563 lines
15 KiB
Python
563 lines
15 KiB
Python
# -*- coding: utf-8 -*-
|
|
# SPDX-FileCopyrightText: : 2020-2023 The PyPSA-Eur Authors
|
|
#
|
|
# SPDX-License-Identifier: MIT
|
|
"""
|
|
Creates plots from summary CSV files.
|
|
"""
|
|
|
|
import logging
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
import matplotlib.gridspec as gridspec
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
plt.style.use("ggplot")
|
|
|
|
from prepare_sector_network import co2_emissions_year
|
|
|
|
|
|
# consolidate and rename
|
|
def rename_techs(label):
|
|
prefix_to_remove = [
|
|
"residential ",
|
|
"services ",
|
|
"urban ",
|
|
"rural ",
|
|
"central ",
|
|
"decentral ",
|
|
]
|
|
|
|
rename_if_contains = [
|
|
"CHP",
|
|
"gas boiler",
|
|
"biogas",
|
|
"solar thermal",
|
|
"air heat pump",
|
|
"ground heat pump",
|
|
"resistive heater",
|
|
"Fischer-Tropsch",
|
|
]
|
|
|
|
rename_if_contains_dict = {
|
|
"water tanks": "hot water storage",
|
|
"retrofitting": "building retrofitting",
|
|
# "H2 Electrolysis": "hydrogen storage",
|
|
# "H2 Fuel Cell": "hydrogen storage",
|
|
# "H2 pipeline": "hydrogen storage",
|
|
"battery": "battery storage",
|
|
# "CC": "CC"
|
|
}
|
|
|
|
rename = {
|
|
"solar": "solar PV",
|
|
"Sabatier": "methanation",
|
|
"offwind": "offshore wind",
|
|
"offwind-ac": "offshore wind (AC)",
|
|
"offwind-dc": "offshore wind (DC)",
|
|
"onwind": "onshore wind",
|
|
"ror": "hydroelectricity",
|
|
"hydro": "hydroelectricity",
|
|
"PHS": "hydroelectricity",
|
|
"NH3": "ammonia",
|
|
"co2 Store": "DAC",
|
|
"co2 stored": "CO2 sequestration",
|
|
"AC": "transmission lines",
|
|
"DC": "transmission lines",
|
|
"B2B": "transmission lines",
|
|
}
|
|
|
|
for ptr in prefix_to_remove:
|
|
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():
|
|
if old in label:
|
|
label = new
|
|
|
|
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",
|
|
]
|
|
)
|
|
|
|
|
|
def plot_costs():
|
|
cost_df = pd.read_csv(
|
|
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
|
|
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"]]
|
|
|
|
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_columns = df.sum().sort_values().index
|
|
|
|
fig, ax = plt.subplots(figsize=(12, 8))
|
|
|
|
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.reverse()
|
|
labels.reverse()
|
|
|
|
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.legend(
|
|
handles, labels, ncol=1, loc="upper left", bbox_to_anchor=[1, 1], frameon=False
|
|
)
|
|
|
|
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))
|
|
)
|
|
|
|
df = energy_df.groupby(energy_df.index.get_level_values(1)).sum()
|
|
|
|
# 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"]
|
|
]
|
|
|
|
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_columns = df.columns.sort_values()
|
|
|
|
fig, ax = plt.subplots(figsize=(12, 8))
|
|
|
|
logger.debug(df.loc[new_index, new_columns])
|
|
|
|
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.reverse()
|
|
labels.reverse()
|
|
|
|
ax.set_ylim(
|
|
[
|
|
snakemake.config["plotting"]["energy_min"],
|
|
snakemake.config["plotting"]["energy_max"],
|
|
]
|
|
)
|
|
|
|
ax.set_ylabel("Energy [TWh/a]")
|
|
|
|
ax.set_xlabel("")
|
|
|
|
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")
|
|
|
|
|
|
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))
|
|
)
|
|
|
|
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))
|
|
|
|
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
|
|
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
|
|
]
|
|
|
|
df = df.groupby(df.index.map(rename_techs)).sum()
|
|
|
|
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(df.loc[to_drop])
|
|
|
|
df = df.drop(to_drop)
|
|
|
|
logger.debug(f"Total energy balance for {v} of {round(df.sum()[0],2)} {units}")
|
|
|
|
if df.empty:
|
|
continue
|
|
|
|
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],
|
|
)
|
|
|
|
handles, labels = ax.get_legend_handles_labels()
|
|
|
|
handles.reverse()
|
|
labels.reverse()
|
|
|
|
if v[0] in co2_carriers:
|
|
ax.set_ylabel("CO2 [MtCO2/a]")
|
|
else:
|
|
ax.set_ylabel("Energy [TWh/a]")
|
|
|
|
ax.set_xlabel("")
|
|
|
|
ax.grid(axis="x")
|
|
|
|
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")
|
|
|
|
plt.cla()
|
|
|
|
|
|
def historical_emissions(countries):
|
|
"""
|
|
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)
|
|
fn = "data/eea/UNFCCC_v23.csv"
|
|
df = pd.read_csv(fn, encoding="latin-1")
|
|
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()
|
|
|
|
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["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["total wL"] = "Total (with LULUCF)"
|
|
e["total woL"] = "Total (without LULUCF)"
|
|
|
|
pol = ["CO2"] # ["All greenhouse gases - (CO2 equivalent)"]
|
|
if "GB" in countries:
|
|
countries.remove("GB")
|
|
countries.append("UK")
|
|
|
|
year = np.arange(1990, 2018).tolist()
|
|
|
|
idx = pd.IndexSlice
|
|
co2_totals = (
|
|
df.loc[idx[year, e.values, countries, 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(input_eurostat):
|
|
"""
|
|
Plot historical carbon emissions in the EU and decarbonization path.
|
|
"""
|
|
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 = "results/" + snakemake.params.RDIR + "/csvs/"
|
|
countries = snakemake.config["countries"]
|
|
e_1990 = co2_emissions_year(countries, input_eurostat, 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(countries)
|
|
|
|
ax1.plot(emissions, color="black", linewidth=3, label=None)
|
|
|
|
# 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(
|
|
[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 committed target",
|
|
)
|
|
|
|
ax1.legend(
|
|
fancybox=True, fontsize=18, loc=(0.01, 0.01), facecolor="white", frameon=True
|
|
)
|
|
|
|
path_cb_plot = "results/" + snakemake.params.RDIR + "/graphs/"
|
|
plt.savefig(path_cb_plot + "carbon_budget_plot.pdf", dpi=300)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
if "snakemake" not in globals():
|
|
from _helpers import mock_snakemake
|
|
|
|
snakemake = mock_snakemake("plot_summary")
|
|
|
|
logging.basicConfig(level=snakemake.config["logging"]["level"])
|
|
|
|
n_header = 4
|
|
|
|
plot_costs()
|
|
|
|
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(snakemake.input.eurostat)
|