from six import iteritems import sys sys.path = ['/home/vres/lib/python3.5/site-packages'] + sys.path import pandas as pd import pypsa from vresutils.costdata import annuity from prepare_network import generate_periodic_profiles import yaml import helper idx = pd.IndexSlice opt_name = {"Store": "e", "Line" : "s", "Transformer" : "s"} #separator to find group name find_by = " " #defaults for group name defaults = {"Load" : "electricity", "Link" : "transmission lines"} def assign_groups(n): for c in n.iterate_components(n.one_port_components|n.branch_components): c.df["group"] = defaults.get(c.name,"default") ifind = pd.Series(c.df.index.str.find(find_by),c.df.index) for i in ifind.value_counts().index: #these have already been assigned defaults if i == -1: continue names = ifind.index[ifind == i] c.df.loc[names,'group'] = names.str[i+1:] def calculate_costs(n,label,costs): for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}): capital_costs = c.df.capital_cost*c.df[opt_name.get(c.name,"p") + "_nom_opt"] capital_costs_grouped = capital_costs.groupby(c.df.group).sum() costs = costs.reindex(costs.index|pd.MultiIndex.from_product([[c.list_name],["capital"],capital_costs_grouped.index])) costs.loc[idx[c.list_name,"capital",list(capital_costs_grouped.index)],label] = capital_costs_grouped.values if c.name == "Link": p = c.pnl.p0.sum() elif c.name == "StorageUnit": p_all = c.pnl.p.copy() p_all[p_all < 0.] = 0. p = p_all.sum() else: p = c.pnl.p.sum() marginal_costs = p*c.df.marginal_cost marginal_costs_grouped = marginal_costs.groupby(c.df.group).sum() costs = costs.reindex(costs.index|pd.MultiIndex.from_product([[c.list_name],["marginal"],marginal_costs_grouped.index])) costs.loc[idx[c.list_name,"marginal",list(marginal_costs_grouped.index)],label] = marginal_costs_grouped.values #add back in costs of links if there is a line volume limit if label[1] != "opt": costs.loc[("links-added","capital","transmission lines"),label] = ((400*1.25*n.links.length+150000.)*n.links.p_nom_opt)[n.links.group == "transmission lines"].sum()*1.5*(annuity(40., 0.07)+0.02) #add back in all hydro costs.loc[("storage_units","capital","hydro"),label] = (0.01)*2e6*n.storage_units.loc[n.storage_units.group=="hydro","p_nom"].sum() costs.loc[("storage_units","capital","PHS"),label] = (0.01)*2e6*n.storage_units.loc[n.storage_units.group=="PHS","p_nom"].sum() costs.loc[("generators","capital","ror"),label] = (0.02)*3e6*n.generators.loc[n.generators.group=="ror","p_nom"].sum() return costs def calculate_curtailment(n,label,curtailment): avail = n.generators_t.p_max_pu.multiply(n.generators.p_nom_opt).sum().groupby(n.generators.group).sum() used = n.generators_t.p.sum().groupby(n.generators.group).sum() curtailment[label] = (((avail - used)/avail)*100).round(3) return curtailment def calculate_energy(n,label,energy): for c in n.iterate_components(n.one_port_components|n.branch_components): if c.name in n.one_port_components: c_energies = c.pnl.p.sum().multiply(c.df.sign).groupby(c.df.group).sum() else: c_energies = (-c.pnl.p1.sum() - c.pnl.p0.sum()).groupby(c.df.group).sum() energy = energy.reindex(energy.index|pd.MultiIndex.from_product([[c.list_name],c_energies.index])) energy.loc[idx[c.list_name,list(c_energies.index)],label] = c_energies.values return energy def calculate_supply(n,label,supply): """calculate the max dispatch of each component at the buses where the loads are attached""" load_types = n.loads.group.value_counts().index for i in load_types: buses = n.loads.bus[n.loads.group == i].values bus_map = pd.Series(False,index=n.buses.index) bus_map.loc[buses] = True for c in n.iterate_components(n.one_port_components): items = c.df.index[c.df.bus.map(bus_map)] if len(items) == 0: continue s = c.pnl.p[items].max().multiply(c.df.loc[items,'sign']).groupby(c.df.loc[items,'group']).sum() supply = supply.reindex(supply.index|pd.MultiIndex.from_product([[i],[c.list_name],s.index])) supply.loc[idx[i,c.list_name,list(s.index)],label] = s.values for c in n.iterate_components(n.branch_components): for end in ["0","1"]: items = c.df.index[c.df["bus" + end].map(bus_map)] if len(items) == 0: continue #lots of sign compensation for direction and to do maximums s = (-1)**(1-int(end))*((-1)**int(end)*c.pnl["p"+end][items]).max().groupby(c.df.loc[items,'group']).sum() supply = supply.reindex(supply.index|pd.MultiIndex.from_product([[i],[c.list_name],s.index])) supply.loc[idx[i,c.list_name,list(s.index)],label] = s.values return supply def calculate_supply_energy(n,label,supply_energy): """calculate the total dispatch of each component at the buses where the loads are attached""" load_types = n.loads.group.value_counts().index for i in load_types: buses = n.loads.bus[n.loads.group == i].values bus_map = pd.Series(False,index=n.buses.index) bus_map.loc[buses] = True for c in n.iterate_components(n.one_port_components): items = c.df.index[c.df.bus.map(bus_map)] if len(items) == 0: continue s = c.pnl.p[items].sum().multiply(c.df.loc[items,'sign']).groupby(c.df.loc[items,'group']).sum() supply_energy = supply_energy.reindex(supply_energy.index|pd.MultiIndex.from_product([[i],[c.list_name],s.index])) supply_energy.loc[idx[i,c.list_name,list(s.index)],label] = s.values for c in n.iterate_components(n.branch_components): for end in ["0","1"]: items = c.df.index[c.df["bus" + end].map(bus_map)] if len(items) == 0: continue s = (-1)*c.pnl["p"+end][items].sum().groupby(c.df.loc[items,'group']).sum() supply_energy = supply_energy.reindex(supply_energy.index|pd.MultiIndex.from_product([[i],[c.list_name],s.index])) supply_energy.loc[idx[i,c.list_name,list(s.index)],label] = s.values return supply_energy def calculate_metrics(n,label,metrics): metrics = metrics.reindex(metrics.index|pd.Index(["line_volume","line_volume_shadow","co2_shadow"])) metrics.at["line_volume",label] = (n.links.length*n.links.p_nom_opt)[n.links.group == "transmission lines"].sum() if "line_volume_limit" in n.shadow_prices.index: metrics.at["line_volume_shadow",label] = n.shadow_prices.at["line_volume_limit","value"] if "co2_constraint" in n.shadow_prices.index: metrics.at["co2_shadow",label] = n.shadow_prices.at["co2_constraint","value"] return metrics def calculate_prices(n,label,prices): bus_type = pd.Series(n.buses.index.str[3:],n.buses.index).replace("","electricity") prices = prices.reindex(prices.index|bus_type.value_counts().index) #WARNING: this is time-averaged, should really be load-weighted average prices[label] = n.buses_t.marginal_price.mean().groupby(bus_type).mean() return prices def calculate_weighted_prices(n,label,weighted_prices): # Warning: doesn't include storage units as loads weighted_prices = weighted_prices.reindex(pd.Index(["electricity","heat","space heat","urban heat","space urban heat","gas","H2"])) link_loads = {"electricity" : ["heat pump", "resistive heater", "battery charger", "H2 Electrolysis"], "heat" : ["water tanks charger"], "urban heat" : ["water tanks charger"], "space heat" : [], "space urban heat" : [], "gas" : ["OCGT","gas boiler","CHP electric","CHP heat"], "H2" : ["Sabatier", "H2 Fuel Cell"]} for carrier in link_loads: if carrier == "electricity": suffix = "" elif carrier[:5] == "space": suffix = carrier[5:] else: suffix = " " + carrier buses = n.buses.index[n.buses.index.str[2:] == suffix] if buses.empty: continue if carrier in ["H2","gas"]: load = pd.DataFrame(index=n.snapshots,columns=buses,data=0.) elif carrier[:5] == "space": load = heat_demand_df[buses.str[:2]].rename(columns=lambda i: str(i)+suffix) else: load = n.loads_t.p_set[buses] for tech in link_loads[carrier]: names = n.links.index[n.links.index.to_series().str[-len(tech):] == tech] if names.empty: continue load += n.links_t.p0[names].groupby(n.links.loc[names,"bus0"],axis=1).sum(axis=1) #Add H2 Store when charging if carrier == "H2": stores = n.stores_t.p[buses+ " Store"].groupby(n.stores.loc[buses+ " Store","bus"],axis=1).sum(axis=1) stores[stores > 0.] = 0. load += -stores weighted_prices.loc[carrier,label] = (load*n.buses_t.marginal_price[buses]).sum().sum()/load.sum().sum() if carrier[:5] == "space": print(load*n.buses_t.marginal_price[buses]) return weighted_prices def calculate_market_values(n, label, market_values): # Warning: doesn't include storage units n.buses["suffix"] = n.buses.index.str[2:] suffix = "" buses = n.buses.index[n.buses.suffix == suffix] ## First do market value of generators ## generators = n.generators.index[n.buses.loc[n.generators.bus,"suffix"] == suffix] techs = n.generators.loc[generators,"carrier"].value_counts().index market_values = market_values.reindex(market_values.index | techs) for tech in techs: gens = generators[n.generators.loc[generators,"carrier"] == tech] dispatch = n.generators_t.p[gens].groupby(n.generators.loc[gens,"bus"],axis=1).sum().reindex(columns=buses,fill_value=0.) revenue = dispatch*n.buses_t.marginal_price[buses] market_values.at[tech,label] = revenue.sum().sum()/dispatch.sum().sum() ## Now do market value of links ## for i in ["0","1"]: all_links = n.links.index[n.buses.loc[n.links["bus"+i],"suffix"] == suffix] techs = n.links.loc[all_links,"group"].value_counts().index market_values = market_values.reindex(market_values.index | techs) for tech in techs: links = all_links[n.links.loc[all_links,"group"] == tech] dispatch = n.links_t["p"+i][links].groupby(n.links.loc[links,"bus"+i],axis=1).sum().reindex(columns=buses,fill_value=0.) revenue = dispatch*n.buses_t.marginal_price[buses] market_values.at[tech,label] = revenue.sum().sum()/dispatch.sum().sum() return market_values def calculate_price_statistics(n, label, price_statistics): price_statistics = price_statistics.reindex(price_statistics.index|pd.Index(["zero_hours","mean","standard_deviation"])) n.buses["suffix"] = n.buses.index.str[2:] suffix = "" buses = n.buses.index[n.buses.suffix == suffix] threshold = 0.1 #higher than phoney marginal_cost of wind/solar df = pd.DataFrame(data=0.,columns=buses,index=n.snapshots) df[n.buses_t.marginal_price[buses] < threshold] = 1. price_statistics.at["zero_hours", label] = df.sum().sum()/(df.shape[0]*df.shape[1]) price_statistics.at["mean", label] = n.buses_t.marginal_price[buses].unstack().mean() price_statistics.at["standard_deviation", label] = n.buses_t.marginal_price[buses].unstack().std() return price_statistics outputs = ["costs", "curtailment", "energy", "supply", "supply_energy", "prices", "weighted_prices", "price_statistics", "market_values", "metrics", ] def make_summaries(networks_dict): columns = pd.MultiIndex.from_tuples(networks_dict.keys(),names=["scenario","line_volume_limit","co2_reduction"]) df = {} for output in outputs: df[output] = pd.DataFrame(columns=columns,dtype=float) for label, filename in iteritems(networks_dict): print(label, filename) n = helper.Network(filename) assign_groups(n) for output in outputs: df[output] = globals()["calculate_" + output](n, label, df[output]) return df def to_csv(df): for key in df: df[key].to_csv(snakemake.output[key]) 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') as f: snakemake.config = yaml.load(f) snakemake.input = Dict() snakemake.input['heat_demand_name'] = 'data/heating/daily_heat_demand.h5' snakemake.output = Dict() for item in outputs: snakemake.output[item] = snakemake.config['summary_dir'] + 'version-{version}/csvs/{item}.csv'.format(version=snakemake.config['version'],item=item) suffix = "nc" if int(snakemake.config['version']) > 100 else "h5" networks_dict = {(flexibility,line_limit,co2_reduction) : '{results_dir}version-{version}/postnetworks/postnetwork-{flexibility}-{line_limit}-{co2_reduction}.{suffix}'\ .format(results_dir=snakemake.config['results_dir'], version=snakemake.config['version'], flexibility=flexibility, line_limit=line_limit, co2_reduction=co2_reduction, suffix=suffix)\ for flexibility in snakemake.config['scenario']['flexibility'] for line_limit in snakemake.config['scenario']['line_limits'] for co2_reduction in snakemake.config['scenario']['co2_reduction']} options = yaml.load(open("options.yml","r")) with pd.HDFStore(snakemake.input.heat_demand_name, mode='r') as store: #the ffill converts daily values into hourly values heat_demand_df = store['heat_demand_profiles'].reindex(index=pd.date_range(options['tmin'],options['tmax'],freq='H'), method="ffill") intraday_profiles = pd.read_csv("data/heating/heat_load_profile_DK_AdamJensen.csv",index_col=0) intraday_year_profiles = generate_periodic_profiles(heat_demand_df.index.tz_localize("UTC"),weekly_profile=(list(intraday_profiles["weekday"])*5 + list(intraday_profiles["weekend"])*2)).tz_localize(None) heat_demand_df = heat_demand_df*intraday_year_profiles df = make_summaries(networks_dict) to_csv(df)