fab31e6524
* Update .gitignore
* Add fictitious load to account for non-transformed shipping emissions
The share of shipping demand that is transformed is defined now for different years to be used with the myopic code.
The carbon emission from the remaining share is treated as a negative load on the atmospheric carbon dioxide bus, just like aviation and land transport emissions.
* Split colours for H2 in Industry and H2 in shipping when plotting balances.
When plotting the balance for H2, the rename dictionary merges all the demands containing H2.
This commit disables such merging and keeps different colours for H2 in shipping and H2 in industry. This is useful when one wants to look at the H2 balance and have an overview of where the H2 is consumed in the model.
* Make transformation of Steel and Aluminum production depends on year
Previously, the transformation of the Steel and Aluminum production was assumed to occur overnight.
This commit enables the definition of a transformation path via the config.yaml file.
This requires adding the {planning_horizon} to the input and output file name of the following rules:
build_industrial_production_per_country_tomorrow
build_industrial_production_per_node
build_industry_energy_demand_per_node
prepare_sector_network
* small follow-up to merge
* Add oil consumed in shipping as a load to EU oil bus
* Update scripts/prepare_sector_network.py
* add planning_horizons wildcard to benchmark paths
* fixup: double fraction_primary for steel
Co-authored-by: Fabian Neumann <fabian.neumann@outlook.de>
453 lines
13 KiB
Python
453 lines
13 KiB
Python
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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plt.style.use('ggplot')
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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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prefix_to_remove = [
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"residential ",
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"services ",
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"urban ",
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"rural ",
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"central ",
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"decentral "
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]
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rename_if_contains = [
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"CHP",
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"gas boiler",
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"biogas",
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"solar thermal",
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"air heat pump",
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"ground heat pump",
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"resistive heater",
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"Fischer-Tropsch"
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]
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rename_if_contains_dict = {
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"water tanks": "hot water storage",
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"retrofitting": "building retrofitting",
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"H2 Electrolysis": "hydrogen storage",
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"H2 Fuel Cell": "hydrogen storage",
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"H2 pipeline": "hydrogen storage",
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"battery": "battery storage",
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"CC": "CC"
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}
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rename = {
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"solar": "solar PV",
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"Sabatier": "methanation",
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"offwind": "offshore wind",
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"offwind-ac": "offshore wind (AC)",
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"offwind-dc": "offshore wind (DC)",
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"onwind": "onshore wind",
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"ror": "hydroelectricity",
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"hydro": "hydroelectricity",
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"PHS": "hydroelectricity",
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"co2 Store": "DAC",
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"co2 stored": "CO2 sequestration",
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"AC": "transmission lines",
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"DC": "transmission lines",
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"B2B": "transmission lines"
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}
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for ptr in prefix_to_remove:
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if label[:len(ptr)] == ptr:
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label = label[len(ptr):]
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for rif in rename_if_contains:
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if rif in label:
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label = rif
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for old,new in rename_if_contains_dict.items():
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if old in label:
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label = new
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for old,new in rename.items():
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if old == label:
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label = new
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return label
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preferred_order = pd.Index([
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"transmission lines",
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"hydroelectricity",
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"hydro reservoir",
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"run of river",
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"pumped hydro storage",
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"solid biomass",
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"biogas",
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"onshore wind",
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"offshore wind",
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"offshore wind (AC)",
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"offshore wind (DC)",
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"solar PV",
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"solar thermal",
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"solar",
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"building retrofitting",
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"ground heat pump",
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"air heat pump",
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"heat pump",
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"resistive heater",
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"power-to-heat",
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"gas-to-power/heat",
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"CHP",
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"OCGT",
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"gas boiler",
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"gas",
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"natural gas",
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"helmeth",
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"methanation",
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"hydrogen storage",
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"power-to-gas",
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"power-to-liquid",
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"battery storage",
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"hot water storage",
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"CO2 sequestration"
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])
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def plot_costs():
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cost_df = pd.read_csv(
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snakemake.input.costs,
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index_col=list(range(3)),
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header=list(range(n_header))
