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| Original file line number | Diff line number | Diff line change |
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| @@ -1,2 +1,3 @@ | ||
| INPUT_FILE_NAMES = "data/input_file_names.json" | ||
| OUTPUT_FILE_NAMES = "data/output_file_names.json" | ||
| OUTPUT_FILE_NAMES = "data/output_file_names.json" | ||
| LOG_LEVEL = "INFO" |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,52 @@ | ||
| from typing import Optional | ||
| from datetime import datetime, timedelta, timezone | ||
| import polars as pl | ||
| from polars import col as c | ||
| import pyomo.environ as pyo | ||
| import tqdm | ||
|
|
||
| from data_federation.input_model import SmallflexInputSchema | ||
| from pyomo_models.input_data_preprocessing import ( | ||
| generate_baseline_index, generate_clean_timeseries, generate_water_flow_factor, generate_basin_volume_table, | ||
| clean_hydro_power_performance_table, generate_hydro_power_state | ||
| ) | ||
| from utility.general_function import pl_to_dict, pl_to_dict_with_tuple, generate_log | ||
|
|
||
| from pyomo_models.baseline.first_stage.sets import baseline_sets | ||
| from pyomo_models.baseline.first_stage.parameters import baseline_parameters | ||
| from pyomo_models.baseline.first_stage.variables import baseline_variables | ||
| from pyomo_models.baseline.first_stage.objective import baseline_objective | ||
| from pyomo_models.baseline.first_stage.constraints.basin_volume import basin_volume_constraints | ||
| from pyomo_models.baseline.first_stage.constraints.turbine import turbine_constraints | ||
| from pyomo_models.baseline.first_stage.constraints.pump import pump_constraints | ||
|
|
||
| class BaseLineInput(): | ||
| def __init__( | ||
| self, input_schema_file_name: str, real_time_step: timedelta, year: int, market_country: str = "CH", | ||
| market: str = "DA", hydro_power_mask: Optional[pl.Expr] = None, max_alpha_error: float = 1.3, | ||
| solver_name: str = 'gurobi'): | ||
|
|
||
| self.real_time_step = real_time_step | ||
| self.min_datetime = datetime(year, 1, 1, tzinfo=timezone.utc) | ||
| self.max_datetime = datetime(year + 1, 1, 1, tzinfo=timezone.utc) | ||
| self.small_flex_input_schema: SmallflexInputSchema = SmallflexInputSchema()\ | ||
| .duckdb_to_schema(file_path=input_schema_file_name) | ||
| self.year = year | ||
| self.market_country = market_country | ||
| self.market = market | ||
| self.hydro_power_mask = hydro_power_mask | ||
| self.max_alpha_error = max_alpha_error | ||
| self.solver= pyo.SolverFactory(solver_name) | ||
|
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| self.discharge_flow_measurement: pl.DataFrame = self.small_flex_input_schema.discharge_flow_measurement\ | ||
| .filter(c("river") == "Griessee")\ | ||
| .with_columns( | ||
| (c("value") * real_time_step.total_seconds()).alias("discharge_volume"), | ||
| pl.lit(0).alias("B") | ||
| ) | ||
|
|
||
| self.market_price_measurement:pl.DataFrame = self.small_flex_input_schema.market_price_measurement\ | ||
| .filter(c("country") == self.market_country)\ | ||
| .filter(c("market") == self.market) | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,137 @@ | ||
| import re | ||
| from typing import Optional | ||
| from datetime import datetime, timedelta, timezone | ||
| import polars as pl | ||
| from polars import col as c | ||
| import pyomo.environ as pyo | ||
| import tqdm | ||
|
|
||
| from data_federation.input_model import SmallflexInputSchema | ||
| from pyomo_models.input_data_preprocessing import ( | ||
| generate_baseline_index, generate_clean_timeseries, generate_water_flow_factor, generate_basin_volume_table, | ||
| clean_hydro_power_performance_table, generate_hydro_power_state | ||
| ) | ||
| from utility.general_function import pl_to_dict, pl_to_dict_with_tuple, generate_log | ||
| from pyomo_models.baseline.baseline_input import BaseLineInput | ||
| from pyomo_models.baseline.first_stage.sets import baseline_sets | ||
| from pyomo_models.baseline.first_stage.parameters import baseline_parameters | ||
| from pyomo_models.baseline.first_stage.variables import baseline_variables | ||
| from pyomo_models.baseline.first_stage.objective import baseline_objective | ||
| from pyomo_models.baseline.first_stage.constraints.basin_volume import basin_volume_constraints | ||
| from pyomo_models.baseline.first_stage.constraints.turbine import turbine_constraints | ||
| from pyomo_models.baseline.first_stage.constraints.pump import pump_constraints | ||
|
|
||
| log = generate_log(name=__name__) | ||
|
|
||
| class BaselineFirstStage(BaseLineInput): | ||
| def __init__(self, input_instance: BaseLineInput, first_time_step: timedelta): | ||
| self.subset_mapping = { | ||
| "T": "T", "H": "H", "B": "B", "BS": ["B", "S_B"], "HS": ["H", "S_H"], "S_BH": {"H", "H", "S_B", "S_H"} | ||
| } | ||
| self.first_time_step = first_time_step | ||
| self.retrieve_input(input_instance) | ||
| self.model_instance: pyo.Model = pyo.ConcreteModel() | ||
| self.generate_index() | ||
| self.process_timeseries() | ||
| self.generate_model() | ||
|
|
||
| def retrieve_input(self, input_instance): | ||
| for name, value in input_instance.__dict__.items(): | ||
| setattr(self, name, value) | ||
