Merge pull request #89 from martavp/distribute_CO2budget
Distribute CO2 budget among the planning horizons for the myopic option
This commit is contained in:
Tom Brown
GitHub
commit
b133cd6656
@ -8,7 +8,7 @@ wildcard_constraints:
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clusters="[0-9]+m?",
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sectors="[+a-zA-Z0-9]+",
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opts="[-+a-zA-Z0-9]*",
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sector_opts="[-+a-zA-Z0-9]*"
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sector_opts="[-+a-zA-Z0-9\.]*"
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@ -292,6 +292,7 @@ rule build_retro_cost:
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output:
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retro_cost="resources/retro_cost_{network}_s{simpl}_{clusters}.csv",
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floor_area="resources/floor_area_{network}_s{simpl}_{clusters}.csv"
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resources: mem_mb=1000
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script: "scripts/build_retro_cost.py"
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@ -24,11 +24,17 @@ scenario:
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# B for biomass supply, I for industry, shipping and aviation
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# solarx or onwindx changes the available installable potential by factor x
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# dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
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# for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
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# emissions throughout the transition path in the timeframe determined by the
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# planning_horizons), be:beta decay; ex:exponential decay
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# cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
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# decay with initial growth rate 0
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planning_horizons : [2030] # investment years for myopic and perfect; or costs year for overnight
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# for example, set to [2020, 2030, 2040, 2050] for myopic foresight
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# CO2 budget as a fraction of 1990 emissions
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# this is over-ridden if CO2Lx is set in sector_opts
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# this is also over-ridden if cb is set in sector_opts
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co2_budget:
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2020: 0.7011648746
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2025: 0.5241935484
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@ -16,7 +16,7 @@ its dependencies. Clone the repository:
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.. code:: bash
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projects % git clone git@github.com:PyPSA/pypsa-eur.git
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projects % git clone https://github.com/PyPSA/pypsa-eur.git
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then download and unpack all the PyPSA-Eur data files by running the following snakemake rule:
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@ -32,7 +32,7 @@ Next install the technology assumptions database `technology-data <https://githu
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.. code:: bash
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projects % git clone git@github.com:PyPSA/technology-data.git
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projects % git clone https://github.com/PyPSA/technology-data.git
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Clone PyPSA-Eur-Sec repository
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@ -42,7 +42,7 @@ Create a parallel directory for `PyPSA-Eur-Sec <https://github.com/PyPSA/pypsa-e
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.. code:: bash
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projects % git clone git@github.com:PyPSA/pypsa-eur-sec.git
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projects % git clone https://github.com/PyPSA/pypsa-eur-sec.git
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Environment/package requirements
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================================
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@ -54,6 +54,13 @@ The requirements are the same as `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>
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xarray version >= 0.15.1, you will need the latest master branch of
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atlite version 0.0.2.
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You can create an enviroment using the environment.yaml file in pypsa-eur/envs:
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.../pypsa-eur % conda env create -f envs/environment.yaml
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.../pypsa-eur % conda activate pypsa-eur
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See details in `PyPSA-Eur Installation <https://pypsa-eur.readthedocs.io/en/latest/installation.html>`_
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Data requirements
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=================
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@ -6,7 +6,7 @@ Myopic transition path
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The myopic code can be used to investigate progressive changes in a network, for instance, those taking place throughout a transition path. The capacities installed in a certain time step are maintained in the network until their operational lifetime expires.
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The myopic approach was initially developed and used in the paper `Early decarbonisation of the European Energy system pays off (2020) <https://arxiv.org/abs/2004.11009>`__ but the current implementation complies with the pypsa-eur-sec standard working flow and is compatible with using the higher resolution electricity transmission model `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`__ rather than a one-node-per-country model.
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The myopic approach was initially developed and used in the paper `Early decarbonisation of the European Energy system pays off (2020) <https://www.nature.com/articles/s41467-020-20015-4>`__ but the current implementation complies with the pypsa-eur-sec standard working flow and is compatible with using the higher resolution electricity transmission model `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`__ rather than a one-node-per-country model.
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The current code applies the myopic approach to generators, storage technologies and links in the power sector and the space and water heating sector.
