, n_p, year):
logger.info(f"Preparing brownfield for the year {year}")
# electric transmission grid set optimised capacities of previous as minimum
n.lines.s_nom_min = n_p.lines.s_nom_opt
dc_i = n.links[n.links.carrier=="DC"].index
dc_i = n.links[n.links.carrier == "DC"].index
n.links.loc[dc_i, "p_nom_min"] = n_p.links.loc[dc_i, "p_nom_opt"]
for c in n_p.iterate_components(["Link", "Generator", "Store"]):
attr = "e" if c.name == "Store" else "p"
# first, remove generators, links and stores that track
# CO2 or global EU values since these are already in n
n_p.mremove(
c.name,
c.df.index[c.df.lifetime==np.inf]
)
n_p.mremove(c.name, c.df.index[c.df.lifetime == np.inf])
# remove assets whose build_year + lifetime < year
n_p.mremove(
c.name,
c.df.index[c.df.build_year + c.df.lifetime < year]
)
n_p.mremove(c.name, c.df.index[c.df.build_year + c.df.lifetime < year])
# remove assets if their optimized nominal capacity is lower than a threshold
# since CHP heat Link is proportional to CHP electric Link, make sure threshold is compatible
chp_heat = c.df.index[(
c.df[attr + "_nom_extendable"]
& c.df.index.str.contains("urban central")
& c.df.index.str.contains("CHP")
& c.df.index.str.contains("heat")
)]
chp_heat = c.df.index[
(
c.df[attr + "_nom_extendable"]
& c.df.index.str.contains("urban central")
& c.df.index.str.contains("CHP")
& c.df.index.str.contains("heat")
)
]
threshold = snakemake.config['existing_capacities']['threshold_capacity']
threshold = snakemake.config["existing_capacities"]["threshold_capacity"]
if not chp_heat.empty:
threshold_chp_heat = (threshold
threshold_chp_heat = (
threshold
* c.df.efficiency[chp_heat.str.replace("heat", "electric")].values
* c.df.p_nom_ratio[chp_heat.str.replace("heat", "electric")].values
/ c.df.efficiency[chp_heat].values
)
n_p.mremove(
c.name,
chp_heat[c.df.loc[chp_heat, attr + "_nom_opt"] < threshold_chp_heat]
chp_heat[c.df.loc[chp_heat, attr + "_nom_opt"] < threshold_chp_heat],
)
n_p.mremove(
c.name,
c.df.index[c.df[attr + "_nom_extendable"] & ~c.df.index.isin(chp_heat) & (c.df[attr + "_nom_opt"] < threshold)]
c.df.index[
c.df[attr + "_nom_extendable"]
& ~c.df.index.isin(chp_heat)
& (c.df[attr + "_nom_opt"] < threshold)
],
)
# copy over assets but fix their capacity
@ -74,55 +74,67 @@ def add_brownfield(n, n_p, year):
n.import_components_from_dataframe(c.df, c.name)
# copy time-dependent
selection = (
n.component_attrs[c.name].type.str.contains("series")
& n.component_attrs[c.name].status.str.contains("Input")
)
selection = n.component_attrs[c.name].type.str.contains(
"series"
) & n.component_attrs[c.name].status.str.contains("Input")
for tattr in n.component_attrs[c.name].index[selection]:
n.import_series_from_dataframe(c.pnl[tattr], c.name, tattr)
# deal with gas network
pipe_carrier = ['gas pipeline']
if snakemake.config["sector"]['H2_retrofit']:
pipe_carrier = ["gas pipeline"]
if snakemake.config["sector"]["H2_retrofit"]:
# drop capacities of previous year to avoid duplicating
to_drop = n.links.carrier.isin(pipe_carrier) & (n.links.build_year!=year)
to_drop = n.links.carrier.isin(pipe_carrier) & (n.links.build_year != year)
n.mremove("Link", n.links.loc[to_drop].index)
# subtract the already retrofitted from today's gas grid capacity
h2_retrofitted_fixed_i = n.links[(n.links.carrier=='H2 pipeline retrofitted') & (n.links.build_year!=year)].index
gas_pipes_i = n.links[n.links.carrier.isin(pipe_carrier)].index
h2_retrofitted_fixed_i = n.links[
(n.links.carrier == "H2 pipeline retrofitted")
& (n.links.build_year != year)
].index
gas_pipes_i = n.links[n.links.carrier.isin(pipe_carrier)].index
CH4_per_H2 = 1 / snakemake.config["sector"]["H2_retrofit_capacity_per_CH4"]
fr = "H2 pipeline retrofitted"
to = "gas pipeline"
# today's pipe capacity
pipe_capacity = n.links.loc[gas_pipes_i, 'p_nom']
pipe_capacity = n.links.loc[gas_pipes_i, "p_nom"]
# already retrofitted capacity from gas -> H2
already_retrofitted = (n.links.loc[h2_retrofitted_fixed_i, 'p_nom']
.rename(lambda x: x.split("-2")[0].replace(fr, to)).groupby(level=0).sum())
remaining_capacity = pipe_capacity - CH4_per_H2 * already_retrofitted.reindex(index=pipe_capacity.index).fillna(0)
already_retrofitted = (
n.links.loc[h2_retrofitted_fixed_i, "p_nom"]
.rename(lambda x: x.split("-2")[0].replace(fr, to))
.groupby(level=0)
.sum()
)
remaining_capacity = (
pipe_capacity
- CH4_per_H2
* already_retrofitted.reindex(index=pipe_capacity.index).fillna(0)
)
n.links.loc[gas_pipes_i, "p_nom"] = remaining_capacity
else:
new_pipes = n.links.carrier.isin(pipe_carrier) & (n.links.build_year==year)
n.links.loc[new_pipes, "p_nom"] = 0.
n.links.loc[new_pipes, "p_nom_min"] = 0.
new_pipes = n.links.carrier.isin(pipe_carrier) & (
n.links.build_year == year
)
n.links.loc[new_pipes, "p_nom"] = 0.0
n.links.loc[new_pipes, "p_nom_min"] = 0.0
#%%
# %%
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'add_brownfield',
simpl='',
"add_brownfield",
simpl="",
clusters="37",
opts="",
lv=1.0,
sector_opts='168H-T-H-B-I-solar+p3-dist1',
sector_opts="168H-T-H-B-I-solar+p3-dist1",
planning_horizons=2030,
)
logging.basicConfig(level=snakemake.config['logging_level'])
logging.basicConfig(level=snakemake.config["logging_level"])
update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts)

496
scripts/add_existing_baseyear.py
View File

@ -1,23 +1,25 @@
# coding: utf-8
# -*- coding: utf-8 -*-
import logging
logger = logging.getLogger(__name__)
import pandas as pd
idx = pd.IndexSlice
import numpy as np
import xarray as xr
import pypsa
import yaml
from prepare_sector_network import prepare_costs, define_spatial, cluster_heat_buses
from helper import override_component_attrs, update_config_with_sector_opts
from types import SimpleNamespace
import numpy as np
import pypsa
import xarray as xr
import yaml
from helper import override_component_attrs, update_config_with_sector_opts
from prepare_sector_network import cluster_heat_buses, define_spatial, prepare_costs
spatial = SimpleNamespace()
def add_build_year_to_new_assets(n, baseyear):
"""
Parameters
@ -29,8 +31,7 @@ def add_build_year_to_new_assets(n, baseyear):
# Give assets with lifetimes and no build year the build year baseyear
for c in n.iterate_components(["Link", "Generator", "Store"]):
assets = c.df.index[(c.df.lifetime!=np.inf) & (c.df.build_year==0)]
assets = c.df.index[(c.df.lifetime != np.inf) & (c.df.build_year == 0)]
c.df.loc[assets, "build_year"] = baseyear
# add -baseyear to name
@ -39,40 +40,34 @@ def add_build_year_to_new_assets(n, baseyear):
c.df.rename(index=rename, inplace=True)
# rename time-dependent
selection = (
n.component_attrs[c.name].type.str.contains("series")
& n.component_attrs[c.name].status.str.contains("Input")
)
selection = n.component_attrs[c.name].type.str.contains(
"series"
) & n.component_attrs[c.name].status.str.contains("Input")
for attr in n.component_attrs[c.name].index[selection]:
c.pnl[attr].rename(columns=rename, inplace=True)
def add_existing_renewables(df_agg):
"""
Append existing renewables to the df_agg pd.DataFrame
with the conventional power plants.
Append existing renewables to the df_agg pd.DataFrame with the conventional
power plants.
"""
cc = pd.read_csv(snakemake.input.country_codes, index_col=0)
carriers = {
"solar": "solar",
"onwind": "onwind",
"offwind": "offwind-ac"
}
for tech in ['solar', 'onwind', 'offwind']:
carriers = {"solar": "solar", "onwind": "onwind", "offwind": "offwind-ac"}
for tech in ["solar", "onwind", "offwind"]:
carrier = carriers[tech]
df = pd.read_csv(snakemake.input[f"existing_{tech}"], index_col=0).fillna(0.)
df = pd.read_csv(snakemake.input[f"existing_{tech}"], index_col=0).fillna(0.0)
df.columns = df.columns.astype(int)
rename_countries = {
'Czechia': 'Czech Republic',
'UK': 'United Kingdom',
'Bosnia Herzg': 'Bosnia Herzegovina',
'North Macedonia': 'Macedonia'
"Czechia": "Czech Republic",
"UK": "United Kingdom",
"Bosnia Herzg": "Bosnia Herzegovina",
"North Macedonia": "Macedonia",
}
df.rename(index=rename_countries, inplace=True)
@ -80,16 +75,21 @@ def add_existing_renewables(df_agg):
df.rename(index=cc["2 letter code (ISO-3166-2)"], inplace=True)
# calculate yearly differences
df.insert(loc=0, value=.0, column='1999')
df = df.diff(axis=1).drop('1999', axis=1).clip(lower=0)
df.insert(loc=0, value=0.0, column="1999")
df = df.diff(axis=1).drop("1999", axis=1).clip(lower=0)
# distribute capacities among nodes according to capacity factor
# weighting with nodal_fraction
elec_buses = n.buses.index[n.buses.carrier == "AC"].union(n.buses.index[n.buses.carrier == "DC"])
nodal_fraction = pd.Series(0., elec_buses)
elec_buses = n.buses.index[n.buses.carrier == "AC"].union(
n.buses.index[n.buses.carrier == "DC"]
)
nodal_fraction = pd.Series(0.0, elec_buses)
for country in n.buses.loc[elec_buses, "country"].unique():
gens = n.generators.index[(n.generators.index.str[:2] == country) & (n.generators.carrier == carrier)]
gens = n.generators.index[
(n.generators.index.str[:2] == country)
& (n.generators.carrier == carrier)
]
cfs = n.generators_t.p_max_pu[gens].mean()
cfs_key = cfs / cfs.sum()
nodal_fraction.loc[n.generators.loc[gens, "bus"]] = cfs_key.values
@ -102,7 +102,7 @@ def add_existing_renewables(df_agg):
for node in nodal_df.index:
name = f"{node}-{tech}-{year}"
capacity = nodal_df.loc[node, year]
if capacity > 0.:
if capacity > 0.0:
df_agg.at[name, "Fueltype"] = tech
df_agg.at[name, "Capacity"] = capacity
df_agg.at[name, "DateIn"] = year
@ -120,35 +120,34 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
to read lifetime to estimate YearDecomissioning
baseyear : int
"""
logger.debug(f"Adding power capacities installed before {baseyear} from powerplants.csv")
logger.debug(
f"Adding power capacities installed before {baseyear} from powerplants.csv"
)
df_agg = pd.read_csv(snakemake.input.powerplants, index_col=0)
rename_fuel = {
'Hard Coal': 'coal',
'Lignite': 'lignite',
'Nuclear': 'nuclear',
'Oil': 'oil',
'OCGT': 'OCGT',
'CCGT': 'CCGT',
'Natural Gas': 'gas',
'Bioenergy': 'urban central solid biomass CHP',
"Hard Coal": "coal",
"Lignite": "lignite",
"Nuclear": "nuclear",
"Oil": "oil",
"OCGT": "OCGT",
"CCGT": "CCGT",
"Natural Gas": "gas",
"Bioenergy": "urban central solid biomass CHP",
}
fueltype_to_drop = [
'Hydro',
'Wind',
'Solar',
'Geothermal',
'Waste',
'Other',
'CCGT, Thermal'
"Hydro",
"Wind",
"Solar",
"Geothermal",
"Waste",
"Other",
"CCGT, Thermal",
]
technology_to_drop = [
'Pv',
'Storage Technologies'
]
technology_to_drop = ["Pv", "Storage Technologies"]
# drop unused fueltyps and technologies
df_agg.drop(df_agg.index[df_agg.Fueltype.isin(fueltype_to_drop)], inplace=True)
@ -157,16 +156,15 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
# Intermediate fix for DateIn & DateOut
# Fill missing DateIn
biomass_i = df_agg.loc[df_agg.Fueltype=='urban central solid biomass CHP'].index
mean = df_agg.loc[biomass_i, 'DateIn'].mean()
df_agg.loc[biomass_i, 'DateIn'] = df_agg.loc[biomass_i, 'DateIn'].fillna(int(mean))
biomass_i = df_agg.loc[df_agg.Fueltype == "urban central solid biomass CHP"].index
mean = df_agg.loc[biomass_i, "DateIn"].mean()
df_agg.loc[biomass_i, "DateIn"] = df_agg.loc[biomass_i, "DateIn"].fillna(int(mean))
# Fill missing DateOut
dateout = df_agg.loc[biomass_i, 'DateIn'] + snakemake.config['costs']['lifetime']
df_agg.loc[biomass_i, 'DateOut'] = df_agg.loc[biomass_i, 'DateOut'].fillna(dateout)
dateout = df_agg.loc[biomass_i, "DateIn"] + snakemake.config["costs"]["lifetime"]
df_agg.loc[biomass_i, "DateOut"] = df_agg.loc[biomass_i, "DateOut"].fillna(dateout)
# drop assets which are already phased out / decommissioned
phased_out = df_agg[df_agg["DateOut"]<baseyear].index
phased_out = df_agg[df_agg["DateOut"] < baseyear].index
df_agg.drop(phased_out, inplace=True)
# calculate remaining lifetime before phase-out (+1 because assuming
@ -190,22 +188,21 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
add_existing_renewables(df_agg)
df_agg["grouping_year"] = np.take(
grouping_years,
np.digitize(df_agg.DateIn, grouping_years, right=True)
grouping_years, np.digitize(df_agg.DateIn, grouping_years, right=True)
)
df = df_agg.pivot_table(
index=["grouping_year", 'Fueltype'],
columns='cluster_bus',
values='Capacity',
aggfunc='sum'
index=["grouping_year", "Fueltype"],
columns="cluster_bus",
values="Capacity",
aggfunc="sum",
)
lifetime = df_agg.pivot_table(
index=["grouping_year", 'Fueltype'],
columns='cluster_bus',
values='lifetime',
aggfunc='mean' # currently taken mean for clustering lifetimes
index=["grouping_year", "Fueltype"],
columns="cluster_bus",
values="lifetime",
aggfunc="mean", # currently taken mean for clustering lifetimes
)
carrier = {
@ -215,78 +212,89 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
"oil": "oil",
"lignite": "lignite",
"nuclear": "uranium",
'urban central solid biomass CHP': "biomass",
"urban central solid biomass CHP": "biomass",
}
for grouping_year, generator in df.index:
# capacity is the capacity in MW at each node for this
capacity = df.loc[grouping_year, generator]
capacity = capacity[~capacity.isna()]
capacity = capacity[capacity > snakemake.config['existing_capacities']['threshold_capacity']]
suffix = '-ac' if generator == 'offwind' else ''
name_suffix = f' {generator}{suffix}-{grouping_year}'
capacity = capacity[
capacity > snakemake.config["existing_capacities"]["threshold_capacity"]
]
suffix = "-ac" if generator == "offwind" else ""
name_suffix = f" {generator}{suffix}-{grouping_year}"
asset_i = capacity.index + name_suffix
if generator in ['solar', 'onwind', 'offwind']:
if generator in ["solar", "onwind", "offwind"]:
# to consider electricity grid connection costs or a split between
# solar utility and rooftop as well, rather take cost assumptions
# from existing network than from the cost database
capital_cost = n.generators.loc[n.generators.carrier==generator+suffix, "capital_cost"].mean()
marginal_cost = n.generators.loc[n.generators.carrier==generator+suffix, "marginal_cost"].mean()
capital_cost = n.generators.loc[
n.generators.carrier == generator + suffix, "capital_cost"
].mean()
marginal_cost = n.generators.loc[
n.generators.carrier == generator + suffix, "marginal_cost"
].mean()
# check if assets are already in network (e.g. for 2020)
already_build = n.generators.index.intersection(asset_i)
new_build = asset_i.difference(n.generators.index)
# this is for the year 2020
if not already_build.empty:
n.generators.loc[already_build, "p_nom_min"] = capacity.loc[already_build.str.replace(name_suffix, "")].values
n.generators.loc[already_build, "p_nom_min"] = capacity.loc[
already_build.str.replace(name_suffix, "")
].values
new_capacity = capacity.loc[new_build.str.replace(name_suffix, "")]
if 'm' in snakemake.wildcards.clusters:
if "m" in snakemake.wildcards.clusters:
for ind in new_capacity.index:
# existing capacities are split evenly among regions in every country
inv_ind = [i for i in inv_busmap[ind]]
# for offshore the splitting only includes coastal regions
inv_ind = [i for i in inv_ind if (i + name_suffix) in n.generators.index]
inv_ind = [
i for i in inv_ind if (i + name_suffix) in n.generators.index
]
p_max_pu = n.generators_t.p_max_pu[[i + name_suffix for i in inv_ind]]
p_max_pu.columns=[i + name_suffix for i in inv_ind ]
p_max_pu = n.generators_t.p_max_pu[
[i + name_suffix for i in inv_ind]
]
p_max_pu.columns = [i + name_suffix for i in inv_ind]
n.madd("Generator",
n.madd(
"Generator",
[i + name_suffix for i in inv_ind],
bus=ind,
carrier=generator,
p_nom=new_capacity[ind] / len(inv_ind), # split among regions in a country
p_nom=new_capacity[ind]
/ len(inv_ind), # split among regions in a country
marginal_cost=marginal_cost,
capital_cost=capital_cost,
efficiency=costs.at[generator, 'efficiency'],
efficiency=costs.at[generator, "efficiency"],
p_max_pu=p_max_pu,
build_year=grouping_year,
lifetime=costs.at[generator,'lifetime']
lifetime=costs.at[generator, "lifetime"],
)
else:
p_max_pu = n.generators_t.p_max_pu[capacity.index + f' {generator}{suffix}-{baseyear}']
p_max_pu = n.generators_t.p_max_pu[
capacity.index + f" {generator}{suffix}-{baseyear}"
]
if not new_build.empty:
n.madd("Generator",
n.madd(
"Generator",
new_capacity.index,
suffix=' ' + name_suffix,
suffix=" " + name_suffix,
bus=new_capacity.index,
carrier=generator,
p_nom=new_capacity,
marginal_cost=marginal_cost,
capital_cost=capital_cost,
efficiency=costs.at[generator, 'efficiency'],
efficiency=costs.at[generator, "efficiency"],
p_max_pu=p_max_pu.rename(columns=n.generators.bus),
build_year=grouping_year,
lifetime=costs.at[generator, 'lifetime']
lifetime=costs.at[generator, "lifetime"],
)
else:
@ -296,56 +304,79 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
already_build = n.links.index.intersection(asset_i)
new_build = asset_i.difference(n.links.index)
lifetime_assets = lifetime.loc[grouping_year,generator].dropna()
lifetime_assets = lifetime.loc[grouping_year, generator].dropna()
# this is for the year 2020
if not already_build.empty:
n.links.loc[already_build, "p_nom_min"] = capacity.loc[already_build.str.replace(name_suffix, "")].values
n.links.loc[already_build, "p_nom_min"] = capacity.loc[
already_build.str.replace(name_suffix, "")
].values
if not new_build.empty:
new_capacity = capacity.loc[new_build.str.replace(name_suffix, "")]
if generator!="urban central solid biomass CHP":
n.madd("Link",
if generator != "urban central solid biomass CHP":
n.madd(
"Link",
new_capacity.index,
suffix= name_suffix,
suffix=name_suffix,
bus0=bus0,
bus1=new_capacity.index,
bus2="co2 atmosphere",
carrier=generator,
marginal_cost=costs.at[generator, 'efficiency'] * costs.at[generator, 'VOM'], #NB: VOM is per MWel
capital_cost=costs.at[generator, 'efficiency'] * costs.at[generator, 'fixed'], #NB: fixed cost is per MWel
p_nom=new_capacity / costs.at[generator, 'efficiency'],
efficiency=costs.at[generator, 'efficiency'],
efficiency2=costs.at[carrier[generator], 'CO2 intensity'],
marginal_cost=costs.at[generator, "efficiency"]
* costs.at[generator, "VOM"], # NB: VOM is per MWel
capital_cost=costs.at[generator, "efficiency"]
* costs.at[generator, "fixed"], # NB: fixed cost is per MWel
p_nom=new_capacity / costs.at[generator, "efficiency"],
efficiency=costs.at[generator, "efficiency"],
efficiency2=costs.at[carrier[generator], "CO2 intensity"],
build_year=grouping_year,
lifetime=lifetime_assets.loc[new_capacity.index],
)
else:
key = 'central solid biomass CHP'
n.madd("Link",
key = "central solid biomass CHP"
n.madd(
"Link",
new_capacity.index,
suffix= name_suffix,
suffix=name_suffix,
bus0=spatial.biomass.df.loc[new_capacity.index]["nodes"].values,
bus1=new_capacity.index,
bus2=new_capacity.index + " urban central heat",
carrier=generator,
p_nom=new_capacity / costs.at[key, 'efficiency'],
capital_cost=costs.at[key, 'fixed'] * costs.at[key, 'efficiency'],
marginal_cost=costs.at[key, 'VOM'],
efficiency=costs.at[key, 'efficiency'],
p_nom=new_capacity / costs.at[key, "efficiency"],
capital_cost=costs.at[key, "fixed"]
* costs.at[key, "efficiency"],
marginal_cost=costs.at[key, "VOM"],
efficiency=costs.at[key, "efficiency"],
build_year=grouping_year,
efficiency2=costs.at[key, 'efficiency-heat'],
lifetime=lifetime_assets.loc[new_capacity.index]
efficiency2=costs.at[key, "efficiency-heat"],
lifetime=lifetime_assets.loc[new_capacity.index],
)
# check if existing capacities are larger than technical potential
existing_large = n.generators[n.generators["p_nom_min"] > n.generators["p_nom_max"]].index
existing_large = n.generators[
n.generators["p_nom_min"] > n.generators["p_nom_max"]
].index
if len(existing_large):
logger.warning(f"Existing capacities larger than technical potential for {existing_large},\
adjust technical potential to existing capacities")
n.generators.loc[existing_large, "p_nom_max"] = n.generators.loc[existing_large, "p_nom_min"]
logger.warning(
f"Existing capacities larger than technical potential for {existing_large},\
adjust technical potential to existing capacities"
)
n.generators.loc[existing_large, "p_nom_max"] = n.generators.loc[
existing_large, "p_nom_min"
]
def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime):
def add_heating_capacities_installed_before_baseyear(
n,
baseyear,
grouping_years,
ashp_cop,
gshp_cop,
time_dep_hp_cop,
costs,
default_lifetime,
):
"""
Parameters
----------
@ -368,20 +399,20 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
# retrieve existing heating capacities
techs = [
'gas boiler',
'oil boiler',
'resistive heater',
'air heat pump',
'ground heat pump'
"gas boiler",
"oil boiler",
"resistive heater",
"air heat pump",
"ground heat pump",
]
df = pd.read_csv(snakemake.input.existing_heating, index_col=0, header=0)
# data for Albania, Montenegro and Macedonia not included in database
df.loc['Albania'] = np.nan
df.loc['Montenegro'] = np.nan
df.loc['Macedonia'] = np.nan
df.loc["Albania"] = np.nan
df.loc["Montenegro"] = np.nan
df.loc["Macedonia"] = np.nan
df.fillna(0., inplace=True)
df.fillna(0.0, inplace=True)
# convert GW to MW
df *= 1e3
@ -391,8 +422,8 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
df.rename(index=cc["2 letter code (ISO-3166-2)"], inplace=True)
# coal and oil boilers are assimilated to oil boilers
df['oil boiler'] = df['oil boiler'] + df['coal boiler']
df.drop(['coal boiler'], axis=1, inplace=True)
df["oil boiler"] = df["oil boiler"] + df["coal boiler"]
df.drop(["coal boiler"], axis=1, inplace=True)
# distribute technologies to nodes by population
pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
@ -403,36 +434,54 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
# split existing capacities between residential and services
# proportional to energy demand
ratio_residential=pd.Series([(n.loads_t.p_set.sum()['{} residential rural heat'.format(node)] /
(n.loads_t.p_set.sum()['{} residential rural heat'.format(node)] +
n.loads_t.p_set.sum()['{} services rural heat'.format(node)] ))
for node in nodal_df.index], index=nodal_df.index)
ratio_residential = pd.Series(
[
(
n.loads_t.p_set.sum()["{} residential rural heat".format(node)]
/ (
n.loads_t.p_set.sum()["{} residential rural heat".format(node)]
+ n.loads_t.p_set.sum()["{} services rural heat".format(node)]
)
)
for node in nodal_df.index
],
index=nodal_df.index,
)
for tech in techs:
nodal_df['residential ' + tech] = nodal_df[tech] * ratio_residential
nodal_df['services ' + tech] = nodal_df[tech] * (1 - ratio_residential)
nodal_df["residential " + tech] = nodal_df[tech] * ratio_residential
nodal_df["services " + tech] = nodal_df[tech] * (1 - ratio_residential)
names = [
"residential rural",
"services rural",
"residential urban decentral",
"services urban decentral",
"urban central"
"urban central",
]
nodes = {}
p_nom = {}
for name in names:
name_type = "central" if name == "urban central" else "decentral"
nodes[name] = pd.Index([n.buses.at[index, "location"] for index in n.buses.index[n.buses.index.str.contains(name) & n.buses.index.str.contains('heat')]])
nodes[name] = pd.Index(
[
n.buses.at[index, "location"]
for index in n.buses.index[
n.buses.index.str.contains(name)
& n.buses.index.str.contains("heat")
]
]
)
heat_pump_type = "air" if "urban" in name else "ground"
heat_type= "residential" if "residential" in name else "services"
heat_type = "residential" if "residential" in name else "services"
if name == "urban central":
p_nom[name] = nodal_df['air heat pump'][nodes[name]]
p_nom[name] = nodal_df["air heat pump"][nodes[name]]
else:
p_nom[name] = nodal_df[f'{heat_type} {heat_pump_type} heat pump'][nodes[name]]
p_nom[name] = nodal_df[f"{heat_type} {heat_pump_type} heat pump"][
nodes[name]
]
# Add heat pumps
costs_name = f"decentral {heat_pump_type}-sourced heat pump"
@ -442,131 +491,182 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
if time_dep_hp_cop:
efficiency = cop[heat_pump_type][nodes[name]]
else:
efficiency = costs.at[costs_name, 'efficiency']
efficiency = costs.at[costs_name, "efficiency"]
for i, grouping_year in enumerate(grouping_years):
if int(grouping_year) + default_lifetime <= int(baseyear):
continue
# installation is assumed to be linear for the past 25 years (default lifetime)
ratio = (int(grouping_year) - int(grouping_years[i-1])) / default_lifetime
ratio = (int(grouping_year) - int(grouping_years[i - 1])) / default_lifetime
n.madd("Link",
n.madd(
"Link",
nodes[name],
suffix=f" {name} {heat_pump_type} heat pump-{grouping_year}",
bus0=nodes[name],
bus1=nodes[name] + " " + name + " heat",
carrier=f"{name} {heat_pump_type} heat pump",
efficiency=efficiency,
capital_cost=costs.at[costs_name, 'efficiency'] * costs.at[costs_name, 'fixed'],
p_nom=p_nom[name] * ratio / costs.at[costs_name, 'efficiency'],
capital_cost=costs.at[costs_name, "efficiency"]
* costs.at[costs_name, "fixed"],
p_nom=p_nom[name] * ratio / costs.at[costs_name, "efficiency"],
build_year=int(grouping_year),
lifetime=costs.at[costs_name, 'lifetime']
lifetime=costs.at[costs_name, "lifetime"],
)
# add resistive heater, gas boilers and oil boilers
# (50% capacities to rural buses, 50% to urban buses)
n.madd("Link",
n.madd(
"Link",
nodes[name],
suffix=f" {name} resistive heater-{grouping_year}",
bus0=nodes[name],
bus1=nodes[name] + " " + name + " heat",
carrier=name + " resistive heater",
efficiency=costs.at[name_type + ' resistive heater', 'efficiency'],
capital_cost=costs.at[name_type + ' resistive heater', 'efficiency'] * costs.at[name_type + ' resistive heater', 'fixed'],
p_nom=0.5 * nodal_df[f'{heat_type} resistive heater'][nodes[name]] * ratio / costs.at[name_type + ' resistive heater', 'efficiency'],
efficiency=costs.at[name_type + " resistive heater", "efficiency"],
capital_cost=costs.at[name_type + " resistive heater", "efficiency"]
* costs.at[name_type + " resistive heater", "fixed"],
p_nom=0.5
* nodal_df[f"{heat_type} resistive heater"][nodes[name]]
* ratio
/ costs.at[name_type + " resistive heater", "efficiency"],
build_year=int(grouping_year),
lifetime=costs.at[costs_name, 'lifetime']
lifetime=costs.at[costs_name, "lifetime"],
)
n.madd("Link",
n.madd(
"Link",
nodes[name],
suffix= f" {name} gas boiler-{grouping_year}",
suffix=f" {name} gas boiler-{grouping_year}",
bus0=spatial.gas.nodes,
bus1=nodes[name] + " " + name + " heat",
bus2="co2 atmosphere",
carrier=name + " gas boiler",
efficiency=costs.at[name_type + ' gas boiler', 'efficiency'],
efficiency2=costs.at['gas', 'CO2 intensity'],
capital_cost=costs.at[name_type + ' gas boiler', 'efficiency'] * costs.at[name_type + ' gas boiler', 'fixed'],
p_nom=0.5*nodal_df[f'{heat_type} gas boiler'][nodes[name]] * ratio / costs.at[name_type + ' gas boiler', 'efficiency'],
efficiency=costs.at[name_type + " gas boiler", "efficiency"],
efficiency2=costs.at["gas", "CO2 intensity"],
capital_cost=costs.at[name_type + " gas boiler", "efficiency"]
* costs.at[name_type + " gas boiler", "fixed"],
p_nom=0.5
* nodal_df[f"{heat_type} gas boiler"][nodes[name]]
* ratio
/ costs.at[name_type + " gas boiler", "efficiency"],
build_year=int(grouping_year),
lifetime=costs.at[name_type + ' gas boiler', 'lifetime']
lifetime=costs.at[name_type + " gas boiler", "lifetime"],
)
n.madd("Link",
n.madd(
"Link",
nodes[name],
suffix=f" {name} oil boiler-{grouping_year}",
bus0=spatial.oil.nodes,
bus1=nodes[name] + " " + name + " heat",
bus2="co2 atmosphere",
carrier=name + " oil boiler",
efficiency=costs.at['decentral oil boiler', 'efficiency'],
efficiency2=costs.at['oil', 'CO2 intensity'],
capital_cost=costs.at['decentral oil boiler', 'efficiency'] * costs.at['decentral oil boiler', 'fixed'],
p_nom=0.5 * nodal_df[f'{heat_type} oil boiler'][nodes[name]] * ratio / costs.at['decentral oil boiler', 'efficiency'],
efficiency=costs.at["decentral oil boiler", "efficiency"],
efficiency2=costs.at["oil", "CO2 intensity"],
capital_cost=costs.at["decentral oil boiler", "efficiency"]
* costs.at["decentral oil boiler", "fixed"],
p_nom=0.5
* nodal_df[f"{heat_type} oil boiler"][nodes[name]]
* ratio
/ costs.at["decentral oil boiler", "efficiency"],
build_year=int(grouping_year),
lifetime=costs.at[name_type + ' gas boiler', 'lifetime']
lifetime=costs.at[name_type + " gas boiler", "lifetime"],
)
# delete links with p_nom=nan corresponding to extra nodes in country
n.mremove("Link", [index for index in n.links.index.to_list() if str(grouping_year) in index and np.isnan(n.links.p_nom[index])])
n.mremove(
"Link",
[
index
for index in n.links.index.to_list()
if str(grouping_year) in index and np.isnan(n.links.p_nom[index])
],
)
# delete links with capacities below threshold
threshold = snakemake.config['existing_capacities']['threshold_capacity']
n.mremove("Link", [index for index in n.links.index.to_list() if str(grouping_year) in index and n.links.p_nom[index] < threshold])
threshold = snakemake.config["existing_capacities"]["threshold_capacity"]
n.mremove(
"Link",
[
index
for index in n.links.index.to_list()
if str(grouping_year) in index and n.links.p_nom[index] < threshold
],
)
#%%
# %%
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'add_existing_baseyear',
simpl='',
"add_existing_baseyear",
simpl="",
clusters="45",
lv=1.0,
opts='',
sector_opts='8760H-T-H-B-I-A-solar+p3-dist1',
opts="",
sector_opts="8760H-T-H-B-I-A-solar+p3-dist1",
planning_horizons=2020,
)
logging.basicConfig(level=snakemake.config['logging_level'])
logging.basicConfig(level=snakemake.config["logging_level"])
update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts)
options = snakemake.config["sector"]
opts = snakemake.wildcards.sector_opts.split('-')
opts = snakemake.wildcards.sector_opts.split("-")
baseyear = snakemake.config['scenario']["planning_horizons"][0]
baseyear = snakemake.config["scenario"]["planning_horizons"][0]
overrides = override_component_attrs(snakemake.input.overrides)
n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
# define spatial resolution of carriers
spatial = define_spatial(n.buses[n.buses.carrier=="AC"].index, options)
spatial = define_spatial(n.buses[n.buses.carrier == "AC"].index, options)
add_build_year_to_new_assets(n, baseyear)
Nyears = n.snapshot_weightings.generators.sum() / 8760.
Nyears = n.snapshot_weightings.generators.sum() / 8760.0
costs = prepare_costs(
snakemake.input.costs,
snakemake.config['costs']['USD2013_to_EUR2013'],
snakemake.config['costs']['discountrate'],
snakemake.config["costs"]["USD2013_to_EUR2013"],
snakemake.config["costs"]["discountrate"],
Nyears,
snakemake.config['costs']['lifetime']
snakemake.config["costs"]["lifetime"],
)
grouping_years_power = snakemake.config['existing_capacities']['grouping_years_power']
grouping_years_heat = snakemake.config['existing_capacities']['grouping_years_heat']
add_power_capacities_installed_before_baseyear(n, grouping_years_power, costs, baseyear)
grouping_years_power = snakemake.config["existing_capacities"][
"grouping_years_power"
]
grouping_years_heat = snakemake.config["existing_capacities"]["grouping_years_heat"]
add_power_capacities_installed_before_baseyear(
n, grouping_years_power, costs, baseyear
)
if "H" in opts:
time_dep_hp_cop = options["time_dep_hp_cop"]
ashp_cop = xr.open_dataarray(snakemake.input.cop_air_total).to_pandas().reindex(index=n.snapshots)
gshp_cop = xr.open_dataarray(snakemake.input.cop_soil_total).to_pandas().reindex(index=n.snapshots)
default_lifetime = snakemake.config['costs']['lifetime']
add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years_heat,
ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime)
ashp_cop = (
xr.open_dataarray(snakemake.input.cop_air_total)
.to_pandas()
.reindex(index=n.snapshots)
)
gshp_cop = (
xr.open_dataarray(snakemake.input.cop_soil_total)
.to_pandas()
.reindex(index=n.snapshots)
)
default_lifetime = snakemake.config["costs"]["lifetime"]
add_heating_capacities_installed_before_baseyear(
n,
baseyear,
grouping_years_heat,
ashp_cop,
gshp_cop,
time_dep_hp_cop,
costs,
default_lifetime,
)
if options.get("cluster_heat_buses", False):
cluster_heat_buses(n)

