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Assign fuel costs
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@ktehranchi
ktehranchi committed Aug 26, 2024
1 parent 2ccc2db commit b3faa53
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Showing 2 changed files with 78 additions and 30 deletions.
17 changes: 11 additions & 6 deletions src/r2x/parser/parser_helpers.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -87,20 +87,25 @@ def fill_missing_timestamps(data_file: pl.DataFrame, date_time_column: list[str]
.alias("timestamp")
).with_columns(pl.col("timestamp").dt.cast_time_unit("ns"))

complete_timestamps_df = pl.from_pandas(pd.DataFrame({"timestamp": date_time_column}))
missing_timestamps_df = complete_timestamps_df.join(data_file, on="timestamp", how="anti")
data_file = data_file.with_columns(
pl.col("year").cast(pl.Int32),
pl.col("month").cast(pl.Int8),
pl.col("day").cast(pl.Int8),
pl.col("hour").cast(pl.Int8),
)

missing_timestamps_df = missing_timestamps_df.with_columns(
complete_timestamps = pl.from_pandas(pd.DataFrame({"timestamp": date_time_column}))
missing_timestamps = complete_timestamps.join(data_file, on="timestamp", how="anti")

missing_timestamps = missing_timestamps.with_columns(
pl.col("timestamp").dt.year().alias("year"),
pl.col("timestamp").dt.month().alias("month"),
pl.col("timestamp").dt.day().alias("day"),
pl.col("timestamp").dt.hour().alias("hour"),
pl.lit(None).alias("value"),
).select(["year", "month", "day", "hour", "value", "timestamp"])

complete_df = (
pl.concat([data_file, missing_timestamps_df]).sort("timestamp").fill_null(strategy="forward")
)
complete_df = pl.concat([data_file, missing_timestamps]).sort("timestamp").fill_null(strategy="forward")
complete_df.drop_in_place("timestamp")
return complete_df

Expand Down
91 changes: 67 additions & 24 deletions src/r2x/parser/plexos.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,6 +54,7 @@
PROPERTY_TS_COLUMNS_BASIC = ["year", "month", "day", "period", "value"]
PROPERTY_TS_COLUMNS_MULTIZONE = ["year", "month", "day", "period"]
PROPERTY_TS_COLUMNS_PIVOT = ["year", "month", "day"]
PROPERTY_TS_COLUMNS_YM = ["year", "month"]
PROPERTY_TS_COLUMNS_MDP = ["month", "day", "period"]
PROPERTY_TS_COLUMNS_MONTH_PIVOT = [
"name",
Expand Down Expand Up @@ -192,8 +193,6 @@ def build_system(self) -> System:
self._add_battery_reserves()

self._construct_load_profiles()
# self._construct_areas()
# self._construct_transformers()
return self.system

def _collect_horizon_data(self, model_name: str) -> dict[str, float]:
Expand Down Expand Up @@ -254,6 +253,37 @@ def _reconcile_timeseries(self, data_file):

return data_file

def _get_fuel_prices(self):
logger.debug("Creating fuel representation")
system_fuels = (pl.col("child_class_name") == ClassEnum.Fuel.value) & (
pl.col("parent_class_name") == ClassEnum.System.value
)
fuels = self._get_model_data(system_fuels)
fuel_prices = {}
for fuel_name, fuel_data in fuels.group_by("name"):
fuel_name = fuel_name[0]
property_records = fuel_data[
[
"band",
"property_name",
"property_value",
"property_unit",
"data_file",
"variable",
"action",
"variable_tag",
]
].to_dicts()

logger.debug("Parsing fuel = {}", fuel_name)
for property in property_records:
property.update({"property_unit": "$/MMBtu"})

mapped_records, multi_band_records = self._parse_property_data(property_records, fuel_name)
mapped_records["name"] = fuel_name
fuel_prices[fuel_name] = mapped_records["Price"]
return fuel_prices

def _construct_load_zones(self, default_model=LoadZone) -> None:
"""Create LoadZone representation.
Expand Down Expand Up @@ -497,6 +527,7 @@ def _construct_generators(self):
"""
generator_fuel = self.db.query(fuel_query)
generator_fuel_map = {key: value for key, value in generator_fuel}
fuel_prices = self._get_fuel_prices()

# Iterate over properties for generator
for generator_name, generator_data in system_generators.group_by("name"):
Expand Down Expand Up @@ -559,6 +590,7 @@ def _construct_generators(self):
continue # Pass if not available

mapped_records = self._construct_value_curves(mapped_records, generator_name)
mapped_records["fuel_price"] = fuel_prices.get(generator_fuel_map.get(generator_name))

valid_fields, ext_data = self._field_filter(mapped_records, model_map.model_fields)