|
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|
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||
| def generate_index(self): | ||
| self.index : dict[str, pl.DataFrame]= generate_baseline_index( | ||
| small_flex_input_schema=self.small_flex_input_schema, | ||
| year=self.year, | ||
| timestep=self.first_time_step, | ||
| real_timestep=self.real_time_step, | ||
| hydro_power_mask=self.hydro_power_mask | ||
| ) | ||
|
|
||
| self.water_flow_factor: pl.DataFrame = generate_water_flow_factor(index=self.index) | ||
| basin_volume_table: dict[int, Optional[pl.DataFrame]] = generate_basin_volume_table( | ||
| small_flex_input_schema=self.small_flex_input_schema, index=self.index) | ||
|
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||
| self.power_performance_table: list[dict] = clean_hydro_power_performance_table( | ||
| small_flex_input_schema=self.small_flex_input_schema, index=self.index, | ||
| basin_volume_table=basin_volume_table) | ||
|
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||
| self.index: dict[str, pl.DataFrame] = generate_hydro_power_state( | ||
| power_performance_table=self.power_performance_table, index=self.index, error_percent=1.3 | ||
| ) | ||
|
|
||
| def process_timeseries(self): | ||
| ### Discharge_flow ############################################################################################## | ||
| self.discharge_volume: pl.DataFrame = generate_clean_timeseries( | ||
| data=self.discharge_flow_measurement, | ||
| col_name="discharge_volume", | ||
| min_datetime=self.min_datetime, | ||
| max_datetime=self.max_datetime, | ||
| timestep=self.first_time_step , | ||
| agg_type="sum" | ||
| ).with_columns( | ||
| pl.concat_list(["T", pl.lit(0).alias("B")]).alias("TB") | ||
| ) | ||
| ### Market price ############################################################################################### | ||
| self.market_price: pl.DataFrame = generate_clean_timeseries( | ||
| data=self.market_price_measurement, | ||
| col_name="avg", | ||
| min_datetime=self.min_datetime, | ||
| max_datetime=self.max_datetime, | ||
| timestep=self.first_time_step, | ||
| agg_type="mean" | ||
| ) | ||
|
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||
|
|
||
| def generate_model(self): | ||
| self.model: pyo.AbstractModel = pyo.AbstractModel() | ||
| self.model = baseline_sets(self.model) | ||
| self.model = baseline_parameters(self.model) | ||
| self.model = baseline_variables(self.model) | ||
|
|
||
| self.model = baseline_objective(self.model) | ||
| self.model = basin_volume_constraints(self.model) | ||
| self.model = turbine_constraints(self.model) | ||
| self.model = pump_constraints(self.model) | ||
|
|
||
| def create_model_instance(self): | ||
| hydropower_state: pl.DataFrame = self.index["state"].drop_nulls("H") | ||
| data: dict = {} | ||
|
|
||
| data["T"] = {None: self.index["datetime"]["T"].to_list()} | ||
| data["H"] = {None: self.index["hydro_power_plant"]["H"].to_list()} | ||
| data["B"] = {None: self.index["water_basin"]["B"].to_list()} | ||
| data["S_b"] = pl_to_dict(self.index["state"].group_by("B", maintain_order=True).agg("S")) | ||
| data["S_h"] = pl_to_dict(self.index["state"].drop_nulls("H").group_by("H", maintain_order=True).agg("S")) | ||
| data["S_BH"] = {None: list(map(tuple, self.index["state"].drop_nulls("H")["S_BH"].to_list()))} | ||
|
|
||
| data["start_basin_volume"] = pl_to_dict(self.index["water_basin"][["B", "start_volume"]]) | ||
| data["water_pumped_factor"] = pl_to_dict_with_tuple(self.water_flow_factor["BH", "pumped_factor"]) | ||
| data["water_turbined_factor"] = pl_to_dict_with_tuple(self.water_flow_factor["BH", "turbined_factor"]) | ||
| data["min_basin_volume"] = pl_to_dict_with_tuple( | ||
| self.index["state"].select("BS", c("volume").struct.field("min"))) | ||
| data["max_basin_volume"] = pl_to_dict_with_tuple( | ||
| self.index["state"].select("BS", c("volume").struct.field("max"))) | ||
| data["max_flow_turbined"] = pl_to_dict_with_tuple( | ||
| hydropower_state.select("HS", c("flow_turbined").struct.field("min"))) | ||
| data["max_flow_pumped"] = pl_to_dict_with_tuple( | ||
| hydropower_state.select("HS", c("flow_pumped").struct.field("min"))) | ||
| data["alpha_turbined"] = pl_to_dict_with_tuple( | ||
| hydropower_state.select("HS", c("alpha_turbined").struct.field("min"))) | ||
| data["alpha_pumped"] = pl_to_dict_with_tuple( | ||
| hydropower_state.select("HS", c("alpha_pumped").struct.field("min"))) | ||
| data["discharge_volume"] = pl_to_dict_with_tuple(self.discharge_volume[["TB", "discharge_volume"]]) | ||
| data["market_price"] = pl_to_dict(self.market_price[["T", "avg"]]) | ||
| data["max_market_price"] = pl_to_dict(self.market_price[["T", "max_avg"]]) | ||
| data["min_market_price"] = pl_to_dict(self.market_price[["T", "min_avg"]]) | ||
| data["nb_hours"] = pl_to_dict(self.index["datetime"][["T", "n_index"]]) | ||
|
|
||
| self.model_instance: pyo.Model = self.model.create_instance({None: data}) | ||
|
|
||
| def solve_model(self): | ||
| with tqdm.tqdm(total=1, desc="Solving first stage optimization problem") as pbar: | ||
|
|
||
| _ = self.solver.solve(self.model_instance) | ||
| pbar.update() | ||
|
|
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