26
scripts/build_ammonia_production.py
View File

@ -1,4 +1,7 @@
"""Build ammonia production."""
# -*- coding: utf-8 -*-
"""
Build ammonia production.
"""
import pandas as pd
@ -27,17 +30,20 @@ country_to_alpha2 = {
"United Kingdom": "GB",
}
if __name__ == '__main__':
if 'snakemake' not in globals():
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake('build_ammonia_production')
ammonia = pd.read_excel(snakemake.input.usgs,
sheet_name="T12",
skiprows=5,
header=0,
index_col=0,
skipfooter=19)
snakemake = mock_snakemake("build_ammonia_production")
ammonia = pd.read_excel(
snakemake.input.usgs,
sheet_name="T12",
skiprows=5,
header=0,
index_col=0,
skipfooter=19,
)
ammonia.rename(country_to_alpha2, inplace=True)

54
scripts/build_biomass_potentials.py
View File

@ -1,15 +1,15 @@
import pandas as pd
# -*- coding: utf-8 -*-
import geopandas as gpd
import pandas as pd
def build_nuts_population_data(year=2013):
pop = pd.read_csv(
snakemake.input.nuts3_population,
sep=r'\,| \t|\t',
engine='python',
sep=r"\,| \t|\t",
engine="python",
na_values=[":"],
index_col=1
index_col=1,
)[str(year)]
# only countries
@ -18,10 +18,12 @@ def build_nuts_population_data(year=2013):
# mapping from Cantons to NUTS3
cantons = pd.read_csv(snakemake.input.swiss_cantons)
cantons = cantons.set_index(cantons.HASC.str[3:]).NUTS
cantons = cantons.str.pad(5, side='right', fillchar='0')
cantons = cantons.str.pad(5, side="right", fillchar="0")
# get population by NUTS3
swiss = pd.read_excel(snakemake.input.swiss_population, skiprows=3, index_col=0).loc["Residents in 1000"]
swiss = pd.read_excel(
snakemake.input.swiss_population, skiprows=3, index_col=0
).loc["Residents in 1000"]
swiss = swiss.rename(cantons).filter(like="CH")
# aggregate also to higher order NUTS levels
@ -64,13 +66,13 @@ def enspreso_biomass_potentials(year=2020, scenario="ENS_Low"):
sheet_name="Glossary",
usecols="B:D",
skiprows=1,
index_col=0
index_col=0,
)
df = pd.read_excel(
str(snakemake.input.enspreso_biomass),
sheet_name="ENER - NUTS2 BioCom E",
usecols="A:H"
usecols="A:H",
)
df["group"] = df["E-Comm"].map(glossary.group)
@ -83,7 +85,7 @@ def enspreso_biomass_potentials(year=2020, scenario="ENS_Low"):
df.rename(columns=to_rename, inplace=True)
# fill up with NUTS0 if NUTS2 is not given
df.NUTS2 = df.apply(lambda x: x.NUTS0 if x.NUTS2 == '-' else x.NUTS2, axis=1)
df.NUTS2 = df.apply(lambda x: x.NUTS0 if x.NUTS2 == "-" else x.NUTS2, axis=1)
# convert PJ to TWh
df.potential /= 3.6
@ -102,9 +104,8 @@ def enspreso_biomass_potentials(year=2020, scenario="ENS_Low"):
def disaggregate_nuts0(bio):
"""
Some commodities are only given on NUTS0 level.
These are disaggregated here using the NUTS2
population as distribution key.
Some commodities are only given on NUTS0 level. These are disaggregated
here using the NUTS2 population as distribution key.
Parameters
----------
@ -141,9 +142,11 @@ def build_nuts2_shapes():
- consistently name ME, MK
"""
nuts2 = gpd.GeoDataFrame(gpd.read_file(snakemake.input.nuts2).set_index('id').geometry)
nuts2 = gpd.GeoDataFrame(
gpd.read_file(snakemake.input.nuts2).set_index("id").geometry
)
countries = gpd.read_file(snakemake.input.country_shapes).set_index('name')
countries = gpd.read_file(snakemake.input.country_shapes).set_index("name")
missing_iso2 = countries.index.intersection(["AL", "RS", "BA"])
missing = countries.loc[missing_iso2]
@ -153,14 +156,16 @@ def build_nuts2_shapes():
def area(gdf):
"""Returns area of GeoDataFrame geometries in square kilometers."""
"""
Returns area of GeoDataFrame geometries in square kilometers.
"""
return gdf.to_crs(epsg=3035).area.div(1e6)
def convert_nuts2_to_regions(bio_nuts2, regions):
"""
Converts biomass potentials given in NUTS2 to PyPSA-Eur regions based on the
overlay of both GeoDataFrames in proportion to the area.
Converts biomass potentials given in NUTS2 to PyPSA-Eur regions based on
the overlay of both GeoDataFrames in proportion to the area.
Parameters
----------
@ -183,7 +188,9 @@ def convert_nuts2_to_regions(bio_nuts2, regions):
overlay["share"] = area(overlay) / overlay["area_nuts2"]
# multiply all nuts2-level values with share of nuts2 inside region
adjust_cols = overlay.columns.difference({"name", "area_nuts2", "geometry", "share"})
adjust_cols = overlay.columns.difference(
{"name", "area_nuts2", "geometry", "share"}
)
overlay[adjust_cols] = overlay[adjust_cols].multiply(overlay["share"], axis=0)
bio_regions = overlay.groupby("name").sum()
@ -194,11 +201,12 @@ def convert_nuts2_to_regions(bio_nuts2, regions):
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake('build_biomass_potentials', simpl='', clusters='5')
config = snakemake.config['biomass']
snakemake = mock_snakemake("build_biomass_potentials", simpl="", clusters="5")
config = snakemake.config["biomass"]
year = config["year"]
scenario = config["scenario"]
@ -219,7 +227,7 @@ if __name__ == "__main__":
grouper = {v: k for k, vv in config["classes"].items() for v in vv}
df = df.groupby(grouper, axis=1).sum()
df *= 1e6 # TWh/a to MWh/a
df *= 1e6 # TWh/a to MWh/a
df.index.name = "MWh/a"
df.to_csv(snakemake.output.biomass_potentials)

28
scripts/build_biomass_transport_costs.py
View File

@ -1,5 +1,6 @@
# -*- coding: utf-8 -*-
"""
Reads biomass transport costs for different countries of the JRC report
Reads biomass transport costs for different countries of the JRC report.
"The JRC-EU-TIMES model.
Bioenergy potentials
@ -18,29 +19,24 @@ import tabula as tbl
ENERGY_CONTENT = 4.8 # unit MWh/t (wood pellets)
def get_countries():
pandas_options = dict(
skiprows=range(6),
header=None,
index_col=0
)
def get_countries():
pandas_options = dict(skiprows=range(6), header=None, index_col=0)
return tbl.read_pdf(
str(snakemake.input.transport_cost_data),
pages="145",
multiple_tables=False,
pandas_options=pandas_options
pandas_options=pandas_options,
)[0].index
def get_cost_per_tkm(page, countries):
pandas_options = dict(
skiprows=range(6),
header=0,
sep=' |,',
engine='python',
sep=" |,",
engine="python",
index_col=False,
)
@ -48,7 +44,7 @@ def get_cost_per_tkm(page, countries):
str(snakemake.input.transport_cost_data),
pages=page,
multiple_tables=False,
pandas_options=pandas_options
pandas_options=pandas_options,
)[0]
sc.index = countries
sc.columns = sc.columns.str.replace("€", "EUR")
@ -57,7 +53,6 @@ def get_cost_per_tkm(page, countries):
def build_biomass_transport_costs():
countries = get_countries()
sc1 = get_cost_per_tkm(146, countries)
@ -72,11 +67,7 @@ def build_biomass_transport_costs():
transport_costs.name = "EUR/km/MWh"
# rename country names
to_rename = {
"UK": "GB",
"XK": "KO",
"EL": "GR"
}
to_rename = {"UK": "GB", "XK": "KO", "EL": "GR"}
transport_costs.rename(to_rename, inplace=True)
# add missing Norway with data from Sweden
@ -86,5 +77,4 @@ def build_biomass_transport_costs():
if __name__ == "__main__":
build_biomass_transport_costs()

35
scripts/build_clustered_population_layouts.py
View File

@ -1,31 +1,38 @@
"""Build clustered population layouts."""
# -*- coding: utf-8 -*-
"""
Build clustered population layouts.
"""
import geopandas as gpd
import xarray as xr
import pandas as pd
import atlite
import geopandas as gpd
import pandas as pd
import xarray as xr
if __name__ == '__main__':
if 'snakemake' not in globals():
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'build_clustered_population_layouts',
simpl='',
"build_clustered_population_layouts",
simpl="",
clusters=48,
)
cutout = atlite.Cutout(snakemake.config['atlite']['cutout'])
cutout = atlite.Cutout(snakemake.config["atlite"]["cutout"])
clustered_regions = gpd.read_file(
snakemake.input.regions_onshore).set_index('name').buffer(0).squeeze()
clustered_regions = (
gpd.read_file(snakemake.input.regions_onshore)
.set_index("name")
.buffer(0)
.squeeze()
)
I = cutout.indicatormatrix(clustered_regions)
pop = {}
for item in ["total", "urban", "rural"]:
pop_layout = xr.open_dataarray(snakemake.input[f'pop_layout_{item}'])
pop[item] = I.dot(pop_layout.stack(spatial=('y', 'x')))
pop_layout = xr.open_dataarray(snakemake.input[f"pop_layout_{item}"])
pop[item] = I.dot(pop_layout.stack(spatial=("y", "x")))
pop = pd.DataFrame(pop, index=clustered_regions.index)

28
scripts/build_cop_profiles.py
View File

@ -1,39 +1,41 @@
"""Build COP time series for air- or ground-sourced heat pumps."""
# -*- coding: utf-8 -*-
"""
Build COP time series for air- or ground-sourced heat pumps.
"""
import xarray as xr
def coefficient_of_performance(delta_T, source='air'):
def coefficient_of_performance(delta_T, source="air"):
"""
COP is function of temp difference source to sink.
The quadratic regression is based on Staffell et al. (2012)
https://doi.org/10.1039/C2EE22653G.
"""
if source == 'air':
if source == "air":
return 6.81 - 0.121 * delta_T + 0.000630 * delta_T**2
elif source == 'soil':
elif source == "soil":
return 8.77 - 0.150 * delta_T + 0.000734 * delta_T**2
else:
raise NotImplementedError("'source' must be one of ['air', 'soil']")
if __name__ == '__main__':
if 'snakemake' not in globals():
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'build_cop_profiles',
simpl='',
"build_cop_profiles",
simpl="",
clusters=48,
)
for area in ["total", "urban", "rural"]:
for source in ["air", "soil"]:
source_T = xr.open_dataarray(snakemake.input[f"temp_{source}_{area}"])
source_T = xr.open_dataarray(
snakemake.input[f"temp_{source}_{area}"])
delta_T = snakemake.config['sector']['heat_pump_sink_T'] - source_T
delta_T = snakemake.config["sector"]["heat_pump_sink_T"] - source_T
cop = coefficient_of_performance(delta_T, source)

214
scripts/build_energy_totals.py
View File

@ -1,25 +1,31 @@
# -*- coding: utf-8 -*-
import logging
logger = logging.getLogger(__name__)
from functools import partial
from tqdm import tqdm
from helper import mute_print
import multiprocessing as mp
import pandas as pd
from functools import partial
import geopandas as gpd
import numpy as np
import pandas as pd
from helper import mute_print
from tqdm import tqdm
idx = pd.IndexSlice
def cartesian(s1, s2):
"""Cartesian product of two pd.Series"""
"""
Cartesian product of two pd.Series.
"""
return pd.DataFrame(np.outer(s1, s2), index=s1.index, columns=s2.index)
def reverse(dictionary):
"""reverses a keys and values of a dictionary"""
"""
Reverses a keys and values of a dictionary.
"""
return {v: k for k, v in dictionary.items()}
@ -122,7 +128,7 @@ to_ipcc = {
"total energy": "1 - Energy",
"industrial processes": "2 - Industrial Processes and Product Use",
"agriculture": "3 - Agriculture",
"agriculture, forestry and fishing": '1.A.4.c - Agriculture/Forestry/Fishing',
"agriculture, forestry and fishing": "1.A.4.c - Agriculture/Forestry/Fishing",
"LULUCF": "4 - Land Use, Land-Use Change and Forestry",
"waste management": "5 - Waste management",
"other": "6 - Other Sector",
@ -131,12 +137,15 @@ to_ipcc = {
"total woL": "Total (without LULUCF)",
}
def build_eurostat(input_eurostat, countries, report_year, year):
"""Return multi-index for all countries' energy data in TWh/a."""
def build_eurostat(input_eurostat, countries, report_year, year):
"""
Return multi-index for all countries' energy data in TWh/a.
"""
filenames = {
2016: f"/{year}-Energy-Balances-June2016edition.xlsx",
2017: f"/{year}-ENERGY-BALANCES-June2017edition.xlsx"
2017: f"/{year}-ENERGY-BALANCES-June2017edition.xlsx",
}
with mute_print():
@ -149,9 +158,11 @@ def build_eurostat(input_eurostat, countries, report_year, year):
# sorted_index necessary for slicing
lookup = eurostat_country_to_alpha2
labelled_dfs = {lookup[df.columns[0]]: df
for df in dfs.values()
if lookup[df.columns[0]] in countries}
labelled_dfs = {
lookup[df.columns[0]]: df
for df in dfs.values()
if lookup[df.columns[0]] in countries
}
df = pd.concat(labelled_dfs, sort=True).sort_index()
# drop non-numeric and country columns
@ -167,11 +178,13 @@ def build_eurostat(input_eurostat, countries, report_year, year):
def build_swiss(year):
"""Return a pd.Series of Swiss energy data in TWh/a"""
"""
Return a pd.Series of Swiss energy data in TWh/a.
"""
fn = snakemake.input.swiss
df = pd.read_csv(fn, index_col=[0,1]).loc["CH", str(year)]
df = pd.read_csv(fn, index_col=[0, 1]).loc["CH", str(year)]
# convert PJ/a to TWh/a
df /= 3.6
@ -180,7 +193,6 @@ def build_swiss(year):
def idees_per_country(ct, year):
base_dir = snakemake.input.idees
ct_totals = {}
@ -220,7 +232,7 @@ def idees_per_country(ct, year):
assert df.index[46] == "Derived heat"
ct_totals["derived heat residential"] = df[46]
assert df.index[50] == 'Thermal uses'
assert df.index[50] == "Thermal uses"
ct_totals["thermal uses residential"] = df[50]
# services
@ -253,10 +265,9 @@ def idees_per_country(ct, year):
assert df.index[49] == "Derived heat"
ct_totals["derived heat services"] = df[49]
assert df.index[53] == 'Thermal uses'
assert df.index[53] == "Thermal uses"
ct_totals["thermal uses services"] = df[53]
# agriculture, forestry and fishing
start = "Detailed split of energy consumption (ktoe)"
@ -268,7 +279,7 @@ def idees_per_country(ct, year):
"Lighting",
"Ventilation",
"Specific electricity uses",
"Pumping devices (electric)"
"Pumping devices (electric)",
]
ct_totals["total agriculture electricity"] = df[rows].sum()
@ -352,7 +363,7 @@ def idees_per_country(ct, year):
assert df.index[8] == "International - Intra-EU"
assert df.index[9] == "International - Extra-EU"
ct_totals["total international aviation passenger"] = df[[8,9]].sum()
ct_totals["total international aviation passenger"] = df[[8, 9]].sum()
assert df.index[11] == "Domestic and International - Intra-EU"
ct_totals["total domestic aviation freight"] = df[11]
@ -360,11 +371,15 @@ def idees_per_country(ct, year):
assert df.index[12] == "International - Extra-EU"
ct_totals["total international aviation freight"] = df[12]
ct_totals["total domestic aviation"] = ct_totals["total domestic aviation freight"] \
+ ct_totals["total domestic aviation passenger"]
ct_totals["total domestic aviation"] = (
ct_totals["total domestic aviation freight"]
+ ct_totals["total domestic aviation passenger"]
)
ct_totals["total international aviation"] = ct_totals["total international aviation freight"] \
+ ct_totals["total international aviation passenger"]
ct_totals["total international aviation"] = (
ct_totals["total international aviation freight"]
+ ct_totals["total international aviation passenger"]
)
df = pd.read_excel(fn_transport, "TrNavi_ene", index_col=0)[year]
@ -380,17 +395,19 @@ def idees_per_country(ct, year):
def build_idees(countries, year):
nprocesses = snakemake.threads
func = partial(idees_per_country, year=year)
tqdm_kwargs = dict(ascii=False, unit=' country', total=len(countries),
desc='Build from IDEES database')
tqdm_kwargs = dict(
ascii=False,
unit=" country",
total=len(countries),
desc="Build from IDEES database",
)
with mute_print():
with mp.Pool(processes=nprocesses) as pool:
totals_list = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))
totals = pd.concat(totals_list, axis=1)
# convert ktoe to TWh
@ -401,19 +418,17 @@ def build_idees(countries, year):
totals.loc["passenger car efficiency"] *= 10
# district heating share
district_heat = totals.loc[["derived heat residential",
"derived heat services"]].sum()
total_heat = totals.loc[["thermal uses residential",
"thermal uses services"]].sum()
district_heat = totals.loc[
["derived heat residential", "derived heat services"]
].sum()
total_heat = totals.loc[["thermal uses residential", "thermal uses services"]].sum()
totals.loc["district heat share"] = district_heat.div(total_heat)
return totals.T
def build_energy_totals(countries, eurostat, swiss, idees):
eurostat_fuels = {"electricity": "Electricity",
"total": "Total all products"}
eurostat_fuels = {"electricity": "Electricity", "total": "Total all products"}
to_drop = ["passenger cars", "passenger car efficiency"]
df = idees.reindex(countries).drop(to_drop, axis=1)
@ -439,36 +454,47 @@ def build_energy_totals(countries, eurostat, swiss, idees):
uses = ["space", "cooking", "water"]
for sector in ["residential", "services", "road", "rail"]:
eurostat_sector = sector.capitalize()
# fuel use
for fuel in ["electricity", "total"]:
slicer = idx[to_fill, :, :, eurostat_sector]
fill_values = eurostat.loc[slicer, eurostat_fuels[fuel]].groupby(level=0).sum()
fill_values = (
eurostat.loc[slicer, eurostat_fuels[fuel]].groupby(level=0).sum()
)
df.loc[to_fill, f"{fuel} {sector}"] = fill_values
for sector in ["residential", "services"]:
# electric use
for use in uses:
fuel_use = df[f"electricity {sector} {use}"]
fuel = df[f"electricity {sector}"]
avg = fuel_use.div(fuel).mean()
logger.debug(f"{sector}: average fraction of electricity for {use} is {avg:.3f}")
df.loc[to_fill, f"electricity {sector} {use}"] = avg * df.loc[to_fill, f"electricity {sector}"]
logger.debug(
f"{sector}: average fraction of electricity for {use} is {avg:.3f}"
)
df.loc[to_fill, f"electricity {sector} {use}"] = (
avg * df.loc[to_fill, f"electricity {sector}"]
)
# non-electric use
for use in uses:
nonelectric_use = df[f"total {sector} {use}"] - df[f"electricity {sector} {use}"]
nonelectric_use = (
df[f"total {sector} {use}"] - df[f"electricity {sector} {use}"]
)
nonelectric = df[f"total {sector}"] - df[f"electricity {sector}"]
avg = nonelectric_use.div(nonelectric).mean()
logger.debug(f"{sector}: average fraction of non-electric for {use} is {avg:.3f}")
logger.debug(
f"{sector}: average fraction of non-electric for {use} is {avg:.3f}"
)
electric_use = df.loc[to_fill, f"electricity {sector} {use}"]
nonelectric = df.loc[to_fill, f"total {sector}"] - df.loc[to_fill, f"electricity {sector}"]
nonelectric = (
df.loc[to_fill, f"total {sector}"]
- df.loc[to_fill, f"electricity {sector}"]
)
df.loc[to_fill, f"total {sector} {use}"] = electric_use + avg * nonelectric
# Fix Norway space and water heating fractions
@ -480,17 +506,25 @@ def build_energy_totals(countries, eurostat, swiss, idees):
no_norway = df.drop("NO")
for sector in ["residential", "services"]:
# assume non-electric is heating
nonelectric = df.loc["NO", f"total {sector}"] - df.loc["NO", f"electricity {sector}"]
nonelectric = (
df.loc["NO", f"total {sector}"] - df.loc["NO", f"electricity {sector}"]
)
total_heating = nonelectric / (1 - elec_fraction)
for use in uses:
nonelectric_use = no_norway[f"total {sector} {use}"] - no_norway[f"electricity {sector} {use}"]
nonelectric = no_norway[f"total {sector}"] - no_norway[f"electricity {sector}"]
nonelectric_use = (
no_norway[f"total {sector} {use}"]
- no_norway[f"electricity {sector} {use}"]
)
nonelectric = (
no_norway[f"total {sector}"] - no_norway[f"electricity {sector}"]
)
fraction = nonelectric_use.div(nonelectric).mean()
df.loc["NO", f"total {sector} {use}"] = total_heating * fraction
df.loc["NO", f"electricity {sector} {use}"] = total_heating * fraction * elec_fraction
df.loc["NO", f"electricity {sector} {use}"] = (
total_heating * fraction * elec_fraction
)
# Missing aviation
@ -517,10 +551,7 @@ def build_energy_totals(countries, eurostat, swiss, idees):
f"{fuel} light duty road freight",
]
if fuel == "total":
selection.extend([
f"{fuel} two-wheel",
f"{fuel} heavy duty road freight"
])
selection.extend([f"{fuel} two-wheel", f"{fuel} heavy duty road freight"])
road = df[selection].sum()
road_fraction = road / road.sum()
fill_values = cartesian(df.loc[missing, f"{fuel} road"], road_fraction)
@ -544,33 +575,40 @@ def build_energy_totals(countries, eurostat, swiss, idees):
]
aviation = df[selection].sum()
aviation_fraction = aviation / aviation.sum()
fill_values = cartesian(df.loc[missing, f"total {destination} aviation"], aviation_fraction)
fill_values = cartesian(
df.loc[missing, f"total {destination} aviation"], aviation_fraction
)
df.loc[missing, aviation_fraction.index] = fill_values
for purpose in ["passenger", "freight"]:
attrs = [f"total domestic aviation {purpose}", f"total international aviation {purpose}"]
df.loc[missing, f"total aviation {purpose}"] = df.loc[missing, attrs].sum(axis=1)
attrs = [
f"total domestic aviation {purpose}",
f"total international aviation {purpose}",
]
df.loc[missing, f"total aviation {purpose}"] = df.loc[missing, attrs].sum(
axis=1
)
if "BA" in df.index:
# fill missing data for BA (services and road energy data)
# proportional to RS with ratio of total residential demand
missing = df.loc["BA"] == 0.0
ratio = df.at["BA", "total residential"] / df.at["RS", "total residential"]
df.loc['BA', missing] = ratio * df.loc["RS", missing]
df.loc["BA", missing] = ratio * df.loc["RS", missing]
# Missing district heating share
dh_share = pd.read_csv(snakemake.input.district_heat_share,
index_col=0, usecols=[0, 1])
dh_share = pd.read_csv(
snakemake.input.district_heat_share, index_col=0, usecols=[0, 1]
)
# make conservative assumption and take minimum from both data sets
df["district heat share"] = (pd.concat([df["district heat share"],
dh_share.reindex(index=df.index)/100],
axis=1).min(axis=1))
df["district heat share"] = pd.concat(
[df["district heat share"], dh_share.reindex(index=df.index) / 100], axis=1
).min(axis=1)
return df
def build_eea_co2(input_co2, year=1990, emissions_scope="CO2"):
# https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16
# downloaded 201228 (modified by EEA last on 201221)
df = pd.read_csv(input_co2, encoding="latin-1", low_memory=False)
@ -589,7 +627,7 @@ def build_eea_co2(input_co2, year=1990, emissions_scope="CO2"):
df.loc[slicer, "emissions"]
.unstack("Sector_name")
.rename(columns=reverse(to_ipcc))
.droplevel([1,2])
.droplevel([1, 2])
)
emissions.rename(index={"EUA": "EU28", "UK": "GB"}, inplace=True)
@ -604,13 +642,20 @@ def build_eea_co2(input_co2, year=1990, emissions_scope="CO2"):
"international aviation",
"domestic navigation",
"international navigation",
"agriculture, forestry and fishing"
"agriculture, forestry and fishing",
]
emissions["industrial non-elec"] = emissions["total energy"] - emissions[to_subtract].sum(axis=1)
emissions["industrial non-elec"] = emissions["total energy"] - emissions[
to_subtract
].sum(axis=1)
emissions["agriculture"] += emissions["agriculture, forestry and fishing"]
to_drop = ["total energy", "total wL", "total woL", "agriculture, forestry and fishing"]
to_drop = [
"total energy",
"total wL",
"total woL",
"agriculture, forestry and fishing",
]
emissions.drop(columns=to_drop, inplace=True)
# convert from Gg to Mt
@ -618,7 +663,6 @@ def build_eea_co2(input_co2, year=1990, emissions_scope="CO2"):
def build_eurostat_co2(input_eurostat, countries, report_year, year=1990):
eurostat = build_eurostat(input_eurostat, countries, report_year, year)
specific_emissions = pd.Series(index=eurostat.columns, dtype=float)
@ -637,13 +681,16 @@ def build_eurostat_co2(input_eurostat, countries, report_year, year=1990):
def build_co2_totals(countries, eea_co2, eurostat_co2):
co2 = eea_co2.reindex(countries)
for ct in countries.intersection(["BA", "RS", "AL", "ME", "MK"]):
mappings = {
"electricity": (ct, "+", "Conventional Thermal Power Stations", "of which From Coal"),
"electricity": (
ct,
"+",
"Conventional Thermal Power Stations",
"of which From Coal",
),
"residential non-elec": (ct, "+", "+", "Residential"),
"services non-elec": (ct, "+", "+", "Services"),
"road non-elec": (ct, "+", "+", "Road"),
@ -655,7 +702,8 @@ def build_co2_totals(countries, eea_co2, eurostat_co2):
# does not include industrial process emissions or fuel processing/refining
"industrial non-elec": (ct, "+", "Industry"),
# does not include non-energy emissions
"agriculture": (eurostat_co2.index.get_level_values(0) == ct) & eurostat_co2.index.isin(["Agriculture / Forestry", "Fishing"], level=3),
"agriculture": (eurostat_co2.index.get_level_values(0) == ct)
& eurostat_co2.index.isin(["Agriculture / Forestry", "Fishing"], level=3),
}
for i, mi in mappings.items():
@ -665,7 +713,6 @@ def build_co2_totals(countries, eea_co2, eurostat_co2):
def build_transport_data(countries, population, idees):
transport_data = pd.DataFrame(index=countries)
# collect number of cars
@ -676,7 +723,9 @@ def build_transport_data(countries, population, idees):
transport_data.at["CH", "number cars"] = 4.136e6
missing = transport_data.index[transport_data["number cars"].isna()]
logger.info(f"Missing data on cars from:\n{list(missing)}\nFilling gaps with averaged data.")
logger.info(
f"Missing data on cars from:\n{list(missing)}\nFilling gaps with averaged data."
)
cars_pp = transport_data["number cars"] / population
transport_data.loc[missing, "number cars"] = cars_pp.mean() * population
@ -686,7 +735,9 @@ def build_transport_data(countries, population, idees):
transport_data["average fuel efficiency"] = idees["passenger car efficiency"]
missing = transport_data.index[transport_data["average fuel efficiency"].isna()]
logger.info(f"Missing data on fuel efficiency from:\n{list(missing)}\nFilling gapswith averaged data.")
logger.info(
f"Missing data on fuel efficiency from:\n{list(missing)}\nFilling gapswith averaged data."
)
fill_values = transport_data["average fuel efficiency"].mean()
transport_data.loc[missing, "average fuel efficiency"] = fill_values
@ -695,11 +746,12 @@ def build_transport_data(countries, population, idees):
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake('build_energy_totals')
logging.basicConfig(level=snakemake.config['logging_level'])
snakemake = mock_snakemake("build_energy_totals")
logging.basicConfig(level=snakemake.config["logging_level"])
config = snakemake.config["energy"]
@ -722,7 +774,9 @@ if __name__ == "__main__":
base_year_emissions = config["base_emissions_year"]
emissions_scope = snakemake.config["energy"]["emissions"]
eea_co2 = build_eea_co2(snakemake.input.co2, base_year_emissions, emissions_scope)
eurostat_co2 = build_eurostat_co2(input_eurostat, countries, report_year, base_year_emissions)
eurostat_co2 = build_eurostat_co2(
input_eurostat, countries, report_year, base_year_emissions
)
co2 = build_co2_totals(countries, eea_co2, eurostat_co2)
co2.to_csv(snakemake.output.co2_name)