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@ -61,12 +61,15 @@ Wildcards
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The {planning_horizons} wildcard indicates the timesteps in which the network is optimized, e.g. planning_horizons: [2020, 2030, 2040, 2050]
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Options
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=============
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The total carbon budget for the entire transition path can be indicated in the ``scenario.sector_opts`` in ``config.yaml``.
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The carbon budget can be split among the ``planning_horizons`` following an exponential or beta decay.
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E.g. ``'cb40ex0'`` splits the a carbon budget equal to 40 GtCO_2 following an exponential decay whose initial linear growth rate $r$ is zero
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**{co2_budget_name} wildcard**
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$e(t) = e_0 (1+ (r+m)t) e^(-mt)$
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The {co2_budget_name} wildcard indicates the name of the co2 budget.
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A csv file is used as input including the planning_horizons as index, the name of co2_budget as column name, and the maximum co2 emissions (relative to 1990) as values.
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See details in Supplementary Note 1 of the paper `Early decarbonisation of the European Energy system pays off (2020) <https://www.nature.com/articles/s41467-020-20015-4>`__
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Rules overview
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=================
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@ -74,17 +77,17 @@ Rules overview
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General myopic code structure
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===============================
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The myopic code solves the network for the time steps included in planning_horizons in a recursive loop, so that:
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The myopic code solves the network for the time steps included in ``planning_horizons`` in a recursive loop, so that:
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1.The existing capacities (those installed before the base year are added as fixed capacities with p_nom=value, p_nom_extendable=False). E.g. for baseyear=2020, capacities installed before 2020 are added. In addition, the network comprises additional generator, storage, and link capacities with p_nom_extendable=True. The non-solved network is saved in ``results/run_name/networks/prenetworks-brownfield``.
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The base year is the first element in planning_horizons. Step 1 is implemented with the rule add_baseyear for the base year and with the rule add_brownfield for the remaining planning_horizons.
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The base year is the first element in ``planning_horizons``. Step 1 is implemented with the rule add_baseyear for the base year and with the rule add_brownfield for the remaining planning_horizons.
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2.The 2020 network is optimized. The solved network is saved in ‘results/run_name/networks/postnetworks’
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2.The 2020 network is optimized. The solved network is saved in ``results/run_name/networks/postnetworks``
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3.For the next planning horizon, e.g. 2030, the capacities from a previous time step are added if they are still in operation (i.e., if they fulfil planning horizon <= commissioned year + lifetime). In addition, the network comprises additional generator, storage, and link capacities with p_nom_extendable=True. The non-solved network is saved in ``results/run_name/networks/prenetworks-brownfield``.
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Steps 2 and 3 are solved recursively for all the planning_horizons included in the configuration file.
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Steps 2 and 3 are solved recursively for all the planning_horizons included in ``config.yaml``.
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rule add_existing baseyear
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@ -110,8 +113,8 @@ Then, the resulting network is saved in ``results/run_name/networks/prenetworks-
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rule add_brownfield
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===================
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The rule add_brownfield loads the network in ‘results/run_name/networks/prenetworks’ and performs the following operation:
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The rule add_brownfield loads the network in ``results/run_name/networks/prenetworks`` and performs the following operation:
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1.Read the capacities optimized in the previous time step and add them to the network if they are still in operation (i.e., if they fulfil planning horizon < commissioned year + lifetime)
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1.Read the capacities optimized in the previous time step and add them to the network if they are still in operation (i.e., if they fulfill planning horizon < commissioned year + lifetime)
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Then, the resulting network is saved in ``results/run_name/networks/prenetworks_brownfield``.
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@ -2,6 +2,9 @@
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Release Notes
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##########################################
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Future release
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===================
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*For the myopic option, a carbon budget and a type of decay (exponential or beta) can be selected in the config file to distribute the budget across the planning_horizons.
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PyPSA-Eur-Sec 0.4.0 (11th December 2020)
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=========================================
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@ -106,7 +106,7 @@ Thermal energy storage using hot water tanks
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Small for decentral applications.
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Big pit storage for district heating.
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Big water pit storage for district heating.
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Hydrogen demand
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@ -122,7 +122,7 @@ Industry (ammonia, precursor to hydrocarbons for chemicals and iron/steel).
