pypsa-eur/scripts/plot_summary.py

import numpy as np
import pandas as pd

#allow plotting without Xwindows
import matplotlib
matplotlib.use('Agg')

import matplotlib.pyplot as plt

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" : "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",
              "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","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"])

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']]

    print("dropping")

    print(df.loc[to_drop])

    df = df.drop(to_drop)

    print(df.sum())

    new_index = (preferred_order&df.index).append(df.index.difference(preferred_order))

    new_columns = df.sum().sort_values().index

    fig, ax = plt.subplots()
    fig.set_size_inches((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="y")

    ax.legend(handles,labels,ncol=4,loc="upper left")


    fig.tight_layout()

    fig.savefig(snakemake.output.costs,transparent=True)


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']]

    print("dropping")

    print(df.loc[to_drop])

    df = df.drop(to_drop)

    print(df.sum())

    print(df)

    new_index = (preferred_order&df.index).append(df.index.difference(preferred_order))

    new_columns = df.columns.sort_values()
    #new_columns = df.sum().sort_values().index
    fig, ax = plt.subplots()
    fig.set_size_inches((12,8))

    print(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="y")

    ax.legend(handles,labels,ncol=4,loc="upper left")


    fig.tight_layout()

    fig.savefig(snakemake.output.energy,transparent=True)



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"] = balances_df.index.levels[0]^co2_carriers

    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 != "co2") 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]

        print("dropping")

        print(df.loc[to_drop])

        df = df.drop(to_drop)

        print(df.sum())

        if df.empty:
            continue

        new_index = (preferred_order&df.index).append(df.index.difference(preferred_order))

        new_columns = df.columns.sort_values()


        fig, ax = plt.subplots()
        fig.set_size_inches((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()

        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="y")

        ax.legend(handles,labels,ncol=4,loc="upper left")


        fig.tight_layout()

        fig.savefig(snakemake.output.balances[:-10] + k + ".pdf",transparent=True)

def historical_emissions(cts):
    """
    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)"] 
    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
    if 'snakemake' not in globals():
        from vresutils import Dict
        import yaml
        snakemake = Dict()
        with open('config.yaml', encoding='utf8') as f:
            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'] + '/{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()

    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()