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Remove old option for DSM import to import pu timeseries
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@birgits
birgits committed Mar 31, 2023
1 parent c599b53 commit b2547c8
Showing 1 changed file with 69 additions and 181 deletions.
250 changes: 69 additions & 181 deletions edisgo/io/dsm_import.py
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Original file line number Diff line number Diff line change
Expand Up @@ -135,125 +135,57 @@ def get_profiles_per_industrial_load(

dsm_dict = {}

if hasattr(egon_sites_ind_load_curves_individual_dsm_timeseries, "p_min_pu"):
with session_scope_egon_data(engine) as session:
query = session.query(
egon_sites_ind_load_curves_individual_dsm_timeseries.site_id,
egon_sites_ind_load_curves_individual_dsm_timeseries.p_nom,
egon_sites_ind_load_curves_individual_dsm_timeseries.p_min_pu,
egon_sites_ind_load_curves_individual_dsm_timeseries.p_max_pu,
egon_sites_ind_load_curves_individual_dsm_timeseries.e_nom,
egon_sites_ind_load_curves_individual_dsm_timeseries.e_min_pu,
egon_sites_ind_load_curves_individual_dsm_timeseries.e_max_pu,
).filter(
egon_sites_ind_load_curves_individual_dsm_timeseries.scn_name
== scenario,
egon_sites_ind_load_curves_individual_dsm_timeseries.site_id.in_(
load_ids
),
)

df_sites_1 = pd.read_sql(sql=query.statement, con=engine)

query = session.query(
sites_ind_dsm_ts.industrial_sites_id.label("site_id"),
sites_ind_dsm_ts.p_nom,
sites_ind_dsm_ts.p_min_pu,
sites_ind_dsm_ts.p_max_pu,
sites_ind_dsm_ts.e_nom,
sites_ind_dsm_ts.e_min_pu,
sites_ind_dsm_ts.e_max_pu,
).filter(
sites_ind_dsm_ts.scn_name == scenario,
sites_ind_dsm_ts.industrial_sites_id.in_(load_ids),
)

df_sites_2 = pd.read_sql(sql=query.statement, con=engine)

query = session.query(
egon_osm_ind_load_curves_individual_dsm_timeseries.osm_id.label(
"site_id"
),
egon_osm_ind_load_curves_individual_dsm_timeseries.p_nom,
egon_osm_ind_load_curves_individual_dsm_timeseries.p_min_pu,
egon_osm_ind_load_curves_individual_dsm_timeseries.p_max_pu,
egon_osm_ind_load_curves_individual_dsm_timeseries.e_nom,
egon_osm_ind_load_curves_individual_dsm_timeseries.e_min_pu,
egon_osm_ind_load_curves_individual_dsm_timeseries.e_max_pu,
).filter(
egon_osm_ind_load_curves_individual_dsm_timeseries.scn_name == scenario,
egon_osm_ind_load_curves_individual_dsm_timeseries.osm_id.in_(load_ids),
)

df_areas = pd.read_sql(sql=query.statement, con=engine)

df = pd.concat([df_sites_1, df_sites_2, df_areas])
# add time step column
df["time_step"] = len(df) * [np.arange(0, 8760)]
# un-nest time series data and pivot so that time_step becomes index and
# site_id column names
dsm_dict["p_min"] = _pivot_pu_helper(df, "p_min", "p_nom")
dsm_dict["p_max"] = _pivot_pu_helper(df, "p_max", "p_nom")
dsm_dict["e_min"] = _pivot_pu_helper(df, "e_min", "e_nom")
dsm_dict["e_max"] = _pivot_pu_helper(df, "e_max", "e_nom")

else:
with session_scope_egon_data(engine) as session:
query = session.query(
egon_sites_ind_load_curves_individual_dsm_timeseries.site_id,
egon_sites_ind_load_curves_individual_dsm_timeseries.p_min,
egon_sites_ind_load_curves_individual_dsm_timeseries.p_max,
egon_sites_ind_load_curves_individual_dsm_timeseries.e_min,
egon_sites_ind_load_curves_individual_dsm_timeseries.e_max,
).filter(
egon_sites_ind_load_curves_individual_dsm_timeseries.scn_name
== scenario,
egon_sites_ind_load_curves_individual_dsm_timeseries.site_id.in_(
load_ids
),
)

df_sites_1 = pd.read_sql(sql=query.statement, con=engine)

