Copy over make_summary.py from sector branch co2
This commit is contained in:
Jonas Hörsch
parent
ac52df13f7
commit
71512c15a5
450
scripts/make_summary.py
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450
scripts/make_summary.py
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from six import iteritems
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import sys
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sys.path = ['/home/vres/lib/python3.5/site-packages'] + sys.path
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import pandas as pd
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import pypsa
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from vresutils.costdata import annuity
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from prepare_network import generate_periodic_profiles
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import yaml
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import helper
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idx = pd.IndexSlice
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opt_name = {"Store": "e", "Line" : "s", "Transformer" : "s"}
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#separator to find group name
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find_by = " "
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#defaults for group name
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defaults = {"Load" : "electricity", "Link" : "transmission lines"}
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def assign_groups(n):
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for c in n.iterate_components(n.one_port_components|n.branch_components):
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c.df["group"] = defaults.get(c.name,"default")
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ifind = pd.Series(c.df.index.str.find(find_by),c.df.index)
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for i in ifind.value_counts().index:
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#these have already been assigned defaults
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if i == -1:
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continue
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names = ifind.index[ifind == i]
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c.df.loc[names,'group'] = names.str[i+1:]
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def calculate_costs(n,label,costs):
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for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}):
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capital_costs = c.df.capital_cost*c.df[opt_name.get(c.name,"p") + "_nom_opt"]
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capital_costs_grouped = capital_costs.groupby(c.df.group).sum()
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costs = costs.reindex(costs.index|pd.MultiIndex.from_product([[c.list_name],["capital"],capital_costs_grouped.index]))
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costs.loc[idx[c.list_name,"capital",list(capital_costs_grouped.index)],label] = capital_costs_grouped.values
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if c.name == "Link":
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p = c.pnl.p0.sum()
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elif c.name == "StorageUnit":
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p_all = c.pnl.p.copy()
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p_all[p_all < 0.] = 0.
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p = p_all.sum()
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else:
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p = c.pnl.p.sum()
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marginal_costs = p*c.df.marginal_cost
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marginal_costs_grouped = marginal_costs.groupby(c.df.group).sum()
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costs = costs.reindex(costs.index|pd.MultiIndex.from_product([[c.list_name],["marginal"],marginal_costs_grouped.index]))
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costs.loc[idx[c.list_name,"marginal",list(marginal_costs_grouped.index)],label] = marginal_costs_grouped.values
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#add back in costs of links if there is a line volume limit
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if label[1] != "opt":
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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)
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#add back in all hydro
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costs.loc[("storage_units","capital","hydro"),label] = (0.01)*2e6*n.storage_units.loc[n.storage_units.group=="hydro","p_nom"].sum()
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costs.loc[("storage_units","capital","PHS"),label] = (0.01)*2e6*n.storage_units.loc[n.storage_units.group=="PHS","p_nom"].sum()
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costs.loc[("generators","capital","ror"),label] = (0.02)*3e6*n.generators.loc[n.generators.group=="ror","p_nom"].sum()
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return costs
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def calculate_curtailment(n,label,curtailment):
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avail = n.generators_t.p_max_pu.multiply(n.generators.p_nom_opt).sum().groupby(n.generators.group).sum()
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used = n.generators_t.p.sum().groupby(n.generators.group).sum()
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curtailment[label] = (((avail - used)/avail)*100).round(3)
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return curtailment
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def calculate_energy(n,label,energy):
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for c in n.iterate_components(n.one_port_components|n.branch_components):
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if c.name in n.one_port_components:
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c_energies = c.pnl.p.sum().multiply(c.df.sign).groupby(c.df.group).sum()
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else:
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c_energies = (-c.pnl.p1.sum() - c.pnl.p0.sum()).groupby(c.df.group).sum()
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energy = energy.reindex(energy.index|pd.MultiIndex.from_product([[c.list_name],c_energies.index]))
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energy.loc[idx[c.list_name,list(c_energies.index)],label] = c_energies.values
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return energy
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def calculate_supply(n,label,supply):
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"""calculate the max dispatch of each component at the buses where the loads are attached"""
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load_types = n.loads.group.value_counts().index
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for i in load_types:
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buses = n.loads.bus[n.loads.group == i].values
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bus_map = pd.Series(False,index=n.buses.index)
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bus_map.loc[buses] = True
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for c in n.iterate_components(n.one_port_components):
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items = c.df.index[c.df.bus.map(bus_map)]
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if len(items) == 0:
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continue
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s = c.pnl.p[items].max().multiply(c.df.loc[items,'sign']).groupby(c.df.loc[items,'group']).sum()
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supply = supply.reindex(supply.index|pd.MultiIndex.from_product([[i],[c.list_name],s.index]))
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supply.loc[idx[i,c.list_name,list(s.index)],label] = s.values