Expand All @@ -573,13 +605,11 @@ def _construct_generators(self):

self.system.add_component(model_map(**valid_fields))
generator = self.system.get_component_by_label(f"{model_map.__name__}.{generator_name}")
# breakpoint()
if ts_fields:
generator = self.system.get_component_by_label(f"{model_map.__name__}.{generator_name}")
ts_dict = {"solve_year": self.study_year}
for ts_name, ts in ts_fields.items():
ts.variable_name = ts_name
# breakpoint()
self.system.add_time_series(ts, generator, **ts_dict)

def _add_buses_to_generators(self):
Expand Down Expand Up @@ -853,11 +883,9 @@ def _construct_value_curves(self, mapped_records, generator_name):
else:
logger.warning("Heat Rate type not implemented for generator={}", generator_name)
fn = None
# breakpoint()
if not vc:
vc = InputOutputCurve(name=f"{generator_name}_HR", function_data=fn)
mapped_records["heat_rate"] = vc
# breakpoint()
return mapped_records

def _select_model_name(self):
Expand Down Expand Up @@ -977,17 +1005,20 @@ def _get_model_data(self, data_filter) -> pl.DataFrame:

base_case_filter = pl.col("scenario").is_null()
if scenario_specific_data.is_empty():
return self.plexos_data.filter(data_filter & base_case_filter)

combined_key_base = pl.col("name") + "_" + pl.col("property_name")
combined_key_scenario = scenario_specific_data["name"] + "_" + scenario_specific_data["property_name"]
system_data = self.plexos_data.filter(data_filter & base_case_filter)
else:
# include both scenario specific and basecase data
combined_key_base = pl.col("name") + "_" + pl.col("property_name")
combined_key_scenario = (
scenario_specific_data["name"] + "_" + scenario_specific_data["property_name"]
)

base_case_filter = base_case_filter & (
~combined_key_base.is_in(combined_key_scenario) | pl.col("property_name").is_null()
)
base_case_data = self.plexos_data.filter(data_filter & base_case_filter)
base_case_filter = base_case_filter & (
~combined_key_base.is_in(combined_key_scenario) | pl.col("property_name").is_null()
)
base_case_data = self.plexos_data.filter(data_filter & base_case_filter)

system_data = pl.concat([scenario_specific_data, base_case_data])
system_data = pl.concat([scenario_specific_data, base_case_data])

# get system variables
variable_filter = (
Expand Down Expand Up @@ -1086,7 +1117,6 @@ def _construct_load_profiles(self):
)
self.system.add_component(load)
ts_dict = {"solve_year": self.study_year}
# breakpoint()
self.system.add_time_series(ts, load, **ts_dict)
return

Expand Down Expand Up @@ -1129,12 +1159,13 @@ def _csv_file_handler(self, property_name, property_data):
time_series_data = self._retrieve_time_series_data(property_name, data_file)
if time_series_data is not None:
return time_series_data
logger.warning("Property {} not supported. Skipping it.", property_name)
logger.warning("Data file {} not supported yet. Skipping it.", relative_path)
logger.warning("Columns not supported: {}", data_file.columns)
logger.debug("Skipped file {}", relative_path)

def _retrieve_single_value_data(self, property_name, data_file):
if all(column in data_file.columns for column in [property_name.lower(), "year"]):
if (
all(column in data_file.columns for column in [property_name.lower(), "year"])
and "month" not in data_file.columns
):
data_file = data_file.filter(pl.col("year") == self.study_year)
return data_file[property_name.lower()][0]

Expand Down Expand Up @@ -1195,6 +1226,14 @@ def _retrieve_time_series_data(self, property_name, data_file):
data_file = data_file.rename({"period": "hour"})
data_file = data_file.select(output_columns)

case columns if all(
column in columns for column in [*PROPERTY_TS_COLUMNS_YM, property_name.lower()]
):
data_file = data_file.filter(pl.col("year") == self.study_year)
data_file = data_file.rename({property_name.lower(): "value"})
data_file = data_file.with_columns(day=pl.lit(1), hour=pl.lit(0))
data_file = data_file.select(output_columns)

case columns if all(column in columns for column in PROPERTY_TS_COLUMNS_BASIC):
data_file = data_file.rename({"period": "hour"})
data_file = data_file.filter(pl.col("year") == self.study_year)
Expand All @@ -1215,11 +1254,15 @@ def _retrieve_time_series_data(self, property_name, data_file):
data_file = data_file.melt(id_vars=PROPERTY_TS_COLUMNS_PIVOT, variable_name="hour")

case _:
if data_file.is_empty():
logger.warning("Weather year doesn't exist in {}. Skipping it.", property_name)
return
logger.warning("Data file columns not supported. Skipping it.")
logger.warning("Datafile Columns: {}", data_file.columns)
return

if data_file.is_empty():
logger.warning("Weather year doesn't exist in {}. Skipping it.", property_name)
return

assert not data_file.is_empty()
# assert not data_file.is_empty()
# Format to SingleTimeSeries
if data_file.columns == output_columns:
resolution = timedelta(hours=1)
Expand Down

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