with session_scope_egon_data(engine) as session:
query = session.query(
sites_ind_dsm_ts.industrial_sites_id.label("site_id"),
sites_ind_dsm_ts.p_min,
sites_ind_dsm_ts.p_max,
sites_ind_dsm_ts.e_min,
sites_ind_dsm_ts.e_max,
).filter(
sites_ind_dsm_ts.scn_name == scenario,
sites_ind_dsm_ts.industrial_sites_id.in_(load_ids),
)
with session_scope_egon_data(engine) as session:
query = session.query(
egon_sites_ind_load_curves_individual_dsm_timeseries.site_id,
egon_sites_ind_load_curves_individual_dsm_timeseries.p_min,
egon_sites_ind_load_curves_individual_dsm_timeseries.p_max,
egon_sites_ind_load_curves_individual_dsm_timeseries.e_min,
egon_sites_ind_load_curves_individual_dsm_timeseries.e_max,
).filter(
egon_sites_ind_load_curves_individual_dsm_timeseries.scn_name == scenario,
egon_sites_ind_load_curves_individual_dsm_timeseries.site_id.in_(load_ids),
)

df_sites_2 = pd.read_sql(sql=query.statement, con=engine)
df_sites_1 = pd.read_sql(sql=query.statement, con=engine)

with session_scope_egon_data(engine) as session:
query = session.query(
sites_ind_dsm_ts.industrial_sites_id.label("site_id"),
sites_ind_dsm_ts.p_min,
sites_ind_dsm_ts.p_max,
sites_ind_dsm_ts.e_min,
sites_ind_dsm_ts.e_max,
).filter(
sites_ind_dsm_ts.scn_name == scenario,
sites_ind_dsm_ts.industrial_sites_id.in_(load_ids),
)

with session_scope_egon_data(engine) as session:
query = session.query(
egon_osm_ind_load_curves_individual_dsm_timeseries.osm_id.label(
"site_id"
),
egon_osm_ind_load_curves_individual_dsm_timeseries.p_min,
egon_osm_ind_load_curves_individual_dsm_timeseries.p_max,
egon_osm_ind_load_curves_individual_dsm_timeseries.e_min,
egon_osm_ind_load_curves_individual_dsm_timeseries.e_max,
).filter(
egon_osm_ind_load_curves_individual_dsm_timeseries.scn_name == scenario,
egon_osm_ind_load_curves_individual_dsm_timeseries.osm_id.in_(load_ids),
)
df_sites_2 = pd.read_sql(sql=query.statement, con=engine)

with session_scope_egon_data(engine) as session:
query = session.query(
egon_osm_ind_load_curves_individual_dsm_timeseries.osm_id.label("site_id"),
egon_osm_ind_load_curves_individual_dsm_timeseries.p_min,
egon_osm_ind_load_curves_individual_dsm_timeseries.p_max,
egon_osm_ind_load_curves_individual_dsm_timeseries.e_min,
egon_osm_ind_load_curves_individual_dsm_timeseries.e_max,
).filter(
egon_osm_ind_load_curves_individual_dsm_timeseries.scn_name == scenario,
egon_osm_ind_load_curves_individual_dsm_timeseries.osm_id.in_(load_ids),
)

df_areas = pd.read_sql(sql=query.statement, con=engine)
df_areas = pd.read_sql(sql=query.statement, con=engine)

df = pd.concat([df_sites_1, df_sites_2, df_areas])
# add time step column
df["time_step"] = len(df) * [np.arange(0, 8760)]
# un-nest time series data and pivot so that time_step becomes index and
# site_id column names
dsm_dict["p_min"] = _pivot_abs_helper(df, "p_min")
dsm_dict["p_max"] = _pivot_abs_helper(df, "p_max")
dsm_dict["e_min"] = _pivot_abs_helper(df, "e_min")
dsm_dict["e_max"] = _pivot_abs_helper(df, "e_max")
df = pd.concat([df_sites_1, df_sites_2, df_areas])
# add time step column
df["time_step"] = len(df) * [np.arange(0, 8760)]
# un-nest time series data and pivot so that time_step becomes index and
# site_id column names
dsm_dict["p_min"] = _pivot_helper(df, "p_min")
dsm_dict["p_max"] = _pivot_helper(df, "p_max")
dsm_dict["e_min"] = _pivot_helper(df, "e_min")
dsm_dict["e_max"] = _pivot_helper(df, "e_max")