74
scripts/build_gas_input_locations.py
View File

@ -1,15 +1,17 @@
# -*- coding: utf-8 -*-
"""
Build import locations for fossil gas from entry-points, LNG terminals and production sites.
Build import locations for fossil gas from entry-points, LNG terminals and
production sites.
"""
import logging
logger = logging.getLogger(__name__)
import pandas as pd
import geopandas as gpd
from shapely import wkt
import pandas as pd
from cluster_gas_network import load_bus_regions
from shapely import wkt
def read_scigrid_gas(fn):
@ -20,24 +22,25 @@ def read_scigrid_gas(fn):
def build_gem_lng_data(lng_fn):
df = pd.read_excel(lng_fn[0], sheet_name='LNG terminals - data')
df = pd.read_excel(lng_fn[0], sheet_name="LNG terminals - data")
df = df.set_index("ComboID")
remove_status = ['Cancelled']
remove_country = ['Cyprus','Turkey']
remove_terminal = ['Puerto de la Luz LNG Terminal', 'Gran Canaria LNG Terminal']
remove_status = ["Cancelled"]
remove_country = ["Cyprus", "Turkey"]
remove_terminal = ["Puerto de la Luz LNG Terminal", "Gran Canaria LNG Terminal"]
df = df.query("Status != 'Cancelled' \
df = df.query(
"Status != 'Cancelled' \
& Country != @remove_country \
& TerminalName != @remove_terminal \
& CapacityInMtpa != '--'")
& CapacityInMtpa != '--'"
)
geometry = gpd.points_from_xy(df['Longitude'], df['Latitude'])
geometry = gpd.points_from_xy(df["Longitude"], df["Latitude"])
return gpd.GeoDataFrame(df, geometry=geometry, crs="EPSG:4326")
def build_gas_input_locations(lng_fn, entry_fn, prod_fn, countries):
# LNG terminals
lng = build_gem_lng_data(lng_fn)
@ -45,21 +48,19 @@ def build_gas_input_locations(lng_fn, entry_fn, prod_fn, countries):
entry = read_scigrid_gas(entry_fn)
entry["from_country"] = entry.from_country.str.rstrip()
entry = entry.loc[
~(entry.from_country.isin(countries) & entry.to_country.isin(countries)) & # only take non-EU entries
~entry.name.str.contains("Tegelen") | # malformed datapoint
(entry.from_country == "NO") # entries from NO to GB
~(entry.from_country.isin(countries) & entry.to_country.isin(countries))
& ~entry.name.str.contains("Tegelen") # only take non-EU entries
| (entry.from_country == "NO") # malformed datapoint # entries from NO to GB
]
# production sites inside the model scope
prod = read_scigrid_gas(prod_fn)
prod = prod.loc[
(prod.geometry.y > 35) &
(prod.geometry.x < 30) &
(prod.country_code != "DE")
(prod.geometry.y > 35) & (prod.geometry.x < 30) & (prod.country_code != "DE")
]
mcm_per_day_to_mw = 437.5 # MCM/day to MWh/h
mtpa_to_mw = 1649.224 # mtpa to MWh/h
mcm_per_day_to_mw = 437.5 # MCM/day to MWh/h
mtpa_to_mw = 1649.224 # mtpa to MWh/h
lng["p_nom"] = lng["CapacityInMtpa"] * mtpa_to_mw
entry["p_nom"] = entry["max_cap_from_to_M_m3_per_d"] * mcm_per_day_to_mw
prod["p_nom"] = prod["max_supply_M_m3_per_d"] * mcm_per_day_to_mw
@ -74,28 +75,29 @@ def build_gas_input_locations(lng_fn, entry_fn, prod_fn, countries):
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'build_gas_input_locations',
simpl='',
clusters='37',
"build_gas_input_locations",
simpl="",
clusters="37",
)
logging.basicConfig(level=snakemake.config['logging_level'])
logging.basicConfig(level=snakemake.config["logging_level"])
regions = load_bus_regions(
snakemake.input.regions_onshore,
snakemake.input.regions_offshore
snakemake.input.regions_onshore, snakemake.input.regions_offshore
)
# add a buffer to eastern countries because some
# entry points are still in Russian or Ukrainian territory.
buffer = 9000 # meters
eastern_countries = ['FI', 'EE', 'LT', 'LV', 'PL', 'SK', 'HU', 'RO']
buffer = 9000 # meters
eastern_countries = ["FI", "EE", "LT", "LV", "PL", "SK", "HU", "RO"]
add_buffer_b = regions.index.str[:2].isin(eastern_countries)
regions.loc[add_buffer_b] = regions[add_buffer_b].to_crs(3035).buffer(buffer).to_crs(4326)
regions.loc[add_buffer_b] = (
regions[add_buffer_b].to_crs(3035).buffer(buffer).to_crs(4326)
)
countries = regions.index.str[:2].unique().str.replace("GB", "UK")
@ -103,16 +105,18 @@ if __name__ == "__main__":
snakemake.input.lng,
snakemake.input.entry,
snakemake.input.production,
countries
countries,
)
gas_input_nodes = gpd.sjoin(gas_input_locations, regions, how='left')
gas_input_nodes = gpd.sjoin(gas_input_locations, regions, how="left")
gas_input_nodes.rename(columns={"index_right": "bus"}, inplace=True)
gas_input_nodes.to_file(snakemake.output.gas_input_nodes, driver='GeoJSON')
gas_input_nodes.to_file(snakemake.output.gas_input_nodes, driver="GeoJSON")
gas_input_nodes_s = gas_input_nodes.groupby(["bus", "type"])["p_nom"].sum().unstack()
gas_input_nodes_s = (
gas_input_nodes.groupby(["bus", "type"])["p_nom"].sum().unstack()
)
gas_input_nodes_s.columns.name = "p_nom"
gas_input_nodes_s.to_csv(snakemake.output.gas_input_nodes_simplified)

76
scripts/build_gas_network.py
View File

@ -1,16 +1,22 @@
"""Preprocess gas network based on data from bthe SciGRID Gas project (https://www.gas.scigrid.de/)."""
# -*- coding: utf-8 -*-
"""
Preprocess gas network based on data from bthe SciGRID Gas project
(https://www.gas.scigrid.de/).
"""
import logging
logger = logging.getLogger(__name__)
import pandas as pd
import geopandas as gpd
from shapely.geometry import Point
import pandas as pd
from pypsa.geo import haversine_pts
from shapely.geometry import Point
def diameter_to_capacity(pipe_diameter_mm):
"""Calculate pipe capacity in MW based on diameter in mm.
"""
Calculate pipe capacity in MW based on diameter in mm.
20 inch (500 mm) 50 bar -> 1.5 GW CH4 pipe capacity (LHV)
24 inch (600 mm) 50 bar -> 5 GW CH4 pipe capacity (LHV)
@ -59,22 +65,31 @@ def prepare_dataset(
length_factor=1.5,
correction_threshold_length=4,
correction_threshold_p_nom=8,
bidirectional_below=10
bidirectional_below=10,
):
# extract start and end from LineString
df["point0"] = df.geometry.apply(lambda x: Point(x.coords[0]))
df["point1"] = df.geometry.apply(lambda x: Point(x.coords[-1]))
conversion_factor = 437.5 # MCM/day to MWh/h
conversion_factor = 437.5 # MCM/day to MWh/h
df["p_nom"] = df.max_cap_M_m3_per_d * conversion_factor
# for inferred diameters, assume 500 mm rather than 900 mm (more conservative)
df.loc[df.diameter_mm_method != 'raw', "diameter_mm"] = 500.
df.loc[df.diameter_mm_method != "raw", "diameter_mm"] = 500.0
keep = ["name", "diameter_mm", "is_H_gas", "is_bothDirection",
"length_km", "p_nom", "max_pressure_bar",
"start_year", "point0", "point1", "geometry"]
keep = [
"name",
"diameter_mm",
"is_H_gas",
"is_bothDirection",
"length_km",
"p_nom",
"max_pressure_bar",
"start_year",
"point0",
"point1",
"geometry",
]
to_rename = {
"is_bothDirection": "bidirectional",
"is_H_gas": "H_gas",
@ -96,37 +111,40 @@ def prepare_dataset(
df["p_nom_diameter"] = df.diameter_mm.apply(diameter_to_capacity)
ratio = df.p_nom / df.p_nom_diameter
not_nordstream = df.max_pressure_bar < 220
df.p_nom.update(df.p_nom_diameter.where(
(df.p_nom <= 500) |
((ratio > correction_threshold_p_nom) & not_nordstream) |
((ratio < 1 / correction_threshold_p_nom) & not_nordstream)
))
df.p_nom.update(
df.p_nom_diameter.where(
(df.p_nom <= 500)
| ((ratio > correction_threshold_p_nom) & not_nordstream)
| ((ratio < 1 / correction_threshold_p_nom) & not_nordstream)
)
)
# lines which have way too discrepant line lengths
# get assigned haversine length * length factor
df["length_haversine"] = df.apply(
lambda p: length_factor * haversine_pts(
[p.point0.x, p.point0.y],
[p.point1.x, p.point1.y]
), axis=1
lambda p: length_factor
* haversine_pts([p.point0.x, p.point0.y], [p.point1.x, p.point1.y]),
axis=1,
)
ratio = df.eval("length / length_haversine")
df["length"].update(df.length_haversine.where(
(df["length"] < 20) |
(ratio > correction_threshold_length) |
(ratio < 1 / correction_threshold_length)
))
df["length"].update(
df.length_haversine.where(
(df["length"] < 20)
| (ratio > correction_threshold_length)
| (ratio < 1 / correction_threshold_length)
)
)
return df
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake('build_gas_network')
logging.basicConfig(level=snakemake.config['logging_level'])
snakemake = mock_snakemake("build_gas_network")
logging.basicConfig(level=snakemake.config["logging_level"])
gas_network = load_dataset(snakemake.input.gas_network)

38
scripts/build_heat_demand.py
View File

@ -1,18 +1,22 @@
"""Build heat demand time series."""
# -*- coding: utf-8 -*-
"""
Build heat demand time series.
"""
import geopandas as gpd
import atlite
import geopandas as gpd
import numpy as np
import pandas as pd
import xarray as xr
import numpy as np
from dask.distributed import Client, LocalCluster
if __name__ == '__main__':
if 'snakemake' not in globals():
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'build_heat_demands',
simpl='',
"build_heat_demands",
simpl="",
clusters=48,
)
@ -20,23 +24,29 @@ if __name__ == '__main__':
cluster = LocalCluster(n_workers=nprocesses, threads_per_worker=1)
client = Client(cluster, asynchronous=True)
time = pd.date_range(freq='h', **snakemake.config['snapshots'])
cutout_config = snakemake.config['atlite']['cutout']
time = pd.date_range(freq="h", **snakemake.config["snapshots"])
cutout_config = snakemake.config["atlite"]["cutout"]
cutout = atlite.Cutout(cutout_config).sel(time=time)
clustered_regions = gpd.read_file(
snakemake.input.regions_onshore).set_index('name').buffer(0).squeeze()
clustered_regions = (
gpd.read_file(snakemake.input.regions_onshore)
.set_index("name")
.buffer(0)
.squeeze()
)
I = cutout.indicatormatrix(clustered_regions)
pop_layout = xr.open_dataarray(snakemake.input.pop_layout)
stacked_pop = pop_layout.stack(spatial=('y', 'x'))
stacked_pop = pop_layout.stack(spatial=("y", "x"))
M = I.T.dot(np.diag(I.dot(stacked_pop)))
heat_demand = cutout.heat_demand(
matrix=M.T, index=clustered_regions.index,
matrix=M.T,
index=clustered_regions.index,
dask_kwargs=dict(scheduler=client),
show_progress=False)
show_progress=False,
)
heat_demand.to_netcdf(snakemake.output.heat_demand)

81
scripts/build_industrial_distribution_key.py
View File

@ -1,40 +1,47 @@
"""Build industrial distribution keys from hotmaps database."""
# -*- coding: utf-8 -*-
"""
Build industrial distribution keys from hotmaps database.
"""
import logging
logger = logging.getLogger(__name__)
import uuid
import pandas as pd
import geopandas as gpd
from itertools import product
import geopandas as gpd
import pandas as pd
from packaging.version import Version, parse
def locate_missing_industrial_sites(df):
"""
Locate industrial sites without valid locations based on
city and countries. Should only be used if the model's
spatial resolution is coarser than individual cities.
Locate industrial sites without valid locations based on city and
countries.
Should only be used if the model's spatial resolution is coarser
than individual cities.
"""
try:
from geopy.geocoders import Nominatim
from geopy.extra.rate_limiter import RateLimiter
from geopy.geocoders import Nominatim
except:
raise ModuleNotFoundError("Optional dependency 'geopy' not found."
"Install via 'conda install -c conda-forge geopy'"
"or set 'industry: hotmaps_locate_missing: false'.")
raise ModuleNotFoundError(
"Optional dependency 'geopy' not found."
"Install via 'conda install -c conda-forge geopy'"
"or set 'industry: hotmaps_locate_missing: false'."
)
locator = Nominatim(user_agent=str(uuid.uuid4()))
geocode = RateLimiter(locator.geocode, min_delay_seconds=2)
def locate_missing(s):
if pd.isna(s.City) or s.City == "CONFIDENTIAL":
return None
loc = geocode([s.City, s.Country], geometry='wkt')
loc = geocode([s.City, s.Country], geometry="wkt")
if loc is not None:
logger.debug(f"Found:\t{loc}\nFor:\t{s['City']}, {s['Country']}\n")
return f"POINT({loc.longitude} {loc.latitude})"
@ -42,14 +49,16 @@ def locate_missing_industrial_sites(df):
return None
missing = df.index[df.geom.isna()]
df.loc[missing, 'coordinates'] = df.loc[missing].apply(locate_missing, axis=1)
df.loc[missing, "coordinates"] = df.loc[missing].apply(locate_missing, axis=1)
# report stats
num_still_missing = df.coordinates.isna().sum()
num_found = len(missing) - num_still_missing
share_missing = len(missing) / len(df) * 100
share_still_missing = num_still_missing / len(df) * 100
logger.warning(f"Found {num_found} missing locations. \nShare of missing locations reduced from {share_missing:.2f}% to {share_still_missing:.2f}%.")
logger.warning(
f"Found {num_found} missing locations. \nShare of missing locations reduced from {share_missing:.2f}% to {share_still_missing:.2f}%."
)
return df
@ -61,19 +70,23 @@ def prepare_hotmaps_database(regions):
df = pd.read_csv(snakemake.input.hotmaps_industrial_database, sep=";", index_col=0)
df[["srid", "coordinates"]] = df.geom.str.split(';', expand=True)
df[["srid", "coordinates"]] = df.geom.str.split(";", expand=True)
if snakemake.config['industry'].get('hotmaps_locate_missing', False):
if snakemake.config["industry"].get("hotmaps_locate_missing", False):
df = locate_missing_industrial_sites(df)
# remove those sites without valid locations
df.drop(df.index[df.coordinates.isna()], inplace=True)
df['coordinates'] = gpd.GeoSeries.from_wkt(df['coordinates'])
df["coordinates"] = gpd.GeoSeries.from_wkt(df["coordinates"])
gdf = gpd.GeoDataFrame(df, geometry='coordinates', crs="EPSG:4326")
gdf = gpd.GeoDataFrame(df, geometry="coordinates", crs="EPSG:4326")
kws = dict(op="within") if parse(gpd.__version__) < Version('0.10') else dict(predicate="within")
kws = (
dict(op="within")
if parse(gpd.__version__) < Version("0.10")
else dict(predicate="within")
)
gdf = gpd.sjoin(gdf, regions, how="inner", **kws)
gdf.rename(columns={"index_right": "bus"}, inplace=True)
@ -83,7 +96,9 @@ def prepare_hotmaps_database(regions):
def build_nodal_distribution_key(hotmaps, regions):
"""Build nodal distribution keys for each sector."""
"""
Build nodal distribution keys for each sector.
"""
sectors = hotmaps.Subsector.unique()
countries = regions.index.str[:2].unique()
@ -91,12 +106,11 @@ def build_nodal_distribution_key(hotmaps, regions):
keys = pd.DataFrame(index=regions.index, columns=sectors, dtype=float)
pop = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
pop['country'] = pop.index.str[:2]
ct_total = pop.total.groupby(pop['country']).sum()
keys['population'] = pop.total / pop.country.map(ct_total)
pop["country"] = pop.index.str[:2]
ct_total = pop.total.groupby(pop["country"]).sum()
keys["population"] = pop.total / pop.country.map(ct_total)
for sector, country in product(sectors, countries):
regions_ct = regions.index[regions.index.str.contains(country)]
facilities = hotmaps.query("country == @country and Subsector == @sector")
@ -106,12 +120,12 @@ def build_nodal_distribution_key(hotmaps, regions):
if emissions.sum() == 0:
key = pd.Series(1 / len(facilities), facilities.index)
else:
#BEWARE: this is a strong assumption
# BEWARE: this is a strong assumption
emissions = emissions.fillna(emissions.mean())
key = emissions / emissions.sum()
key = key.groupby(facilities.bus).sum().reindex(regions_ct, fill_value=0.)
key = key.groupby(facilities.bus).sum().reindex(regions_ct, fill_value=0.0)
else:
key = keys.loc[regions_ct, 'population']
key = keys.loc[regions_ct, "population"]
keys.loc[regions_ct, sector] = key
@ -119,17 +133,18 @@ def build_nodal_distribution_key(hotmaps, regions):
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'build_industrial_distribution_key',
simpl='',
"build_industrial_distribution_key",
simpl="",
clusters=48,
)
logging.basicConfig(level=snakemake.config['logging_level'])
logging.basicConfig(level=snakemake.config["logging_level"])
regions = gpd.read_file(snakemake.input.regions_onshore).set_index('name')
regions = gpd.read_file(snakemake.input.regions_onshore).set_index("name")
hotmaps = prepare_hotmaps_database(regions)

192
scripts/build_industrial_energy_demand_per_country_today.py
View File

@ -1,84 +1,116 @@
"""Build industrial energy demand per country."""
# -*- coding: utf-8 -*-
"""
Build industrial energy demand per country.
"""
import multiprocessing as mp
import pandas as pd
import multiprocessing as mp
from tqdm import tqdm
ktoe_to_twh = 0.011630
# name in JRC-IDEES Energy Balances
sector_sheets = {'Integrated steelworks': 'cisb',
'Electric arc': 'cise',
'Alumina production': 'cnfa',
'Aluminium - primary production': 'cnfp',
'Aluminium - secondary production': 'cnfs',
'Other non-ferrous metals': 'cnfo',
'Basic chemicals': 'cbch',
'Other chemicals': 'coch',
'Pharmaceutical products etc.': 'cpha',
'Basic chemicals feedstock': 'cpch',
'Cement': 'ccem',
'Ceramics & other NMM': 'ccer',
'Glass production': 'cgla',
'Pulp production': 'cpul',
'Paper production': 'cpap',
'Printing and media reproduction': 'cprp',
'Food, beverages and tobacco': 'cfbt',
'Transport Equipment': 'ctre',
'Machinery Equipment': 'cmae',
'Textiles and leather': 'ctel',
'Wood and wood products': 'cwwp',
'Mining and quarrying': 'cmiq',
'Construction': 'ccon',
'Non-specified': 'cnsi',
}
sector_sheets = {
"Integrated steelworks": "cisb",
"Electric arc": "cise",
"Alumina production": "cnfa",
"Aluminium - primary production": "cnfp",
"Aluminium - secondary production": "cnfs",
"Other non-ferrous metals": "cnfo",
"Basic chemicals": "cbch",
"Other chemicals": "coch",
"Pharmaceutical products etc.": "cpha",
"Basic chemicals feedstock": "cpch",
"Cement": "ccem",
"Ceramics & other NMM": "ccer",
"Glass production": "cgla",
"Pulp production": "cpul",
"Paper production": "cpap",
"Printing and media reproduction": "cprp",
"Food, beverages and tobacco": "cfbt",
"Transport Equipment": "ctre",
"Machinery Equipment": "cmae",
"Textiles and leather": "ctel",
"Wood and wood products": "cwwp",
"Mining and quarrying": "cmiq",
"Construction": "ccon",
"Non-specified": "cnsi",
}
fuels = {'All Products': 'all',
'Solid Fuels': 'solid',
'Total petroleum products (without biofuels)': 'liquid',
'Gases': 'gas',
'Nuclear heat': 'heat',
'Derived heat': 'heat',
'Biomass and Renewable wastes': 'biomass',
'Wastes (non-renewable)': 'waste',
'Electricity': 'electricity'
}
fuels = {
"All Products": "all",
"Solid Fuels": "solid",
"Total petroleum products (without biofuels)": "liquid",
"Gases": "gas",
"Nuclear heat": "heat",
"Derived heat": "heat",
"Biomass and Renewable wastes": "biomass",
"Wastes (non-renewable)": "waste",
"Electricity": "electricity",
}
eu28 = ['FR', 'DE', 'GB', 'IT', 'ES', 'PL', 'SE', 'NL', 'BE', 'FI',
'DK', 'PT', 'RO', 'AT', 'BG', 'EE', 'GR', 'LV', 'CZ',
'HU', 'IE', 'SK', 'LT', 'HR', 'LU', 'SI', 'CY', 'MT']
eu28 = [
"FR",
"DE",
"GB",
"IT",
"ES",
"PL",
"SE",
"NL",
"BE",
"FI",
"DK",
"PT",
"RO",
"AT",
"BG",
"EE",
"GR",
"LV",
"CZ",
"HU",
"IE",
"SK",
"LT",
"HR",
"LU",
"SI",
"CY",
"MT",
]
jrc_names = {"GR": "EL", "GB": "UK"}
def industrial_energy_demand_per_country(country):
jrc_dir = snakemake.input.jrc
jrc_country = jrc_names.get(country, country)
fn = f'{jrc_dir}/JRC-IDEES-2015_EnergyBalance_{jrc_country}.xlsx'
fn = f"{jrc_dir}/JRC-IDEES-2015_EnergyBalance_{jrc_country}.xlsx"
sheets = list(sector_sheets.values())
df_dict = pd.read_excel(fn, sheet_name=sheets, index_col=0)
def get_subsector_data(sheet):
df = df_dict[sheet][year].groupby(fuels).sum()
df["ammonia"] = 0.
df["ammonia"] = 0.0
df['other'] = df['all'] - df.loc[df.index != 'all'].sum()
df["other"] = df["all"] - df.loc[df.index != "all"].sum()
return df
df = pd.concat({sub: get_subsector_data(sheet)
for sub, sheet in sector_sheets.items()}, axis=1)
df = pd.concat(
{sub: get_subsector_data(sheet) for sub, sheet in sector_sheets.items()}, axis=1
)
sel = ['Mining and quarrying', 'Construction', 'Non-specified']
df['Other Industrial Sectors'] = df[sel].sum(axis=1)
df['Basic chemicals'] += df['Basic chemicals feedstock']
sel = ["Mining and quarrying", "Construction", "Non-specified"]
df["Other Industrial Sectors"] = df[sel].sum(axis=1)
df["Basic chemicals"] += df["Basic chemicals feedstock"]
df.drop(columns=sel+['Basic chemicals feedstock'], index='all', inplace=True)
df.drop(columns=sel + ["Basic chemicals feedstock"], index="all", inplace=True)
df *= ktoe_to_twh
@ -86,41 +118,44 @@ def industrial_energy_demand_per_country(country):
def add_ammonia_energy_demand(demand):
# MtNH3/a
fn = snakemake.input.ammonia_production
ammonia = pd.read_csv(fn, index_col=0)[str(year)] / 1e3
def get_ammonia_by_fuel(x):
fuels = {
"gas": config["MWh_CH4_per_tNH3_SMR"],
"electricity": config["MWh_elec_per_tNH3_SMR"],
}
fuels = {'gas': config['MWh_CH4_per_tNH3_SMR'],
'electricity': config['MWh_elec_per_tNH3_SMR']}
return pd.Series({k: x*v for k,v in fuels.items()})
return pd.Series({k: x * v for k, v in fuels.items()})
ammonia_by_fuel = ammonia.apply(get_ammonia_by_fuel).T
ammonia_by_fuel = ammonia_by_fuel.unstack().reindex(index=demand.index, fill_value=0.)
ammonia_by_fuel = ammonia_by_fuel.unstack().reindex(
index=demand.index, fill_value=0.0
)
ammonia = pd.DataFrame({"ammonia": ammonia * config['MWh_NH3_per_tNH3']}).T
ammonia = pd.DataFrame({"ammonia": ammonia * config["MWh_NH3_per_tNH3"]}).T
demand['Ammonia'] = ammonia.unstack().reindex(index=demand.index, fill_value=0.)
demand["Ammonia"] = ammonia.unstack().reindex(index=demand.index, fill_value=0.0)
demand['Basic chemicals (without ammonia)'] = demand["Basic chemicals"] - ammonia_by_fuel
demand["Basic chemicals (without ammonia)"] = (
demand["Basic chemicals"] - ammonia_by_fuel
)
demand['Basic chemicals (without ammonia)'].clip(lower=0, inplace=True)
demand["Basic chemicals (without ammonia)"].clip(lower=0, inplace=True)
demand.drop(columns='Basic chemicals', inplace=True)
demand.drop(columns="Basic chemicals", inplace=True)
return demand
def add_non_eu28_industrial_energy_demand(demand):
# output in MtMaterial/a
fn = snakemake.input.industrial_production_per_country
production = pd.read_csv(fn, index_col=0) / 1e3
#recombine HVC, Chlorine and Methanol to Basic chemicals (without ammonia)
# recombine HVC, Chlorine and Methanol to Basic chemicals (without ammonia)
chemicals = ["HVC", "Chlorine", "Methanol"]
production["Basic chemicals (without ammonia)"] = production[chemicals].sum(axis=1)
production.drop(columns=chemicals, inplace=True)
@ -131,18 +166,22 @@ def add_non_eu28_industrial_energy_demand(demand):
non_eu28 = production.index.symmetric_difference(eu28)
demand_non_eu28 = pd.concat({k: v * eu28_averages
for k, v in production.loc[non_eu28].iterrows()})
demand_non_eu28 = pd.concat(
{k: v * eu28_averages for k, v in production.loc[non_eu28].iterrows()}
)
return pd.concat([demand, demand_non_eu28])
def industrial_energy_demand(countries):
nprocesses = snakemake.threads
func = industrial_energy_demand_per_country
tqdm_kwargs = dict(ascii=False, unit=' country', total=len(countries),
desc="Build industrial energy demand")
tqdm_kwargs = dict(
ascii=False,
unit=" country",
total=len(countries),
desc="Build industrial energy demand",
)
with mp.Pool(processes=nprocesses) as pool:
demand_l = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))
@ -151,13 +190,14 @@ def industrial_energy_demand(countries):
return demand
if __name__ == '__main__':
if 'snakemake' not in globals():
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake('build_industrial_energy_demand_per_country_today')
config = snakemake.config['industry']
year = config.get('reference_year', 2015)
snakemake = mock_snakemake("build_industrial_energy_demand_per_country_today")
config = snakemake.config["industry"]
year = config.get("reference_year", 2015)
demand = industrial_energy_demand(eu28)
@ -166,10 +206,10 @@ if __name__ == '__main__':
demand = add_non_eu28_industrial_energy_demand(demand)
# for format compatibility
demand = demand.stack(dropna=False).unstack(level=[0,2])
demand = demand.stack(dropna=False).unstack(level=[0, 2])
# style and annotation
demand.index.name = 'TWh/a'
demand.index.name = "TWh/a"
demand.sort_index(axis=1, inplace=True)
fn = snakemake.output.industrial_energy_demand_per_country_today

22
scripts/build_industrial_energy_demand_per_node.py
View File

@ -1,13 +1,17 @@
"""Build industrial energy demand per node."""
# -*- coding: utf-8 -*-
"""
Build industrial energy demand per node.
"""
import pandas as pd
if __name__ == '__main__':
if 'snakemake' not in globals():
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'build_industrial_energy_demand_per_node',
simpl='',
"build_industrial_energy_demand_per_node",
simpl="",
clusters=48,
planning_horizons=2030,
)
@ -31,9 +35,9 @@ if __name__ == '__main__':
nodal_df *= 0.001
rename_sectors = {
'elec': 'electricity',
'biomass': 'solid biomass',
'heat': 'low-temperature heat'
"elec": "electricity",
"biomass": "solid biomass",
"heat": "low-temperature heat",
}
nodal_df.rename(columns=rename_sectors, inplace=True)
@ -42,4 +46,4 @@ if __name__ == '__main__':
nodal_df.index.name = "TWh/a (MtCO2/a)"
fn = snakemake.output.industrial_energy_demand_per_node
nodal_df.to_csv(fn, float_format='%.2f')
nodal_df.to_csv(fn, float_format="%.2f")

