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Better workflow
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Juliette-Gerbaux committed Aug 23, 2024
1 parent 9dccf80 commit 4f47103
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84 changes: 83 additions & 1 deletion src/andromede/hydro_heuristic/data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,10 +11,18 @@
# This file is part of the Antares project.

from dataclasses import dataclass
from pathlib import Path
from typing import List, Optional

import numpy as np
import pandas as pd

from andromede.study import (
ConstantData,
DataBase,
TimeIndex,
TimeScenarioSeriesData,
TimeSeriesData,
)


@dataclass(frozen=False)
Expand Down Expand Up @@ -164,3 +172,77 @@ def compute_weekly_target(all_daily_generation: List[float]) -> List[float]:
]

return weekly_target


def get_database(hydro_data: HydroHeuristicData, id: str = "H") -> DataBase:
database = DataBase()

database.add_data(id, "capacity", ConstantData(hydro_data.reservoir_data.capacity))
database.add_data(
id,
"initial_level",
ConstantData(hydro_data.reservoir_data.initial_level),
)

inflow_data = pd.DataFrame(
hydro_data.inflow,
index=[i for i in range(len(hydro_data.inflow))],
columns=[0],
)
database.add_data(id, "inflow", TimeScenarioSeriesData(inflow_data))

target_data = pd.DataFrame(
hydro_data.target,
index=[i for i in range(len(hydro_data.target))],
columns=[0],
)
database.add_data(id, "generating_target", TimeScenarioSeriesData(target_data))
database.add_data(id, "overall_target", ConstantData(sum(hydro_data.target)))

database.add_data(
id,
"lower_rule_curve",
TimeSeriesData(
{
TimeIndex(i): hydro_data.lower_rule_curve[i]
* hydro_data.reservoir_data.capacity
for i in range(len(hydro_data.lower_rule_curve))
}
),
)
database.add_data(
id,
"upper_rule_curve",
TimeSeriesData(
{
TimeIndex(i): hydro_data.upper_rule_curve[i]
* hydro_data.reservoir_data.capacity
for i in range(len(hydro_data.lower_rule_curve))
}
),
)
database.add_data(id, "min_generating", ConstantData(0))

database.add_data(
id,
"max_generating",
TimeSeriesData(
{
TimeIndex(i): hydro_data.max_generating[i]
for i in range(len(hydro_data.max_generating))
}
),
)

database.add_data(
id,
"max_epsilon",
TimeSeriesData(
{
TimeIndex(i): (hydro_data.reservoir_data.capacity if i == 0 else 0)
for i in range(len(hydro_data.max_generating))
}
),
)

return database
2 changes: 1 addition & 1 deletion src/andromede/hydro_heuristic/heuristic_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -46,7 +46,7 @@ def __init__(
"gamma_s": 0 if horizon == "monthly" else -1 / 32,
}

def get_model(
def build_model(
self,
) -> Model:
if "level" not in self.hydro_model.variables.keys():
Expand Down
190 changes: 36 additions & 154 deletions src/andromede/hydro_heuristic/problem.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,34 +11,26 @@
# This file is part of the Antares project.

from dataclasses import dataclass

from typing import List, Tuple

import ortools.linear_solver.pywraplp as pywraplp
import pandas as pd

from andromede.hydro_heuristic.data import (
DataAggregatorParameters,
HydroHeuristicData,
HydroHeuristicParameters,
ReservoirParameters,
get_database,
)
from andromede.model import Model
from andromede.simulation import (
BlockBorderManagement,
OptimizationProblem,
OutputValues,
TimeBlock,
build_problem,
)
from andromede.study import (
ConstantData,
DataBase,
Network,
TimeIndex,
TimeScenarioSeriesData,
TimeSeriesData,
create_component,
)
from andromede.study import DataBase, Network, create_component


