""" Creates summaries of aggregated energy and costs as ``.csv`` files. Relevant Settings ----------------- .. code:: yaml costs: USD2013_to_EUR2013: discountrate: marginal_cost: capital_cost: electricity: max_hours: .. seealso:: Documentation of the configuration file ``config.yaml`` at :ref:`costs_cf`, :ref:`electricity_cf` Inputs ------ Outputs ------- Description ----------- The following rule can be used to summarize the results in seperate .csv files: .. code:: snakemake results/summaries/elec_s_all_lall_Co2L-3H_all clusters line volume or cost cap - options - all countries the line volume/cost cap field can be set to one of the following: * ``lv1.25`` for a particular line volume extension by 25% * ``lc1.25`` for a line cost extension by 25 % * ``lall`` for all evalutated caps * ``lvall`` for all line volume caps * ``lcall`` for all line cost caps Replacing '/summaries/' with '/plots/' creates nice colored maps of the results. """ import os from six import iteritems from itertools import product import pandas as pd import pypsa from add_electricity import load_costs, update_transmission_costs idx = pd.IndexSlice opt_name = {"Store": "e", "Line" : "s", "Transformer" : "s"} def assign_carriers(n): if "carrier" not in n.loads: n.loads["carrier"] = "electricity" for carrier in ["transport","heat","urban heat"]: n.loads.loc[n.loads.index.str.contains(carrier),"carrier"] = carrier n.storage_units['carrier'].replace({'hydro': 'hydro+PHS', 'PHS': 'hydro+PHS'}, inplace=True) if "carrier" not in n.lines: n.lines["carrier"] = "AC" n.lines["carrier"].replace({"AC": "lines"}, inplace=True) if n.links.empty: n.links["carrier"] = pd.Series(dtype=str) n.links["carrier"].replace({"DC": "lines"}, inplace=True) if "EU gas store" in n.stores.index and n.stores.loc["EU gas Store","carrier"] == "": n.stores.loc["EU gas Store","carrier"] = "gas Store" 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.carrier).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.multiply(n.snapshot_weightings,axis=0).sum() elif c.name == "Line": continue elif c.name == "StorageUnit": p_all = c.pnl.p.multiply(n.snapshot_weightings,axis=0) p_all[p_all < 0.] = 0. p = p_all.sum() else: p = c.pnl.p.multiply(n.snapshot_weightings,axis=0).sum() marginal_costs = p*c.df.marginal_cost marginal_costs_grouped = marginal_costs.groupby(c.df.carrier).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 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.carrier).sum() used = n.generators_t.p.sum().groupby(n.generators.carrier).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.multiply(n.snapshot_weightings,axis=0).sum().multiply(c.df.sign).groupby(c.df.carrier).sum() else: c_energies = (-c.pnl.p1.multiply(n.snapshot_weightings,axis=0).sum() - c.pnl.p0.multiply(n.snapshot_weightings,axis=0).sum()).groupby(c.df.carrier).sum() energy = include_in_summary(energy, [c.list_name], label, c_energies) return energy def include_in_summary(summary, multiindexprefix, label, item): summary = summary.reindex(summary.index | pd.MultiIndex.from_product([[p] for p in multiindexprefix] + [item.index])) summary.loc[idx[tuple(multiindexprefix + [list(item.index)])], label] = item.values return summary def calculate_capacity(n,label,capacity): for c in n.iterate_components(n.one_port_components): if 'p_nom_opt' in c.df.columns: c_capacities = abs(c.df.p_nom_opt.multiply(c.df.sign)).groupby(c.df.carrier).sum() capacity = include_in_summary(capacity, [c.list_name], label, c_capacities) for c in n.iterate_components(n.passive_branch_components): c_capacities = c.df['s_nom_opt'].groupby(c.df.carrier).sum() capacity = include_in_summary(capacity, [c.list_name], label, c_capacities) for c in n.iterate_components(n.controllable_branch_components): c_capacities = c.df.p_nom_opt.groupby(c.df.carrier).sum() capacity = include_in_summary(capacity, [c.list_name], label, c_capacities) return capacity 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.carrier.value_counts().index for i in load_types: buses = n.loads.bus[n.loads.carrier == 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,'carrier']).