67
scripts/build_industrial_energy_demand_per_node_today.py
View File

@ -1,33 +1,36 @@
"""Build industrial energy demand per node."""
# -*- coding: utf-8 -*-
"""
Build industrial energy demand per node.
"""
import pandas as pd
import numpy as np
from itertools import product
import numpy as np
import pandas as pd
# map JRC/our sectors to hotmaps sector, where mapping exist
sector_mapping = {
'Electric arc': 'Iron and steel',
'Integrated steelworks': 'Iron and steel',
'DRI + Electric arc': 'Iron and steel',
'Ammonia': 'Chemical industry',
'Basic chemicals (without ammonia)': 'Chemical industry',
'Other chemicals': 'Chemical industry',
'Pharmaceutical products etc.': 'Chemical industry',
'Cement': 'Cement',
'Ceramics & other NMM': 'Non-metallic mineral products',
'Glass production': 'Glass',
'Pulp production': 'Paper and printing',
'Paper production': 'Paper and printing',
'Printing and media reproduction': 'Paper and printing',
'Alumina production': 'Non-ferrous metals',
'Aluminium - primary production': 'Non-ferrous metals',
'Aluminium - secondary production': 'Non-ferrous metals',
'Other non-ferrous metals': 'Non-ferrous metals',
"Electric arc": "Iron and steel",
"Integrated steelworks": "Iron and steel",
"DRI + Electric arc": "Iron and steel",
"Ammonia": "Chemical industry",
"Basic chemicals (without ammonia)": "Chemical industry",
"Other chemicals": "Chemical industry",
"Pharmaceutical products etc.": "Chemical industry",
"Cement": "Cement",
"Ceramics & other NMM": "Non-metallic mineral products",
"Glass production": "Glass",
"Pulp production": "Paper and printing",
"Paper production": "Paper and printing",
"Printing and media reproduction": "Paper and printing",
"Alumina production": "Non-ferrous metals",
"Aluminium - primary production": "Non-ferrous metals",
"Aluminium - secondary production": "Non-ferrous metals",
"Other non-ferrous metals": "Non-ferrous metals",
}
def build_nodal_industrial_energy_demand():
fn = snakemake.input.industrial_energy_demand_per_country_today
industrial_demand = pd.read_csv(fn, header=[0, 1], index_col=0)
@ -35,24 +38,23 @@ def build_nodal_industrial_energy_demand():
keys = pd.read_csv(fn, index_col=0)
keys["country"] = keys.index.str[:2]
nodal_demand = pd.DataFrame(0., dtype=float,
index=keys.index,
columns=industrial_demand.index)
nodal_demand = pd.DataFrame(
0.0, dtype=float, index=keys.index, columns=industrial_demand.index
)
countries = keys.country.unique()
sectors = industrial_demand.columns.levels[1]
for country, sector in product(countries, sectors):
buses = keys.index[keys.country == country]
mapping = sector_mapping.get(sector, 'population')
mapping = sector_mapping.get(sector, "population")
key = keys.loc[buses, mapping]
demand = industrial_demand[country, sector]
outer = pd.DataFrame(np.outer(key, demand),
index=key.index,
columns=demand.index)
outer = pd.DataFrame(
np.outer(key, demand), index=key.index, columns=demand.index
)
nodal_demand.loc[buses] += outer
@ -62,11 +64,12 @@ def build_nodal_industrial_energy_demand():
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'build_industrial_energy_demand_per_node_today',
simpl='',
"build_industrial_energy_demand_per_node_today",
simpl="",
clusters=48,
)

290
scripts/build_industrial_production_per_country.py
View File

@ -1,132 +1,204 @@
"""Build industrial production per country."""
# -*- coding: utf-8 -*-
"""
Build industrial production per country.
"""
import logging
logger = logging.getLogger(__name__)
import pandas as pd
import numpy as np
import multiprocessing as mp
from tqdm import tqdm
import numpy as np
import pandas as pd
from helper import mute_print
from tqdm import tqdm
tj_to_ktoe = 0.0238845
ktoe_to_twh = 0.01163
sub_sheet_name_dict = {'Iron and steel': 'ISI',
'Chemicals Industry': 'CHI',
'Non-metallic mineral products': 'NMM',
'Pulp, paper and printing': 'PPA',
'Food, beverages and tobacco': 'FBT',
'Non Ferrous Metals': 'NFM',
'Transport Equipment': 'TRE',
'Machinery Equipment': 'MAE',
'Textiles and leather': 'TEL',
'Wood and wood products': 'WWP',
'Other Industrial Sectors': 'OIS'}
sub_sheet_name_dict = {
"Iron and steel": "ISI",
"Chemicals Industry": "CHI",
"Non-metallic mineral products": "NMM",
"Pulp, paper and printing": "PPA",
"Food, beverages and tobacco": "FBT",
"Non Ferrous Metals": "NFM",
"Transport Equipment": "TRE",
"Machinery Equipment": "MAE",
"Textiles and leather": "TEL",
"Wood and wood products": "WWP",
"Other Industrial Sectors": "OIS",
}
non_EU = ['NO', 'CH', 'ME', 'MK', 'RS', 'BA', 'AL']
non_EU = ["NO", "CH", "ME", "MK", "RS", "BA", "AL"]
jrc_names = {"GR": "EL", "GB": "UK"}
eu28 = ['FR', 'DE', 'GB', 'IT', 'ES', 'PL', 'SE', 'NL', 'BE', 'FI',
'DK', 'PT', 'RO', 'AT', 'BG', 'EE', 'GR', 'LV', 'CZ',
'HU', 'IE', 'SK', 'LT', 'HR', 'LU', 'SI', 'CY', 'MT']
eu28 = [
"FR",
"DE",
"GB",
"IT",
"ES",
"PL",
"SE",
"NL",
"BE",
"FI",
"DK",
"PT",
"RO",
"AT",
"BG",
"EE",
"GR",
"LV",
"CZ",
"HU",
"IE",
"SK",
"LT",
"HR",
"LU",
"SI",
"CY",
"MT",
]
sect2sub = {'Iron and steel': ['Electric arc', 'Integrated steelworks'],
'Chemicals Industry': ['Basic chemicals', 'Other chemicals', 'Pharmaceutical products etc.'],
'Non-metallic mineral products': ['Cement', 'Ceramics & other NMM', 'Glass production'],
'Pulp, paper and printing': ['Pulp production', 'Paper production', 'Printing and media reproduction'],
'Food, beverages and tobacco': ['Food, beverages and tobacco'],
'Non Ferrous Metals': ['Alumina production', 'Aluminium - primary production', 'Aluminium - secondary production', 'Other non-ferrous metals'],
'Transport Equipment': ['Transport Equipment'],
'Machinery Equipment': ['Machinery Equipment'],
'Textiles and leather': ['Textiles and leather'],
'Wood and wood products': ['Wood and wood products'],
'Other Industrial Sectors': ['Other Industrial Sectors']}
sect2sub = {
"Iron and steel": ["Electric arc", "Integrated steelworks"],
"Chemicals Industry": [
"Basic chemicals",
"Other chemicals",
"Pharmaceutical products etc.",
],
"Non-metallic mineral products": [
"Cement",
"Ceramics & other NMM",
"Glass production",
],
"Pulp, paper and printing": [
"Pulp production",
"Paper production",
"Printing and media reproduction",
],
"Food, beverages and tobacco": ["Food, beverages and tobacco"],
"Non Ferrous Metals": [
"Alumina production",
"Aluminium - primary production",
"Aluminium - secondary production",
"Other non-ferrous metals",
],
"Transport Equipment": ["Transport Equipment"],
"Machinery Equipment": ["Machinery Equipment"],
"Textiles and leather": ["Textiles and leather"],
"Wood and wood products": ["Wood and wood products"],
"Other Industrial Sectors": ["Other Industrial Sectors"],
}
sub2sect = {v: k for k, vv in sect2sub.items() for v in vv}
fields = {'Electric arc': 'Electric arc',
'Integrated steelworks': 'Integrated steelworks',
'Basic chemicals': 'Basic chemicals (kt ethylene eq.)',
'Other chemicals': 'Other chemicals (kt ethylene eq.)',
'Pharmaceutical products etc.': 'Pharmaceutical products etc. (kt ethylene eq.)',
'Cement': 'Cement (kt)',
'Ceramics & other NMM': 'Ceramics & other NMM (kt bricks eq.)',
'Glass production': 'Glass production (kt)',
'Pulp production': 'Pulp production (kt)',
'Paper production': 'Paper production (kt)',
'Printing and media reproduction': 'Printing and media reproduction (kt paper eq.)',
'Food, beverages and tobacco': 'Physical output (index)',
'Alumina production': 'Alumina production (kt)',
'Aluminium - primary production': 'Aluminium - primary production',
'Aluminium - secondary production': 'Aluminium - secondary production',
'Other non-ferrous metals': 'Other non-ferrous metals (kt lead eq.)',
'Transport Equipment': 'Physical output (index)',
'Machinery Equipment': 'Physical output (index)',
'Textiles and leather': 'Physical output (index)',
'Wood and wood products': 'Physical output (index)',
'Other Industrial Sectors': 'Physical output (index)'}
fields = {
"Electric arc": "Electric arc",
"Integrated steelworks": "Integrated steelworks",
"Basic chemicals": "Basic chemicals (kt ethylene eq.)",
"Other chemicals": "Other chemicals (kt ethylene eq.)",
"Pharmaceutical products etc.": "Pharmaceutical products etc. (kt ethylene eq.)",
"Cement": "Cement (kt)",
"Ceramics & other NMM": "Ceramics & other NMM (kt bricks eq.)",
"Glass production": "Glass production (kt)",
"Pulp production": "Pulp production (kt)",
"Paper production": "Paper production (kt)",
"Printing and media reproduction": "Printing and media reproduction (kt paper eq.)",
"Food, beverages and tobacco": "Physical output (index)",
"Alumina production": "Alumina production (kt)",
"Aluminium - primary production": "Aluminium - primary production",
"Aluminium - secondary production": "Aluminium - secondary production",
"Other non-ferrous metals": "Other non-ferrous metals (kt lead eq.)",
"Transport Equipment": "Physical output (index)",
"Machinery Equipment": "Physical output (index)",
"Textiles and leather": "Physical output (index)",
"Wood and wood products": "Physical output (index)",
"Other Industrial Sectors": "Physical output (index)",
}
eb_names = {'NO': 'Norway', 'AL': 'Albania', 'BA': 'Bosnia and Herzegovina',
'MK': 'FYR of Macedonia', 'GE': 'Georgia', 'IS': 'Iceland',
'KO': 'Kosovo', 'MD': 'Moldova', 'ME': 'Montenegro', 'RS': 'Serbia',
'UA': 'Ukraine', 'TR': 'Turkey', }
eb_names = {
"NO": "Norway",
"AL": "Albania",
"BA": "Bosnia and Herzegovina",
"MK": "FYR of Macedonia",
"GE": "Georgia",
"IS": "Iceland",
"KO": "Kosovo",
"MD": "Moldova",
"ME": "Montenegro",
"RS": "Serbia",
"UA": "Ukraine",
"TR": "Turkey",
}
eb_sectors = {'Iron & steel industry': 'Iron and steel',
'Chemical and Petrochemical industry': 'Chemicals Industry',
'Non-ferrous metal industry': 'Non-metallic mineral products',
'Paper, Pulp and Print': 'Pulp, paper and printing',
'Food and Tabacco': 'Food, beverages and tobacco',
'Non-metallic Minerals (Glass, pottery & building mat. Industry)': 'Non Ferrous Metals',
'Transport Equipment': 'Transport Equipment',
'Machinery': 'Machinery Equipment',
'Textile and Leather': 'Textiles and leather',
'Wood and Wood Products': 'Wood and wood products',
'Non-specified (Industry)': 'Other Industrial Sectors'}
eb_sectors = {
"Iron & steel industry": "Iron and steel",
"Chemical and Petrochemical industry": "Chemicals Industry",
"Non-ferrous metal industry": "Non-metallic mineral products",
"Paper, Pulp and Print": "Pulp, paper and printing",
"Food and Tabacco": "Food, beverages and tobacco",
"Non-metallic Minerals (Glass, pottery & building mat. Industry)": "Non Ferrous Metals",
"Transport Equipment": "Transport Equipment",
"Machinery": "Machinery Equipment",
"Textile and Leather": "Textiles and leather",
"Wood and Wood Products": "Wood and wood products",
"Non-specified (Industry)": "Other Industrial Sectors",
}
# TODO: this should go in a csv in `data`
# Annual energy consumption in Switzerland by sector in 2015 (in TJ)
# From: Energieverbrauch in der Industrie und im Dienstleistungssektor, Der Bundesrat
# http://www.bfe.admin.ch/themen/00526/00541/00543/index.html?lang=de&dossier_id=00775
e_switzerland = pd.Series({'Iron and steel': 7889.,
'Chemicals Industry': 26871.,
'Non-metallic mineral products': 15513.+3820.,
'Pulp, paper and printing': 12004.,
'Food, beverages and tobacco': 17728.,
'Non Ferrous Metals': 3037.,
'Transport Equipment': 14993.,
'Machinery Equipment': 4724.,
'Textiles and leather': 1742.,
'Wood and wood products': 0.,
'Other Industrial Sectors': 10825.,
'current electricity': 53760.})
e_switzerland = pd.Series(
{
"Iron and steel": 7889.0,
"Chemicals Industry": 26871.0,
"Non-metallic mineral products": 15513.0 + 3820.0,
"Pulp, paper and printing": 12004.0,
"Food, beverages and tobacco": 17728.0,
"Non Ferrous Metals": 3037.0,
"Transport Equipment": 14993.0,
"Machinery Equipment": 4724.0,
"Textiles and leather": 1742.0,
"Wood and wood products": 0.0,
"Other Industrial Sectors": 10825.0,
"current electricity": 53760.0,
}
)
def find_physical_output(df):
start = np.where(df.index.str.contains('Physical output', na=''))[0][0]
start = np.where(df.index.str.contains("Physical output", na=""))[0][0]
empty_row = np.where(df.index.isnull())[0]
end = empty_row[np.argmax(empty_row > start)]
return slice(start, end)
def get_energy_ratio(country):
if country == 'CH':
if country == "CH":
e_country = e_switzerland * tj_to_ktoe
else:
# estimate physical output, energy consumption in the sector and country
fn = f"{eurostat_dir}/{eb_names[country]}.XLSX"
with mute_print():
df = pd.read_excel(fn, sheet_name='2016', index_col=2,
header=0, skiprows=1).squeeze('columns')
e_country = df.loc[eb_sectors.keys(
), 'Total all products'].rename(eb_sectors)
df = pd.read_excel(
fn, sheet_name="2016", index_col=2, header=0, skiprows=1
).squeeze("columns")
e_country = df.loc[eb_sectors.keys(), "Total all products"].rename(eb_sectors)
fn = f'{jrc_dir}/JRC-IDEES-2015_Industry_EU28.xlsx'
fn = f"{jrc_dir}/JRC-IDEES-2015_Industry_EU28.xlsx"
with mute_print():
df = pd.read_excel(fn, sheet_name='Ind_Summary',
index_col=0, header=0).squeeze('columns')
df = pd.read_excel(fn, sheet_name="Ind_Summary", index_col=0, header=0).squeeze(
"columns"
)
assert df.index[48] == "by sector"
year_i = df.columns.get_loc(year)
@ -139,15 +211,14 @@ def get_energy_ratio(country):
def industry_production_per_country(country):
def get_sector_data(sector, country):
jrc_country = jrc_names.get(country, country)
fn = f'{jrc_dir}/JRC-IDEES-2015_Industry_{jrc_country}.xlsx'
fn = f"{jrc_dir}/JRC-IDEES-2015_Industry_{jrc_country}.xlsx"
sheet = sub_sheet_name_dict[sector]
with mute_print():
df = pd.read_excel(fn, sheet_name=sheet,
index_col=0, header=0).squeeze('columns')
df = pd.read_excel(fn, sheet_name=sheet, index_col=0, header=0).squeeze(
"columns"
)
year_i = df.columns.get_loc(year)
df = df.iloc[find_physical_output(df), year_i]
@ -169,11 +240,14 @@ def industry_production_per_country(country):
def industry_production(countries):
nprocesses = snakemake.threads
func = industry_production_per_country
tqdm_kwargs = dict(ascii=False, unit=' country', total=len(countries),
desc="Build industry production")
tqdm_kwargs = dict(
ascii=False,
unit=" country",
total=len(countries),
desc="Build industry production",
)
with mp.Pool(processes=nprocesses) as pool:
demand_l = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))
@ -185,7 +259,9 @@ def industry_production(countries):
def separate_basic_chemicals(demand):
"""Separate basic chemicals into ammonia, chlorine, methanol and HVC."""
"""
Separate basic chemicals into ammonia, chlorine, methanol and HVC.
"""
ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0)
@ -194,14 +270,14 @@ def separate_basic_chemicals(demand):
logger.info(f"Following countries have no ammonia demand: {missing.tolist()}")
demand["Ammonia"] = 0.
demand["Ammonia"] = 0.0
demand.loc[there, "Ammonia"] = ammonia.loc[there, str(year)]
demand["Basic chemicals"] -= demand["Ammonia"]
# EE, HR and LT got negative demand through subtraction - poor data
demand['Basic chemicals'].clip(lower=0., inplace=True)
demand["Basic chemicals"].clip(lower=0.0, inplace=True)
# assume HVC, methanol, chlorine production proportional to non-ammonia basic chemicals
distribution_key = demand["Basic chemicals"] / demand["Basic chemicals"].sum()
@ -211,16 +287,18 @@ def separate_basic_chemicals(demand):
demand.drop(columns=["Basic chemicals"], inplace=True)
if __name__ == '__main__':
if 'snakemake' not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake('build_industrial_production_per_country')
logging.basicConfig(level=snakemake.config['logging_level'])
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake("build_industrial_production_per_country")
logging.basicConfig(level=snakemake.config["logging_level"])
countries = non_EU + eu28
year = snakemake.config['industry']['reference_year']
year = snakemake.config["industry"]["reference_year"]
config = snakemake.config["industry"]
@ -232,4 +310,4 @@ if __name__ == '__main__':
separate_basic_chemicals(demand)
fn = snakemake.output.industrial_production_per_country
demand.to_csv(fn, float_format='%.2f')
demand.to_csv(fn, float_format="%.2f")

47
scripts/build_industrial_production_per_country_tomorrow.py
View File

@ -1,13 +1,16 @@
"""Build future industrial production per country."""
# -*- coding: utf-8 -*-
"""
Build future industrial production per country.
"""
import pandas as pd
from prepare_sector_network import get
if __name__ == '__main__':
if 'snakemake' not in globals():
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake('build_industrial_production_per_country_tomorrow')
snakemake = mock_snakemake("build_industrial_production_per_country_tomorrow")
config = snakemake.config["industry"]
@ -24,12 +27,20 @@ if __name__ == '__main__':
int_steel = production["Integrated steelworks"].sum()
fraction_persistent_primary = st_primary_fraction * total_steel.sum() / int_steel
dri = dri_fraction * fraction_persistent_primary * production["Integrated steelworks"]
dri = (
dri_fraction * fraction_persistent_primary * production["Integrated steelworks"]
)
production.insert(2, "DRI + Electric arc", dri)
not_dri = (1 - dri_fraction)
production["Integrated steelworks"] = not_dri * fraction_persistent_primary * production["Integrated steelworks"]
production["Electric arc"] = total_steel - production["DRI + Electric arc"] - production["Integrated steelworks"]
not_dri = 1 - dri_fraction
production["Integrated steelworks"] = (
not_dri * fraction_persistent_primary * production["Integrated steelworks"]
)
production["Electric arc"] = (
total_steel
- production["DRI + Electric arc"]
- production["Integrated steelworks"]
)
keys = ["Aluminium - primary production", "Aluminium - secondary production"]
total_aluminium = production[keys].sum(axis=1)
@ -38,15 +49,23 @@ if __name__ == '__main__':
key_sec = "Aluminium - secondary production"
al_primary_fraction = get(config["Al_primary_fraction"], investment_year)
fraction_persistent_primary = al_primary_fraction * total_aluminium.sum() / production[key_pri].sum()
fraction_persistent_primary = (
al_primary_fraction * total_aluminium.sum() / production[key_pri].sum()
)
production[key_pri] = fraction_persistent_primary * production[key_pri]
production[key_sec] = total_aluminium - production[key_pri]
production["HVC (mechanical recycling)"] = get(config["HVC_mechanical_recycling_fraction"], investment_year) * production["HVC"]
production["HVC (chemical recycling)"] = get(config["HVC_chemical_recycling_fraction"], investment_year) * production["HVC"]
production["HVC (mechanical recycling)"] = (
get(config["HVC_mechanical_recycling_fraction"], investment_year)
* production["HVC"]
)
production["HVC (chemical recycling)"] = (
get(config["HVC_chemical_recycling_fraction"], investment_year)
* production["HVC"]
)
production["HVC"] *= get(config['HVC_primary_fraction'], investment_year)
production["HVC"] *= get(config["HVC_primary_fraction"], investment_year)
fn = snakemake.output.industrial_production_per_country_tomorrow
production.to_csv(fn, float_format='%.2f')
production.to_csv(fn, float_format="%.2f")

70
scripts/build_industrial_production_per_node.py
View File

@ -1,36 +1,39 @@
"""Build industrial production per node."""
# -*- coding: utf-8 -*-
"""
Build industrial production per node.
"""
from itertools import product
import pandas as pd
from itertools import product
# map JRC/our sectors to hotmaps sector, where mapping exist
sector_mapping = {
'Electric arc': 'Iron and steel',
'Integrated steelworks': 'Iron and steel',
'DRI + Electric arc': 'Iron and steel',
'Ammonia': 'Chemical industry',
'HVC': 'Chemical industry',
'HVC (mechanical recycling)': 'Chemical industry',
'HVC (chemical recycling)': 'Chemical industry',
'Methanol': 'Chemical industry',
'Chlorine': 'Chemical industry',
'Other chemicals': 'Chemical industry',
'Pharmaceutical products etc.': 'Chemical industry',
'Cement': 'Cement',
'Ceramics & other NMM': 'Non-metallic mineral products',
'Glass production': 'Glass',
'Pulp production': 'Paper and printing',
'Paper production': 'Paper and printing',
'Printing and media reproduction': 'Paper and printing',
'Alumina production': 'Non-ferrous metals',
'Aluminium - primary production': 'Non-ferrous metals',
'Aluminium - secondary production': 'Non-ferrous metals',
'Other non-ferrous metals': 'Non-ferrous metals',
"Electric arc": "Iron and steel",
"Integrated steelworks": "Iron and steel",
"DRI + Electric arc": "Iron and steel",
"Ammonia": "Chemical industry",
"HVC": "Chemical industry",
"HVC (mechanical recycling)": "Chemical industry",
"HVC (chemical recycling)": "Chemical industry",
"Methanol": "Chemical industry",
"Chlorine": "Chemical industry",
"Other chemicals": "Chemical industry",
"Pharmaceutical products etc.": "Chemical industry",
"Cement": "Cement",
"Ceramics & other NMM": "Non-metallic mineral products",
"Glass production": "Glass",
"Pulp production": "Paper and printing",
"Paper production": "Paper and printing",
"Printing and media reproduction": "Paper and printing",
"Alumina production": "Non-ferrous metals",
"Aluminium - primary production": "Non-ferrous metals",
"Aluminium - secondary production": "Non-ferrous metals",
"Other non-ferrous metals": "Non-ferrous metals",
}
def build_nodal_industrial_production():
fn = snakemake.input.industrial_production_per_country_tomorrow
industrial_production = pd.read_csv(fn, index_col=0)
@ -38,29 +41,32 @@ def build_nodal_industrial_production():
keys = pd.read_csv(fn, index_col=0)
keys["country"] = keys.index.str[:2]
nodal_production = pd.DataFrame(index=keys.index,
columns=industrial_production.columns,
dtype=float)
nodal_production = pd.DataFrame(
index=keys.index, columns=industrial_production.columns, dtype=float
)
countries = keys.country.unique()
sectors = industrial_production.columns
for country, sector in product(countries, sectors):
buses = keys.index[keys.country == country]
mapping = sector_mapping.get(sector, "population")
key = keys.loc[buses, mapping]
nodal_production.loc[buses, sector] = industrial_production.at[country, sector] * key
nodal_production.loc[buses, sector] = (
industrial_production.at[country, sector] * key
)
nodal_production.to_csv(snakemake.output.industrial_production_per_node)
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake('build_industrial_production_per_node',
simpl='',
snakemake = mock_snakemake(
"build_industrial_production_per_node",
simpl="",
clusters=48,
)

21
scripts/build_industry_sector_ratios.py
View File

@ -1,4 +1,7 @@
"""Build industry sector ratios."""
# -*- coding: utf-8 -*-
"""
Build industry sector ratios.
"""
import pandas as pd
from helper import mute_print
@ -68,7 +71,6 @@ index = [
def load_idees_data(sector, country="EU28"):
suffixes = {"out": "", "fec": "_fec", "ued": "_ued", "emi": "_emi"}
sheets = {k: sheet_names[sector] + v for k, v in suffixes.items()}
@ -91,7 +93,6 @@ def load_idees_data(sector, country="EU28"):
def iron_and_steel():
# There are two different approaches to produce iron and steel:
# i.e., integrated steelworks and electric arc.
# Electric arc approach has higher efficiency and relies more on electricity.
@ -602,7 +603,6 @@ def chemicals_industry():
def nonmetalic_mineral_products():
# This includes cement, ceramic and glass production.
# This includes process emissions related to the fabrication of clinker.
@ -789,7 +789,6 @@ def nonmetalic_mineral_products():
def pulp_paper_printing():
# Pulp, paper and printing can be completely electrified.
# There are no process emissions associated to this sector.
@ -942,7 +941,6 @@ def pulp_paper_printing():
def food_beverages_tobacco():
# Food, beverages and tobaco can be completely electrified.
# There are no process emissions associated to this sector.
@ -1002,7 +1000,6 @@ def food_beverages_tobacco():
def non_ferrous_metals():
sector = "Non Ferrous Metals"
idees = load_idees_data(sector)
@ -1205,7 +1202,6 @@ def non_ferrous_metals():
def transport_equipment():
sector = "Transport Equipment"
idees = load_idees_data(sector)
@ -1256,7 +1252,6 @@ def transport_equipment():
def machinery_equipment():
sector = "Machinery Equipment"
idees = load_idees_data(sector)
@ -1309,7 +1304,6 @@ def machinery_equipment():
def textiles_and_leather():
sector = "Textiles and leather"
idees = load_idees_data(sector)
@ -1358,7 +1352,6 @@ def textiles_and_leather():
def wood_and_wood_products():
sector = "Wood and wood products"
idees = load_idees_data(sector)
@ -1404,7 +1397,6 @@ def wood_and_wood_products():
def other_industrial_sectors():
sector = "Other Industrial Sectors"
idees = load_idees_data(sector)
@ -1465,9 +1457,10 @@ def other_industrial_sectors():
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake('build_industry_sector_ratios')
snakemake = mock_snakemake("build_industry_sector_ratios")
# TODO make config option
year = 2015

58
scripts/build_population_layouts.py
View File

@ -1,29 +1,35 @@
"""Build mapping between grid cells and population (total, urban, rural)"""
# -*- coding: utf-8 -*-
"""
Build mapping between grid cells and population (total, urban, rural)
"""
import logging
logger = logging.getLogger(__name__)
import multiprocessing as mp
import atlite
import geopandas as gpd
import numpy as np
import pandas as pd
import xarray as xr
import geopandas as gpd
if __name__ == '__main__':
if 'snakemake' not in globals():
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake('build_population_layouts')
logging.basicConfig(level=snakemake.config['logging_level'])
snakemake = mock_snakemake("build_population_layouts")
cutout = atlite.Cutout(snakemake.config['atlite']['cutout'])
logging.basicConfig(level=snakemake.config["logging_level"])
cutout = atlite.Cutout(snakemake.config["atlite"]["cutout"])
grid_cells = cutout.grid.geometry
# nuts3 has columns country, gdp, pop, geometry
# population is given in dimensions of 1e3=k
nuts3 = gpd.read_file(snakemake.input.nuts3_shapes).set_index('index')
nuts3 = gpd.read_file(snakemake.input.nuts3_shapes).set_index("index")
# Indicator matrix NUTS3 -> grid cells
I = atlite.cutout.compute_indicatormatrix(nuts3.geometry, grid_cells)
@ -34,9 +40,12 @@ if __name__ == '__main__':
countries = np.sort(nuts3.country.unique())
urban_fraction = pd.read_csv(snakemake.input.urban_percent,
header=None, index_col=0,
names=['fraction']).squeeze() / 100.
urban_fraction = (
pd.read_csv(
snakemake.input.urban_percent, header=None, index_col=0, names=["fraction"]
).squeeze()
/ 100.0
)
# fill missing Balkans values
missing = ["AL", "ME", "MK"]
@ -46,7 +55,7 @@ if __name__ == '__main__':
urban_fraction = pd.concat([urban_fraction, fill_values])
# population in each grid cell
pop_cells = pd.Series(I.dot(nuts3['pop']))
pop_cells = pd.Series(I.dot(nuts3["pop"]))
# in km^2
cell_areas = grid_cells.to_crs(3035).area / 1e6
@ -55,13 +64,15 @@ if __name__ == '__main__':
density_cells = pop_cells / cell_areas
# rural or urban population in grid cell
pop_rural = pd.Series(0., density_cells.index)
pop_urban = pd.Series(0., density_cells.index)
pop_rural = pd.Series(0.0, density_cells.index)
pop_urban = pd.Series(0.0, density_cells.index)
for ct in countries:
logger.debug(f"The urbanization rate for {ct} is {round(urban_fraction[ct]*100)}%")
logger.debug(
f"The urbanization rate for {ct} is {round(urban_fraction[ct]*100)}%"
)
indicator_nuts3_ct = nuts3.country.apply(lambda x: 1. if x == ct else 0.)
indicator_nuts3_ct = nuts3.country.apply(lambda x: 1.0 if x == ct else 0.0)
indicator_cells_ct = pd.Series(Iinv.T.dot(indicator_nuts3_ct))
@ -70,7 +81,7 @@ if __name__ == '__main__':
pop_cells_ct = indicator_cells_ct * pop_cells
# correct for imprecision of Iinv*I
pop_ct = nuts3.loc[nuts3.country==ct,'pop'].sum()
pop_ct = nuts3.loc[nuts3.country == ct, "pop"].sum()
pop_cells_ct *= pop_ct / pop_cells_ct.sum()
# The first low density grid cells to reach rural fraction are rural
@ -80,20 +91,19 @@ if __name__ == '__main__':
pop_ct_rural_b = asc_density_cumsum < rural_fraction_ct
pop_ct_urban_b = ~pop_ct_rural_b
pop_ct_rural_b[indicator_cells_ct == 0.] = False
pop_ct_urban_b[indicator_cells_ct == 0.] = False
pop_ct_rural_b[indicator_cells_ct == 0.0] = False
pop_ct_urban_b[indicator_cells_ct == 0.0] = False
pop_rural += pop_cells_ct.where(pop_ct_rural_b, 0.)
pop_urban += pop_cells_ct.where(pop_ct_urban_b, 0.)
pop_rural += pop_cells_ct.where(pop_ct_rural_b, 0.0)
pop_urban += pop_cells_ct.where(pop_ct_urban_b, 0.0)
pop_cells = {"total": pop_cells}
pop_cells["rural"] = pop_rural
pop_cells["urban"] = pop_urban
for key, pop in pop_cells.items():
ycoords = ('y', cutout.coords['y'].data)
xcoords = ('x', cutout.coords['x'].data)
ycoords = ("y", cutout.coords["y"].data)
xcoords = ("x", cutout.coords["x"].data)
values = pop.values.reshape(cutout.shape)
layout = xr.DataArray(values, [ycoords, xcoords])

16
scripts/build_population_weighted_energy_totals.py
View File

@ -1,13 +1,17 @@
"""Build population-weighted energy totals."""
# -*- coding: utf-8 -*-
"""
Build population-weighted energy totals.
"""
import pandas as pd
if __name__ == '__main__':
if 'snakemake' not in globals():
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'build_population_weighted_energy_totals',
simpl='',
"build_population_weighted_energy_totals",
simpl="",
clusters=48,
)
@ -15,7 +19,7 @@ if __name__ == '__main__':
energy_totals = pd.read_csv(snakemake.input.energy_totals, index_col=0)
nodal_energy_totals = energy_totals.loc[pop_layout.ct].fillna(0.)
nodal_energy_totals = energy_totals.loc[pop_layout.ct].fillna(0.0)
nodal_energy_totals.index = pop_layout.index
nodal_energy_totals = nodal_energy_totals.multiply(pop_layout.fraction, axis=0)