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Hydrogen supply
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=================
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SMR, SMR+CCS, electrolysers.
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Steam Methane Reforming (SMR), SMR+CCS, electrolysers.
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Methane demand
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@ -390,12 +390,12 @@ def build_energy_totals():
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return clean_df
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def build_eea_co2():
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def build_eea_co2(year=1990):
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# see ../notebooks/compute_1990_Europe_emissions_for_targets.ipynb
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#https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-14
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#downloaded 190222 (modified by EEA last on 181130)
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fn = "data/eea/UNFCCC_v21.csv"
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#https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16
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#downloaded 201228 (modified by EEA last on 201221)
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fn = "data/eea/UNFCCC_v23.csv"
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df = pd.read_csv(fn, encoding="latin-1")
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df.loc[df["Year"] == "1985-1987","Year"] = 1986
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df["Year"] = df["Year"].astype(int)
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@ -418,16 +418,14 @@ def build_eea_co2():
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e['waste management'] = '5 - Waste management'
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e['other'] = '6 - Other Sector'
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e['indirect'] = 'ind_CO2 - Indirect CO2'
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e["total wL"] = "Total (with LULUCF, with indirect CO2)"
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e["total woL"] = "Total (without LULUCF, with indirect CO2)"
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e["total wL"] = "Total (with LULUCF)"
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e["total woL"] = "Total (without LULUCF)"
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pol = "CO2" #["All greenhouse gases - (CO2 equivalent)","CO2"]
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cts = ["CH","EUA","NO"] + eu28_eea
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year = 1990
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emissions = df.loc[idx[cts,pol,year,e.values],"emissions"].unstack("Sector_name").rename(columns=pd.Series(e.index,e.values)).rename(index={"All greenhouse gases - (CO2 equivalent)" : "GHG"},level=1)
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#only take level 0, since level 1 (pol) and level 2 (year) are trivial
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@ -467,7 +465,7 @@ def build_eurostat_co2(year=1990):
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return eurostat_co2
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def build_co2_totals(year=1990):
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def build_co2_totals(eea_co2, eurostat_co2, year=1990):
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co2 = eea_co2.reindex(["EU28","NO","CH","BA","RS","AL","ME","MK"] + eu28)
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@ -486,10 +484,6 @@ def build_co2_totals(year=1990):
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#doesn't include non-energy emissions
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co2.loc[ct,'agriculture'] = eurostat_co2[ct,"+","+","Agriculture / Forestry"].sum()
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co2.to_csv(snakemake.output.co2_name)
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return co2
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@ -547,7 +541,7 @@ if __name__ == "__main__":
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snakemake.output['transport_name'] = "data/transport_data.csv"
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snakemake.input = Dict()
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snakemake.input['nuts3_shapes'] = 'resources/nuts3_shapes.geojson'
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snakemake.input['nuts3_shapes'] = '../pypsa-eur/resources/nuts3_shapes.geojson'
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nuts3 = gpd.read_file(snakemake.input.nuts3_shapes).set_index('index')
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population = nuts3['pop'].groupby(nuts3.country).sum()
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@ -566,6 +560,7 @@ if __name__ == "__main__":
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eurostat_co2 = build_eurostat_co2()
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build_co2_totals()
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co2=build_co2_totals(eea_co2, eurostat_co2, year)
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co2.to_csv(snakemake.output.co2_name)
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build_transport_data()
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@ -196,7 +196,25 @@ def calculate_costs(n,label,costs):
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return costs
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def calculate_cumulative_cost():
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planning_horizons = snakemake.config['scenario']['planning_horizons']
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cumulative_cost = pd.DataFrame(index = df["costs"].sum().index,
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columns=pd.Series(data=np.arange(0,0.1, 0.01), name='social discount rate'))
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#discount cost and express them in money value of planning_horizons[0]
