"""
Data models for MPC configurations in flexibility quantification.
This module defines Pydantic data models that encapsulate configuration parameters
for baseline, positive flexibility, and negative flexibility MPC controllers used
in flexquant. The models handle file paths, module configurations, variable
mappings, and optimization weights for MPC implementations.
"""
import pydantic
from agentlib_mpc.data_structures.mpc_datamodels import MPCVariable
from pydantic import ConfigDict, model_validator
import agentlib_flexquant.data_structures.globals as glbs
import agentlib_flexquant.utils.config_management as cmng
[docs]class BaseMPCData(pydantic.BaseModel):
"""Base class containing necessary data for the code creation of the different mpcs"""
# files and paths
created_flex_mpcs_file: str = "flex_agents.py"
name_of_created_file: str
results_suffix: str
# modules
module_types: dict
class_name: str
module_id: str
# variables
power_alias: str
stored_energy_alias: str
config_inputs_appendix: list[MPCVariable] = []
config_parameters_appendix: list[MPCVariable] = []
weights: list[MPCVariable] = pydantic.Field(
default=[], description="Name and value of weights",
)
model_config = ConfigDict(json_encoders={MPCVariable: lambda v: v.dict()})
[docs]class BaselineMPCData(BaseMPCData):
"""Data class for Baseline MPC"""
# files and paths
results_suffix: str = "_base.csv"
name_of_created_file: str = "baseline.json"
# modules
module_types: dict = cmng.BASELINE_MODULE_TYPE_DICT
class_name: str = "BaselineMPCModel"
module_id: str = "Baseline"
# variables
power_alias: str = glbs.POWER_ALIAS_BASE
stored_energy_alias: str = glbs.STORED_ENERGY_ALIAS_BASE
power_variable: str = pydantic.Field(
default="P_el",
description=(
"Name of the variable representing the electrical power in the baseline config"
),
)
profile_deviation_weight: float = pydantic.Field(
default=0,
description="Weight of soft constraint for deviation from accepted flexible profile",
)
power_unit: str = pydantic.Field(
default="kW", description="Unit of the power variable"
)
comfort_variable: str = pydantic.Field(
default=None,
description=(
"Name of the slack variable representing the thermal comfort in the baseline config"
),
)
profile_comfort_weight: float = pydantic.Field(
default=1, description="Weight of soft constraint for discomfort",
)
config_inputs_appendix: list[MPCVariable] = [
MPCVariable(name="_P_external", value=0, unit="W"),
MPCVariable(name="in_provision", value=False),
MPCVariable(name="rel_start", value=0, unit="s"),
MPCVariable(name="rel_end", value=0, unit="s"),
]
config_parameters_appendix: list[MPCVariable] = []
weights: list[MPCVariable] = pydantic.Field(
default=[], description="Name and value of weights",
)
model_config = ConfigDict(json_encoders={MPCVariable: lambda v: v.dict()})
[docs] @model_validator(mode="after")
def update_config_parameters_appendix(self) -> "BaselineMPCData":
"""Update the config parameters appendix with profile deviation and comfort weights.
Adds the profile deviation weight parameter and optionally the profile comfort
weight parameter (if comfort_variable is enabled) to the config_parameters_appendix
list as MPCVariable instances.
"""
self.config_parameters_appendix = [
MPCVariable(
name=glbs.PROFILE_DEVIATION_WEIGHT,
value=self.profile_deviation_weight,
unit="-",
description=(
"Weight of soft constraint for deviation from accepted flexible profile"
),
)
]
if self.comfort_variable:
self.config_parameters_appendix.append(
MPCVariable(
name=glbs.PROFILE_COMFORT_WEIGHT,
value=self.profile_comfort_weight,
unit="-",
description="Weight of soft constraint for discomfort",
)
)
return self
[docs]class PFMPCData(BaseMPCData):
"""Data class for PF-MPC"""
# files and paths
results_suffix: str = "_pos_flex.csv"
name_of_created_file: str = "pos_flex.json"
# modules
module_types: dict = cmng.SHADOW_MODULE_TYPE_DICT
class_name: str = "PosFlexModel"
module_id: str = "PosFlexMPC"
# variables
power_alias: str = glbs.POWER_ALIAS_POS
stored_energy_alias: str = glbs.STORED_ENERGY_ALIAS_POS
flex_cost_function: str = pydantic.Field(
default=None, description="Cost function of the PF-MPC",
)
# initialize market parameters with dummy values (0)
config_parameters_appendix: list[MPCVariable] = [
MPCVariable(name=glbs.PREP_TIME, value=0, unit="s"),
MPCVariable(name=glbs.MARKET_TIME, value=0, unit="s"),
MPCVariable(name=glbs.FLEX_EVENT_DURATION, value=0, unit="s")
]
config_inputs_appendix: list[MPCVariable] = [
MPCVariable(name="in_provision", value=False),
]
weights: list[MPCVariable] = pydantic.Field(
default=[], description="Name and value of weights",
)
model_config = ConfigDict(json_encoders={MPCVariable: lambda v: v.dict()})
[docs]class NFMPCData(BaseMPCData):
"""Data class for PF-MPC"""
# files and paths
results_suffix: str = "_neg_flex.csv"
name_of_created_file: str = "neg_flex.json"
# modules
module_types: dict = cmng.SHADOW_MODULE_TYPE_DICT
class_name: str = "NegFlexModel"
module_id: str = "NegFlexMPC"
# variables
power_alias: str = glbs.POWER_ALIAS_NEG
stored_energy_alias: str = glbs.STORED_ENERGY_ALIAS_NEG
flex_cost_function: str = pydantic.Field(
default=None, description="Cost function of the NF-MPC",
)
# initialize market parameters with dummy values (0)
config_parameters_appendix: list[MPCVariable] = [
MPCVariable(name=glbs.PREP_TIME, value=0, unit="s"),
MPCVariable(name=glbs.MARKET_TIME, value=0, unit="s"),
MPCVariable(name=glbs.FLEX_EVENT_DURATION, value=0, unit="s")
]
config_inputs_appendix: list[MPCVariable] = [
MPCVariable(name="in_provision", value=False),
]
weights: list[MPCVariable] = pydantic.Field(
default=[], description="Name and value of weights",
)
model_config = ConfigDict(json_encoders={MPCVariable: lambda v: v.dict()})