return dsm_dict

Expand Down Expand Up @@ -289,60 +221,27 @@ def get_profile_cts(

# get data
dsm_dict = {}
if hasattr(egon_etrago_electricity_cts_dsm_timeseries, "p_min_pu"):
with session_scope_egon_data(engine) as session:
query = session.query(
egon_etrago_electricity_cts_dsm_timeseries.bus.label("site_id"),
egon_etrago_electricity_cts_dsm_timeseries.p_nom,
egon_etrago_electricity_cts_dsm_timeseries.p_min_pu,
egon_etrago_electricity_cts_dsm_timeseries.p_max_pu,
egon_etrago_electricity_cts_dsm_timeseries.e_nom,
egon_etrago_electricity_cts_dsm_timeseries.e_min_pu,
egon_etrago_electricity_cts_dsm_timeseries.e_max_pu,
).filter(
egon_etrago_electricity_cts_dsm_timeseries.scn_name == scenario,
egon_etrago_electricity_cts_dsm_timeseries.bus
== edisgo_obj.topology.id,
)
df = pd.read_sql(sql=query.statement, con=engine)
# add time step column
df["time_step"] = len(df) * [np.arange(0, 8760)]
# un-nest time series data and pivot so that time_step becomes index and
# site_id column names
dsm_dict["p_min"] = _pivot_pu_helper(df, "p_min", "p_nom")
dsm_dict["p_max"] = _pivot_pu_helper(df, "p_max", "p_nom")
dsm_dict["e_min"] = _pivot_pu_helper(df, "e_min", "e_nom")
dsm_dict["e_max"] = _pivot_pu_helper(df, "e_max", "e_nom")

# temporary! set negative p_max and e_max values and positive p_min and e_min
# values to zero
dsm_dict["p_min"][dsm_dict["p_min"] > 0.0] = 0.0
dsm_dict["e_min"][dsm_dict["e_min"] > 0.0] = 0.0
dsm_dict["p_max"][dsm_dict["p_max"] < 0.0] = 0.0
dsm_dict["e_max"][dsm_dict["e_max"] < 0.0] = 0.0

else:
with session_scope_egon_data(engine) as session:
query = session.query(
egon_etrago_electricity_cts_dsm_timeseries.bus.label("site_id"),
egon_etrago_electricity_cts_dsm_timeseries.p_min,
egon_etrago_electricity_cts_dsm_timeseries.p_max,
egon_etrago_electricity_cts_dsm_timeseries.e_min,
egon_etrago_electricity_cts_dsm_timeseries.e_max,
).filter(
egon_etrago_electricity_cts_dsm_timeseries.scn_name == scenario,
egon_etrago_electricity_cts_dsm_timeseries.bus
== edisgo_obj.topology.id,
)
df = pd.read_sql(sql=query.statement, con=engine)
# add time step column
df["time_step"] = len(df) * [np.arange(0, 8760)]
# un-nest time series data and pivot so that time_step becomes index and
# site_id column names
dsm_dict["p_min"] = _pivot_abs_helper(df, "p_min")
dsm_dict["p_max"] = _pivot_abs_helper(df, "p_max")
dsm_dict["e_min"] = _pivot_abs_helper(df, "e_min")
dsm_dict["e_max"] = _pivot_abs_helper(df, "e_max")

with session_scope_egon_data(engine) as session:
query = session.query(
egon_etrago_electricity_cts_dsm_timeseries.bus.label("site_id"),
egon_etrago_electricity_cts_dsm_timeseries.p_min,
egon_etrago_electricity_cts_dsm_timeseries.p_max,
egon_etrago_electricity_cts_dsm_timeseries.e_min,
egon_etrago_electricity_cts_dsm_timeseries.e_max,
).filter(
egon_etrago_electricity_cts_dsm_timeseries.scn_name == scenario,
egon_etrago_electricity_cts_dsm_timeseries.bus == edisgo_obj.topology.id,
)
df = pd.read_sql(sql=query.statement, con=engine)
# add time step column
df["time_step"] = len(df) * [np.arange(0, 8760)]
# un-nest time series data and pivot so that time_step becomes index and
# site_id column names
dsm_dict["p_min"] = _pivot_helper(df, "p_min")
dsm_dict["p_max"] = _pivot_helper(df, "p_max")
dsm_dict["e_min"] = _pivot_helper(df, "e_min")
dsm_dict["e_max"] = _pivot_helper(df, "e_max")

# distribute over all CTS loads
cts_loads = edisgo_obj.topology.loads_df[
Expand All @@ -367,18 +266,7 @@ def get_profile_cts(
return dsm_dict


def _pivot_pu_helper(df_db, pu_col, scale_col):
df = (
df_db.loc[:, ["site_id", scale_col, f"{pu_col}_pu", "time_step"]]
.explode([f"{pu_col}_pu", "time_step"])
.astype({f"{pu_col}_pu": "float"})
)
df[pu_col] = df[f"{pu_col}_pu"] * df[scale_col]
df = df.pivot(index="time_step", columns="site_id", values=pu_col)
return df


def _pivot_abs_helper(df_db, col):
def _pivot_helper(df_db, col):
df = (
df_db.loc[:, ["site_id", col, "time_step"]]
.explode([col, "time_step"])
Expand Down

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