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for r in cumulative_cost.columns:
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cumulative_cost[r]=[df["costs"].sum()[index]/((1+r)**(index[-1]-planning_horizons[0])) for index in cumulative_cost.index]
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#integrate cost throughout the transition path
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for r in cumulative_cost.columns:
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for cluster in cumulative_cost.index.get_level_values(level=0).unique():
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for lv in cumulative_cost.index.get_level_values(level=1).unique():
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for sector_opts in cumulative_cost.index.get_level_values(level=2).unique():
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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)
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return cumulative_cost
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def calculate_nodal_capacities(n,label,nodal_capacities):
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#Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff
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for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}):
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@ -564,16 +582,17 @@ if __name__ == "__main__":
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snakemake.config = yaml.safe_load(f)
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#overwrite some options
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snakemake.config["run"] = "test"
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snakemake.config["run"] = "version-8"
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snakemake.config["scenario"]["lv"] = [1.0]
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snakemake.config["scenario"]["sector_opts"] = ["Co2L0-168H-T-H-B-I-solar3-dist1"]
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snakemake.config["scenario"]["sector_opts"] = ["3H-T-H-B-I-solar3-dist1"]
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snakemake.config["planning_horizons"] = ['2020', '2030', '2040', '2050']
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snakemake.input = Dict()
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snakemake.input['heat_demand_name'] = 'data/heating/daily_heat_demand.h5'
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snakemake.input['costs'] = snakemake.config['costs_dir'] + "costs_{}.csv".format(snakemake.config['scenario']['planning_horizons'][0])
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snakemake.output = Dict()
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for item in outputs:
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snakemake.output[item] = snakemake.config['summary_dir'] + '/{name}/csvs/{item}.csv'.format(name=snakemake.config['run'],item=item)
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snakemake.output['cumulative_cost'] = snakemake.config['summary_dir'] + '/{name}/csvs/cumulative_cost.csv'.format(name=snakemake.config['run'])
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networks_dict = {(cluster, lv, opt+sector_opt, planning_horizon) :
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snakemake.config['results_dir'] + snakemake.config['run'] + '/postnetworks/elec_s{simpl}_{cluster}_lv{lv}_{opt}_{sector_opt}_{planning_horizon}.nc'\
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.format(simpl=simpl,
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@ -592,6 +611,7 @@ if __name__ == "__main__":
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print(networks_dict)
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Nyears = 1
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costs_db = prepare_costs(snakemake.input.costs,
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snakemake.config['costs']['USD2013_to_EUR2013'],
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snakemake.config['costs']['discountrate'],
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@ -603,3 +623,10 @@ if __name__ == "__main__":
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df["metrics"].loc["total costs"] = df["costs"].sum()
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to_csv(df)
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if snakemake.config["foresight"]=='myopic':
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cumulative_cost=calculate_cumulative_cost()
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cumulative_cost.to_csv(snakemake.config['summary_dir'] + '/' + snakemake.config['run'] + '/csvs/cumulative_cost.csv')
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@ -1,6 +1,6 @@
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import numpy as np
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import pandas as pd
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#allow plotting without Xwindows
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@ -9,7 +9,7 @@ matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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from prepare_sector_network import co2_emissions_year
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#consolidate and rename
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def rename_techs(label):
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@ -237,7 +237,137 @@ def plot_balances():
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fig.savefig(snakemake.output.balances[:-10] + k + ".pdf",transparent=True)
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def historical_emissions(cts):
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"""
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read historical emissions to add them to the carbon budget plot
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"""
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#https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16
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#downloaded 201228 (modified by EEA last on 201221)
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fn = "data/eea/UNFCCC_v23.csv"