869
scripts/build_retro_cost.py
View File

File diff suppressed because it is too large Load Diff

35
scripts/build_salt_cavern_potentials.py
View File

@ -1,3 +1,4 @@
# -*- coding: utf-8 -*-
"""
Build salt cavern potentials for hydrogen storage.
@ -22,29 +23,35 @@ import geopandas as gpd
import pandas as pd
def concat_gdf(gdf_list, crs='EPSG:4326'):
"""Concatenate multiple geopandas dataframes with common coordinate reference system (crs)."""
def concat_gdf(gdf_list, crs="EPSG:4326"):
"""
Concatenate multiple geopandas dataframes with common coordinate reference
system (crs).
"""
return gpd.GeoDataFrame(pd.concat(gdf_list), crs=crs)
def load_bus_regions(onshore_path, offshore_path):
"""Load pypsa-eur on- and offshore regions and concat."""
"""
Load pypsa-eur on- and offshore regions and concat.
"""
bus_regions_offshore = gpd.read_file(offshore_path)
bus_regions_onshore = gpd.read_file(onshore_path)
bus_regions = concat_gdf([bus_regions_offshore, bus_regions_onshore])
bus_regions = bus_regions.dissolve(by='name', aggfunc='sum')
bus_regions = bus_regions.dissolve(by="name", aggfunc="sum")
return bus_regions
def area(gdf):
"""Returns area of GeoDataFrame geometries in square kilometers."""
"""
Returns area of GeoDataFrame geometries in square kilometers.
"""
return gdf.to_crs(epsg=3035).area.div(1e6)
def salt_cavern_potential_by_region(caverns, regions):
# calculate area of caverns shapes
caverns["area_caverns"] = area(caverns)
@ -53,18 +60,24 @@ def salt_cavern_potential_by_region(caverns, regions):
# calculate share of cavern area inside region
overlay["share"] = area(overlay) / overlay["area_caverns"]
overlay["e_nom"] = overlay.eval("capacity_per_area * share * area_caverns / 1000") # TWh
overlay["e_nom"] = overlay.eval(
"capacity_per_area * share * area_caverns / 1000"
) # TWh
caverns_regions = overlay.groupby(['name', "storage_type"]).e_nom.sum().unstack("storage_type")
caverns_regions = (
overlay.groupby(["name", "storage_type"]).e_nom.sum().unstack("storage_type")
)
return caverns_regions
if __name__ == '__main__':
if 'snakemake' not in globals():
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake('build_salt_cavern_potentials', simpl='', clusters='37')
snakemake = mock_snakemake(
"build_salt_cavern_potentials", simpl="", clusters="37"
)
fn_onshore = snakemake.input.regions_onshore
fn_offshore = snakemake.input.regions_offshore

27
scripts/build_sequestration_potentials.py
View File

@ -1,12 +1,18 @@
import pandas as pd
# -*- coding: utf-8 -*-
import geopandas as gpd
import pandas as pd
def area(gdf):
"""Returns area of GeoDataFrame geometries in square kilometers."""
"""
Returns area of GeoDataFrame geometries in square kilometers.
"""
return gdf.to_crs(epsg=3035).area.div(1e6)
def allocate_sequestration_potential(gdf, regions, attr='conservative estimate Mt', threshold=3):
def allocate_sequestration_potential(
gdf, regions, attr="conservative estimate Mt", threshold=3
):
gdf = gdf.loc[gdf[attr] > threshold, [attr, "geometry"]]
gdf["area_sqkm"] = area(gdf)
overlay = gpd.overlay(regions, gdf, keep_geom_type=True)
@ -19,12 +25,11 @@ def allocate_sequestration_potential(gdf, regions, attr='conservative estimate M
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'build_sequestration_potentials',
simpl='',
clusters="181"
"build_sequestration_potentials", simpl="", clusters="181"
)
cf = snakemake.config["sector"]["regional_co2_sequestration_potential"]
@ -34,10 +39,12 @@ if __name__ == "__main__":
regions = gpd.read_file(snakemake.input.regions_offshore)
if cf["include_onshore"]:
onregions = gpd.read_file(snakemake.input.regions_onshore)
regions = pd.concat([regions, onregions]).dissolve(by='name').reset_index()
regions = pd.concat([regions, onregions]).dissolve(by="name").reset_index()
s = allocate_sequestration_potential(gdf, regions, attr=cf["attribute"], threshold=cf["min_size"])
s = allocate_sequestration_potential(
gdf, regions, attr=cf["attribute"], threshold=cf["min_size"]
)
s = s.where(s>cf["min_size"]).dropna()
s = s.where(s > cf["min_size"]).dropna()
s.to_csv(snakemake.output.sequestration_potential)

36
scripts/build_shipping_demand.py
View File

@ -1,25 +1,33 @@
"""Build regional demand for international navigation based on outflow volume of ports."""
# -*- coding: utf-8 -*-
"""
Build regional demand for international navigation based on outflow volume of
ports.
"""
import pandas as pd
import geopandas as gpd
import json
if __name__ == '__main__':
if 'snakemake' not in globals():
import geopandas as gpd
import pandas as pd
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'build_shipping_demand_per_node',
simpl='',
"build_shipping_demand_per_node",
simpl="",
clusters=48,
)
scope = gpd.read_file(snakemake.input.scope).geometry[0]
regions = gpd.read_file(snakemake.input.regions).set_index('name')
demand = pd.read_csv(snakemake.input.demand, index_col=0)["total international navigation"]
regions = gpd.read_file(snakemake.input.regions).set_index("name")
demand = pd.read_csv(snakemake.input.demand, index_col=0)[
"total international navigation"
]
# read port data into GeoDataFrame
with open(snakemake.input.ports, 'r', encoding='latin_1') as f:
ports = json.load(f)
with open(snakemake.input.ports, "r", encoding="latin_1") as f:
ports = json.load(f)
ports = pd.json_normalize(ports, "features", sep="_")
coordinates = ports.geometry_coordinates
geometry = gpd.points_from_xy(coordinates.str[0], coordinates.str[1])
@ -31,7 +39,9 @@ if __name__ == '__main__':
# assign ports to nearest region
p = european_ports.to_crs(3857)
r = regions.to_crs(3857)
outflows = p.sjoin_nearest(r).groupby("index_right").properties_outflows.sum().div(1e3)
outflows = (
p.sjoin_nearest(r).groupby("index_right").properties_outflows.sum().div(1e3)
)
# calculate fraction of each country's port outflows
countries = outflows.index.str[:2]
@ -39,7 +49,7 @@ if __name__ == '__main__':
fraction = outflows / countries.map(outflows_per_country)
# distribute per-country demands to nodes based on these fractions
nodal_demand = demand.loc[countries].fillna(0.)
nodal_demand = demand.loc[countries].fillna(0.0)
nodal_demand.index = fraction.index
nodal_demand = nodal_demand.multiply(fraction, axis=0)
nodal_demand = nodal_demand.reindex(regions.index, fill_value=0)

47
scripts/build_solar_thermal_profiles.py
View File

@ -1,18 +1,22 @@
"""Build solar thermal collector time series."""
# -*- coding: utf-8 -*-
"""
Build solar thermal collector time series.
"""
import geopandas as gpd
import atlite
import geopandas as gpd
import numpy as np
import pandas as pd
import xarray as xr
import numpy as np
from dask.distributed import Client, LocalCluster
if __name__ == '__main__':
if 'snakemake' not in globals():
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'build_solar_thermal_profiles',
simpl='',
"build_solar_thermal_profiles",
simpl="",
clusters=48,
)
@ -20,29 +24,36 @@ if __name__ == '__main__':
cluster = LocalCluster(n_workers=nprocesses, threads_per_worker=1)
client = Client(cluster, asynchronous=True)
config = snakemake.config['solar_thermal']
config = snakemake.config["solar_thermal"]
time = pd.date_range(freq='h', **snakemake.config['snapshots'])
cutout_config = snakemake.config['atlite']['cutout']
time = pd.date_range(freq="h", **snakemake.config["snapshots"])
cutout_config = snakemake.config["atlite"]["cutout"]
cutout = atlite.Cutout(cutout_config).sel(time=time)
clustered_regions = gpd.read_file(
snakemake.input.regions_onshore).set_index('name').buffer(0).squeeze()
clustered_regions = (
gpd.read_file(snakemake.input.regions_onshore)
.set_index("name")
.buffer(0)
.squeeze()
)
I = cutout.indicatormatrix(clustered_regions)
pop_layout = xr.open_dataarray(snakemake.input.pop_layout)
stacked_pop = pop_layout.stack(spatial=('y', 'x'))
stacked_pop = pop_layout.stack(spatial=("y", "x"))
M = I.T.dot(np.diag(I.dot(stacked_pop)))
nonzero_sum = M.sum(axis=0, keepdims=True)
nonzero_sum[nonzero_sum == 0.] = 1.
nonzero_sum[nonzero_sum == 0.0] = 1.0
M_tilde = M / nonzero_sum
solar_thermal = cutout.solar_thermal(**config, matrix=M_tilde.T,
index=clustered_regions.index,
dask_kwargs=dict(scheduler=client),
show_progress=False)
solar_thermal = cutout.solar_thermal(
**config,
matrix=M_tilde.T,
index=clustered_regions.index,
dask_kwargs=dict(scheduler=client),
show_progress=False
)
solar_thermal.to_netcdf(snakemake.output.solar_thermal)

46
scripts/build_temperature_profiles.py
View File

@ -1,18 +1,22 @@
"""Build temperature profiles."""
# -*- coding: utf-8 -*-
"""
Build temperature profiles.
"""
import geopandas as gpd
import atlite
import geopandas as gpd
import numpy as np
import pandas as pd
import xarray as xr
import numpy as np
from dask.distributed import Client, LocalCluster
if __name__ == '__main__':
if 'snakemake' not in globals():
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'build_temperature_profiles',
simpl='',
"build_temperature_profiles",
simpl="",
clusters=48,
)
@ -20,34 +24,42 @@ if __name__ == '__main__':
cluster = LocalCluster(n_workers=nprocesses, threads_per_worker=1)
client = Client(cluster, asynchronous=True)
time = pd.date_range(freq='h', **snakemake.config['snapshots'])
cutout_config = snakemake.config['atlite']['cutout']
time = pd.date_range(freq="h", **snakemake.config["snapshots"])
cutout_config = snakemake.config["atlite"]["cutout"]
cutout = atlite.Cutout(cutout_config).sel(time=time)
clustered_regions = gpd.read_file(
snakemake.input.regions_onshore).set_index('name').buffer(0).squeeze()
clustered_regions = (
gpd.read_file(snakemake.input.regions_onshore)
.set_index("name")
.buffer(0)
.squeeze()
)
I = cutout.indicatormatrix(clustered_regions)
pop_layout = xr.open_dataarray(snakemake.input.pop_layout)
stacked_pop = pop_layout.stack(spatial=('y', 'x'))
stacked_pop = pop_layout.stack(spatial=("y", "x"))
M = I.T.dot(np.diag(I.dot(stacked_pop)))
nonzero_sum = M.sum(axis=0, keepdims=True)
nonzero_sum[nonzero_sum == 0.] = 1.
nonzero_sum[nonzero_sum == 0.0] = 1.0
M_tilde = M / nonzero_sum
temp_air = cutout.temperature(
matrix=M_tilde.T, index=clustered_regions.index,
matrix=M_tilde.T,
index=clustered_regions.index,
dask_kwargs=dict(scheduler=client),
show_progress=False)
show_progress=False,
)
temp_air.to_netcdf(snakemake.output.temp_air)
temp_soil = cutout.soil_temperature(
matrix=M_tilde.T, index=clustered_regions.index,
matrix=M_tilde.T,
index=clustered_regions.index,
dask_kwargs=dict(scheduler=client),
show_progress=False)
show_progress=False,
)
temp_soil.to_netcdf(snakemake.output.temp_soil)

33
scripts/build_transport_demand.py
View File

@ -1,13 +1,15 @@
"""Build transport demand."""
# -*- coding: utf-8 -*-
"""
Build transport demand.
"""
import pandas as pd
import numpy as np
import pandas as pd
import xarray as xr
from helper import generate_periodic_profiles
def build_nodal_transport_data(fn, pop_layout):
transport_data = pd.read_csv(fn, index_col=0)
nodal_transport_data = transport_data.loc[pop_layout.ct].fillna(0.0)
@ -24,12 +26,9 @@ def build_nodal_transport_data(fn, pop_layout):
def build_transport_demand(traffic_fn, airtemp_fn, nodes, nodal_transport_data):
## Get overall demand curve for all vehicles
traffic = pd.read_csv(
traffic_fn, skiprows=2, usecols=["count"]
).squeeze("columns")
traffic = pd.read_csv(traffic_fn, skiprows=2, usecols=["count"]).squeeze("columns")
transport_shape = generate_periodic_profiles(
dt_index=snapshots,
@ -94,9 +93,11 @@ def transport_degree_factor(
upper_degree_factor=1.6,
):
"""
Work out how much energy demand in vehicles increases due to heating and cooling.
There is a deadband where there is no increase.
Degree factors are % increase in demand compared to no heating/cooling fuel consumption.
Work out how much energy demand in vehicles increases due to heating and
cooling.
There is a deadband where there is no increase. Degree factors are %
increase in demand compared to no heating/cooling fuel consumption.
Returns per unit increase in demand for each place and time
"""
@ -137,7 +138,6 @@ def bev_availability_profile(fn, snapshots, nodes, options):
def bev_dsm_profile(snapshots, nodes, options):
dsm_week = np.zeros((24 * 7,))
dsm_week[(np.arange(0, 7, 1) * 24 + options["bev_dsm_restriction_time"])] = options[
@ -173,24 +173,23 @@ if __name__ == "__main__":
options = snakemake.config["sector"]
snapshots = pd.date_range(freq='h', **snakemake.config["snapshots"], tz="UTC")
snapshots = pd.date_range(freq="h", **snakemake.config["snapshots"], tz="UTC")
Nyears = 1
nodal_transport_data = build_nodal_transport_data(
snakemake.input.transport_data,
pop_layout
snakemake.input.transport_data, pop_layout
)
transport_demand = build_transport_demand(
snakemake.input.traffic_data_KFZ,
snakemake.input.temp_air_total,
nodes, nodal_transport_data
nodes,
nodal_transport_data,
)
avail_profile = bev_availability_profile(
snakemake.input.traffic_data_Pkw,
snapshots, nodes, options
snakemake.input.traffic_data_Pkw, snapshots, nodes, options
)
dsm_profile = bev_dsm_profile(snapshots, nodes, options)

78
scripts/cluster_gas_network.py
View File

@ -1,45 +1,57 @@
"""Cluster gas network."""
# -*- coding: utf-8 -*-
"""
Cluster gas network.
"""
import logging
logger = logging.getLogger(__name__)
import pandas as pd
import geopandas as gpd
from shapely import wkt
from pypsa.geo import haversine_pts
import pandas as pd
from packaging.version import Version, parse
from pypsa.geo import haversine_pts
from shapely import wkt
def concat_gdf(gdf_list, crs='EPSG:4326'):
"""Concatenate multiple geopandas dataframes with common coordinate reference system (crs)."""
def concat_gdf(gdf_list, crs="EPSG:4326"):
"""
Concatenate multiple geopandas dataframes with common coordinate reference
system (crs).
"""
return gpd.GeoDataFrame(pd.concat(gdf_list), crs=crs)
def load_bus_regions(onshore_path, offshore_path):
"""Load pypsa-eur on- and offshore regions and concat."""
"""
Load pypsa-eur on- and offshore regions and concat.
"""
bus_regions_offshore = gpd.read_file(offshore_path)
bus_regions_onshore = gpd.read_file(onshore_path)
bus_regions = concat_gdf([bus_regions_offshore, bus_regions_onshore])
bus_regions = bus_regions.dissolve(by='name', aggfunc='sum')
bus_regions = bus_regions.dissolve(by="name", aggfunc="sum")
return bus_regions
def build_clustered_gas_network(df, bus_regions, length_factor=1.25):
for i in [0,1]:
for i in [0, 1]:
gdf = gpd.GeoDataFrame(geometry=df[f"point{i}"], crs="EPSG:4326")
kws = dict(op="within") if parse(gpd.__version__) < Version('0.10') else dict(predicate="within")
kws = (
dict(op="within")
if parse(gpd.__version__) < Version("0.10")
else dict(predicate="within")
)
bus_mapping = gpd.sjoin(gdf, bus_regions, how="left", **kws).index_right
bus_mapping = bus_mapping.groupby(bus_mapping.index).first()
df[f"bus{i}"] = bus_mapping
df[f"point{i}"] = df[f"bus{i}"].map(bus_regions.to_crs(3035).centroid.to_crs(4326))
df[f"point{i}"] = df[f"bus{i}"].map(
bus_regions.to_crs(3035).centroid.to_crs(4326)
)
# drop pipes where not both buses are inside regions
df = df.loc[~df.bus0.isna() & ~df.bus1.isna()]
@ -49,10 +61,9 @@ def build_clustered_gas_network(df, bus_regions, length_factor=1.25):
# recalculate lengths as center to center * length factor
df["length"] = df.apply(
lambda p: length_factor * haversine_pts(
[p.point0.x, p.point0.y],
[p.point1.x, p.point1.y]
), axis=1
lambda p: length_factor
* haversine_pts([p.point0.x, p.point0.y], [p.point1.x, p.point1.y]),
axis=1,
)
# tidy and create new numbered index
@ -63,7 +74,6 @@ def build_clustered_gas_network(df, bus_regions, length_factor=1.25):
def reindex_pipes(df):
def make_index(x):
connector = " <-> " if x.bidirectional else " -> "
return "gas pipeline " + x.bus0 + connector + x.bus1
@ -77,33 +87,28 @@ def reindex_pipes(df):
def aggregate_parallel_pipes(df):
strategies = {
'bus0': 'first',
'bus1': 'first',
"p_nom": 'sum',
"p_nom_diameter": 'sum',
"bus0": "first",
"bus1": "first",
"p_nom": "sum",
"p_nom_diameter": "sum",
"max_pressure_bar": "mean",
"build_year": "mean",
"diameter_mm": "mean",
"length": 'mean',
'name': ' '.join,
"p_min_pu": 'min',
"length": "mean",
"name": " ".join,
"p_min_pu": "min",
}
return df.groupby(df.index).agg(strategies)
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'cluster_gas_network',
simpl='',
clusters='37'
)
logging.basicConfig(level=snakemake.config['logging_level'])
snakemake = mock_snakemake("cluster_gas_network", simpl="", clusters="37")
logging.basicConfig(level=snakemake.config["logging_level"])
fn = snakemake.input.cleaned_gas_network
df = pd.read_csv(fn, index_col=0)
@ -111,8 +116,7 @@ if __name__ == "__main__":
df[col] = df[col].apply(wkt.loads)
bus_regions = load_bus_regions(
snakemake.input.regions_onshore,
snakemake.input.regions_offshore
snakemake.input.regions_onshore, snakemake.input.regions_offshore
)
gas_network = build_clustered_gas_network(df, bus_regions)

19
scripts/copy_config.py
View File

@ -1,5 +1,7 @@
# -*- coding: utf-8 -*-
from shutil import copy
import yaml
files = {
@ -7,24 +9,27 @@ files = {
"Snakefile": "Snakefile",
"scripts/solve_network.py": "solve_network.py",
"scripts/prepare_sector_network.py": "prepare_sector_network.py",
"../pypsa-eur/config.yaml": "config.pypsaeur.yaml"
"../pypsa-eur/config.yaml": "config.pypsaeur.yaml",
}
if __name__ == '__main__':
if 'snakemake' not in globals():
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake('copy_config')
basepath = snakemake.config['summary_dir'] + '/' + snakemake.config['run'] + '/configs/'
snakemake = mock_snakemake("copy_config")
basepath = (
snakemake.config["summary_dir"] + "/" + snakemake.config["run"] + "/configs/"
)
for f, name in files.items():
copy(f, basepath + name)
with open(basepath + 'config.snakemake.yaml', 'w') as yaml_file:
with open(basepath + "config.snakemake.yaml", "w") as yaml_file:
yaml.dump(
snakemake.config,
yaml_file,
default_flow_style=False,
allow_unicode=True,
sort_keys=False
sort_keys=False,
)

56
scripts/helper.py
View File

@ -1,22 +1,24 @@
# -*- coding: utf-8 -*-
import contextlib
import logging
import os
import sys
import contextlib
import yaml
import pytz
import pandas as pd
from pathlib import Path
from snakemake.utils import update_config
from pypsa.descriptors import Dict
from pypsa.components import components, component_attrs
import logging
import pandas as pd
import pytz
import yaml
from pypsa.components import component_attrs, components
from pypsa.descriptors import Dict
from snakemake.utils import update_config
logger = logging.getLogger(__name__)
# Define a context manager to temporarily mute print statements
@contextlib.contextmanager
def mute_print():
with open(os.devnull, 'w') as devnull:
with open(os.devnull, "w") as devnull:
with contextlib.redirect_stdout(devnull):
yield
@ -38,7 +40,7 @@ def override_component_attrs(directory):
Dictionary of overridden component attributes.
"""
attrs = Dict({k : v.copy() for k,v in component_attrs.items()})
attrs = Dict({k: v.copy() for k, v in component_attrs.items()})
for component, list_name in components.list_name.items():
fn = f"{directory}/{list_name}.csv"
@ -66,15 +68,17 @@ def mock_snakemake(rulename, **wildcards):
keyword arguments fixing the wildcards. Only necessary if wildcards are
needed.
"""
import snakemake as sm
import os
import snakemake as sm
from packaging.version import Version, parse
from pypsa.descriptors import Dict
from snakemake.script import Snakemake
from packaging.version import Version, parse
script_dir = Path(__file__).parent.resolve()
assert Path.cwd().resolve() == script_dir, \
f'mock_snakemake has to be run from the repository scripts directory {script_dir}'
assert (
Path.cwd().resolve() == script_dir
), f"mock_snakemake has to be run from the repository scripts directory {script_dir}"
os.chdir(script_dir.parent)
for p in sm.SNAKEFILE_CHOICES:
if os.path.exists(p):
@ -95,9 +99,18 @@ def mock_snakemake(rulename, **wildcards):
io[i] = os.path.abspath(io[i])
make_accessable(job.input, job.output, job.log)
snakemake = Snakemake(job.input, job.output, job.params, job.wildcards,
job.threads, job.resources, job.log,
job.dag.workflow.config, job.rule.name, None,)
snakemake = Snakemake(
job.input,
job.output,
job.params,
job.wildcards,
job.threads,
job.resources,
job.log,
job.dag.workflow.config,
job.rule.name,
None,
)
# create log and output dir if not existent
for path in list(snakemake.log) + list(snakemake.output):
Path(path).parent.mkdir(parents=True, exist_ok=True)
@ -105,15 +118,17 @@ def mock_snakemake(rulename, **wildcards):
os.chdir(script_dir)
return snakemake
# from pypsa-eur/_helpers.py
def progress_retrieve(url, file):
import urllib
from progressbar import ProgressBar
pbar = ProgressBar(0, 100)
def dlProgress(count, blockSize, totalSize):
pbar.update( int(count * blockSize * 100 / totalSize) )
pbar.update(int(count * blockSize * 100 / totalSize))
urllib.request.urlretrieve(url, file, reporthook=dlProgress)
@ -121,10 +136,11 @@ def progress_retrieve(url, file):
def generate_periodic_profiles(dt_index, nodes, weekly_profile, localize=None):
"""
Give a 24*7 long list of weekly hourly profiles, generate this for each
country for the period dt_index, taking account of time zones and summer time.
country for the period dt_index, taking account of time zones and summer
time.
"""
weekly_profile = pd.Series(weekly_profile, range(24*7))
weekly_profile = pd.Series(weekly_profile, range(24 * 7))
week_df = pd.DataFrame(index=dt_index, columns=nodes)