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)
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df = cost_df.groupby(cost_df.index.get_level_values(2)).sum()
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#convert to billions
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df = df / 1e9
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df = df.groupby(df.index.map(rename_techs)).sum()
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to_drop = df.index[df.max(axis=1) < snakemake.config['plotting']['costs_threshold']]
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print("dropping")
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print(df.loc[to_drop])
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df = df.drop(to_drop)
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print(df.sum())
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new_index = preferred_order.intersection(df.index).append(df.index.difference(preferred_order))
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new_columns = df.sum().sort_values().index
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fig, ax = plt.subplots(figsize=(12,8))
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df.loc[new_index,new_columns].T.plot(
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kind="bar",
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ax=ax,
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stacked=True,
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color=[snakemake.config['plotting']['tech_colors'][i] for i in new_index]
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)
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handles,labels = ax.get_legend_handles_labels()
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handles.reverse()
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labels.reverse()
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ax.set_ylim([0,snakemake.config['plotting']['costs_max']])
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ax.set_ylabel("System Cost [EUR billion per year]")
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ax.set_xlabel("")
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ax.grid(axis='x')
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ax.legend(handles, labels, ncol=1, loc="upper left", bbox_to_anchor=[1,1], frameon=False)
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fig.savefig(snakemake.output.costs, bbox_inches='tight')
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def plot_energy():
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energy_df = pd.read_csv(
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snakemake.input.energy,
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index_col=list(range(2)),
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header=list(range(n_header))
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)
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df = energy_df.groupby(energy_df.index.get_level_values(1)).sum()
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#convert MWh to TWh
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df = df / 1e6
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df = df.groupby(df.index.map(rename_techs)).sum()
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to_drop = df.index[df.abs().max(axis=1) < snakemake.config['plotting']['energy_threshold']]
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print("dropping")
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print(df.loc[to_drop])
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df = df.drop(to_drop)
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print(df.sum())
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print(df)
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new_index = preferred_order.intersection(df.index).append(df.index.difference(preferred_order))
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new_columns = df.columns.sort_values()
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fig, ax = plt.subplots(figsize=(12,8))
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print(df.loc[new_index, new_columns])
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df.loc[new_index, new_columns].T.plot(
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kind="bar",
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ax=ax,
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stacked=True,
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color=[snakemake.config['plotting']['tech_colors'][i] for i in new_index]
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)
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handles,labels = ax.get_legend_handles_labels()
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handles.reverse()
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labels.reverse()
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ax.set_ylim([snakemake.config['plotting']['energy_min'], snakemake.config['plotting']['energy_max']])
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ax.set_ylabel("Energy [TWh/a]")
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ax.set_xlabel("")
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ax.grid(axis="x")
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ax.legend(handles, labels, ncol=1, loc="upper left", bbox_to_anchor=[1, 1], frameon=False)
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fig.savefig(snakemake.output.energy, bbox_inches='tight')
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def plot_balances():
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co2_carriers = ["co2", "co2 stored", "process emissions"]
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balances_df = pd.read_csv(
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snakemake.input.balances,
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index_col=list(range(3)),
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header=list(range(n_header))
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)
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balances = {i.replace(" ","_"): [i] for i in balances_df.index.levels[0]}
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balances["energy"] = [i for i in balances_df.index.levels[0] if i not in co2_carriers]
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for k, v in balances.items():
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df = balances_df.loc[v]
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df = df.groupby(df.index.get_level_values(2)).sum()
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#convert MWh to TWh
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df = df / 1e6
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#remove trailing link ports
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df.index = [i[:-1] if ((i != "co2") and (i[-1:] in ["0","1","2","3"])) else i for i in df.index]
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df = df.groupby(df.index.map(rename_techs)).sum()
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to_drop = df.index[df.abs().max(axis=1) < snakemake.config['plotting']['energy_threshold']/10]
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print("dropping")
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print(df.loc[to_drop])
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df = df.drop(to_drop)
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print(df.sum())
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if df.empty:
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continue
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new_index = preferred_order.intersection(df.index).append(df.index.difference(preferred_order))
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new_columns = df.columns.sort_values()
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fig, ax = plt.subplots(figsize=(12,8))
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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])
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handles,labels = ax.get_legend_handles_labels()
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handles.reverse()
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labels.reverse()
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if v[0] in co2_carriers:
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ax.set_ylabel("CO2 [MtCO2/a]")
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else:
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ax.set_ylabel("Energy [TWh/a]")
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ax.set_xlabel("")
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ax.grid(axis="x")
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ax.legend(handles, labels, ncol=1, loc="upper left", bbox_to_anchor=[1, 1], frameon=False)
