from statkraft.ltm.io.run_repository import RunRepository
import shyft.api as sa
from statkraft.ltm.state import quantity
t0 = sa.utctime_now()
labels = ["norway", "operational", "season"]
rr = RunRepository()
run_id = rr.find_closest_operational_run(t=t0, labels=labels)
run = rr.recreate(run_id=run_id)
time_axis = run.fine_time_axis
min_share = 0.01
prod_price_area = {}
for key, val in run.model.market.areas.items():
for m_area in val.aggregation_list:
prod = quantity(0, "GWh")
m_area_hyd = m_area.detailed_hydro
if not m_area_hyd is None:
for pws in m_area_hyd.power_stations.values():
if pws.statkraft_share > min_share:
print(pws)
prod += pws.production.mean(time_axis=time_axis,
unit="GWh")
t0 = sa.utctime_now()
run_id = rr.find_closest_operational_run(t0)
run = rr.recreate(run_id=run_id)
calendar = sa.Calendar("Europe/Oslo")
dt = calendar.WEEK
start = calendar.time(2018, 9, 1, 0, 0, 0)
end = calendar.time(2021, 9, 1, 0, 0, 0)
n = calendar.diff_units(start, end, dt)
time_axis = sa.TimeAxis(start, dt, n)
l_reservoirs = {}
legend_list = []
for key in run.model.areas.keys():
emps_area = run.model.areas[key]
max_volume_as_energy = quantity(0, "GWh")
max_tag = None
#print(emps_area.name)
if not emps_area.detailed_hydro is None:
for res_key in emps_area.detailed_hydro.reservoirs.keys():
res = emps_area.detailed_hydro.reservoirs[res_key]
res_volume_as_energy = res.max_volume_as_energy
if res_volume_as_energy > max_volume_as_energy:
max_volume_as_energy = res_volume_as_energy
max_tag = res.local_tag
if max_tag is not None:
max_res = emps_area.detailed_hydro.reservoirs[max_tag]
l_reservoirs[key] = max_res
else:
print("hæh?")
from statkraft.ltm.io.run_repository import RunRepository
import shyft.api as sa
from statkraft.ltm.state import quantity
from statkraft.ltm.io.converter import to_pandas
rr = RunRepository()
t0 = sa.utctime_now() - sa.Calendar.DAY
run_id = rr.find_closest_operational_run(t0)
run = rr.recreate(run_id=run_id)
areas = ["SE1", "SE2", "SE3", "SE4"]
energy_inflow = quantity(sa.TsVector(), "GWh")
time_axis = sa.TimeAxis(sa.utctime_now(), sa.Calendar.MONTH, 24)
years = run.run_info.scenario_years
for key in run.model.areas.keys():
area = run.model.areas[key]
if area.market_price_area != None and area.market_price_area.name in areas:
storable_inflow = area.aggregated_hydro.storable_inflow(
unit="GWh", time_axis=time_axis)
non_storable_inflow = area.aggregated_hydro.nonstorable_inflow(
unit="GWh", time_axis=time_axis)
bypass = area.aggregated_hydro.bypass(unit="GWh", time_axis=time_axis)
spill = area.aggregated_hydro.spillage(unit="GWh", time_axis=time_axis)
energy_inflow += storable_inflow + non_storable_inflow - bypass - spill
df, pip = to_pandas(energy_inflow)
df = df.magnitude
df.columns = years + [df.columns[-1]]
df.to_csv("inflow_sweden.csv")
import matplotlib.pyplot as plt
import shyft.api as sa
from statkraft.ltm.io.run_repository import RunRepository
from statkraft.ltm.scripting import plot_ts
from statkraft.ltm.state import quantity
rr = RunRepository()
rid = rr.find_closest_operational_run(sa.utctime_now())
run = rr.recreate(run_id=rid)
time_axis = sa.TimeAxis(run.start_utc, sa.Calendar.DAY, 52 * 7)
tsv = quantity(sa.TsVector(), "EUR/MWh")
legend_list = []
for area_name, area in run.model.areas.items():
legend_list.append(area_name)
tsv.extend(area.power_price.mean(time_axis=time_axis).magnitude)
plot_ts(tsv)
plt.legend(legend_list)
plt.show()
from statkraft.ltm.io.run_repository import RunRepository
import shyft.api as sa
from statkraft.ltm.state import quantity
from statkraft.ltm.scripting import plot_ts
import matplotlib.pyplot as plt
from statkraft.ltm.io.converter import to_pandas
rr = RunRepository()
t0 = sa.utctime_now() - sa.Calendar.DAY * 2
res = rr.search(labels=["operational", "norway"], created_from=t0)
areas = ["NO1", "NO2", "NO5"]
tsv = quantity(sa.TsVector(), "GWh")
tot_cons_list = []
legend_list = []
for key in res.keys():
run_info = res[key]
run = rr.recreate(run_id=key)
legend_list.append(key)
time_axis = sa.TimeAxis(sa.utctime_now(), sa.Calendar.DAY, 365)
tot_cons = quantity(sa.TsVector(), "GWh")
for key1 in run.model.market.areas.keys():
this_area = run.model.market.areas[key1]
if key1 in areas:
cons = this_area.consumption.mean(unit="GWh", time_axis=time_axis)
tot_cons += cons
tot_cons_list.append(tot_cons)
diff_cons = tot_cons_list[0] - tot_cons_list[1]
tsv.extend(diff_cons.magnitude)
from statkraft.ltm.state import quantity
from statkraft.ltm.scripting import plot_ts
import matplotlib.pyplot as plt
rr = RunRepository()
t0 = sa.utctime_now()
run_id = rr.find_closest_operational_run(t0)
run = rr.recreate(run_id=run_id)
calendar = sa.Calendar("Europe/Oslo")
dt = 3 * calendar.HOUR
n = calendar.diff_units(run.time_axis.total_period().start,
run.time_axis.total_period().end, dt)
time_axis = sa.TimeAxis(run.start_utc, dt, n)
norway = run.model.countries['Norway']
tsv = quantity(sa.TsVector(), "EUR")
legend_list = []
#val_in_eur = quantity(sa.TsVector(), "EUR")
values = None
for m_area in norway.aggregation_list:
price = m_area.power_price(time_axis=time_axis, unit="EUR/MWh")
production = m_area.production(time_axis=time_axis, unit="MWh")
val_in_eur = price * production
if values is None:
values = val_in_eur
else:
values += val_in_eur
l_reservoirs = {}
legend_list = []
for key in run.model.areas.keys():
emps_area = run.model.areas[key]
max_volume = 0
max_tag = None
if not emps_area.detailed_hydro is None:
for res_key in emps_area.detailed_hydro.reservoirs.keys():
res = emps_area.detailed_hydro.reservoirs[res_key]
res_volume = res.max_volume.magnitude
if res_volume > max_volume:
max_volume = res_volume
max_tag = res.local_tag
if max_tag is not None:
max_res = emps_area.detailed_hydro.reservoirs[max_tag]
l_reservoirs[key] = max_res
else:
print("hæh?")
tsv = quantity(sa.TsVector(), "m**3")
print(l_reservoirs)
for key in l_reservoirs.keys():
obj_res = l_reservoirs[key]
legend_list.append(obj_res.name)
obj_res_mean_volume = obj_res.volume.mean(time_axis=time_axis, unit="m**3")
tsv.extend(obj_res_mean_volume.magnitude)
plot_ts(tsv)
plt.legend(legend_list)
plt.show(block=True)