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for c in n.iterate_components(n.branch_components):
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for end in ["0","1"]:
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items = c.df.index[c.df["bus" + end].map(bus_map)]
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if len(items) == 0:
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continue
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#lots of sign compensation for direction and to do maximums
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s = (-1)**(1-int(end))*((-1)**int(end)*c.pnl["p"+end][items]).max().groupby(c.df.loc[items,'group']).sum()
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supply = supply.reindex(supply.index|pd.MultiIndex.from_product([[i],[c.list_name],s.index]))
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supply.loc[idx[i,c.list_name,list(s.index)],label] = s.values
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return supply
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def calculate_supply_energy(n,label,supply_energy):
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"""calculate the total dispatch of each component at the buses where the loads are attached"""
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load_types = n.loads.group.value_counts().index
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for i in load_types:
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buses = n.loads.bus[n.loads.group == i].values
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bus_map = pd.Series(False,index=n.buses.index)
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bus_map.loc[buses] = True
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for c in n.iterate_components(n.one_port_components):
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items = c.df.index[c.df.bus.map(bus_map)]
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if len(items) == 0:
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continue
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s = c.pnl.p[items].sum().multiply(c.df.loc[items,'sign']).groupby(c.df.loc[items,'group']).sum()
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supply_energy = supply_energy.reindex(supply_energy.index|pd.MultiIndex.from_product([[i],[c.list_name],s.index]))
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supply_energy.loc[idx[i,c.list_name,list(s.index)],label] = s.values
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for c in n.iterate_components(n.branch_components):
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for end in ["0","1"]:
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items = c.df.index[c.df["bus" + end].map(bus_map)]
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if len(items) == 0:
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continue
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s = (-1)*c.pnl["p"+end][items].sum().groupby(c.df.loc[items,'group']).sum()
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supply_energy = supply_energy.reindex(supply_energy.index|pd.MultiIndex.from_product([[i],[c.list_name],s.index]))
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supply_energy.loc[idx[i,c.list_name,list(s.index)],label] = s.values
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return supply_energy
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def calculate_metrics(n,label,metrics):
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metrics = metrics.reindex(metrics.index|pd.Index(["line_volume","line_volume_shadow","co2_shadow"]))
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metrics.at["line_volume",label] = (n.links.length*n.links.p_nom_opt)[n.links.group == "transmission lines"].sum()
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if "line_volume_limit" in n.shadow_prices.index:
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metrics.at["line_volume_shadow",label] = n.shadow_prices.at["line_volume_limit","value"]
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if "co2_constraint" in n.shadow_prices.index:
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metrics.at["co2_shadow",label] = n.shadow_prices.at["co2_constraint","value"]
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return metrics
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def calculate_prices(n,label,prices):
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bus_type = pd.Series(n.buses.index.str[3:],n.buses.index).replace("","electricity")
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prices = prices.reindex(prices.index|bus_type.value_counts().index)
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#WARNING: this is time-averaged, should really be load-weighted average
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prices[label] = n.buses_t.marginal_price.mean().groupby(bus_type).mean()
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return prices
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def calculate_weighted_prices(n,label,weighted_prices):
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# Warning: doesn't include storage units as loads
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weighted_prices = weighted_prices.reindex(pd.Index(["electricity","heat","space heat","urban heat","space urban heat","gas","H2"]))
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link_loads = {"electricity" : ["heat pump", "resistive heater", "battery charger", "H2 Electrolysis"],
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"heat" : ["water tanks charger"],
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"urban heat" : ["water tanks charger"],
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"space heat" : [],
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"space urban heat" : [],
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"gas" : ["OCGT","gas boiler","CHP electric","CHP heat"],
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"H2" : ["Sabatier", "H2 Fuel Cell"]}
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for carrier in link_loads:
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if carrier == "electricity":
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suffix = ""
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elif carrier[:5] == "space":
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suffix = carrier[5:]
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else:
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suffix = " " + carrier
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buses = n.buses.index[n.buses.index.str[2:] == suffix]
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if buses.empty:
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continue
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if carrier in ["H2","gas"]:
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load = pd.DataFrame(index=n.snapshots,columns=buses,data=0.)
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elif carrier[:5] == "space":
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load = heat_demand_df[buses.str[:2]].rename(columns=lambda i: str(i)+suffix)
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else:
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load = n.loads_t.p_set[buses]
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for tech in link_loads[carrier]:
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names = n.links.index[n.links.index.to_series().str[-len(tech):] == tech]
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if names.empty:
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continue
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load += n.links_t.p0[names].groupby(n.links.loc[names,"bus0"],axis=1).sum(axis=1)
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#Add H2 Store when charging
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if carrier == "H2":
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stores = n.stores_t.p[buses+ " Store"].groupby(n.stores.loc[buses+ " Store","bus"],axis=1).sum(axis=1)
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stores[stores > 0.] = 0.
