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Tom Brown 2018-08-02 12:10:46 +02:00 committed by Jonas Hörsch
parent 71512c15a5
commit f3a12e7aee
2 changed files with 65 additions and 75 deletions
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2
config.yaml
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@ -1,6 +1,8 @@
version: 0.1
logging_level: INFO
summary_dir: results
scenario:
sectors: [E] # ,E+EV,E+BEV,E+BEV+V2G] # [ E+EV, E+BEV, E+BEV+V2G ]
simpl: ['']

136
scripts/make_summary.py
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@ -13,9 +13,9 @@ from vresutils.costdata import annuity
from prepare_network import generate_periodic_profiles
import yaml
from add_electricity import load_costs
import helper
import yaml
idx = pd.IndexSlice
@ -25,34 +25,25 @@ 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_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
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:]
if "carrier" not in n.lines:
n.lines["carrier"] = "AC"
if 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.group).sum()
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]))
@ -60,6 +51,8 @@ def calculate_costs(n,label,costs):
if c.name == "Link":
p = c.pnl.p0.sum()
elif c.name == "Line":
continue
elif c.name == "StorageUnit":
p_all = c.pnl.p.copy()
p_all[p_all < 0.] = 0.
@ -69,7 +62,7 @@ def calculate_costs(n,label,costs):
marginal_costs = p*c.df.marginal_cost
marginal_costs_grouped = marginal_costs.groupby(c.df.group).sum()
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]))
@ -77,12 +70,14 @@ def calculate_costs(n,label,costs):
#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)
costs.loc[("links-added","capital","transmission lines"),label] = ((costs_db.at['HVDC overhead', 'capital_cost']*n.links.length + costs_db.at['HVDC inverter pair', 'capital_cost'])*n.links.p_nom_opt)[n.links.carrier == "DC"].sum()
costs.loc[("lines-added","capital","transmission lines"),label] = costs_db.at["HVAC overhead", "capital_cost"]*(n.lines.length*n.lines.s_nom_opt).sum()
#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()
#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
@ -90,8 +85,8 @@ def calculate_costs(n,label,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()
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)
@ -102,9 +97,9 @@ 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()
c_energies = c.pnl.p.sum().multiply(c.df.sign).groupby(c.df.carrier).sum()
else:
c_energies = (-c.pnl.p1.sum() - c.pnl.p0.sum()).groupby(c.df.group).sum()
c_energies = (-c.pnl.p1.sum() - c.pnl.p0.sum()).groupby(c.df.carrier).sum()
energy = energy.reindex(energy.index|pd.MultiIndex.from_product([[c.list_name],c_energies.index]))
@ -116,11 +111,11 @@ def calculate_energy(n,label,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
load_types = n.loads.carrier.value_counts().index
for i in load_types:
buses = n.loads.bus[n.loads.group == i].values
buses = n.loads.bus[n.loads.carrier == i].values
bus_map = pd.Series(False,index=n.buses.index)
@ -133,7 +128,7 @@ def calculate_supply(n,label,supply):
if len(items) == 0:
continue
s = c.pnl.p[items].max().multiply(c.df.loc[items,'sign']).groupby(c.df.loc[items,'group']).sum()
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
@ -149,7 +144,7 @@ def calculate_supply(n,label,supply):
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()
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
@ -159,11 +154,11 @@ def calculate_supply(n,label,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
load_types = n.loads.carrier.value_counts().index
for i in load_types:
buses = n.loads.bus[n.loads.group == i].values
buses = n.loads.bus[n.loads.carrier == i].values
bus_map = pd.Series(False,index=n.buses.index)
@ -176,7 +171,7 @@ def calculate_supply_energy(n,label,supply_energy):
if len(items) == 0:
continue
s = c.pnl.p[items].sum().multiply(c.df.loc[items,'sign']).groupby(c.df.loc[items,'group']).sum()
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
@ -191,7 +186,7 @@ def calculate_supply_energy(n,label,supply_energy):
if len(items) == 0:
continue
s = (-1)*c.pnl["p"+end][items].sum().groupby(c.df.loc[items,'group']).sum()
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
@ -200,15 +195,18 @@ def calculate_supply_energy(n,label,supply_energy):
def calculate_metrics(n,label,metrics):
metrics = metrics.reindex(metrics.index|pd.Index(["line_volume","line_volume_shadow","co2_shadow"]))
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",label] = (n.links.length*n.links.p_nom_opt)[n.links.group == "transmission lines"].sum()
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 "line_volume_limit" in n.shadow_prices.index:
metrics.at["line_volume_shadow",label] = n.shadow_prices.at["line_volume_limit","value"]
if hasattr(n,"line_volume_limit"):
metrics.at["line_volume_limit",label] = n.line_volume_limit
metrics.at["line_volume_shadow",label] = n.line_volume_limit_dual
if "co2_constraint" in n.shadow_prices.index:
metrics.at["co2_shadow",label] = n.shadow_prices.at["co2_constraint","value"]
if "CO2Limit" in n.global_constraints.index:
metrics.at["co2_shadow",label] = n.global_constraints.at["CO2Limit","mu"]
return metrics
@ -322,12 +320,12 @@ def calculate_market_values(n, label, market_values):
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
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,"group"] == tech]
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.)
@ -379,7 +377,7 @@ outputs = ["costs",
def make_summaries(networks_dict):
columns = pd.MultiIndex.from_tuples(networks_dict.keys(),names=["scenario","line_volume_limit","co2_reduction"])
columns = pd.MultiIndex.from_tuples(networks_dict.keys(),names=["cluster","lv","opt"])
df = {}
@ -389,9 +387,9 @@ def make_summaries(networks_dict):
for label, filename in iteritems(networks_dict):
print(label, filename)
n = helper.Network(filename)
n = pypsa.Network(filename)
assign_groups(n)
assign_carriers(n)
for output in outputs:
df[output] = globals()["calculate_" + output](n, label, df[output])
@ -416,34 +414,24 @@ if __name__ == "__main__":
snakemake.input = Dict()
snakemake.input['heat_demand_name'] = 'data/heating/daily_heat_demand.h5'
snakemake.output = Dict()
name = "37-lv"
clusters = [37]
lvs = [1.0,1.25,2.0,3.0]
opts = ["Co2L-3H-T-H"]
for item in outputs:
snakemake.output[item] = snakemake.config['summary_dir'] + 'version-{version}/csvs/{item}.csv'.format(version=snakemake.config['version'],item=item)
snakemake.output[item] = snakemake.config['summary_dir'] + '/{name}/csvs/{item}.csv'.format(name=name,item=item)
suffix = "nc" if int(snakemake.config['version']) > 100 else "h5"
networks_dict = {(cluster,lv,opt) :
'results/networks/elec_s_{cluster}_lv{lv}_{opt}.nc'\
.format(cluster=cluster,
opt=opt,
lv=lv)\
for cluster in clusters \
for opt in opts \
for lv in lvs}
print(networks_dict)
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
costs_db = load_costs(Nyears=1.,tech_costs="data/costs.csv",config=snakemake.config["costs"],elec_config=snakemake.config['electricity'])
df = make_summaries(networks_dict)