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,'carrier']).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.carrier.value_counts().index for i in load_types: buses = n.loads.bus[n.loads.carrier == 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,'carrier']).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,'carrier']).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_limit","line_volume_AC","line_volume_DC","line_volume_shadow","co2_shadow"])) metrics.at["line_volume_DC",label] = (n.links.length*n.links.p_nom_opt)[n.links.carrier == "DC"].sum() metrics.at["line_volume_AC",label] = (n.lines.length*n.lines.s_nom_opt).sum() metrics.at["line_volume",label] = metrics.loc[["line_volume_AC","line_volume_DC"],label].sum() if hasattr(n,"line_volume_limit"): metrics.at["line_volume_limit",label] = n.line_volume_limit if hasattr(n,"line_volume_limit_dual"): metrics.at["line_volume_shadow",label] = n.line_volume_limit_dual if "CO2Limit" in n.global_constraints.index: metrics.at["co2_shadow",label] = n.global_constraints.at["CO2Limit","mu"] 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 # BROKEN don't use # # 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,"carrier"].value_counts().index # market_values = market_values.reindex(market_values.index | techs) # for tech in techs: # links = all_links[n.links.loc[all_links,"carrier"] == 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 # OLD CODE must be adapted # 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", "capacity", "supply", "supply_energy", "prices", "weighted_prices", # "price_statistics", # "market_values", "metrics", ] def make_summaries(networks_dict, country='all'): columns = pd.MultiIndex.from_tuples(networks_dict.keys(),names=["simpl","clusters","ll","opts"]) dfs = {} for output in outputs: dfs[output] = pd.DataFrame(columns=columns,dtype=float) for label, filename in iteritems(networks_dict): print(label, filename) if not os.path.exists(filename): print("does not exist!!") continue try: n = pypsa.Network(filename) except OSError: logger.warning("Skipping {filename}".format(filename=filename)) continue if country != 'all': n = n[n.buses.country == country] Nyears = n.snapshot_weightings.sum()/8760. costs = load_costs(Nyears, snakemake.input[0], snakemake.config['costs'], snakemake.config['electricity']) update_transmission_costs(n, costs, simple_hvdc_costs=False) assign_carriers(n) for output in outputs: dfs[output] = globals()["calculate_" + output](n, label, dfs[output]) return dfs def to_csv(dfs): dir = snakemake.output[0] os.makedirs(dir, exist_ok=True) for key, df in iteritems(dfs): df.to_csv(os.path.join(dir, f"{key}.csv")) if __name__ == "__main__": # Detect running outside of snakemake and mock snakemake for testing def expand_from_wildcard(key): w = getattr(snakemake.wildcards, key) return snakemake.config["scenario"][key] if w == "all" else [w] if snakemake.wildcards.ll.endswith("all"): ll = snakemake.config["scenario"]["ll"] if len(snakemake.wildcards.ll) == 4: ll = [l for l in ll if l[0] == snakemake.wildcards.ll[0]] else: ll = [snakemake.wildcards.ll] networks_dict = {(simpl,clusters,l,opts) : ('results/networks/{network}_s{simpl}_{clusters}_l{ll}_{opts}.nc' .format(network=snakemake.wildcards.network, simpl=simpl, clusters=clusters, opts=opts, ll=l)) for simpl in expand_from_wildcard("simpl") for clusters in expand_from_wildcard("clusters") for l in ll for opts in expand_from_wildcard("opts")} print(networks_dict) dfs = make_summaries(networks_dict, country=snakemake.wildcards.country) to_csv(dfs)