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df = pd.read_csv(fn, encoding="latin-1")
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df.loc[df["Year"] == "1985-1987","Year"] = 1986
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df["Year"] = df["Year"].astype(int)
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df = df.set_index(['Year', 'Sector_name', 'Country_code', 'Pollutant_name']).sort_index()
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e = pd.Series()
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e["electricity"] = '1.A.1.a - Public Electricity and Heat Production'
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e['residential non-elec'] = '1.A.4.b - Residential'
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e['services non-elec'] = '1.A.4.a - Commercial/Institutional'
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e['rail non-elec'] = "1.A.3.c - Railways"
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e["road non-elec"] = '1.A.3.b - Road Transportation'
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e["domestic navigation"] = "1.A.3.d - Domestic Navigation"
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e['international navigation'] = '1.D.1.b - International Navigation'
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e["domestic aviation"] = '1.A.3.a - Domestic Aviation'
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e["international aviation"] = '1.D.1.a - International Aviation'
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e['total energy'] = '1 - Energy'
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e['industrial processes'] = '2 - Industrial Processes and Product Use'
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e['agriculture'] = '3 - Agriculture'
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e['LULUCF'] = '4 - Land Use, Land-Use Change and Forestry'
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e['waste management'] = '5 - Waste management'
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e['other'] = '6 - Other Sector'
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e['indirect'] = 'ind_CO2 - Indirect CO2'
|
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e["total wL"] = "Total (with LULUCF)"
|
||||
e["total woL"] = "Total (without LULUCF)"
|
||||
|
||||
pol = ["CO2"] # ["All greenhouse gases - (CO2 equivalent)"]
|
||||
cts
|
||||
if "GB" in cts:
|
||||
cts.remove("GB")
|
||||
cts.append("UK")
|
||||
|
||||
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 = (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()
|
||||
|
||||
emissions = co2_totals.loc["electricity"]
|
||||
if "T" in opts:
|
||||
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()
|
||||
if "I" in opts:
|
||||
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():
|
||||
"""
|
||||
Plot historical carbon emissions in the EU and decarbonization path
|
||||
"""
|
||||
|
||||
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
|
||||
|
||||
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])
|
||||
|
||||
path_cb = snakemake.config['results_dir'] + snakemake.config['run'] + '/csvs/'
|
||||
countries=pd.read_csv(path_cb + 'countries.csv', index_col=1)
|
||||
cts=countries.index.to_list()
|
||||
e_1990 = co2_emissions_year(cts, opts, year=1990)
|
||||
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)
|
||||
|
||||
emissions = historical_emissions(cts)
|
||||
|
||||
ax1.plot(emissions, color='black', linewidth=3, label=None)
|
||||
|
||||
#plot commited 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([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([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],[0.125*emissions[1990]],'ro',
|
||||
marker='*', markersize=12, markerfacecolor='black',
|
||||
markeredgecolor='black', label='EU commited target')
|
||||
|
||||
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)
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Detect running outside of snakemake and mock snakemake for testing
|
||||
@ -249,13 +379,16 @@ if __name__ == "__main__":
|
||||
snakemake.config = yaml.safe_load(f)
|
||||
snakemake.input = Dict()
|
||||
snakemake.output = Dict()
|
||||
|
||||
snakemake.wildcards = Dict()
|
||||
#snakemake.wildcards['sector_opts']='3H-T-H-B-I-solar3-dist1-cb48be3'
|
||||
|
||||
for item in ["costs", "energy"]:
|
||||
snakemake.input[item] = snakemake.config['summary_dir'] + '/{name}/csvs/{item}.csv'.format(name=snakemake.config['run'],item=item)
|
||||
snakemake.output[item] = snakemake.config['summary_dir'] + '/{name}/graphs/{item}.pdf'.format(name=snakemake.config['run'],item=item)
|
||||
snakemake.input["balances"] = snakemake.config['summary_dir'] + '/test/csvs/supply_energy.csv'
|
||||
snakemake.output["balances"] = snakemake.config['summary_dir'] + '/test/graphs/balances-energy.csv'
|
||||
|
||||
snakemake.input["balances"] = snakemake.config['summary_dir'] + '/{name}/csvs/supply_energy.csv'.format(name=snakemake.config['run'],item=item)
|
||||
snakemake.output["balances"] = snakemake.config['summary_dir'] + '/{name}/graphs/balances-energy.csv'.format(name=snakemake.config['run'],item=item)
|
||||
|
||||
|
||||
n_header = 4
|
||||
|
||||
plot_costs()
|
||||
@ -263,3 +396,9 @@ if __name__ == "__main__":
|
||||
plot_energy()
|
||||
|
||||
plot_balances()
|
||||
|
||||
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()
|
||||
@ -20,6 +20,8 @@ import pytz
|
||||
|
||||
from vresutils.costdata import annuity
|
||||
|
||||
from scipy.stats import beta
|
||||
from build_energy_totals import build_eea_co2, build_eurostat_co2, build_co2_totals
|
||||
|
||||
#First tell PyPSA that links can have multiple outputs by
|
||||
#overriding the component_attrs. This can be done for
|
||||
@ -45,6 +47,83 @@ override_component_attrs["Generator"].loc["lifetime"] = ["float","years",np.nan,
|
||||
override_component_attrs["Store"].loc["build_year"] = ["integer","year",np.nan,"build year","Input (optional)"]
|
||||
override_component_attrs["Store"].loc["lifetime"] = ["float","years",np.nan,"lifetime","Input (optional)"]
|
||||
|
||||
|
||||
def co2_emissions_year(cts, opts, year):
|
||||
"""
|
||||
calculate co2 emissions in one specific year (e.g. 1990 or 2018).