449
scripts/make_summary.py
View File

@ -1,23 +1,20 @@
# -*- coding: utf-8 -*-
import logging
logger = logging.getLogger(__name__)
import sys
import yaml
import pypsa
import numpy as np
import pandas as pd
from prepare_sector_network import prepare_costs
import pypsa
import yaml
from helper import override_component_attrs
from prepare_sector_network import prepare_costs
idx = pd.IndexSlice
opt_name = {
"Store": "e",
"Line": "s",
"Transformer": "s"
}
opt_name = {"Store": "e", "Line": "s", "Transformer": "s"}
def assign_carriers(n):
@ -26,20 +23,25 @@ def assign_carriers(n):
def assign_locations(n):
for c in n.iterate_components(n.one_port_components|n.branch_components):
ifind = pd.Series(c.df.index.str.find(" ",start=4),c.df.index)
for c in n.iterate_components(n.one_port_components | n.branch_components):
ifind = pd.Series(c.df.index.str.find(" ", start=4), c.df.index)
for i in ifind.unique():
names = ifind.index[ifind == i]
if i == -1:
c.df.loc[names, 'location'] = ""
c.df.loc[names, "location"] = ""
else:
c.df.loc[names, 'location'] = names.str[:i]
c.df.loc[names, "location"] = names.str[:i]
def calculate_nodal_cfs(n, label, nodal_cfs):
#Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff
for c in n.iterate_components((n.branch_components^{"Line","Transformer"})|n.controllable_one_port_components^{"Load","StorageUnit"}):
capacities_c = c.df.groupby(["location","carrier"])[opt_name.get(c.name,"p") + "_nom_opt"].sum()
# Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff
for c in n.iterate_components(
(n.branch_components ^ {"Line", "Transformer"})
| n.controllable_one_port_components ^ {"Load", "StorageUnit"}
):
capacities_c = c.df.groupby(["location", "carrier"])[
opt_name.get(c.name, "p") + "_nom_opt"
].sum()
if c.name == "Link":
p = c.pnl.p0.abs().mean()
@ -53,21 +55,27 @@ def calculate_nodal_cfs(n, label, nodal_cfs):
c.df["p"] = p
p_c = c.df.groupby(["location", "carrier"])["p"].sum()
cf_c = p_c/capacities_c
cf_c = p_c / capacities_c
index = pd.MultiIndex.from_tuples([(c.list_name,) + t for t in cf_c.index.to_list()])
index = pd.MultiIndex.from_tuples(
[(c.list_name,) + t for t in cf_c.index.to_list()]
)
nodal_cfs = nodal_cfs.reindex(index.union(nodal_cfs.index))
nodal_cfs.loc[index,label] = cf_c.values
nodal_cfs.loc[index, label] = cf_c.values
return nodal_cfs
def calculate_cfs(n, label, cfs):
for c in n.iterate_components(
n.branch_components
| n.controllable_one_port_components ^ {"Load", "StorageUnit"}
):
capacities_c = (
c.df[opt_name.get(c.name, "p") + "_nom_opt"].groupby(c.df.carrier).sum()
)
for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load","StorageUnit"}):
capacities_c = c.df[opt_name.get(c.name,"p") + "_nom_opt"].groupby(c.df.carrier).sum()
if c.name in ["Link","Line","Transformer"]:
if c.name in ["Link", "Line", "Transformer"]:
p = c.pnl.p0.abs().mean()
elif c.name == "Store":
p = c.pnl.e.abs().mean()
@ -76,25 +84,31 @@ def calculate_cfs(n, label, cfs):
p_c = p.groupby(c.df.carrier).sum()
cf_c = p_c/capacities_c
cf_c = p_c / capacities_c
cf_c = pd.concat([cf_c], keys=[c.list_name])
cfs = cfs.reindex(cf_c.index.union(cfs.index))
cfs.loc[cf_c.index,label] = cf_c
cfs.loc[cf_c.index, label] = cf_c
return cfs
def calculate_nodal_costs(n, label, nodal_costs):
#Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff
for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}):
c.df["capital_costs"] = c.df.capital_cost * c.df[opt_name.get(c.name, "p") + "_nom_opt"]
# Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff
for c in n.iterate_components(
n.branch_components | n.controllable_one_port_components ^ {"Load"}
):
c.df["capital_costs"] = (
c.df.capital_cost * c.df[opt_name.get(c.name, "p") + "_nom_opt"]
)
capital_costs = c.df.groupby(["location", "carrier"])["capital_costs"].sum()
index = pd.MultiIndex.from_tuples([(c.list_name, "capital") + t for t in capital_costs.index.to_list()])
index = pd.MultiIndex.from_tuples(
[(c.list_name, "capital") + t for t in capital_costs.index.to_list()]
)
nodal_costs = nodal_costs.reindex(index.union(nodal_costs.index))
nodal_costs.loc[index,label] = capital_costs.values
nodal_costs.loc[index, label] = capital_costs.values
if c.name == "Link":
p = c.pnl.p0.multiply(n.snapshot_weightings.generators, axis=0).sum()
@ -102,19 +116,23 @@ def calculate_nodal_costs(n, label, nodal_costs):
continue
elif c.name == "StorageUnit":
p_all = c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0)
p_all[p_all < 0.] = 0.
p_all[p_all < 0.0] = 0.0
p = p_all.sum()
else:
p = c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0).sum()
#correct sequestration cost
# correct sequestration cost
if c.name == "Store":
items = c.df.index[(c.df.carrier == "co2 stored") & (c.df.marginal_cost <= -100.)]
c.df.loc[items, "marginal_cost"] = -20.
items = c.df.index[
(c.df.carrier == "co2 stored") & (c.df.marginal_cost <= -100.0)
]
c.df.loc[items, "marginal_cost"] = -20.0
c.df["marginal_costs"] = p*c.df.marginal_cost
c.df["marginal_costs"] = p * c.df.marginal_cost
marginal_costs = c.df.groupby(["location", "carrier"])["marginal_costs"].sum()
index = pd.MultiIndex.from_tuples([(c.list_name, "marginal") + t for t in marginal_costs.index.to_list()])
index = pd.MultiIndex.from_tuples(
[(c.list_name, "marginal") + t for t in marginal_costs.index.to_list()]
)
nodal_costs = nodal_costs.reindex(index.union(nodal_costs.index))
nodal_costs.loc[index, label] = marginal_costs.values
@ -122,9 +140,10 @@ def calculate_nodal_costs(n, label, nodal_costs):
def calculate_costs(n, label, costs):
for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}):
capital_costs = c.df.capital_cost*c.df[opt_name.get(c.name,"p") + "_nom_opt"]
for c in n.iterate_components(
n.branch_components | n.controllable_one_port_components ^ {"Load"}
):
capital_costs = c.df.capital_cost * c.df[opt_name.get(c.name, "p") + "_nom_opt"]
capital_costs_grouped = capital_costs.groupby(c.df.carrier).sum()
capital_costs_grouped = pd.concat([capital_costs_grouped], keys=["capital"])
@ -140,17 +159,19 @@ def calculate_costs(n, label, costs):
continue
elif c.name == "StorageUnit":
p_all = c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0)
p_all[p_all < 0.] = 0.
p_all[p_all < 0.0] = 0.0
p = p_all.sum()
else:
p = c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0).sum()
#correct sequestration cost
# correct sequestration cost
if c.name == "Store":
items = c.df.index[(c.df.carrier == "co2 stored") & (c.df.marginal_cost <= -100.)]
c.df.loc[items, "marginal_cost"] = -20.
items = c.df.index[
(c.df.carrier == "co2 stored") & (c.df.marginal_cost <= -100.0)
]
c.df.loc[items, "marginal_cost"] = -20.0
marginal_costs = p*c.df.marginal_cost
marginal_costs = p * c.df.marginal_cost
marginal_costs_grouped = marginal_costs.groupby(c.df.carrier).sum()
@ -159,54 +180,79 @@ def calculate_costs(n, label, costs):
costs = costs.reindex(marginal_costs_grouped.index.union(costs.index))
costs.loc[marginal_costs_grouped.index,label] = marginal_costs_grouped
costs.loc[marginal_costs_grouped.index, label] = marginal_costs_grouped
# add back in all hydro
#costs.loc[("storage_units", "capital", "hydro"),label] = (0.01)*2e6*n.storage_units.loc[n.storage_units.group=="hydro", "p_nom"].sum()
#costs.loc[("storage_units", "capital", "PHS"),label] = (0.01)*2e6*n.storage_units.loc[n.storage_units.group=="PHS", "p_nom"].sum()
#costs.loc[("generators", "capital", "ror"),label] = (0.02)*3e6*n.generators.loc[n.generators.group=="ror", "p_nom"].sum()
# costs.loc[("storage_units", "capital", "hydro"),label] = (0.01)*2e6*n.storage_units.loc[n.storage_units.group=="hydro", "p_nom"].sum()
# costs.loc[("storage_units", "capital", "PHS"),label] = (0.01)*2e6*n.storage_units.loc[n.storage_units.group=="PHS", "p_nom"].sum()
# costs.loc[("generators", "capital", "ror"),label] = (0.02)*3e6*n.generators.loc[n.generators.group=="ror", "p_nom"].sum()
return costs
def calculate_cumulative_cost():
planning_horizons = snakemake.config['scenario']['planning_horizons']
planning_horizons = snakemake.config["scenario"]["planning_horizons"]
cumulative_cost = pd.DataFrame(index = df["costs"].sum().index,
columns=pd.Series(data=np.arange(0,0.1, 0.01), name='social discount rate'))
cumulative_cost = pd.DataFrame(
index=df["costs"].sum().index,
columns=pd.Series(data=np.arange(0, 0.1, 0.01), name="social discount rate"),
)
#discount cost and express them in money value of planning_horizons[0]
# discount cost and express them in money value of planning_horizons[0]
for r in cumulative_cost.columns:
cumulative_cost[r]=[df["costs"].sum()[index]/((1+r)**(index[-1]-planning_horizons[0])) for index in cumulative_cost.index]
cumulative_cost[r] = [
df["costs"].sum()[index] / ((1 + r) ** (index[-1] - planning_horizons[0]))
for index in cumulative_cost.index
]
#integrate cost throughout the transition path
# integrate cost throughout the transition path
for r in cumulative_cost.columns:
for cluster in cumulative_cost.index.get_level_values(level=0).unique():
for lv in cumulative_cost.index.get_level_values(level=1).unique():
for sector_opts in cumulative_cost.index.get_level_values(level=2).unique():
cumulative_cost.loc[(cluster, lv, sector_opts, 'cumulative cost'),r] = np.trapz(cumulative_cost.loc[idx[cluster, lv, sector_opts,planning_horizons],r].values, x=planning_horizons)
for sector_opts in cumulative_cost.index.get_level_values(
level=2
).unique():
cumulative_cost.loc[
(cluster, lv, sector_opts, "cumulative cost"), r
] = np.trapz(
cumulative_cost.loc[
idx[cluster, lv, sector_opts, planning_horizons], r
].values,
x=planning_horizons,
)
return cumulative_cost
def calculate_nodal_capacities(n, label, nodal_capacities):
#Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff
for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}):
nodal_capacities_c = c.df.groupby(["location","carrier"])[opt_name.get(c.name,"p") + "_nom_opt"].sum()
index = pd.MultiIndex.from_tuples([(c.list_name,) + t for t in nodal_capacities_c.index.to_list()])
# Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff
for c in n.iterate_components(
n.branch_components | n.controllable_one_port_components ^ {"Load"}
):
nodal_capacities_c = c.df.groupby(["location", "carrier"])[
opt_name.get(c.name, "p") + "_nom_opt"
].sum()
index = pd.MultiIndex.from_tuples(
[(c.list_name,) + t for t in nodal_capacities_c.index.to_list()]
)
nodal_capacities = nodal_capacities.reindex(index.union(nodal_capacities.index))
nodal_capacities.loc[index,label] = nodal_capacities_c.values
nodal_capacities.loc[index, label] = nodal_capacities_c.values
return nodal_capacities
def calculate_capacities(n, label, capacities):
for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}):
capacities_grouped = c.df[opt_name.get(c.name,"p") + "_nom_opt"].groupby(c.df.carrier).sum()
for c in n.iterate_components(
n.branch_components | n.controllable_one_port_components ^ {"Load"}
):
capacities_grouped = (
c.df[opt_name.get(c.name, "p") + "_nom_opt"].groupby(c.df.carrier).sum()
)
capacities_grouped = pd.concat([capacities_grouped], keys=[c.list_name])
capacities = capacities.reindex(capacities_grouped.index.union(capacities.index))
capacities = capacities.reindex(
capacities_grouped.index.union(capacities.index)
)
capacities.loc[capacities_grouped.index, label] = capacities_grouped
@ -214,28 +260,42 @@ def calculate_capacities(n, label, capacities):
def calculate_curtailment(n, label, curtailment):
avail = n.generators_t.p_max_pu.multiply(n.generators.p_nom_opt).sum().groupby(n.generators.carrier).sum()
avail = (
n.generators_t.p_max_pu.multiply(n.generators.p_nom_opt)
.sum()
.groupby(n.generators.carrier)
.sum()
)
used = n.generators_t.p.sum().groupby(n.generators.carrier).sum()
curtailment[label] = (((avail - used)/avail)*100).round(3)
curtailment[label] = (((avail - used) / avail) * 100).round(3)
return curtailment
def calculate_energy(n, label, energy):
for c in n.iterate_components(n.one_port_components|n.branch_components):
for c in n.iterate_components(n.one_port_components | n.branch_components):
if c.name in n.one_port_components:
c_energies = c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0).sum().multiply(c.df.sign).groupby(c.df.carrier).sum()
c_energies = (
c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0)
.sum()
.multiply(c.df.sign)
.groupby(c.df.carrier)
.sum()
)
else:
c_energies = pd.Series(0., c.df.carrier.unique())
c_energies = pd.Series(0.0, c.df.carrier.unique())
for port in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
totals = c.pnl["p" + port].multiply(n.snapshot_weightings.generators, axis=0).sum()
#remove values where bus is missing (bug in nomopyomo)
totals = (
c.pnl["p" + port]
.multiply(n.snapshot_weightings.generators, axis=0)
.sum()
)
# remove values where bus is missing (bug in nomopyomo)
no_bus = c.df.index[c.df["bus" + port] == ""]
totals.loc[no_bus] = n.component_attrs[c.name].loc["p" + port, "default"]
totals.loc[no_bus] = n.component_attrs[c.name].loc[
"p" + port, "default"
]
c_energies -= totals.groupby(c.df.carrier).sum()
c_energies = pd.concat([c_energies], keys=[c.list_name])
@ -248,40 +308,47 @@ def calculate_energy(n, label, energy):
def calculate_supply(n, label, supply):
"""calculate the max dispatch of each component at the buses aggregated by carrier"""
"""
Calculate the max dispatch of each component at the buses aggregated by
carrier.
"""
bus_carriers = n.buses.carrier.unique()
for i in bus_carriers:
bus_map = (n.buses.carrier == i)
bus_map = n.buses.carrier == i
bus_map.at[""] = False
for c in n.iterate_components(n.one_port_components):
items = c.df.index[c.df.bus.map(bus_map).fillna(False)]
if len(items) == 0:
continue
s = c.pnl.p[items].max().multiply(c.df.loc[items, 'sign']).groupby(c.df.loc[items, 'carrier']).sum()
s = (
c.pnl.p[items]
.max()
.multiply(c.df.loc[items, "sign"])
.groupby(c.df.loc[items, "carrier"])
.sum()
)
s = pd.concat([s], keys=[c.list_name])
s = pd.concat([s], keys=[i])
supply = supply.reindex(s.index.union(supply.index))
supply.loc[s.index,label] = s
supply.loc[s.index, label] = s
for c in n.iterate_components(n.branch_components):
for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
items = c.df.index[c.df["bus" + end].map(bus_map).fillna(False)]
if len(items) == 0:
continue
#lots of sign compensation for direction and to do maximums
s = (-1)**(1-int(end))*((-1)**int(end)*c.pnl["p"+end][items]).max().groupby(c.df.loc[items, 'carrier']).sum()
# lots of sign compensation for direction and to do maximums
s = (-1) ** (1 - int(end)) * (
(-1) ** int(end) * c.pnl["p" + end][items]
).max().groupby(c.df.loc[items, "carrier"]).sum()
s.index = s.index + end
s = pd.concat([s], keys=[c.list_name])
s = pd.concat([s], keys=[i])
@ -291,46 +358,56 @@ def calculate_supply(n, label, supply):
return supply
def calculate_supply_energy(n, label, supply_energy):
"""calculate the total energy supply/consuption of each component at the buses aggregated by carrier"""
def calculate_supply_energy(n, label, supply_energy):
"""
Calculate the total energy supply/consuption of each component at the buses
aggregated by carrier.
"""
bus_carriers = n.buses.carrier.unique()
for i in bus_carriers:
bus_map = (n.buses.carrier == i)
bus_map = n.buses.carrier == i
bus_map.at[""] = False
for c in n.iterate_components(n.one_port_components):
items = c.df.index[c.df.bus.map(bus_map).fillna(False)]
if len(items) == 0:
continue
s = c.pnl.p[items].multiply(n.snapshot_weightings.generators,axis=0).sum().multiply(c.df.loc[items, 'sign']).groupby(c.df.loc[items, 'carrier']).sum()
s = (
c.pnl.p[items]
.multiply(n.snapshot_weightings.generators, axis=0)
.sum()
.multiply(c.df.loc[items, "sign"])
.groupby(c.df.loc[items, "carrier"])
.sum()
)
s = pd.concat([s], keys=[c.list_name])
s = pd.concat([s], keys=[i])
supply_energy = supply_energy.reindex(s.index.union(supply_energy.index))
supply_energy.loc[s.index, label] = s
for c in n.iterate_components(n.branch_components):
for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
items = c.df.index[c.df["bus" + str(end)].map(bus_map).fillna(False)]
if len(items) == 0:
continue
s = (-1)*c.pnl["p"+end][items].multiply(n.snapshot_weightings.generators,axis=0).sum().groupby(c.df.loc[items, 'carrier']).sum()
s = (-1) * c.pnl["p" + end][items].multiply(
n.snapshot_weightings.generators, axis=0
).sum().groupby(c.df.loc[items, "carrier"]).sum()
s.index = s.index + end
s = pd.concat([s], keys=[c.list_name])
s = pd.concat([s], keys=[i])
supply_energy = supply_energy.reindex(s.index.union(supply_energy.index))
supply_energy = supply_energy.reindex(
s.index.union(supply_energy.index)
)
supply_energy.loc[s.index, label] = s
@ -338,21 +415,24 @@ def calculate_supply_energy(n, label, supply_energy):
def calculate_metrics(n, label, metrics):
metrics_list = [
"line_volume",
"line_volume_limit",
"line_volume_AC",
"line_volume_DC",
"line_volume_shadow",
"co2_shadow"
"co2_shadow",
]
metrics = metrics.reindex(pd.Index(metrics_list).union(metrics.index))
metrics.at["line_volume_DC",label] = (n.links.length * n.links.p_nom_opt)[n.links.carrier == "DC"].sum()
metrics.at["line_volume_AC",label] = (n.lines.length * n.lines.s_nom_opt).sum()
metrics.at["line_volume",label] = metrics.loc[["line_volume_AC", "line_volume_DC"], label].sum()
metrics.at["line_volume_DC", label] = (n.links.length * n.links.p_nom_opt)[
n.links.carrier == "DC"
].sum()
metrics.at["line_volume_AC", label] = (n.lines.length * n.lines.s_nom_opt).sum()
metrics.at["line_volume", label] = metrics.loc[
["line_volume_AC", "line_volume_DC"], label
].sum()
if hasattr(n, "line_volume_limit"):
metrics.at["line_volume_limit", label] = n.line_volume_limit
@ -365,10 +445,9 @@ def calculate_metrics(n, label, metrics):
def calculate_prices(n, label, prices):
prices = prices.reindex(prices.index.union(n.buses.carrier.unique()))
#WARNING: this is time-averaged, see weighted_prices for load-weighted average
# WARNING: this is time-averaged, see weighted_prices for load-weighted average
prices[label] = n.buses_t.marginal_price.mean().groupby(n.buses.carrier).mean()
return prices
@ -377,32 +456,42 @@ def calculate_prices(n, label, prices):
def calculate_weighted_prices(n, label, weighted_prices):
# Warning: doesn't include storage units as loads
weighted_prices = weighted_prices.reindex(pd.Index([
"electricity",
"heat",
"space heat",
"urban heat",
"space urban heat",
"gas",
"H2"
]))
weighted_prices = weighted_prices.reindex(
pd.Index(
[
"electricity",
"heat",
"space heat",
"urban heat",
"space urban heat",
"gas",
"H2",
]
)
)
link_loads = {"electricity": ["heat pump", "resistive heater", "battery charger", "H2 Electrolysis"],
"heat": ["water tanks charger"],
"urban heat": ["water tanks charger"],
"space heat": [],
"space urban heat": [],
"gas": ["OCGT", "gas boiler", "CHP electric", "CHP heat"],
"H2": ["Sabatier", "H2 Fuel Cell"]}
link_loads = {
"electricity": [
"heat pump",
"resistive heater",
"battery charger",
"H2 Electrolysis",
],
"heat": ["water tanks charger"],
"urban heat": ["water tanks charger"],
"space heat": [],
"space urban heat": [],
"gas": ["OCGT", "gas boiler", "CHP electric", "CHP heat"],
"H2": ["Sabatier", "H2 Fuel Cell"],
}
for carrier in link_loads:
if carrier == "electricity":
suffix = ""
elif carrier[:5] == "space":
suffix = carrier[5:]
else:
suffix = " " + carrier
suffix = " " + carrier
buses = n.buses.index[n.buses.index.str[2:] == suffix]
@ -410,28 +499,33 @@ def calculate_weighted_prices(n, label, weighted_prices):
continue
if carrier in ["H2", "gas"]:
load = pd.DataFrame(index=n.snapshots, columns=buses, data=0.)
load = pd.DataFrame(index=n.snapshots, columns=buses, data=0.0)
elif carrier[:5] == "space":
load = heat_demand_df[buses.str[:2]].rename(columns=lambda i: str(i)+suffix)
load = heat_demand_df[buses.str[:2]].rename(
columns=lambda i: str(i) + suffix
)
else:
load = n.loads_t.p_set[buses]
for tech in link_loads[carrier]:
names = n.links.index[n.links.index.to_series().str[-len(tech):] == tech]
names = n.links.index[n.links.index.to_series().str[-len(tech) :] == tech]
if names.empty:
continue
load += n.links_t.p0[names].groupby(n.links.loc[names, "bus0"],axis=1).sum()
load += (
n.links_t.p0[names].groupby(n.links.loc[names, "bus0"], axis=1).sum()
)
# Add H2 Store when charging
#if carrier == "H2":
# if carrier == "H2":
# stores = n.stores_t.p[buses+ " Store"].groupby(n.stores.loc[buses+ " Store", "bus"],axis=1).sum(axis=1)
# stores[stores > 0.] = 0.
# load += -stores
weighted_prices.loc[carrier,label] = (load * n.buses_t.marginal_price[buses]).sum().sum() / load.sum().sum()
weighted_prices.loc[carrier, label] = (
load * n.buses_t.marginal_price[buses]
).sum().sum() / load.sum().sum()
# still have no idea what this is for, only for debug reasons.
if carrier[:5] == "space":
@ -455,21 +549,24 @@ def calculate_market_values(n, label, market_values):
market_values = market_values.reindex(market_values.index.union(techs))
for tech in techs:
gens = generators[n.generators.loc[generators, "carrier"] == tech]
dispatch = n.generators_t.p[gens].groupby(n.generators.loc[gens, "bus"], axis=1).sum().reindex(columns=buses, fill_value=0.)
dispatch = (
n.generators_t.p[gens]
.groupby(n.generators.loc[gens, "bus"], axis=1)
.sum()
.reindex(columns=buses, fill_value=0.0)
)
revenue = dispatch * n.buses_t.marginal_price[buses]
market_values.at[tech,label] = revenue.sum().sum() / dispatch.sum().sum()
market_values.at[tech, label] = revenue.sum().sum() / dispatch.sum().sum()
## Now do market value of links ##
for i in ["0", "1"]:
all_links = n.links.index[n.buses.loc[n.links["bus"+i], "carrier"] == carrier]
all_links = n.links.index[n.buses.loc[n.links["bus" + i], "carrier"] == carrier]
techs = n.links.loc[all_links, "carrier"].value_counts().index
@ -478,39 +575,51 @@ def calculate_market_values(n, label, market_values):
for tech in techs:
links = all_links[n.links.loc[all_links, "carrier"] == tech]
dispatch = n.links_t["p"+i][links].groupby(n.links.loc[links, "bus"+i], axis=1).sum().reindex(columns=buses, fill_value=0.)
dispatch = (
n.links_t["p" + i][links]
.groupby(n.links.loc[links, "bus" + i], axis=1)
.sum()
.reindex(columns=buses, fill_value=0.0)
)
revenue = dispatch * n.buses_t.marginal_price[buses]
market_values.at[tech,label] = revenue.sum().sum() / dispatch.sum().sum()
market_values.at[tech, label] = revenue.sum().sum() / dispatch.sum().sum()
return market_values
def calculate_price_statistics(n, label, price_statistics):
price_statistics = price_statistics.reindex(price_statistics.index.union(pd.Index(["zero_hours", "mean", "standard_deviation"])))
price_statistics = price_statistics.reindex(
price_statistics.index.union(
pd.Index(["zero_hours", "mean", "standard_deviation"])
)
)
buses = n.buses.index[n.buses.carrier == "AC"]
threshold = 0.1 # higher than phoney marginal_cost of wind/solar
threshold = 0.1 # higher than phoney marginal_cost of wind/solar
df = pd.DataFrame(data=0., columns=buses, index=n.snapshots)
df = pd.DataFrame(data=0.0, columns=buses, index=n.snapshots)
df[n.buses_t.marginal_price[buses] < threshold] = 1.
df[n.buses_t.marginal_price[buses] < threshold] = 1.0
price_statistics.at["zero_hours", label] = df.sum().sum() / (df.shape[0] * df.shape[1])
price_statistics.at["zero_hours", label] = df.sum().sum() / (
df.shape[0] * df.shape[1]
)
price_statistics.at["mean", label] = n.buses_t.marginal_price[buses].unstack().mean()
price_statistics.at["mean", label] = (
n.buses_t.marginal_price[buses].unstack().mean()
)
price_statistics.at["standard_deviation", label] = n.buses_t.marginal_price[buses].unstack().std()
price_statistics.at["standard_deviation", label] = (
n.buses_t.marginal_price[buses].unstack().std()
)
return price_statistics
def make_summaries(networks_dict):
outputs = [
"nodal_costs",
"nodal_capacities",
@ -530,8 +639,7 @@ def make_summaries(networks_dict):
]
columns = pd.MultiIndex.from_tuples(
networks_dict.keys(),
names=["cluster", "lv", "opt", "planning_horizon"]
networks_dict.keys(), names=["cluster", "lv", "opt", "planning_horizon"]
)
df = {}
@ -560,41 +668,48 @@ def to_csv(df):
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake('make_summary')
logging.basicConfig(level=snakemake.config['logging_level'])
snakemake = mock_snakemake("make_summary")
logging.basicConfig(level=snakemake.config["logging_level"])
networks_dict = {
(cluster, lv, opt+sector_opt, planning_horizon) :
snakemake.config['results_dir'] + snakemake.config['run'] + f'/postnetworks/elec_s{simpl}_{cluster}_lv{lv}_{opt}_{sector_opt}_{planning_horizon}.nc' \
for simpl in snakemake.config['scenario']['simpl'] \
for cluster in snakemake.config['scenario']['clusters'] \
for opt in snakemake.config['scenario']['opts'] \
for sector_opt in snakemake.config['scenario']['sector_opts'] \
for lv in snakemake.config['scenario']['lv'] \
for planning_horizon in snakemake.config['scenario']['planning_horizons']
(cluster, lv, opt + sector_opt, planning_horizon): snakemake.config[
"results_dir"
]
+ snakemake.config["run"]
+ f"/postnetworks/elec_s{simpl}_{cluster}_lv{lv}_{opt}_{sector_opt}_{planning_horizon}.nc"
for simpl in snakemake.config["scenario"]["simpl"]
for cluster in snakemake.config["scenario"]["clusters"]
for opt in snakemake.config["scenario"]["opts"]
for sector_opt in snakemake.config["scenario"]["sector_opts"]
for lv in snakemake.config["scenario"]["lv"]
for planning_horizon in snakemake.config["scenario"]["planning_horizons"]
}
Nyears = 1
costs_db = prepare_costs(
snakemake.input.costs,
snakemake.config['costs']['USD2013_to_EUR2013'],
snakemake.config['costs']['discountrate'],
snakemake.config["costs"]["USD2013_to_EUR2013"],
snakemake.config["costs"]["discountrate"],
Nyears,
snakemake.config['costs']['lifetime']
snakemake.config["costs"]["lifetime"],
)
df = make_summaries(networks_dict)
df["metrics"].loc["total costs"] = df["costs"].sum()
df["metrics"].loc["total costs"] = df["costs"].sum()
to_csv(df)
if snakemake.config["foresight"]=='myopic':
cumulative_cost=calculate_cumulative_cost()
cumulative_cost.to_csv(snakemake.config['summary_dir'] + '/' + snakemake.config['run'] + '/csvs/cumulative_cost.csv')
if snakemake.config["foresight"] == "myopic":
cumulative_cost = calculate_cumulative_cost()
cumulative_cost.to_csv(
snakemake.config["summary_dir"]
+ "/"
+ snakemake.config["run"]
+ "/csvs/cumulative_cost.csv"
)

