def export_power(
stats: List[StatExport] = None,
priority: Optional[PriorityType] = None,
latest: Optional[bool] = False,
) -> None:
"""
Export power stats from the export map
"""
# Not passed a stat map so go and get one
if not stats:
export_map = None
if priority and priority == PriorityType.history:
export_map = get_weekly_export_map()
else:
export_map = get_export_map().get_by_stat_type(StatType.power)
if priority:
export_map = export_map.get_by_priority(priority)
stats = export_map.resources
output_count: int = 0
logger.info("Running {}export {} with {} stats".format(
"latest " if latest else "", priority, len(stats)))
for power_stat in stats:
if power_stat.stat_type != StatType.power:
continue
if output_count >= 1 and latest:
return None
date_range_networks = power_stat.networks or []
if NetworkNEM in date_range_networks:
date_range_networks = [NetworkNEM]
date_range: ScadaDateRange = get_scada_range(
network=power_stat.network, networks=date_range_networks)
logger.debug("Date range for {}: {} => {}".format(
power_stat.network.code, date_range.start, date_range.end))
# Migrate to this time_series
time_series = TimeSeries(
start=date_range.start,
end=date_range.end,
network=power_stat.network,
year=power_stat.year,
interval=power_stat.interval,
period=power_stat.period,
)
stat_set = power_week(
time_series=time_series,
network_region_code=power_stat.network_region_query
or power_stat.network_region,
networks_query=power_stat.networks,
)
if not stat_set:
logger.info("No power stat set for {} {} {}".format(
power_stat.period,
power_stat.networks,
power_stat.network_region,
))
continue
demand_set = demand_week(
time_series=time_series,
networks_query=power_stat.networks,
network_region_code=power_stat.network_region_query
or power_stat.network_region,
)
stat_set.append_set(demand_set)
if power_stat.network_region:
flow_set = power_flows_region_week(
time_series=time_series,
network_region_code=power_stat.network_region,
)
if flow_set:
stat_set.append_set(flow_set)
time_series_weather = time_series.copy()
time_series_weather.interval = human_to_interval("30m")
if power_stat.bom_station:
try:
weather_set = weather_daily(
time_series=time_series_weather,
station_code=power_stat.bom_station,
network_region=power_stat.network_region,
include_min_max=False,
unit_name="temperature",
)
stat_set.append_set(weather_set)
except Exception:
pass
write_output(power_stat.path, stat_set)
output_count += 1
def power_week(
time_series: TimeSeries,
network_region_code: str = None,
networks_query: Optional[List[NetworkSchema]] = None,
include_capacities: bool = False,
include_code: Optional[bool] = True,
) -> Optional[OpennemDataSet]:
engine = get_database_engine()
query = power_network_fueltech_query(
time_series=time_series,
networks_query=networks_query,
network_region=network_region_code,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
stats = [
DataQueryResult(interval=i[0], result=i[2], group_by=i[1] if len(i) > 1 else None)
for i in row
]
if len(stats) < 1:
logger.error("No results from power week query with {}".format(time_series))
return None
result = stats_factory(
stats,
# code=network_region_code or network.code,
network=time_series.network,
interval=time_series.interval,
period=time_series.period,
units=get_unit("power"),
region=network_region_code,
fueltech_group=True,
include_code=include_code,
)
if not result:
logger.error("No results from power week status factory with {}".format(time_series))
return None
if include_capacities and network_region_code:
region_fueltech_capacities = get_facility_capacities(
time_series.network, network_region_code
)
for ft in result.data:
if ft.fuel_tech in region_fueltech_capacities:
ft.x_capacity_at_present = region_fueltech_capacities[ft.fuel_tech]
# price
time_series_price = time_series.copy()
time_series_price.interval = human_to_interval("30m")
query = price_network_query(
time_series=time_series_price,
networks_query=networks_query,
network_region=network_region_code,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
stats_price = [
DataQueryResult(interval=i[0], result=i[2], group_by=i[1] if len(i) > 1 else None)
for i in row
]
stats_market_value = stats_factory(
stats=stats_price,
code=network_region_code or time_series.network.code.lower(),
units=get_unit("price_energy_mega"),
network=time_series.network,
interval=human_to_interval("30m"),
region=network_region_code,
period=time_series.period,
include_code=include_code,
)
result.append_set(stats_market_value)
# rooftop solar
time_series_rooftop = time_series.copy()
time_series_rooftop.interval = human_to_interval("30m")
query = power_network_rooftop_query(
time_series=time_series_rooftop,
networks_query=networks_query,
network_region=network_region_code,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
rooftop_power = [