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load += -stores
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weighted_prices.loc[carrier,label] = (load*n.buses_t.marginal_price[buses]).sum().sum()/load.sum().sum()
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if carrier[:5] == "space":
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print(load*n.buses_t.marginal_price[buses])
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return weighted_prices
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def calculate_market_values(n, label, market_values):
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# Warning: doesn't include storage units
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n.buses["suffix"] = n.buses.index.str[2:]
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suffix = ""
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buses = n.buses.index[n.buses.suffix == suffix]
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## First do market value of generators ##
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generators = n.generators.index[n.buses.loc[n.generators.bus,"suffix"] == suffix]
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techs = n.generators.loc[generators,"carrier"].value_counts().index
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market_values = market_values.reindex(market_values.index | techs)
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for tech in techs:
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gens = generators[n.generators.loc[generators,"carrier"] == tech]
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dispatch = n.generators_t.p[gens].groupby(n.generators.loc[gens,"bus"],axis=1).sum().reindex(columns=buses,fill_value=0.)
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revenue = dispatch*n.buses_t.marginal_price[buses]
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market_values.at[tech,label] = revenue.sum().sum()/dispatch.sum().sum()
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## Now do market value of links ##
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for i in ["0","1"]:
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all_links = n.links.index[n.buses.loc[n.links["bus"+i],"suffix"] == suffix]
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techs = n.links.loc[all_links,"group"].value_counts().index
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market_values = market_values.reindex(market_values.index | techs)
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for tech in techs:
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links = all_links[n.links.loc[all_links,"group"] == tech]
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dispatch = n.links_t["p"+i][links].groupby(n.links.loc[links,"bus"+i],axis=1).sum().reindex(columns=buses,fill_value=0.)
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revenue = dispatch*n.buses_t.marginal_price[buses]
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market_values.at[tech,label] = revenue.sum().sum()/dispatch.sum().sum()
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return market_values
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def calculate_price_statistics(n, label, price_statistics):
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price_statistics = price_statistics.reindex(price_statistics.index|pd.Index(["zero_hours","mean","standard_deviation"]))
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n.buses["suffix"] = n.buses.index.str[2:]
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suffix = ""
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buses = n.buses.index[n.buses.suffix == suffix]
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threshold = 0.1 #higher than phoney marginal_cost of wind/solar
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df = pd.DataFrame(data=0.,columns=buses,index=n.snapshots)
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df[n.buses_t.marginal_price[buses] < threshold] = 1.
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price_statistics.at["zero_hours", label] = df.sum().sum()/(df.shape[0]*df.shape[1])
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price_statistics.at["mean", label] = n.buses_t.marginal_price[buses].unstack().mean()
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price_statistics.at["standard_deviation", label] = n.buses_t.marginal_price[buses].unstack().std()
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return price_statistics
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outputs = ["costs",
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"curtailment",
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"energy",
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"supply",
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"supply_energy",
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"prices",
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"weighted_prices",
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"price_statistics",
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"market_values",
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"metrics",
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]
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def make_summaries(networks_dict):
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columns = pd.MultiIndex.from_tuples(networks_dict.keys(),names=["scenario","line_volume_limit","co2_reduction"])
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df = {}
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for output in outputs:
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df[output] = pd.DataFrame(columns=columns,dtype=float)
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for label, filename in iteritems(networks_dict):
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print(label, filename)
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n = helper.Network(filename)
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assign_groups(n)
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for output in outputs:
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df[output] = globals()["calculate_" + output](n, label, df[output])
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return df
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def to_csv(df):
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for key in df:
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df[key].to_csv(snakemake.output[key])
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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') as f:
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snakemake.config = yaml.load(f)
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snakemake.input = Dict()
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snakemake.input['heat_demand_name'] = 'data/heating/daily_heat_demand.h5'
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snakemake.output = Dict()
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for item in outputs:
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snakemake.output[item] = snakemake.config['summary_dir'] + 'version-{version}/csvs/{item}.csv'.format(version=snakemake.config['version'],item=item)
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suffix = "nc" if int(snakemake.config['version']) > 100 else "h5"
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networks_dict = {(flexibility,line_limit,co2_reduction) :
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'{results_dir}version-{version}/postnetworks/postnetwork-{flexibility}-{line_limit}-{co2_reduction}.{suffix}'\
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.format(results_dir=snakemake.config['results_dir'],
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version=snakemake.config['version'],
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flexibility=flexibility,
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line_limit=line_limit,
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co2_reduction=co2_reduction,
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suffix=suffix)\
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for flexibility in snakemake.config['scenario']['flexibility'] for line_limit in snakemake.config['scenario']['line_limits']
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for co2_reduction in snakemake.config['scenario']['co2_reduction']}
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options = yaml.load(open("options.yml","r"))
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with pd.HDFStore(snakemake.input.heat_demand_name, mode='r') as store:
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#the ffill converts daily values into hourly values
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heat_demand_df = store['heat_demand_profiles'].reindex(index=pd.date_range(options['tmin'],options['tmax'],freq='H'), method="ffill")
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intraday_profiles = pd.read_csv("data/heating/heat_load_profile_DK_AdamJensen.csv",index_col=0)
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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)
|
||||
Loading…
Reference in New Issue
Block a user