508
scripts/plot_network.py
View File

@ -1,20 +1,19 @@
# -*- coding: utf-8 -*-
import logging
logger = logging.getLogger(__name__)
import pypsa
import pandas as pd
import cartopy.crs as ccrs
import geopandas as gpd
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
from pypsa.plot import add_legend_circles, add_legend_patches, add_legend_lines
from make_summary import assign_carriers
from plot_summary import rename_techs, preferred_order
import pandas as pd
import pypsa
from helper import override_component_attrs
from make_summary import assign_carriers
from plot_summary import preferred_order, rename_techs
from pypsa.plot import add_legend_circles, add_legend_lines, add_legend_patches
plt.style.use(['ggplot', "matplotlibrc"])
plt.style.use(["ggplot", "matplotlibrc"])
def rename_techs_tyndp(tech):
@ -46,15 +45,20 @@ def assign_location(n):
ifind = pd.Series(c.df.index.str.find(" ", start=4), c.df.index)
for i in ifind.value_counts().index:
# these have already been assigned defaults
if i == -1: continue
if i == -1:
continue
names = ifind.index[ifind == i]
c.df.loc[names, 'location'] = names.str[:i]
c.df.loc[names, "location"] = names.str[:i]
def plot_map(network, components=["links", "stores", "storage_units", "generators"],
bus_size_factor=1.7e10, transmission=False, with_legend=True):
tech_colors = snakemake.config['plotting']['tech_colors']
def plot_map(
network,
components=["links", "stores", "storage_units", "generators"],
bus_size_factor=1.7e10,
transmission=False,
with_legend=True,
):
tech_colors = snakemake.config["plotting"]["tech_colors"]
n = network.copy()
assign_location(n)
@ -73,19 +77,24 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
attr = "e_nom_opt" if comp == "stores" else "p_nom_opt"
costs_c = ((df_c.capital_cost * df_c[attr])
.groupby([df_c.location, df_c.nice_group]).sum()
.unstack().fillna(0.))
costs_c = (
(df_c.capital_cost * df_c[attr])
.groupby([df_c.location, df_c.nice_group])
.sum()
.unstack()
.fillna(0.0)
)
costs = pd.concat([costs, costs_c], axis=1)
logger.debug(f"{comp}, {costs}")
costs = costs.groupby(costs.columns, axis=1).sum()
costs.drop(list(costs.columns[(costs == 0.).all()]), axis=1, inplace=True)
costs.drop(list(costs.columns[(costs == 0.0).all()]), axis=1, inplace=True)
new_columns = (preferred_order.intersection(costs.columns)
.append(costs.columns.difference(preferred_order)))
new_columns = preferred_order.intersection(costs.columns).append(
costs.columns.difference(preferred_order)
)
costs = costs[new_columns]
for item in new_columns:
@ -95,12 +104,16 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
costs = costs.stack() # .sort_index()
# hack because impossible to drop buses...
eu_location = snakemake.config["plotting"].get("eu_node_location", dict(x=-5.5, y=46))
eu_location = snakemake.config["plotting"].get(
"eu_node_location", dict(x=-5.5, y=46)
)
n.buses.loc["EU gas", "x"] = eu_location["x"]
n.buses.loc["EU gas", "y"] = eu_location["y"]
n.links.drop(n.links.index[(n.links.carrier != "DC") & (
n.links.carrier != "B2B")], inplace=True)
n.links.drop(
n.links.index[(n.links.carrier != "DC") & (n.links.carrier != "B2B")],
inplace=True,
)
# drop non-bus
to_drop = costs.index.levels[0].symmetric_difference(n.buses.index)
@ -111,13 +124,13 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
# make sure they are removed from index
costs.index = pd.MultiIndex.from_tuples(costs.index.values)
threshold = 100e6 # 100 mEUR/a
threshold = 100e6 # 100 mEUR/a
carriers = costs.groupby(level=1).sum()
carriers = carriers.where(carriers > threshold).dropna()
carriers = list(carriers.index)
# PDF has minimum width, so set these to zero
line_lower_threshold = 500.
line_lower_threshold = 500.0
line_upper_threshold = 1e4
linewidth_factor = 4e3
ac_color = "rosybrown"
@ -133,7 +146,7 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
line_widths = n.lines.s_nom_opt
link_widths = n.links.p_nom_opt
linewidth_factor = 2e3
line_lower_threshold = 0.
line_lower_threshold = 0.0
title = "current grid"
else:
line_widths = n.lines.s_nom_opt - n.lines.s_nom_min
@ -145,11 +158,11 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
link_widths = n.links.p_nom_opt
title = "total grid"
line_widths = line_widths.clip(line_lower_threshold,line_upper_threshold)
link_widths = link_widths.clip(line_lower_threshold,line_upper_threshold)
line_widths = line_widths.clip(line_lower_threshold, line_upper_threshold)
link_widths = link_widths.clip(line_lower_threshold, line_upper_threshold)
line_widths = line_widths.replace(line_lower_threshold,0)
link_widths = link_widths.replace(line_lower_threshold,0)
line_widths = line_widths.replace(line_lower_threshold, 0)
link_widths = link_widths.replace(line_lower_threshold, 0)
fig, ax = plt.subplots(subplot_kw={"projection": ccrs.EqualEarth()})
fig.set_size_inches(7, 6)
@ -161,12 +174,13 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
link_colors=dc_color,
line_widths=line_widths / linewidth_factor,
link_widths=link_widths / linewidth_factor,
ax=ax, **map_opts
ax=ax,
**map_opts,
)
sizes = [20, 10, 5]
labels = [f"{s} bEUR/a" for s in sizes]
sizes = [s/bus_size_factor*1e9 for s in sizes]
sizes = [s / bus_size_factor * 1e9 for s in sizes]
legend_kw = dict(
loc="upper left",
@ -174,7 +188,7 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
labelspacing=0.8,
frameon=False,
handletextpad=0,
title='system cost',
title="system cost",
)
add_legend_circles(
@ -183,13 +197,13 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
labels,
srid=n.srid,
patch_kw=dict(facecolor="lightgrey"),
legend_kw=legend_kw
legend_kw=legend_kw,
)
sizes = [10, 5]
labels = [f"{s} GW" for s in sizes]
scale = 1e3 / linewidth_factor
sizes = [s*scale for s in sizes]
sizes = [s * scale for s in sizes]
legend_kw = dict(
loc="upper left",
@ -197,15 +211,11 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
frameon=False,
labelspacing=0.8,
handletextpad=1,
title=title
title=title,
)
add_legend_lines(
ax,
sizes,
labels,
patch_kw=dict(color='lightgrey'),
legend_kw=legend_kw
ax, sizes, labels, patch_kw=dict(color="lightgrey"), legend_kw=legend_kw
)
legend_kw = dict(
@ -214,7 +224,6 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
)
if with_legend:
colors = [tech_colors[c] for c in carriers] + [ac_color, dc_color]
labels = carriers + ["HVAC line", "HVDC link"]
@ -225,14 +234,12 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
legend_kw=legend_kw,
)
fig.savefig(
snakemake.output.map,
transparent=True,
bbox_inches="tight"
)
fig.savefig(snakemake.output.map, transparent=True, bbox_inches="tight")
def group_pipes(df, drop_direction=False):
"""Group pipes which connect same buses and return overall capacity.
"""
Group pipes which connect same buses and return overall capacity.
"""
if drop_direction:
positive_order = df.bus0 < df.bus1
@ -244,16 +251,17 @@ def group_pipes(df, drop_direction=False):
# there are pipes for each investment period rename to AC buses name for plotting
df.index = df.apply(
lambda x: f"H2 pipeline {x.bus0.replace(' H2', '')} -> {x.bus1.replace(' H2', '')}",
axis=1
axis=1,
)
# group pipe lines connecting the same buses and rename them for plotting
pipe_capacity = df.groupby(level=0).agg({"p_nom_opt": sum, "bus0": "first", "bus1": "first"})
pipe_capacity = df.groupby(level=0).agg(
{"p_nom_opt": sum, "bus0": "first", "bus1": "first"}
)
return pipe_capacity
def plot_h2_map(network, regions):
n = network.copy()
if "H2 pipeline" not in n.links.carrier.unique():
return
@ -261,7 +269,11 @@ def plot_h2_map(network, regions):
assign_location(n)
h2_storage = n.stores.query("carrier == 'H2'")
regions["H2"] = h2_storage.rename(index=h2_storage.bus.map(n.buses.location)).e_nom_opt.div(1e6) # TWh
regions["H2"] = h2_storage.rename(
index=h2_storage.bus.map(n.buses.location)
).e_nom_opt.div(
1e6
) # TWh
regions["H2"] = regions["H2"].where(regions["H2"] > 0.1)
bus_size_factor = 1e5
@ -276,26 +288,33 @@ def plot_h2_map(network, regions):
elec = n.links[n.links.carrier.isin(carriers)].index
bus_sizes = n.links.loc[elec,"p_nom_opt"].groupby([n.links["bus0"], n.links.carrier]).sum() / bus_size_factor
bus_sizes = (
n.links.loc[elec, "p_nom_opt"].groupby([n.links["bus0"], n.links.carrier]).sum()
/ bus_size_factor
)
# make a fake MultiIndex so that area is correct for legend
bus_sizes.rename(index=lambda x: x.replace(" H2", ""), level=0, inplace=True)
# drop all links which are not H2 pipelines
n.links.drop(n.links.index[~n.links.carrier.str.contains("H2 pipeline")], inplace=True)
n.links.drop(
n.links.index[~n.links.carrier.str.contains("H2 pipeline")], inplace=True
)
h2_new = n.links[n.links.carrier=="H2 pipeline"]
h2_retro = n.links[n.links.carrier=='H2 pipeline retrofitted']
h2_new = n.links[n.links.carrier == "H2 pipeline"]
h2_retro = n.links[n.links.carrier == "H2 pipeline retrofitted"]
if snakemake.config['foresight'] == 'myopic':
if snakemake.config["foresight"] == "myopic":
# sum capacitiy for pipelines from different investment periods
h2_new = group_pipes(h2_new)
if not h2_retro.empty:
h2_retro = group_pipes(h2_retro, drop_direction=True).reindex(h2_new.index).fillna(0)
h2_retro = (
group_pipes(h2_retro, drop_direction=True)
.reindex(h2_new.index)
.fillna(0)
)
if not h2_retro.empty:
positive_order = h2_retro.bus0 < h2_retro.bus1
h2_retro_p = h2_retro[positive_order]
swap_buses = {"bus0": "bus1", "bus1": "bus0"}
@ -305,7 +324,7 @@ def plot_h2_map(network, regions):
h2_retro["index_orig"] = h2_retro.index
h2_retro.index = h2_retro.apply(
lambda x: f"H2 pipeline {x.bus0.replace(' H2', '')} -> {x.bus1.replace(' H2', '')}",
axis=1
axis=1,
)
retro_w_new_i = h2_retro.index.intersection(h2_new.index)
@ -319,19 +338,20 @@ def plot_h2_map(network, regions):
h2_total = pd.concat(to_concat).p_nom_opt.groupby(level=0).sum()
else:
h2_total = h2_new.p_nom_opt
link_widths_total = h2_total / linewidth_factor
n.links.rename(index=lambda x: x.split("-2")[0], inplace=True)
n.links = n.links.groupby(level=0).first()
link_widths_total = link_widths_total.reindex(n.links.index).fillna(0.)
link_widths_total[n.links.p_nom_opt < line_lower_threshold] = 0.
link_widths_total = link_widths_total.reindex(n.links.index).fillna(0.0)
link_widths_total[n.links.p_nom_opt < line_lower_threshold] = 0.0
retro = n.links.p_nom_opt.where(n.links.carrier=='H2 pipeline retrofitted', other=0.)
retro = n.links.p_nom_opt.where(
n.links.carrier == "H2 pipeline retrofitted", other=0.0
)
link_widths_retro = retro / linewidth_factor
link_widths_retro[n.links.p_nom_opt < line_lower_threshold] = 0.
link_widths_retro[n.links.p_nom_opt < line_lower_threshold] = 0.0
n.links.bus0 = n.links.bus0.str.replace(" H2", "")
n.links.bus1 = n.links.bus1.str.replace(" H2", "")
@ -339,18 +359,12 @@ def plot_h2_map(network, regions):
proj = ccrs.EqualEarth()
regions = regions.to_crs(proj.proj4_init)
fig, ax = plt.subplots(
figsize=(7, 6),
subplot_kw={"projection": proj}
)
fig, ax = plt.subplots(figsize=(7, 6), subplot_kw={"projection": proj})
color_h2_pipe = '#b3f3f4'
color_retrofit = '#499a9c'
color_h2_pipe = "#b3f3f4"
color_retrofit = "#499a9c"
bus_colors = {
"H2 Electrolysis": "#ff29d9",
"H2 Fuel Cell": '#805394'
}
bus_colors = {"H2 Electrolysis": "#ff29d9", "H2 Fuel Cell": "#805394"}
n.plot(
geomap=True,
@ -360,7 +374,7 @@ def plot_h2_map(network, regions):
link_widths=link_widths_total,
branch_components=["Link"],
ax=ax,
**map_opts
**map_opts,
)
n.plot(
@ -371,13 +385,13 @@ def plot_h2_map(network, regions):
branch_components=["Link"],
ax=ax,
color_geomap=False,
boundaries=map_opts["boundaries"]
boundaries=map_opts["boundaries"],
)
regions.plot(
ax=ax,
column="H2",
cmap='Blues',
cmap="Blues",
linewidths=0,
legend=True,
vmax=6,
@ -391,7 +405,7 @@ def plot_h2_map(network, regions):
sizes = [50, 10]
labels = [f"{s} GW" for s in sizes]
sizes = [s/bus_size_factor*1e3 for s in sizes]
sizes = [s / bus_size_factor * 1e3 for s in sizes]
legend_kw = dict(
loc="upper left",
@ -401,16 +415,19 @@ def plot_h2_map(network, regions):
frameon=False,
)
add_legend_circles(ax, sizes, labels,
add_legend_circles(
ax,
sizes,
labels,
srid=n.srid,
patch_kw=dict(facecolor='lightgrey'),
legend_kw=legend_kw
patch_kw=dict(facecolor="lightgrey"),
legend_kw=legend_kw,
)
sizes = [30, 10]
labels = [f"{s} GW" for s in sizes]
scale = 1e3 / linewidth_factor
sizes = [s*scale for s in sizes]
sizes = [s * scale for s in sizes]
legend_kw = dict(
loc="upper left",
@ -424,7 +441,7 @@ def plot_h2_map(network, regions):
ax,
sizes,
labels,
patch_kw=dict(color='lightgrey'),
patch_kw=dict(color="lightgrey"),
legend_kw=legend_kw,
)
@ -438,23 +455,16 @@ def plot_h2_map(network, regions):
frameon=False,
)
add_legend_patches(
ax,
colors,
labels,
legend_kw=legend_kw
)
add_legend_patches(ax, colors, labels, legend_kw=legend_kw)
ax.set_facecolor("white")
fig.savefig(
snakemake.output.map.replace("-costs-all","-h2_network"),
bbox_inches="tight"
snakemake.output.map.replace("-costs-all", "-h2_network"), bbox_inches="tight"
)
def plot_ch4_map(network):
n = network.copy()
if "gas pipeline" not in n.links.carrier.unique():
@ -470,22 +480,54 @@ def plot_ch4_map(network):
# Drop non-electric buses so they don't clutter the plot
n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
fossil_gas_i = n.generators[n.generators.carrier=="gas"].index
fossil_gas = n.generators_t.p.loc[:,fossil_gas_i].mul(n.snapshot_weightings.generators, axis=0).sum().groupby(n.generators.loc[fossil_gas_i,"bus"]).sum() / bus_size_factor
fossil_gas_i = n.generators[n.generators.carrier == "gas"].index
fossil_gas = (
n.generators_t.p.loc[:, fossil_gas_i]
.mul(n.snapshot_weightings.generators, axis=0)
.sum()
.groupby(n.generators.loc[fossil_gas_i, "bus"])
.sum()
/ bus_size_factor
)
fossil_gas.rename(index=lambda x: x.replace(" gas", ""), inplace=True)
fossil_gas = fossil_gas.reindex(n.buses.index).fillna(0)
# make a fake MultiIndex so that area is correct for legend
fossil_gas.index = pd.MultiIndex.from_product([fossil_gas.index, ["fossil gas"]])
methanation_i = n.links[n.links.carrier.isin(["helmeth", "Sabatier"])].index
methanation = abs(n.links_t.p1.loc[:,methanation_i].mul(n.snapshot_weightings.generators, axis=0)).sum().groupby(n.links.loc[methanation_i,"bus1"]).sum() / bus_size_factor
methanation = methanation.groupby(methanation.index).sum().rename(index=lambda x: x.replace(" gas", ""))
methanation = (
abs(
n.links_t.p1.loc[:, methanation_i].mul(
n.snapshot_weightings.generators, axis=0
)
)
.sum()
.groupby(n.links.loc[methanation_i, "bus1"])
.sum()
/ bus_size_factor
)
methanation = (
methanation.groupby(methanation.index)
.sum()
.rename(index=lambda x: x.replace(" gas", ""))
)
# make a fake MultiIndex so that area is correct for legend
methanation.index = pd.MultiIndex.from_product([methanation.index, ["methanation"]])
biogas_i = n.stores[n.stores.carrier=="biogas"].index
biogas = n.stores_t.p.loc[:,biogas_i].mul(n.snapshot_weightings.generators, axis=0).sum().groupby(n.stores.loc[biogas_i,"bus"]).sum() / bus_size_factor
biogas = biogas.groupby(biogas.index).sum().rename(index=lambda x: x.replace(" biogas", ""))
biogas_i = n.stores[n.stores.carrier == "biogas"].index
biogas = (
n.stores_t.p.loc[:, biogas_i]
.mul(n.snapshot_weightings.generators, axis=0)
.sum()
.groupby(n.stores.loc[biogas_i, "bus"])
.sum()
/ bus_size_factor
)
biogas = (
biogas.groupby(biogas.index)
.sum()
.rename(index=lambda x: x.replace(" biogas", ""))
)
# make a fake MultiIndex so that area is correct for legend
biogas.index = pd.MultiIndex.from_product([biogas.index, ["biogas"]])
@ -496,22 +538,22 @@ def plot_ch4_map(network):
n.links.drop(to_remove, inplace=True)
link_widths_rem = n.links.p_nom_opt / linewidth_factor
link_widths_rem[n.links.p_nom_opt < line_lower_threshold] = 0.
link_widths_rem[n.links.p_nom_opt < line_lower_threshold] = 0.0
link_widths_orig = n.links.p_nom / linewidth_factor
link_widths_orig[n.links.p_nom < line_lower_threshold] = 0.
link_widths_orig[n.links.p_nom < line_lower_threshold] = 0.0
max_usage = n.links_t.p0.abs().max(axis=0)
link_widths_used = max_usage / linewidth_factor
link_widths_used[max_usage < line_lower_threshold] = 0.
link_widths_used = max_usage / linewidth_factor
link_widths_used[max_usage < line_lower_threshold] = 0.0
tech_colors = snakemake.config['plotting']['tech_colors']
tech_colors = snakemake.config["plotting"]["tech_colors"]
pipe_colors = {
"gas pipeline": "#f08080",
"gas pipeline new": "#c46868",
"gas pipeline (in 2020)": 'lightgrey',
"gas pipeline (available)": '#e8d1d1',
"gas pipeline (in 2020)": "lightgrey",
"gas pipeline (available)": "#e8d1d1",
}
link_color_used = n.links.carrier.map(pipe_colors)
@ -522,44 +564,44 @@ def plot_ch4_map(network):
bus_colors = {
"fossil gas": tech_colors["fossil gas"],
"methanation": tech_colors["methanation"],
"biogas": "seagreen"
"biogas": "seagreen",
}
fig, ax = plt.subplots(figsize=(7,6), subplot_kw={"projection": ccrs.EqualEarth()})
fig, ax = plt.subplots(figsize=(7, 6), subplot_kw={"projection": ccrs.EqualEarth()})
n.plot(
bus_sizes=bus_sizes,
bus_colors=bus_colors,
link_colors=pipe_colors['gas pipeline (in 2020)'],
link_colors=pipe_colors["gas pipeline (in 2020)"],
link_widths=link_widths_orig,
branch_components=["Link"],
ax=ax,
**map_opts
**map_opts,
)
n.plot(
ax=ax,
bus_sizes=0.,
link_colors=pipe_colors['gas pipeline (available)'],
bus_sizes=0.0,
link_colors=pipe_colors["gas pipeline (available)"],
link_widths=link_widths_rem,
branch_components=["Link"],
color_geomap=False,
boundaries=map_opts["boundaries"]
boundaries=map_opts["boundaries"],
)
n.plot(
ax=ax,
bus_sizes=0.,
bus_sizes=0.0,
link_colors=link_color_used,
link_widths=link_widths_used,
branch_components=["Link"],
color_geomap=False,
boundaries=map_opts["boundaries"]
boundaries=map_opts["boundaries"],
)
sizes = [100, 10]
labels = [f"{s} TWh" for s in sizes]
sizes = [s/bus_size_factor*1e6 for s in sizes]
sizes = [s / bus_size_factor * 1e6 for s in sizes]
legend_kw = dict(
loc="upper left",
@ -567,7 +609,7 @@ def plot_ch4_map(network):
labelspacing=0.8,
frameon=False,
handletextpad=1,
title='gas sources',
title="gas sources",
)
add_legend_circles(
@ -575,14 +617,14 @@ def plot_ch4_map(network):
sizes,
labels,
srid=n.srid,
patch_kw=dict(facecolor='lightgrey'),
patch_kw=dict(facecolor="lightgrey"),
legend_kw=legend_kw,
)
sizes = [50, 10]
labels = [f"{s} GW" for s in sizes]
scale = 1e3 / linewidth_factor
sizes = [s*scale for s in sizes]
sizes = [s * scale for s in sizes]
legend_kw = dict(
loc="upper left",
@ -590,14 +632,14 @@ def plot_ch4_map(network):
frameon=False,
labelspacing=0.8,
handletextpad=1,
title='gas pipeline'
title="gas pipeline",
)
add_legend_lines(
ax,
sizes,
labels,
patch_kw=dict(color='lightgrey'),
patch_kw=dict(color="lightgrey"),
legend_kw=legend_kw,
)
@ -611,7 +653,7 @@ def plot_ch4_map(network):
# )
legend_kw = dict(
loc='upper left',
loc="upper left",
bbox_to_anchor=(0, 1.24),
ncol=2,
frameon=False,
@ -625,26 +667,21 @@ def plot_ch4_map(network):
)
fig.savefig(
snakemake.output.map.replace("-costs-all","-ch4_network"),
bbox_inches="tight"
snakemake.output.map.replace("-costs-all", "-ch4_network"), bbox_inches="tight"
)
def plot_map_without(network):
n = network.copy()
assign_location(n)
# Drop non-electric buses so they don't clutter the plot
n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
fig, ax = plt.subplots(
figsize=(7, 6),
subplot_kw={"projection": ccrs.EqualEarth()}
)
fig, ax = plt.subplots(figsize=(7, 6), subplot_kw={"projection": ccrs.EqualEarth()})
# PDF has minimum width, so set these to zero
line_lower_threshold = 200.
line_lower_threshold = 200.0
line_upper_threshold = 1e4
linewidth_factor = 3e3
ac_color = "rosybrown"
@ -652,9 +689,11 @@ def plot_map_without(network):
# hack because impossible to drop buses...
if "EU gas" in n.buses.index:
eu_location = snakemake.config["plotting"].get("eu_node_location", dict(x=-5.5, y=46))
n.buses.loc["EU gas", "x"] = eu_location["x"]
n.buses.loc["EU gas", "y"] = eu_location["y"]
eu_location = snakemake.config["plotting"].get(
"eu_node_location", dict(x=-5.5, y=46)
)
n.buses.loc["EU gas", "x"] = eu_location["x"]
n.buses.loc["EU gas", "y"] = eu_location["y"]
to_drop = n.links.index[(n.links.carrier != "DC") & (n.links.carrier != "B2B")]
n.links.drop(to_drop, inplace=True)
@ -666,11 +705,11 @@ def plot_map_without(network):
line_widths = n.lines.s_nom_min
link_widths = n.links.p_nom_min
line_widths = line_widths.clip(line_lower_threshold,line_upper_threshold)
link_widths = link_widths.clip(line_lower_threshold,line_upper_threshold)
line_widths = line_widths.clip(line_lower_threshold, line_upper_threshold)
link_widths = link_widths.clip(line_lower_threshold, line_upper_threshold)
line_widths = line_widths.replace(line_lower_threshold,0)
link_widths = link_widths.replace(line_lower_threshold,0)
line_widths = line_widths.replace(line_lower_threshold, 0)
link_widths = link_widths.replace(line_lower_threshold, 0)
n.plot(
bus_colors="k",
@ -678,32 +717,34 @@ def plot_map_without(network):
link_colors=dc_color,
line_widths=line_widths / linewidth_factor,
link_widths=link_widths / linewidth_factor,
ax=ax, **map_opts
ax=ax,
**map_opts,
)
handles = []
labels = []
for s in (10, 5):
handles.append(plt.Line2D([0], [0], color=ac_color,
linewidth=s * 1e3 / linewidth_factor))
handles.append(
plt.Line2D([0], [0], color=ac_color, linewidth=s * 1e3 / linewidth_factor)
)
labels.append(f"{s} GW")
l1_1 = ax.legend(handles, labels,
loc="upper left", bbox_to_anchor=(0.05, 1.01),
frameon=False,
labelspacing=0.8, handletextpad=1.5,
title='Today\'s transmission')
l1_1 = ax.legend(
handles,
labels,
loc="upper left",
bbox_to_anchor=(0.05, 1.01),
frameon=False,
labelspacing=0.8,
handletextpad=1.5,
title="Today's transmission",
)
ax.add_artist(l1_1)
fig.savefig(
snakemake.output.today,
transparent=True,
bbox_inches="tight"
)
fig.savefig(snakemake.output.today, transparent=True, bbox_inches="tight")
def plot_series(network, carrier="AC", name="test"):
n = network.copy()
assign_location(n)
assign_carriers(n)
@ -712,28 +753,41 @@ def plot_series(network, carrier="AC", name="test"):
supply = pd.DataFrame(index=n.snapshots)
for c in n.iterate_components(n.branch_components):
n_port = 4 if c.name=='Link' else 2
n_port = 4 if c.name == "Link" else 2
for i in range(n_port):
supply = pd.concat((supply,
(-1) * c.pnl["p" + str(i)].loc[:,
c.df.index[c.df["bus" + str(i)].isin(buses)]].groupby(c.df.carrier,
axis=1).sum()),
axis=1)
supply = pd.concat(
(
supply,
(-1)
* c.pnl["p" + str(i)]
.loc[:, c.df.index[c.df["bus" + str(i)].isin(buses)]]
.groupby(c.df.carrier, axis=1)
.sum(),
),
axis=1,
)
for c in n.iterate_components(n.one_port_components):
comps = c.df.index[c.df.bus.isin(buses)]
supply = pd.concat((supply, ((c.pnl["p"].loc[:, comps]).multiply(
c.df.loc[comps, "sign"])).groupby(c.df.carrier, axis=1).sum()), axis=1)
supply = pd.concat(
(
supply,
((c.pnl["p"].loc[:, comps]).multiply(c.df.loc[comps, "sign"]))
.groupby(c.df.carrier, axis=1)
.sum(),
),
axis=1,
)
supply = supply.groupby(rename_techs_tyndp, axis=1).sum()
both = supply.columns[(supply < 0.).any() & (supply > 0.).any()]
both = supply.columns[(supply < 0.0).any() & (supply > 0.0).any()]
positive_supply = supply[both]
negative_supply = supply[both]
positive_supply[positive_supply < 0.] = 0.
negative_supply[negative_supply > 0.] = 0.
positive_supply[positive_supply < 0.0] = 0.0
negative_supply[negative_supply > 0.0] = 0.0
supply[both] = positive_supply
@ -761,48 +815,61 @@ def plot_series(network, carrier="AC", name="test"):
supply = supply / 1e3
supply.rename(columns={"electricity": "electric demand",
"heat": "heat demand"},
inplace=True)
supply.rename(
columns={"electricity": "electric demand", "heat": "heat demand"}, inplace=True
)
supply.columns = supply.columns.str.replace("residential ", "")
supply.columns = supply.columns.str.replace("services ", "")
supply.columns = supply.columns.str.replace("urban decentral ", "decentral ")
preferred_order = pd.Index(["electric demand",
"transmission lines",
"hydroelectricity",
"hydro reservoir",
"run of river",
"pumped hydro storage",
"CHP",
"onshore wind",
"offshore wind",
"solar PV",
"solar thermal",
"building retrofitting",
"ground heat pump",
"air heat pump",
"resistive heater",
"OCGT",
"gas boiler",
"gas",
"natural gas",
"methanation",
"hydrogen storage",
"battery storage",
"hot water storage"])
preferred_order = pd.Index(
[
"electric demand",
"transmission lines",
"hydroelectricity",
"hydro reservoir",
"run of river",
"pumped hydro storage",
"CHP",
"onshore wind",
"offshore wind",
"solar PV",
"solar thermal",
"building retrofitting",
"ground heat pump",
"air heat pump",
"resistive heater",
"OCGT",
"gas boiler",
"gas",
"natural gas",
"methanation",
"hydrogen storage",
"battery storage",
"hot water storage",
]
)
new_columns = (preferred_order.intersection(supply.columns)
.append(supply.columns.difference(preferred_order)))
new_columns = preferred_order.intersection(supply.columns).append(
supply.columns.difference(preferred_order)
)
supply = supply.groupby(supply.columns, axis=1).sum()
supply = supply.groupby(supply.columns, axis=1).sum()
fig, ax = plt.subplots()
fig.set_size_inches((8, 5))
(supply.loc[start:stop, new_columns]
.plot(ax=ax, kind="area", stacked=True, linewidth=0.,
color=[snakemake.config['plotting']['tech_colors'][i.replace(suffix, "")]
for i in new_columns]))
(
supply.loc[start:stop, new_columns].plot(
ax=ax,
kind="area",
stacked=True,
linewidth=0.0,
color=[
snakemake.config["plotting"]["tech_colors"][i.replace(suffix, "")]
for i in new_columns
],
)
)
handles, labels = ax.get_legend_handles_labels()
@ -824,44 +891,53 @@ def plot_series(network, carrier="AC", name="test"):
ax.set_ylabel("Power [GW]")
fig.tight_layout()
fig.savefig("{}{}/maps/series-{}-{}-{}-{}-{}.pdf".format(
snakemake.config['results_dir'], snakemake.config['run'],
snakemake.wildcards["lv"],
carrier, start, stop, name),
transparent=True)
fig.savefig(
"{}{}/maps/series-{}-{}-{}-{}-{}.pdf".format(
snakemake.config["results_dir"],
snakemake.config["run"],
snakemake.wildcards["lv"],
carrier,
start,
stop,
name,
),
transparent=True,
)
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'plot_network',
simpl='',
"plot_network",
simpl="",
clusters="181",
lv='opt',
opts='',
sector_opts='Co2L0-730H-T-H-B-I-A-solar+p3-linemaxext10',
lv="opt",
opts="",
sector_opts="Co2L0-730H-T-H-B-I-A-solar+p3-linemaxext10",
planning_horizons="2050",
)
logging.basicConfig(level=snakemake.config['logging_level'])
logging.basicConfig(level=snakemake.config["logging_level"])
overrides = override_component_attrs(snakemake.input.overrides)
n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
regions = gpd.read_file(snakemake.input.regions).set_index("name")
map_opts = snakemake.config['plotting']['map']
map_opts = snakemake.config["plotting"]["map"]
plot_map(n,
plot_map(
n,
components=["generators", "links", "stores", "storage_units"],
bus_size_factor=2e10,
transmission=False
transmission=False,
)
plot_h2_map(n, regions)
plot_ch4_map(n)
plot_map_without(n)
#plot_series(n, carrier="AC", name=suffix)
#plot_series(n, carrier="heat", name=suffix)
# plot_series(n, carrier="AC", name=suffix)
# plot_series(n, carrier="heat", name=suffix)

