451 lines
13 KiB
Python
451 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": "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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