|
||||
"""
|
||||
eea_co2 = build_eea_co2(year)
|
||||
|
||||
#TODO: read Eurostat data from year>2014, this only affects the estimation of
|
||||
# CO2 emissions for "BA","RS","AL","ME","MK"
|
||||
if year > 2014:
|
||||
eurostat_co2 = build_eurostat_co2(year=2014)
|
||||
else:
|
||||
eurostat_co2 = build_eurostat_co2(year)
|
||||
|
||||
co2_totals=build_co2_totals(eea_co2, eurostat_co2, year)
|
||||
|
||||
co2_emissions = co2_totals.loc[cts, "electricity"].sum()
|
||||
|
||||
if "T" in opts:
|
||||
co2_emissions += co2_totals.loc[cts, [i+ " non-elec" for i in ["rail","road"]]].sum().sum()
|
||||
if "H" in opts:
|
||||
co2_emissions += co2_totals.loc[cts, [i+ " non-elec" for i in ["residential","services"]]].sum().sum()
|
||||
if "I" in opts:
|
||||
co2_emissions += co2_totals.loc[cts, ["industrial non-elec","industrial processes",
|
||||
"domestic aviation","international aviation",
|
||||
"domestic navigation","international navigation"]].sum().sum()
|
||||
co2_emissions *=0.001 #MtCO2 to GtCO2
|
||||
return co2_emissions
|
||||
|
||||
|
||||
def build_carbon_budget(o):
|
||||
#distribute carbon budget following beta or exponential transition path
|
||||
if "be" in o:
|
||||
#beta decay
|
||||
carbon_budget = float(o[o.find("cb")+2:o.find("be")])
|
||||
be=float(o[o.find("be")+2:])
|
||||
if "ex" in o:
|
||||
#exponential decay
|
||||
carbon_budget = float(o[o.find("cb")+2:o.find("ex")])
|
||||
r=float(o[o.find("ex")+2:])
|
||||
|
||||
|
||||
pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
|
||||
pop_layout["ct"] = pop_layout.index.str[:2]
|
||||
cts = pop_layout.ct.value_counts().index
|
||||
|
||||
e_1990 = co2_emissions_year(cts, opts, year=1990)
|
||||
|
||||
#emissions at the beginning of the path (last year available 2018)
|
||||
e_0 = co2_emissions_year(cts, opts, year=2018)
|
||||
#emissions in 2019 and 2020 assumed equal to 2018 and substracted
|
||||
carbon_budget -= 2*e_0
|
||||
planning_horizons = snakemake.config['scenario']['planning_horizons']
|
||||
CO2_CAP = pd.DataFrame(index = pd.Series(data=planning_horizons,
|
||||
name='planning_horizon'),
|
||||
columns=pd.Series(data=[],
|
||||
name='paths',
|
||||
dtype='float'))
|
||||
t_0 = planning_horizons[0]
|
||||
if "be" in o:
|
||||
#beta decay
|
||||
t_f = t_0 + (2*carbon_budget/e_0).round(0) # final year in the path
|
||||
#emissions (relative to 1990)
|
||||
CO2_CAP[o] = [(e_0/e_1990)*(1-beta.cdf((t-t_0)/(t_f-t_0), be, be)) for t in planning_horizons]
|
||||
|
||||
if "ex" in o:
|
||||
#exponential decay without delay
|
||||
T=carbon_budget/e_0
|
||||
m=(1+np.sqrt(1+r*T))/T
|
||||
CO2_CAP[o] = [(e_0/e_1990)*(1+(m+r)*(t-t_0))*np.exp(-m*(t-t_0)) for t in planning_horizons]
|
||||
|
||||
CO2_CAP.to_csv(path_cb + 'carbon_budget_distribution.csv', sep=',',
|
||||
line_terminator='\n', float_format='%.3f')
|
||||
countries=pd.Series(data=cts)