@dataclass(frozen=True)
Expand All @@ -53,153 +45,43 @@ class OutputHeuristic:
level: float


class HydroHeuristicProblem:
def __init__(self, hydro_data: HydroHeuristicData, heuristic_model: Model) -> None:
self.hydro_data = hydro_data
self.id = "H"
database = self.generate_database()

hydro = create_component(
model=heuristic_model,
id=self.id,
)

network = Network("test")
network.add_component(hydro)

problem = build_problem(
network,
database,
TimeBlock(1, [i for i in range(len(hydro_data.target))]),
1,
border_management=(BlockBorderManagement.CYCLE),
)

self.problem = problem

def solve_hydro_problem(self) -> Tuple[SolvingOutput, OutputHeuristic]:
parameters = pywraplp.MPSolverParameters()
parameters.SetIntegerParam(parameters.PRESOLVE, parameters.PRESOLVE_OFF)
parameters.SetIntegerParam(parameters.SCALING, 0)

status = self.problem.solver.Solve(parameters)

output = OutputValues(self.problem)

return (
SolvingOutput(status, self.problem.solver.Objective().Value()),
OutputHeuristic(
output.component(self.id).var("generating").value[0], # type:ignore
output.component(self.id).var("level").value[0][-1], # type:ignore
),
)

def generate_database(
self,
) -> DataBase:
database = DataBase()

database.add_data(
self.id, "capacity", ConstantData(self.hydro_data.reservoir_data.capacity)
)
database.add_data(
self.id,
"initial_level",
ConstantData(self.hydro_data.reservoir_data.initial_level),
)

inflow_data = pd.DataFrame(
self.hydro_data.inflow,
index=[i for i in range(len(self.hydro_data.inflow))],
columns=[0],
)
database.add_data(self.id, "inflow", TimeScenarioSeriesData(inflow_data))

target_data = pd.DataFrame(
self.hydro_data.target,
index=[i for i in range(len(self.hydro_data.target))],
columns=[0],
)
database.add_data(
self.id, "generating_target", TimeScenarioSeriesData(target_data)
)
database.add_data(
self.id, "overall_target", ConstantData(sum(self.hydro_data.target))
)

database.add_data(
self.id,
"lower_rule_curve",
TimeSeriesData(
{
TimeIndex(i): self.hydro_data.lower_rule_curve[i]
* self.hydro_data.reservoir_data.capacity
for i in range(len(self.hydro_data.lower_rule_curve))
}
),
)
database.add_data(
self.id,
"upper_rule_curve",
TimeSeriesData(
{
TimeIndex(i): self.hydro_data.upper_rule_curve[i]
* self.hydro_data.reservoir_data.capacity
for i in range(len(self.hydro_data.lower_rule_curve))
}
),
)
database.add_data(self.id, "min_generating", ConstantData(0))

database.add_data(
self.id,
"max_generating",
TimeSeriesData(
{
TimeIndex(i): self.hydro_data.max_generating[i]
for i in range(len(self.hydro_data.max_generating))
}
),
)

database.add_data(
self.id,
"max_epsilon",
TimeSeriesData(
{
TimeIndex(i): (
self.hydro_data.reservoir_data.capacity if i == 0 else 0
)
for i in range(len(self.hydro_data.max_generating))
}
),
)

return database


def optimize_target(
heuristic_parameters: HydroHeuristicParameters,
data_aggregator_parameters: DataAggregatorParameters,
reservoir_data: ReservoirParameters,
heuristic_model: Model,
) -> Tuple[SolvingOutput, OutputHeuristic]:
# Récupération des données
data = HydroHeuristicData(
data_aggregator_parameters,
reservoir_data,
)
# Calcul de la préallocation
data.compute_target(heuristic_parameters)
def build_hydro_heuristic_problem(
database: DataBase, heuristic_model: Model, timesteps: List[int], id: str = "H"
) -> OptimizationProblem:
hydro = create_component(model=heuristic_model, id=id)
network = Network("test")
network.add_component(hydro)

# Ajustement de la réapartition
heuristic_problem = HydroHeuristicProblem(
hydro_data=data, heuristic_model=heuristic_model
problem = build_problem(
network,
database,
TimeBlock(1, timesteps),
1,
border_management=(BlockBorderManagement.CYCLE),
)

solving_output, heuristic_output = heuristic_problem.solve_hydro_problem()
return problem


def get_default_solver_parameters() -> pywraplp.MPSolverParameters:
parameters = pywraplp.MPSolverParameters()
parameters.SetIntegerParam(parameters.PRESOLVE, parameters.PRESOLVE_OFF)
parameters.SetIntegerParam(parameters.SCALING, 0)
return parameters


def retrieve_important_heuristic_output(
heuristic_problem: OptimizationProblem,
id: str = "H",
) -> OutputHeuristic:
output = OutputValues(heuristic_problem)

heuristic_output = OutputHeuristic(
output.component(id).var("generating").value[0], # type:ignore
output.component(id).var("level").value[0][-1], # type:ignore
)

return solving_output, heuristic_output
return heuristic_output


def update_initial_level(
Expand Down
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