265 lines
7.5 KiB
Python
265 lines
7.5 KiB
Python
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import pandas as pd
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#allow plotting without Xwindows
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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#consolidate and rename
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def rename_techs(label):
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prefix_to_remove = ["residential ","services ","urban ","rural ","central ","decentral "]
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rename_if_contains = ["CHP","gas boiler","biogas","solar thermal","air heat pump","ground heat pump","resistive heater","Fischer-Tropsch"]
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rename_if_contains_dict = {"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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"CCS" : "CCS"}
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rename = {"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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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(["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","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","hydrogen storage","power-to-gas","power-to-liquid","battery storage","hot water storage","CO2 sequestration"])
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def plot_costs():
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cost_df = pd.read_csv(snakemake.input.costs,index_col=list(range(3)),header=list(range(n_header)))
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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&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()
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fig.set_size_inches((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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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="y")
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ax.legend(handles,labels,ncol=4,loc="upper left")
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fig.tight_layout()
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fig.savefig(snakemake.output.costs,transparent=True)
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def plot_energy():
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energy_df = pd.read_csv(snakemake.input.energy,index_col=list(range(2)),header=list(range(n_header)))
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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&df.index).append(df.index.difference(preferred_order))
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new_columns = df.columns.sort_values()
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#new_columns = df.sum().sort_values().index
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fig, ax = plt.subplots()
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fig.set_size_inches((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(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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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="y")
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ax.legend(handles,labels,ncol=4,loc="upper left")
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fig.tight_layout()
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fig.savefig(snakemake.output.energy,transparent=True)
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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(snakemake.input.balances,index_col=list(range(3)),header=list(range(n_header)))
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balances = {i.replace(" ","_") : [i] for i in balances_df.index.levels[0]}
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balances["energy"] = balances_df.index.levels[0]^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&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()
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fig.set_size_inches((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="y")
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ax.legend(handles,labels,ncol=4,loc="upper left")
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fig.tight_layout()
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fig.savefig(snakemake.output.balances[:-10] + k + ".pdf",transparent=True)
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if __name__ == "__main__":
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# Detect running outside of snakemake and mock snakemake for testing
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if 'snakemake' not in globals():
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from vresutils import Dict
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import yaml
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snakemake = Dict()
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with open('config.yaml', encoding='utf8') as f:
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snakemake.config = yaml.safe_load(f)
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snakemake.input = Dict()
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snakemake.output = Dict()
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for item in ["costs", "energy"]:
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snakemake.input[item] = snakemake.config['summary_dir'] + '/{name}/csvs/{item}.csv'.format(name=snakemake.config['run'],item=item)
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snakemake.output[item] = snakemake.config['summary_dir'] + '/{name}/graphs/{item}.pdf'.format(name=snakemake.config['run'],item=item)
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snakemake.input["balances"] = snakemake.config['summary_dir'] + '/test/csvs/supply_energy.csv'
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snakemake.output["balances"] = snakemake.config['summary_dir'] + '/test/graphs/balances-energy.csv'
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n_header = 5
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plot_costs()
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plot_energy()
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plot_balances()
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