DataQueryResult(interval=i[0], result=i[2], group_by=i[1] if len(i) > 1 else None)
for i in row
]
rooftop = stats_factory(
rooftop_power,
# code=network_region_code or network.code,
network=time_series.network,
interval=human_to_interval("30m"),
period=time_series.period,
units=get_unit("power"),
region=network_region_code,
fueltech_group=True,
include_code=include_code,
cast_nulls=False,
)
# rooftop forecast
rooftop_forecast = None
if rooftop and rooftop.data and len(rooftop.data) > 0:
time_series_rooftop_forecast = time_series_rooftop.copy()
time_series_rooftop_forecast.start = rooftop.data[0].history.last
time_series_rooftop_forecast.forecast = True
query = power_network_rooftop_query(
time_series=time_series_rooftop_forecast,
networks_query=networks_query,
network_region=network_region_code,
forecast=True,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
rooftop_forecast_power = [
DataQueryResult(interval=i[0], result=i[2], group_by=i[1] if len(i) > 1 else None)
for i in row
]
rooftop_forecast = stats_factory(
rooftop_forecast_power,
# code=network_region_code or network.code,
network=time_series.network,
interval=human_to_interval("30m"),
period=time_series.period,
units=get_unit("power"),
region=network_region_code,
fueltech_group=True,
include_code=include_code,
cast_nulls=False,
)
if rooftop and rooftop_forecast:
if (
hasattr(rooftop, "data")
and len(rooftop.data) > 0
and rooftop_forecast.data
and len(rooftop_forecast.data) > 0
):
rooftop.data[0].forecast = rooftop_forecast.data[0].history
result.append_set(rooftop)
return result
def power_week(
time_series: TimeSeries,
network_region_code: str = None,
networks_query: Optional[List[NetworkSchema]] = None,
) -> Optional[OpennemDataSet]:
engine = get_database_engine()
query = power_network_fueltech_query(
time_series=time_series,
networks_query=networks_query,
network_region=network_region_code,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
stats = [
DataQueryResult(interval=i[0],
result=i[2],
group_by=i[1] if len(i) > 1 else None) for i in row
]
if len(stats) < 1:
raise Exception("No results from query: {}".format(query))
result = stats_factory(
stats,
# code=network_region_code or network.code,
network=time_series.network,
interval=time_series.interval,
period=time_series.period,
units=get_unit("power"),
region=network_region_code,
fueltech_group=True,
)
if not result:
raise Exception("No results")
# price
time_series_price = time_series.copy()
time_series_price.interval = human_to_interval("30m")
query = price_network_query(
time_series=time_series_price,
networks_query=networks_query,
network_region=network_region_code,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
stats_price = [
DataQueryResult(interval=i[0],
result=i[2],
group_by=i[1] if len(i) > 1 else None) for i in row
]
stats_market_value = stats_factory(
stats=stats_price,
code=network_region_code or time_series.network.code.lower(),
units=get_unit("price_energy_mega"),
network=time_series.network,
interval=human_to_interval("30m"),
region=network_region_code,
period=time_series.period,
)
result.append_set(stats_market_value)
# rooftop solar
time_series_rooftop = time_series.copy()
time_series_rooftop.interval = human_to_interval("30m")
query = power_network_rooftop_query(
time_series=time_series_rooftop,
networks_query=networks_query,
network_region=network_region_code,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
rooftop_power = [
DataQueryResult(interval=i[0],
result=i[2],
group_by=i[1] if len(i) > 1 else None) for i in row
]
rooftop = stats_factory(
rooftop_power,
# code=network_region_code or network.code,
network=time_series.network,
interval=human_to_interval("30m"),
period=time_series.period,
units=get_unit("power"),
region=network_region_code,
fueltech_group=True,
)
# rooftop forecast
time_series_rooftop_forecast = time_series.copy()
time_series_rooftop_forecast.interval = human_to_interval("30m")
time_series_rooftop_forecast.forecast = True
query = power_network_rooftop_query(
time_series=time_series_rooftop,
networks_query=networks_query,
network_region=network_region_code,
forecast=True,
)
with engine.connect() as c:
logger.debug(query)
row = list(c.execute(query))
rooftop_forecast_power = [
DataQueryResult(interval=i[0],
result=i[2],
group_by=i[1] if len(i) > 1 else None) for i in row
]
rooftop_forecast = stats_factory(
rooftop_forecast_power,
# code=network_region_code or network.code,
network=time_series.network,
interval=human_to_interval("30m"),
period=time_series.period,
units=get_unit("power"),
region=network_region_code,
fueltech_group=True,
)
if rooftop and rooftop_forecast:
if (hasattr(rooftop, "data") and len(rooftop.data) > 0
and rooftop_forecast.data and len(rooftop_forecast.data) > 0):
rooftop.data[0].forecast = rooftop_forecast.data[0].history
result.append_set(rooftop)
return result