|
||||
countries.to_csv(path_cb + 'countries.csv', sep=',',
|
||||
line_terminator='\n', float_format='%.3f')
|
||||
|
||||
def add_lifetime_wind_solar(n):
|
||||
"""
|
||||
Add lifetime for solar and wind generators
|
||||
@ -1775,15 +1854,15 @@ def get_parameter(item):
|
||||
return item
|
||||
|
||||
|
||||
#%%
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Detect running outside of snakemake and mock snakemake for testing
|
||||
if 'snakemake' not in globals():
|
||||
from vresutils.snakemake import MockSnakemake
|
||||
snakemake = MockSnakemake(
|
||||
wildcards=dict(network='elec', simpl='', clusters='37', lv='1.0',
|
||||
opts='', planning_horizons='2030', co2_budget_name="go",
|
||||
sector_opts='Co2L0-120H-T-H-B-I-solar3-dist1'),
|
||||
opts='', planning_horizons='2020',
|
||||
sector_opts='120H-T-H-B-I-solar3-dist1-cb48be3'),
|
||||
input=dict( network='../pypsa-eur/networks/{network}_s{simpl}_{clusters}_ec_lv{lv}_{opts}.nc',
|
||||
energy_totals_name='resources/energy_totals.csv',
|
||||
co2_totals_name='resources/co2_totals.csv',
|
||||
@ -1819,10 +1898,10 @@ if __name__ == "__main__":
|
||||
solar_thermal_total="resources/solar_thermal_total_{network}_s{simpl}_{clusters}.nc",
|
||||
solar_thermal_urban="resources/solar_thermal_urban_{network}_s{simpl}_{clusters}.nc",
|
||||
solar_thermal_rural="resources/solar_thermal_rural_{network}_s{simpl}_{clusters}.nc",
|
||||
retro_cost_energy = "resources/retro_cost_{network}_s{simpl}_{clusters}.csv",
|
||||
retro_cost_energy = "resources/retro_cost_{network}_s{simpl}_{clusters}.csv",
|
||||
floor_area = "resources/floor_area_{network}_s{simpl}_{clusters}.csv"
|
||||
),
|
||||
output=['pypsa-eur-sec/results/test/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{co2_budget_name}_{planning_horizons}.nc']
|
||||
output=['results/version-cb48be3/prenetworks/{network}_s{simpl}_{clusters}_lv{lv}__{sector_opts}_{planning_horizons}.nc']
|
||||
)
|
||||
import yaml
|
||||
with open('config.yaml', encoding='utf8') as f:
|
||||
@ -1926,6 +2005,20 @@ if __name__ == "__main__":
|
||||
limit = get_parameter(snakemake.config["co2_budget"])
|
||||
print("CO2 limit set to",limit)
|
||||
|
||||
for o in opts:
|
||||
if "cb" in o:
|
||||
path_cb = snakemake.config['results_dir'] + snakemake.config['run'] + '/csvs/'
|
||||
if not os.path.exists(path_cb):
|
||||
os.makedirs(path_cb)
|
||||
try:
|
||||
CO2_CAP=pd.read_csv(path_cb + 'carbon_budget_distribution.csv', index_col=0)
|
||||
except:
|
||||
build_carbon_budget(o)
|
||||
CO2_CAP=pd.read_csv(path_cb + 'carbon_budget_distribution.csv', index_col=0)
|
||||
|
||||
limit=CO2_CAP.loc[investment_year]
|
||||
print("overriding CO2 limit with scenario limit",limit)
|
||||
|
||||
for o in opts:
|
||||
if "Co2L" in o:
|
||||
limit = o[o.find("Co2L")+4:]
|
||||
|
||||
Loading…
Reference in New Issue
Block a user