464
scripts/plot_summary.py
View File

@ -1,25 +1,29 @@
# -*- coding: utf-8 -*-
import logging
logger = logging.getLogger(__name__)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
plt.style.use('ggplot')
plt.style.use("ggplot")
from prepare_sector_network import co2_emissions_year
#consolidate and rename
def rename_techs(label):
# consolidate and rename
def rename_techs(label):
prefix_to_remove = [
"residential ",
"services ",
"urban ",
"rural ",
"central ",
"decentral "
"decentral ",
]
rename_if_contains = [
@ -30,7 +34,7 @@ def rename_techs(label):
"air heat pump",
"ground heat pump",
"resistive heater",
"Fischer-Tropsch"
"Fischer-Tropsch",
]
rename_if_contains_dict = {
@ -58,151 +62,159 @@ def rename_techs(label):
"co2 stored": "CO2 sequestration",
"AC": "transmission lines",
"DC": "transmission lines",
"B2B": "transmission lines"
"B2B": "transmission lines",
}
for ptr in prefix_to_remove:
if label[:len(ptr)] == ptr:
label = label[len(ptr):]
if label[: len(ptr)] == ptr:
label = label[len(ptr) :]
for rif in rename_if_contains:
if rif in label:
label = rif
for old,new in rename_if_contains_dict.items():
for old, new in rename_if_contains_dict.items():
if old in label:
label = new
for old,new in rename.items():
for old, new in rename.items():
if old == label:
label = new
return label
preferred_order = pd.Index([
"transmission lines",
"hydroelectricity",
"hydro reservoir",
"run of river",
"pumped hydro storage",
"solid biomass",
"biogas",
"onshore wind",
"offshore wind",
"offshore wind (AC)",
"offshore wind (DC)",
"solar PV",
"solar thermal",
"solar rooftop",
"solar",
"building retrofitting",
"ground heat pump",
"air heat pump",
"heat pump",
"resistive heater",
"power-to-heat",
"gas-to-power/heat",
"CHP",
"OCGT",
"gas boiler",
"gas",
"natural gas",
"helmeth",
"methanation",
"ammonia",
"hydrogen storage",
"power-to-gas",
"power-to-liquid",
"battery storage",
"hot water storage",
"CO2 sequestration"
])
preferred_order = pd.Index(
[
"transmission lines",
"hydroelectricity",
"hydro reservoir",
"run of river",
"pumped hydro storage",
"solid biomass",
"biogas",
"onshore wind",
"offshore wind",
"offshore wind (AC)",
"offshore wind (DC)",
"solar PV",
"solar thermal",
"solar rooftop",
"solar",
"building retrofitting",
"ground heat pump",
"air heat pump",
"heat pump",
"resistive heater",
"power-to-heat",
"gas-to-power/heat",
"CHP",
"OCGT",
"gas boiler",
"gas",
"natural gas",
"helmeth",
"methanation",
"ammonia",
"hydrogen storage",
"power-to-gas",
"power-to-liquid",
"battery storage",
"hot water storage",
"CO2 sequestration",
]
)
def plot_costs():
cost_df = pd.read_csv(
snakemake.input.costs,
index_col=list(range(3)),
header=list(range(n_header))
snakemake.input.costs, index_col=list(range(3)), header=list(range(n_header))
)
df = cost_df.groupby(cost_df.index.get_level_values(2)).sum()
#convert to billions
# convert to billions
df = df / 1e9
df = df.groupby(df.index.map(rename_techs)).sum()
to_drop = df.index[df.max(axis=1) < snakemake.config['plotting']['costs_threshold']]
to_drop = df.index[df.max(axis=1) < snakemake.config["plotting"]["costs_threshold"]]
logger.info(f"Dropping technology with costs below {snakemake.config['plotting']['costs_threshold']} EUR billion per year")
logger.info(
f"Dropping technology with costs below {snakemake.config['plotting']['costs_threshold']} EUR billion per year"
)
logger.debug(df.loc[to_drop])
df = df.drop(to_drop)
logger.info(f"Total system cost of {round(df.sum()[0])} EUR billion per year")
new_index = preferred_order.intersection(df.index).append(df.index.difference(preferred_order))
new_index = preferred_order.intersection(df.index).append(
df.index.difference(preferred_order)
)
new_columns = df.sum().sort_values().index
fig, ax = plt.subplots(figsize=(12,8))
fig, ax = plt.subplots(figsize=(12, 8))
df.loc[new_index,new_columns].T.plot(
df.loc[new_index, new_columns].T.plot(
kind="bar",
ax=ax,
stacked=True,
color=[snakemake.config['plotting']['tech_colors'][i] for i in new_index]
color=[snakemake.config["plotting"]["tech_colors"][i] for i in new_index],
)
handles,labels = ax.get_legend_handles_labels()
handles, labels = ax.get_legend_handles_labels()
handles.reverse()
labels.reverse()
ax.set_ylim([0,snakemake.config['plotting']['costs_max']])
ax.set_ylim([0, snakemake.config["plotting"]["costs_max"]])
ax.set_ylabel("System Cost [EUR billion per year]")
ax.set_xlabel("")
ax.grid(axis='x')
ax.grid(axis="x")
ax.legend(handles, labels, ncol=1, loc="upper left", bbox_to_anchor=[1,1], frameon=False)
ax.legend(
handles, labels, ncol=1, loc="upper left", bbox_to_anchor=[1, 1], frameon=False
)
fig.savefig(snakemake.output.costs, bbox_inches='tight')
fig.savefig(snakemake.output.costs, bbox_inches="tight")
def plot_energy():
energy_df = pd.read_csv(
snakemake.input.energy,
index_col=list(range(2)),
header=list(range(n_header))
snakemake.input.energy, index_col=list(range(2)), header=list(range(n_header))
)
df = energy_df.groupby(energy_df.index.get_level_values(1)).sum()
#convert MWh to TWh
# convert MWh to TWh
df = df / 1e6
df = df.groupby(df.index.map(rename_techs)).sum()
to_drop = df.index[df.abs().max(axis=1) < snakemake.config['plotting']['energy_threshold']]
to_drop = df.index[
df.abs().max(axis=1) < snakemake.config["plotting"]["energy_threshold"]
]
logger.info(f"Dropping all technology with energy consumption or production below {snakemake.config['plotting']['energy_threshold']} TWh/a")
logger.info(
f"Dropping all technology with energy consumption or production below {snakemake.config['plotting']['energy_threshold']} TWh/a"
)
logger.debug(df.loc[to_drop])
df = df.drop(to_drop)
logger.info(f"Total energy of {round(df.sum()[0])} TWh/a")
new_index = preferred_order.intersection(df.index).append(df.index.difference(preferred_order))
new_index = preferred_order.intersection(df.index).append(
df.index.difference(preferred_order)
)
new_columns = df.columns.sort_values()
fig, ax = plt.subplots(figsize=(12,8))
fig, ax = plt.subplots(figsize=(12, 8))
logger.debug(df.loc[new_index, new_columns])
@ -210,15 +222,20 @@ def plot_energy():
kind="bar",
ax=ax,
stacked=True,
color=[snakemake.config['plotting']['tech_colors'][i] for i in new_index]
color=[snakemake.config["plotting"]["tech_colors"][i] for i in new_index],
)
handles,labels = ax.get_legend_handles_labels()
handles, labels = ax.get_legend_handles_labels()
handles.reverse()
labels.reverse()
ax.set_ylim([snakemake.config['plotting']['energy_min'], snakemake.config['plotting']['energy_max']])
ax.set_ylim(
[
snakemake.config["plotting"]["energy_min"],
snakemake.config["plotting"]["energy_max"],
]
)
ax.set_ylabel("Energy [TWh/a]")
@ -226,48 +243,56 @@ def plot_energy():
ax.grid(axis="x")
ax.legend(handles, labels, ncol=1, loc="upper left", bbox_to_anchor=[1, 1], frameon=False)
fig.savefig(snakemake.output.energy, bbox_inches='tight')
ax.legend(
handles, labels, ncol=1, loc="upper left", bbox_to_anchor=[1, 1], frameon=False
)
fig.savefig(snakemake.output.energy, bbox_inches="tight")
def plot_balances():
co2_carriers = ["co2", "co2 stored", "process emissions"]
balances_df = pd.read_csv(
snakemake.input.balances,
index_col=list(range(3)),
header=list(range(n_header))
snakemake.input.balances, index_col=list(range(3)), header=list(range(n_header))
)
balances = {i.replace(" ","_"): [i] for i in balances_df.index.levels[0]}
balances["energy"] = [i for i in balances_df.index.levels[0] if i not in co2_carriers]
balances = {i.replace(" ", "_"): [i] for i in balances_df.index.levels[0]}
balances["energy"] = [
i for i in balances_df.index.levels[0] if i not in co2_carriers
]
fig, ax = plt.subplots(figsize=(12,8))
fig, ax = plt.subplots(figsize=(12, 8))
for k, v in balances.items():
df = balances_df.loc[v]
df = df.groupby(df.index.get_level_values(2)).sum()
#convert MWh to TWh
# convert MWh to TWh
df = df / 1e6
#remove trailing link ports
df.index = [i[:-1] if ((i not in ["co2", "NH3"]) and (i[-1:] in ["0","1","2","3"])) else i for i in df.index]
# remove trailing link ports
df.index = [
i[:-1]
if ((i not in ["co2", "NH3"]) and (i[-1:] in ["0", "1", "2", "3"]))
else i
for i in df.index
]
df = df.groupby(df.index.map(rename_techs)).sum()
to_drop = df.index[df.abs().max(axis=1) < snakemake.config['plotting']['energy_threshold']/10]
to_drop = df.index[
df.abs().max(axis=1) < snakemake.config["plotting"]["energy_threshold"] / 10
]
if v[0] in co2_carriers:
units = "MtCO2/a"
else:
units = "TWh/a"
logger.debug(f"Dropping technology energy balance smaller than {snakemake.config['plotting']['energy_threshold']/10} {units}")
logger.debug(
f"Dropping technology energy balance smaller than {snakemake.config['plotting']['energy_threshold']/10} {units}"
)
logger.debug(df.loc[to_drop])
df = df.drop(to_drop)
@ -277,14 +302,20 @@ def plot_balances():
if df.empty:
continue
new_index = preferred_order.intersection(df.index).append(df.index.difference(preferred_order))
new_index = preferred_order.intersection(df.index).append(
df.index.difference(preferred_order)
)
new_columns = df.columns.sort_values()
df.loc[new_index,new_columns].T.plot(kind="bar",ax=ax,stacked=True,color=[snakemake.config['plotting']['tech_colors'][i] for i in new_index])
df.loc[new_index, new_columns].T.plot(
kind="bar",
ax=ax,
stacked=True,
color=[snakemake.config["plotting"]["tech_colors"][i] for i in new_index],
)
handles,labels = ax.get_legend_handles_labels()
handles, labels = ax.get_legend_handles_labels()
handles.reverse()
labels.reverse()
@ -298,81 +329,115 @@ def plot_balances():
ax.grid(axis="x")
ax.legend(handles, labels, ncol=1, loc="upper left", bbox_to_anchor=[1, 1], frameon=False)
ax.legend(
handles,
labels,
ncol=1,
loc="upper left",
bbox_to_anchor=[1, 1],
frameon=False,
)
fig.savefig(snakemake.output.balances[:-10] + k + ".pdf", bbox_inches='tight')
fig.savefig(snakemake.output.balances[:-10] + k + ".pdf", bbox_inches="tight")
plt.cla()
def historical_emissions(cts):
"""
read historical emissions to add them to the carbon budget plot
Read historical emissions to add them to the carbon budget plot.
"""
#https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16
#downloaded 201228 (modified by EEA last on 201221)
# https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16
# downloaded 201228 (modified by EEA last on 201221)
fn = "data/eea/UNFCCC_v23.csv"
df = pd.read_csv(fn, encoding="latin-1")
df.loc[df["Year"] == "1985-1987","Year"] = 1986
df.loc[df["Year"] == "1985-1987", "Year"] = 1986
df["Year"] = df["Year"].astype(int)
df = df.set_index(['Year', 'Sector_name', 'Country_code', 'Pollutant_name']).sort_index()
df = df.set_index(
["Year", "Sector_name", "Country_code", "Pollutant_name"]
).sort_index()
e = pd.Series()
e["electricity"] = '1.A.1.a - Public Electricity and Heat Production'
e['residential non-elec'] = '1.A.4.b - Residential'
e['services non-elec'] = '1.A.4.a - Commercial/Institutional'
e['rail non-elec'] = "1.A.3.c - Railways"
e["road non-elec"] = '1.A.3.b - Road Transportation'
e["electricity"] = "1.A.1.a - Public Electricity and Heat Production"
e["residential non-elec"] = "1.A.4.b - Residential"
e["services non-elec"] = "1.A.4.a - Commercial/Institutional"
e["rail non-elec"] = "1.A.3.c - Railways"
e["road non-elec"] = "1.A.3.b - Road Transportation"
e["domestic navigation"] = "1.A.3.d - Domestic Navigation"
e['international navigation'] = '1.D.1.b - International Navigation'
e["domestic aviation"] = '1.A.3.a - Domestic Aviation'
e["international aviation"] = '1.D.1.a - International Aviation'
e['total energy'] = '1 - Energy'
e['industrial processes'] = '2 - Industrial Processes and Product Use'
e['agriculture'] = '3 - Agriculture'
e['LULUCF'] = '4 - Land Use, Land-Use Change and Forestry'
e['waste management'] = '5 - Waste management'
e['other'] = '6 - Other Sector'
e['indirect'] = 'ind_CO2 - Indirect CO2'
e["international navigation"] = "1.D.1.b - International Navigation"
e["domestic aviation"] = "1.A.3.a - Domestic Aviation"
e["international aviation"] = "1.D.1.a - International Aviation"
e["total energy"] = "1 - Energy"
e["industrial processes"] = "2 - Industrial Processes and Product Use"
e["agriculture"] = "3 - Agriculture"
e["LULUCF"] = "4 - Land Use, Land-Use Change and Forestry"
e["waste management"] = "5 - Waste management"
e["other"] = "6 - Other Sector"
e["indirect"] = "ind_CO2 - Indirect CO2"
e["total wL"] = "Total (with LULUCF)"
e["total woL"] = "Total (without LULUCF)"
pol = ["CO2"] # ["All greenhouse gases - (CO2 equivalent)"]
pol = ["CO2"] # ["All greenhouse gases - (CO2 equivalent)"]
cts
if "GB" in cts:
cts.remove("GB")
cts.append("UK")
year = np.arange(1990,2018).tolist()
year = np.arange(1990, 2018).tolist()
idx = pd.IndexSlice
co2_totals = df.loc[idx[year,e.values,cts,pol],"emissions"].unstack("Year").rename(index=pd.Series(e.index,e.values))
co2_totals = (
df.loc[idx[year, e.values, cts, pol], "emissions"]
.unstack("Year")
.rename(index=pd.Series(e.index, e.values))
)
co2_totals = (1/1e6)*co2_totals.groupby(level=0, axis=0).sum() #Gton CO2
co2_totals = (1 / 1e6) * co2_totals.groupby(level=0, axis=0).sum() # Gton CO2
co2_totals.loc['industrial non-elec'] = co2_totals.loc['total energy'] - co2_totals.loc[['electricity', 'services non-elec','residential non-elec', 'road non-elec',
'rail non-elec', 'domestic aviation', 'international aviation', 'domestic navigation',
'international navigation']].sum()
co2_totals.loc["industrial non-elec"] = (
co2_totals.loc["total energy"]
- co2_totals.loc[
[
"electricity",
"services non-elec",
"residential non-elec",
"road non-elec",
"rail non-elec",
"domestic aviation",
"international aviation",
"domestic navigation",
"international navigation",
]
].sum()
)
emissions = co2_totals.loc["electricity"]
if "T" in opts:
emissions += co2_totals.loc[[i+ " non-elec" for i in ["rail","road"]]].sum()
emissions += co2_totals.loc[[i + " non-elec" for i in ["rail", "road"]]].sum()
if "H" in opts:
emissions += co2_totals.loc[[i+ " non-elec" for i in ["residential","services"]]].sum()
emissions += co2_totals.loc[
[i + " non-elec" for i in ["residential", "services"]]
].sum()
if "I" in opts:
emissions += co2_totals.loc[["industrial non-elec","industrial processes",
"domestic aviation","international aviation",
"domestic navigation","international navigation"]].sum()
emissions += co2_totals.loc[
[
"industrial non-elec",
"industrial processes",
"domestic aviation",
"international aviation",
"domestic navigation",
"international navigation",
]
].sum()
return emissions
def plot_carbon_budget_distribution(input_eurostat):
"""
Plot historical carbon emissions in the EU and decarbonization path
Plot historical carbon emissions in the EU and decarbonization path.
"""
<<<<<<< HEAD
import seaborn as sns; sns.set()
sns.set_style('ticks')
plt.style.use('seaborn-ticks')
@ -380,70 +445,119 @@ def plot_carbon_budget_distribution(input_eurostat):
plt.rcParams['ytick.direction'] = 'in'
plt.rcParams['xtick.labelsize'] = 20
plt.rcParams['ytick.labelsize'] = 20
=======
import matplotlib.gridspec as gridspec
import seaborn as sns
sns.set()
sns.set_style("ticks")
plt.style.use("seaborn-ticks")
plt.rcParams["xtick.direction"] = "in"
plt.rcParams["ytick.direction"] = "in"
plt.rcParams["xtick.labelsize"] = 20
plt.rcParams["ytick.labelsize"] = 20
>>>>>>> 13769f90af4500948b0376d57df4cceaa13e78b5
plt.figure(figsize=(10, 7))
gs1 = gridspec.GridSpec(1, 1)
ax1 = plt.subplot(gs1[0,0])
ax1.set_ylabel('CO$_2$ emissions (Gt per year)',fontsize=22)
ax1.set_ylim([0,5])
ax1.set_xlim([1990,snakemake.config['scenario']['planning_horizons'][-1]+1])
ax1 = plt.subplot(gs1[0, 0])
ax1.set_ylabel("CO$_2$ emissions (Gt per year)", fontsize=22)
ax1.set_ylim([0, 5])
ax1.set_xlim([1990, snakemake.config["scenario"]["planning_horizons"][-1] + 1])
path_cb = snakemake.config['results_dir'] + snakemake.config['run'] + '/csvs/'
path_cb = snakemake.config["results_dir"] + snakemake.config["run"] + "/csvs/"
countries = pd.read_csv(snakemake.input.country_codes, index_col=1)
cts = countries.index.to_list()
e_1990 = co2_emissions_year(cts, input_eurostat, opts, year=1990)
CO2_CAP=pd.read_csv(path_cb + 'carbon_budget_distribution.csv',
index_col=0)
CO2_CAP = pd.read_csv(path_cb + "carbon_budget_distribution.csv", index_col=0)
ax1.plot(e_1990*CO2_CAP[o],linewidth=3,
color='dodgerblue', label=None)
ax1.plot(e_1990 * CO2_CAP[o], linewidth=3, color="dodgerblue", label=None)
emissions = historical_emissions(cts)
ax1.plot(emissions, color='black', linewidth=3, label=None)
ax1.plot(emissions, color="black", linewidth=3, label=None)
#plot committed and uder-discussion targets
#(notice that historical emissions include all countries in the
# plot committed and uder-discussion targets
# (notice that historical emissions include all countries in the
# network, but targets refer to EU)
ax1.plot([2020],[0.8*emissions[1990]],
marker='*', markersize=12, markerfacecolor='black',
markeredgecolor='black')
ax1.plot(
[2020],
[0.8 * emissions[1990]],
marker="*",
markersize=12,
markerfacecolor="black",
markeredgecolor="black",
)
ax1.plot([2030],[0.45*emissions[1990]],
marker='*', markersize=12, markerfacecolor='white',
markeredgecolor='black')
ax1.plot(
[2030],
[0.45 * emissions[1990]],
marker="*",
markersize=12,
markerfacecolor="white",
markeredgecolor="black",
)
ax1.plot([2030],[0.6*emissions[1990]],
marker='*', markersize=12, markerfacecolor='black',
markeredgecolor='black')
ax1.plot(
[2030],
[0.6 * emissions[1990]],
marker="*",
markersize=12,
markerfacecolor="black",
markeredgecolor="black",
)
ax1.plot([2050, 2050],[x*emissions[1990] for x in [0.2, 0.05]],
color='gray', linewidth=2, marker='_', alpha=0.5)
ax1.plot(
[2050, 2050],
[x * emissions[1990] for x in [0.2, 0.05]],
color="gray",
linewidth=2,
marker="_",
alpha=0.5,
)
ax1.plot([2050],[0.01*emissions[1990]],
marker='*', markersize=12, markerfacecolor='white',
linewidth=0, markeredgecolor='black',
label='EU under-discussion target', zorder=10,
clip_on=False)
ax1.plot(
[2050],
[0.01 * emissions[1990]],
marker="*",
markersize=12,
markerfacecolor="white",
linewidth=0,
markeredgecolor="black",
label="EU under-discussion target",
zorder=10,
clip_on=False,
)
ax1.plot([2050],[0.125*emissions[1990]],'ro',
marker='*', markersize=12, markerfacecolor='black',
markeredgecolor='black', label='EU committed target')
ax1.plot(
[2050],
[0.125 * emissions[1990]],
"ro",
marker="*",
markersize=12,
markerfacecolor="black",
markeredgecolor="black",
label="EU committed target",
)
ax1.legend(fancybox=True, fontsize=18, loc=(0.01,0.01),
facecolor='white', frameon=True)
ax1.legend(
fancybox=True, fontsize=18, loc=(0.01, 0.01), facecolor="white", frameon=True
)
path_cb_plot = snakemake.config['results_dir'] + snakemake.config['run'] + '/graphs/'
plt.savefig(path_cb_plot+'carbon_budget_plot.pdf', dpi=300)
path_cb_plot = (
snakemake.config["results_dir"] + snakemake.config["run"] + "/graphs/"
)
plt.savefig(path_cb_plot + "carbon_budget_plot.pdf", dpi=300)
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake('plot_summary')
logging.basicConfig(level=snakemake.config['logging_level'])
snakemake = mock_snakemake("plot_summary")
logging.basicConfig(level=snakemake.config["logging_level"])
n_header = 4
@ -453,8 +567,8 @@ if __name__ == "__main__":
plot_balances()
for sector_opts in snakemake.config['scenario']['sector_opts']:
opts=sector_opts.split('-')
for sector_opts in snakemake.config["scenario"]["sector_opts"]:
opts = sector_opts.split("-")
for o in opts:
if "cb" in o:
plot_carbon_budget_distribution(snakemake.input.eurostat)

2077
scripts/prepare_sector_network.py
View File

File diff suppressed because it is too large Load Diff

17
scripts/retrieve_gas_infrastructure_data.py
View File

@ -1,23 +1,26 @@
# -*- coding: utf-8 -*-
"""
Retrieve gas infrastructure data from https://zenodo.org/record/4767098/files/IGGIELGN.zip
Retrieve gas infrastructure data from
https://zenodo.org/record/4767098/files/IGGIELGN.zip.
"""
import logging
from helper import progress_retrieve
import zipfile
from pathlib import Path
from helper import progress_retrieve
logger = logging.getLogger(__name__)
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake('retrieve_gas_network_data')
rootpath = '..'
snakemake = mock_snakemake("retrieve_gas_network_data")
rootpath = ".."
else:
rootpath = '.'
rootpath = "."
url = "https://zenodo.org/record/4767098/files/IGGIELGN.zip"

5
scripts/retrieve_sector_databundle.py
View File

@ -1,8 +1,10 @@
# -*- coding: utf-8 -*-
"""
Retrieve and extract sector data bundle.
"""
import logging
logger = logging.getLogger(__name__)
import os
@ -13,8 +15,7 @@ from pathlib import Path
# Add pypsa-eur scripts to path for import of _helpers
sys.path.insert(0, os.getcwd() + "/../pypsa-eur/scripts")
from _helpers import progress_retrieve, configure_logging
from _helpers import configure_logging, progress_retrieve
if __name__ == "__main__":
configure_logging(snakemake)

258
scripts/solve_network.py
View File

@ -1,40 +1,51 @@
"""Solve network."""
import pypsa
import numpy as np
from vresutils.benchmark import memory_logger
from helper import override_component_attrs, update_config_with_sector_opts
# -*- coding: utf-8 -*-
"""
Solve network.
"""
import logging
import numpy as np
import pypsa
from helper import override_component_attrs, update_config_with_sector_opts
from vresutils.benchmark import memory_logger
logger = logging.getLogger(__name__)
pypsa.pf.logger.setLevel(logging.WARNING)
def add_land_use_constraint(n):
if 'm' in snakemake.wildcards.clusters:
if "m" in snakemake.wildcards.clusters:
_add_land_use_constraint_m(n)
else:
_add_land_use_constraint(n)
def _add_land_use_constraint(n):
#warning: this will miss existing offwind which is not classed AC-DC and has carrier 'offwind'
# warning: this will miss existing offwind which is not classed AC-DC and has carrier 'offwind'
for carrier in ['solar', 'onwind', 'offwind-ac', 'offwind-dc']:
ext_i = (n.generators.carrier==carrier) & ~n.generators.p_nom_extendable
existing = n.generators.loc[ext_i,"p_nom"].groupby(n.generators.bus.map(n.buses.location)).sum()
for carrier in ["solar", "onwind", "offwind-ac", "offwind-dc"]:
ext_i = (n.generators.carrier == carrier) & ~n.generators.p_nom_extendable
existing = (
n.generators.loc[ext_i, "p_nom"]
.groupby(n.generators.bus.map(n.buses.location))
.sum()
)
existing.index += " " + carrier + "-" + snakemake.wildcards.planning_horizons
n.generators.loc[existing.index,"p_nom_max"] -= existing
n.generators.loc[existing.index, "p_nom_max"] -= existing
# check if existing capacities are larger than technical potential
existing_large = n.generators[n.generators["p_nom_min"] > n.generators["p_nom_max"]].index
existing_large = n.generators[
n.generators["p_nom_min"] > n.generators["p_nom_max"]
].index
if len(existing_large):
logger.warning(f"Existing capacities larger than technical potential for {existing_large},\
adjust technical potential to existing capacities")
n.generators.loc[existing_large, "p_nom_max"] = n.generators.loc[existing_large, "p_nom_min"]
logger.warning(
f"Existing capacities larger than technical potential for {existing_large},\
adjust technical potential to existing capacities"
)
n.generators.loc[existing_large, "p_nom_max"] = n.generators.loc[
existing_large, "p_nom_min"
]
n.generators.p_nom_max.clip(lower=0, inplace=True)
@ -46,80 +57,109 @@ def _add_land_use_constraint_m(n):
grouping_years = snakemake.config["existing_capacities"]["grouping_years"]
current_horizon = snakemake.wildcards.planning_horizons
for carrier in ['solar', 'onwind', 'offwind-ac', 'offwind-dc']:
existing = n.generators.loc[n.generators.carrier==carrier,"p_nom"]
ind = list(set([i.split(sep=" ")[0] + ' ' + i.split(sep=" ")[1] for i in existing.index]))
for carrier in ["solar", "onwind", "offwind-ac", "offwind-dc"]:
existing = n.generators.loc[n.generators.carrier == carrier, "p_nom"]
ind = list(
set(
[
i.split(sep=" ")[0] + " " + i.split(sep=" ")[1]
for i in existing.index
]
)
)
previous_years = [
str(y) for y in
planning_horizons + grouping_years
str(y)
for y in planning_horizons + grouping_years
if y < int(snakemake.wildcards.planning_horizons)
]
for p_year in previous_years:
ind2 = [i for i in ind if i + " " + carrier + "-" + p_year in existing.index]
ind2 = [
i for i in ind if i + " " + carrier + "-" + p_year in existing.index
]
sel_current = [i + " " + carrier + "-" + current_horizon for i in ind2]
sel_p_year = [i + " " + carrier + "-" + p_year for i in ind2]
n.generators.loc[sel_current, "p_nom_max"] -= existing.loc[sel_p_year].rename(lambda x: x[:-4] + current_horizon)
n.generators.loc[sel_current, "p_nom_max"] -= existing.loc[
sel_p_year
].rename(lambda x: x[:-4] + current_horizon)
n.generators.p_nom_max.clip(lower=0, inplace=True)
def add_co2_sequestration_limit(n, limit=200):
"""Add a global constraint on the amount of Mt CO2 that can be sequestered."""
"""
Add a global constraint on the amount of Mt CO2 that can be sequestered.
"""
n.carriers.loc["co2 stored", "co2_absorptions"] = -1
n.carriers.co2_absorptions = n.carriers.co2_absorptions.fillna(0)
limit = limit * 1e6
for o in opts:
if not "seq" in o: continue
limit = float(o[o.find("seq")+3:]) * 1e6
if not "seq" in o:
continue
limit = float(o[o.find("seq") + 3 :]) * 1e6
break
n.add("GlobalConstraint", 'co2_sequestration_limit', sense="<=", constant=limit,
type="primary_energy", carrier_attribute="co2_absorptions")
n.add(
"GlobalConstraint",
"co2_sequestration_limit",
sense="<=",
constant=limit,
type="primary_energy",
carrier_attribute="co2_absorptions",
)
def prepare_network(n, solve_opts=None, config=None):
if "clip_p_max_pu" in solve_opts:
for df in (
n.generators_t.p_max_pu,
n.generators_t.p_min_pu,
n.storage_units_t.inflow,
):
df.where(df > solve_opts["clip_p_max_pu"], other=0.0, inplace=True)
if 'clip_p_max_pu' in solve_opts:
for df in (n.generators_t.p_max_pu, n.generators_t.p_min_pu, n.storage_units_t.inflow):
df.where(df>solve_opts['clip_p_max_pu'], other=0., inplace=True)
if solve_opts.get('load_shedding'):
if solve_opts.get("load_shedding"):
# intersect between macroeconomic and surveybased willingness to pay
# http://journal.frontiersin.org/article/10.3389/fenrg.2015.00055/full
n.add("Carrier", "Load")
n.madd("Generator", n.buses.index, " load",
bus=n.buses.index,
carrier='load',
sign=1e-3, # Adjust sign to measure p and p_nom in kW instead of MW
marginal_cost=1e2, # Eur/kWh
p_nom=1e9 # kW
n.madd(
"Generator",
n.buses.index,
" load",
bus=n.buses.index,
carrier="load",
sign=1e-3, # Adjust sign to measure p and p_nom in kW instead of MW
marginal_cost=1e2, # Eur/kWh
p_nom=1e9, # kW
)
if solve_opts.get('noisy_costs'):
if solve_opts.get("noisy_costs"):
for t in n.iterate_components():
#if 'capital_cost' in t.df:
# if 'capital_cost' in t.df:
# t.df['capital_cost'] += 1e1 + 2.*(np.random.random(len(t.df)) - 0.5)
if 'marginal_cost' in t.df:
if "marginal_cost" in t.df:
np.random.seed(174)
t.df['marginal_cost'] += 1e-2 + 2e-3 * (np.random.random(len(t.df)) - 0.5)
t.df["marginal_cost"] += 1e-2 + 2e-3 * (
np.random.random(len(t.df)) - 0.5
)
for t in n.iterate_components(['Line', 'Link']):
for t in n.iterate_components(["Line", "Link"]):
np.random.seed(123)
t.df['capital_cost'] += (1e-1 + 2e-2 * (np.random.random(len(t.df)) - 0.5)) * t.df['length']
t.df["capital_cost"] += (
1e-1 + 2e-2 * (np.random.random(len(t.df)) - 0.5)
) * t.df["length"]
if solve_opts.get('nhours'):
nhours = solve_opts['nhours']
if solve_opts.get("nhours"):
nhours = solve_opts["nhours"]
n.set_snapshots(n.snapshots[:nhours])
n.snapshot_weightings[:] = 8760./nhours
n.snapshot_weightings[:] = 8760.0 / nhours
if snakemake.config['foresight'] == 'myopic':
if snakemake.config["foresight"] == "myopic":
add_land_use_constraint(n)
if n.stores.carrier.eq('co2 stored').any():
if n.stores.carrier.eq("co2 stored").any():
limit = config["sector"].get("co2_sequestration_potential", 200)
add_co2_sequestration_limit(n, limit=limit)
@ -134,23 +174,29 @@ def add_battery_constraints(n):
discharger_bool = n.links.index.str.contains("battery discharger")
charger_bool = n.links.index.str.contains("battery charger")
dischargers_ext= n.links[discharger_bool].query("p_nom_extendable").index
chargers_ext= n.links[charger_bool].query("p_nom_extendable").index
dischargers_ext = n.links[discharger_bool].query("p_nom_extendable").index
chargers_ext = n.links[charger_bool].query("p_nom_extendable").index
eff = n.links.efficiency[dischargers_ext].values
lhs = n.model["Link-p_nom"].loc[chargers_ext] - n.model["Link-p_nom"].loc[dischargers_ext] * eff
lhs = (
n.model["Link-p_nom"].loc[chargers_ext]
- n.model["Link-p_nom"].loc[dischargers_ext] * eff
)
n.model.add_constraints(lhs == 0, name="Link-charger_ratio")
def add_chp_constraints(n):
electric = (n.links.index.str.contains("urban central")
& n.links.index.str.contains("CHP")
& n.links.index.str.contains("electric"))
heat = (n.links.index.str.contains("urban central")
& n.links.index.str.contains("CHP")
& n.links.index.str.contains("heat"))
electric = (
n.links.index.str.contains("urban central")
& n.links.index.str.contains("CHP")
& n.links.index.str.contains("electric")
)
heat = (
n.links.index.str.contains("urban central")
& n.links.index.str.contains("CHP")
& n.links.index.str.contains("heat")
)
electric_ext = n.links[electric].query("p_nom_extendable").index
heat_ext = n.links[heat].query("p_nom_extendable").index
@ -158,38 +204,50 @@ def add_chp_constraints(n):
electric_fix = n.links[electric].query("~p_nom_extendable").index
heat_fix = n.links[heat].query("~p_nom_extendable").index
p = n.model["Link-p"] # dimension: [time, link]
p = n.model["Link-p"] # dimension: [time, link]
# output ratio between heat and electricity and top_iso_fuel_line for extendable
if not electric_ext.empty:
p_nom = n.model["Link-p_nom"]
lhs = (p_nom.loc[electric_ext] * (n.links.p_nom_ratio * n.links.efficiency)[electric_ext].values -
p_nom.loc[heat_ext] * n.links.efficiency[heat_ext].values)
n.model.add_constraints(lhs == 0, name='chplink-fix_p_nom_ratio')
lhs = (
p_nom.loc[electric_ext]
* (n.links.p_nom_ratio * n.links.efficiency)[electric_ext].values
- p_nom.loc[heat_ext] * n.links.efficiency[heat_ext].values
)
n.model.add_constraints(lhs == 0, name="chplink-fix_p_nom_ratio")
rename = {"Link-ext": "Link"}
lhs = p.loc[:, electric_ext] + p.loc[:, heat_ext] - p_nom.rename(rename).loc[electric_ext]
n.model.add_constraints(lhs <= 0, name='chplink-top_iso_fuel_line_ext')
lhs = (
p.loc[:, electric_ext]
+ p.loc[:, heat_ext]
- p_nom.rename(rename).loc[electric_ext]
)
n.model.add_constraints(lhs <= 0, name="chplink-top_iso_fuel_line_ext")
# top_iso_fuel_line for fixed
if not electric_fix.empty:
lhs = p.loc[:, electric_fix] + p.loc[:, heat_fix]
rhs = n.links.p_nom[electric_fix]
n.model.add_constraints(lhs <= rhs, name='chplink-top_iso_fuel_line_fix')
n.model.add_constraints(lhs <= rhs, name="chplink-top_iso_fuel_line_fix")
# back-pressure
if not electric.empty:
lhs = (p.loc[:, heat] * (n.links.efficiency[heat] * n.links.c_b[electric].values) -
p.loc[:, electric] * n.links.efficiency[electric])
n.model.add_constraints(lhs <= rhs, name='chplink-backpressure')
lhs = (
p.loc[:, heat] * (n.links.efficiency[heat] * n.links.c_b[electric].values)
- p.loc[:, electric] * n.links.efficiency[electric]
)
n.model.add_constraints(lhs <= rhs, name="chplink-backpressure")
def add_pipe_retrofit_constraint(n):
"""Add constraint for retrofitting existing CH4 pipelines to H2 pipelines."""
"""
Add constraint for retrofitting existing CH4 pipelines to H2 pipelines.
"""
gas_pipes_i = n.links.query("carrier == 'gas pipeline' and p_nom_extendable").index
h2_retrofitted_i = n.links.query("carrier == 'H2 pipeline retrofitted' and p_nom_extendable").index
h2_retrofitted_i = n.links.query(
"carrier == 'H2 pipeline retrofitted' and p_nom_extendable"
).index
if h2_retrofitted_i.empty or gas_pipes_i.empty:
return
@ -200,7 +258,7 @@ def add_pipe_retrofit_constraint(n):
lhs = p_nom.loc[gas_pipes_i] + CH4_per_H2 * p_nom.loc[h2_retrofitted_i]
rhs = n.links.p_nom[gas_pipes_i].rename_axis("Link-ext")
n.model.add_constraints(lhs == rhs, name='Link-pipe_retrofit')
n.model.add_constraints(lhs == rhs, name="Link-pipe_retrofit")
def extra_functionality(n, snapshots):
@ -209,9 +267,11 @@ def extra_functionality(n, snapshots):
def solve_network(n, config, opts="", **kwargs):
set_of_options = config['solving']['solver']['options']
solver_options = config['solving']["solver_options"][set_of_options] if set_of_options else {}
solver_name = config['solving']['solver']['name']
set_of_options = config["solving"]["solver"]["options"]
solver_options = (
config["solving"]["solver_options"][set_of_options] if set_of_options else {}
)
solver_name = config["solving"]["solver"]["name"]
cf_solving = config["solving"]["options"]
track_iterations = cf_solving.get("track_iterations", False)
min_iterations = cf_solving.get("min_iterations", 4)
@ -245,46 +305,52 @@ def solve_network(n, config, opts="", **kwargs):
)
if status != "ok":
logger.warning(f"Solving status '{status}' with termination condition '{condition}'")
logger.warning(
f"Solving status '{status}' with termination condition '{condition}'"
)
return n
#%%
# %%
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'solve_network_myopic',
simpl='',
"solve_network_myopic",
simpl="",
opts="",
clusters="45",
lv=1.0,
sector_opts='8760H-T-H-B-I-A-solar+p3-dist1',
sector_opts="8760H-T-H-B-I-A-solar+p3-dist1",
planning_horizons="2020",
)
logging.basicConfig(filename=snakemake.log.python,
level=snakemake.config['logging_level'])
logging.basicConfig(
filename=snakemake.log.python, level=snakemake.config["logging_level"]
)
update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts)
tmpdir = snakemake.config['solving'].get('tmpdir')
tmpdir = snakemake.config["solving"].get("tmpdir")
if tmpdir is not None:
from pathlib import Path
Path(tmpdir).mkdir(parents=True, exist_ok=True)
opts = snakemake.wildcards.sector_opts.split('-')
solve_opts = snakemake.config['solving']['options']
fn = getattr(snakemake.log, 'memory', None)
with memory_logger(filename=fn, interval=30.) as mem:
opts = snakemake.wildcards.sector_opts.split("-")
solve_opts = snakemake.config["solving"]["options"]
fn = getattr(snakemake.log, "memory", None)
with memory_logger(filename=fn, interval=30.0) as mem:
overrides = override_component_attrs(snakemake.input.overrides)
n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
n = prepare_network(n, solve_opts, config=snakemake.config)
n = solve_network(n, config=snakemake.config, opts=opts, log_fn=snakemake.log.solver)
n = solve_network(
n, config=snakemake.config, opts=opts, log_fn=snakemake.log.solver
)
if "lv_limit" in n.global_constraints.index:
n.line_volume_limit = n.global_constraints.at["lv_limit", "constant"]

13
test/config.myopic.yaml
View File

@ -3,15 +3,15 @@ foresight: myopic
scenario:
lv:
- 1.5
- 1.5
clusters:
- 5
- 5
sector_opts:
- 191H-T-H-B-I-A-solar+p3-dist1
- 191H-T-H-B-I-A-solar+p3-dist1
planning_horizons:
- 2030
- 2040
- 2050
- 2030
- 2040
- 2050
snapshots:
start: "2013-03-01"
@ -25,4 +25,3 @@ solving:
name: cbc
options: cbc-default
mem: 4000

8
test/config.overnight.yaml
View File

@ -3,13 +3,13 @@ foresight: overnight
scenario:
lv:
- 1.5
- 1.5
clusters:
- 5
- 5
sector_opts:
- CO2L0-191H-T-H-B-I-A-solar+p3-dist1
- CO2L0-191H-T-H-B-I-A-solar+p3-dist1
planning_horizons:
- 2030
- 2030
snapshots:
start: "2013-03-01"