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fig.savefig(snakemake.output.balances[:-10] + k + ".pdf", bbox_inches='tight')
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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)"
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e["total woL"] = "Total (without LULUCF)"
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pol = ["CO2"] # ["All greenhouse gases - (CO2 equivalent)"]
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cts
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if "GB" in cts:
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cts.remove("GB")
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cts.append("UK")
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year = np.arange(1990,2018).tolist()
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idx = pd.IndexSlice
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co2_totals = df.loc[idx[year,e.values,cts,pol],"emissions"].unstack("Year").rename(index=pd.Series(e.index,e.values))
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co2_totals = (1/1e6)*co2_totals.groupby(level=0, axis=0).sum() #Gton CO2
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co2_totals.loc['industrial non-elec'] = co2_totals.loc['total energy'] - co2_totals.loc[['electricity', 'services non-elec','residential non-elec', 'road non-elec',
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'rail non-elec', 'domestic aviation', 'international aviation', 'domestic navigation',
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'international navigation']].sum()
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emissions = co2_totals.loc["electricity"]
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if "T" in opts:
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emissions += co2_totals.loc[[i+ " non-elec" for i in ["rail","road"]]].sum()
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if "H" in opts:
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emissions += co2_totals.loc[[i+ " non-elec" for i in ["residential","services"]]].sum()
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if "I" in opts:
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emissions += co2_totals.loc[["industrial non-elec","industrial processes",
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"domestic aviation","international aviation",
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"domestic navigation","international navigation"]].sum()
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return emissions
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def plot_carbon_budget_distribution():
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"""
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Plot historical carbon emissions in the EU and decarbonization path
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"""
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import matplotlib.gridspec as gridspec
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import seaborn as sns; sns.set()
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sns.set_style('ticks')
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plt.style.use('seaborn-ticks')
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plt.rcParams['xtick.direction'] = 'in'
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plt.rcParams['ytick.direction'] = 'in'
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plt.rcParams['xtick.labelsize'] = 20
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plt.rcParams['ytick.labelsize'] = 20
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plt.figure(figsize=(10, 7))
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gs1 = gridspec.GridSpec(1, 1)
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ax1 = plt.subplot(gs1[0,0])
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ax1.set_ylabel('CO$_2$ emissions (Gt per year)',fontsize=22)
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ax1.set_ylim([0,5])
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ax1.set_xlim([1990,snakemake.config['scenario']['planning_horizons'][-1]+1])
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path_cb = snakemake.config['results_dir'] + snakemake.config['run'] + '/csvs/'
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countries=pd.read_csv(path_cb + 'countries.csv', index_col=1)
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cts=countries.index.to_list()
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e_1990 = co2_emissions_year(cts, opts, year=1990)
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CO2_CAP=pd.read_csv(path_cb + 'carbon_budget_distribution.csv',
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index_col=0)
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ax1.plot(e_1990*CO2_CAP[o],linewidth=3,
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color='dodgerblue', label=None)
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emissions = historical_emissions(cts)
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ax1.plot(emissions, color='black', linewidth=3, label=None)
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#plot commited and uder-discussion targets
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#(notice that historical emissions include all countries in the
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# network, but targets refer to EU)
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ax1.plot([2020],[0.8*emissions[1990]],
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marker='*', markersize=12, markerfacecolor='black',
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markeredgecolor='black')
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ax1.plot([2030],[0.45*emissions[1990]],
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marker='*', markersize=12, markerfacecolor='white',
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markeredgecolor='black')
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ax1.plot([2030],[0.6*emissions[1990]],
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marker='*', markersize=12, markerfacecolor='black',
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markeredgecolor='black')
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ax1.plot([2050, 2050],[x*emissions[1990] for x in [0.2, 0.05]],
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color='gray', linewidth=2, marker='_', alpha=0.5)
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ax1.plot([2050],[0.01*emissions[1990]],
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marker='*', markersize=12, markerfacecolor='white',
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linewidth=0, markeredgecolor='black',
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label='EU under-discussion target', zorder=10,
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clip_on=False)
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ax1.plot([2050],[0.125*emissions[1990]],'ro',
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marker='*', markersize=12, markerfacecolor='black',
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markeredgecolor='black', label='EU commited target')
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ax1.legend(fancybox=True, fontsize=18, loc=(0.01,0.01),
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facecolor='white', frameon=True)
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path_cb_plot = snakemake.config['results_dir'] + snakemake.config['run'] + '/graphs/'
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plt.savefig(path_cb_plot+'carbon_budget_plot.pdf', dpi=300)
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if __name__ == "__main__":
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if 'snakemake' not in globals():
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from helper import mock_snakemake
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snakemake = mock_snakemake('plot_summary')
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n_header = 4
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plot_costs()
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plot_energy()
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plot_balances()
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for sector_opts in snakemake.config['scenario']['sector_opts']:
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opts=sector_opts.split('-')
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for o in opts:
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if "cb" in o:
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plot_carbon_budget_distribution()
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