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EOS/src/akkudoktoreos/optimization/genetic/geneticdevices.py
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AndreasandClaude Opus 5 f24d9ea0eb feat(optimization): deadlines for consumers and EV, graded grid export
Three related scheduling improvements, all opt-in and behaviour-preserving
when the new fields are not set.

Flexible consumers get absolute time bounds next to the recurring
time_windows: earliest_start_datetime and deadline_datetime, where the
deadline requires the complete run to have *finished* before that moment
("clean dishes by 03:00 tonight"). When no start can meet it,
deadline_policy decides between BEST_EFFORT (run as early as possible, so
the delay rather than the cost is minimized) and STRICT (keep the
deadline; a ONCE consumer then fails the optimization). The solution
reports appliance_deadline_missed per device.

The EV charging target can be given the same kind of deadline, as an
absolute min_soc_deadline_datetime and/or a relative min_soc_max_duration_h
("full in 6 hours"), the earlier of the two winning. The ev_soc_miss
penalty is then evaluated at that slot instead of at the end of the
horizon, and the seeding heuristic only proposes charge slots before it.

Battery-to-grid export under direct marketing is no longer all-or-nothing:
grid_export_rates configures the selectable export levels as a factor of
the rated discharge power (default [0.25, 0.5, 0.75, 1.0]). Each rate is
its own optimizer state, with the full-power state keeping its previous
index so existing seeds and heuristics are unaffected. The chosen level
per slot is reported in battery_grid_export_factor and as the
GRID_SUPPORT_EXPORT operation factor.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-09-03 17:53:33 +02:00

384 lines
14 KiB
Python

"""Genetic optimization algorithm device interfaces/ parameters."""
from typing import Any, Optional
from pydantic import Field, field_validator, model_validator
from typing_extensions import Self
from akkudoktoreos.config.configabc import TimeWindowSequence
from akkudoktoreos.devices.devicesabc import (
ConsumerDeadlinePolicy,
ConsumerScheduleMode,
validate_home_appliance_load_definition,
)
from akkudoktoreos.optimization.genetic.geneticabc import GeneticParametersBaseModel
from akkudoktoreos.utils.datetimeutil import DateTime, compare_datetimes, to_datetime
class DeviceParameters(GeneticParametersBaseModel):
device_id: str = Field(json_schema_extra={"description": "ID of device", "examples": "device1"})
hours: Optional[int] = Field(
default=None,
gt=0,
json_schema_extra={
"description": "Number of prediction hours. Defaults to global config prediction hours.",
"examples": [None],
},
)
def max_charging_power_field(description: Optional[str] = None) -> float:
if description is None:
description = "Maximum charging power in watts."
return Field(default=5000, gt=0, json_schema_extra={"description": description})
def initial_soc_percentage_field(description: str) -> int:
return Field(
default=0, ge=0, le=100, json_schema_extra={"description": description, "examples": [42]}
)
def discharging_efficiency_field(default_value: float) -> float:
return Field(
default=default_value,
gt=0,
le=1,
json_schema_extra={
"description": "A float representing the discharge efficiency of the battery."
},
)
class BaseBatteryParameters(DeviceParameters):
"""Battery Device Simulation Configuration."""
device_id: str = Field(
json_schema_extra={"description": "ID of battery", "examples": ["battery1"]}
)
capacity_wh: int = Field(
gt=0,
json_schema_extra={
"description": "An integer representing the capacity of the battery in watt-hours.",
"examples": [8000],
},
)
charging_efficiency: float = Field(
default=0.88,
gt=0,
le=1,
json_schema_extra={
"description": "A float representing the charging efficiency of the battery."
},
)
discharging_efficiency: float = discharging_efficiency_field(0.88)
max_charge_power_w: Optional[float] = max_charging_power_field()
initial_soc_percentage: int = initial_soc_percentage_field(
"An integer representing the state of charge of the battery at the **start** of the current hour (not the current state)."
)
min_soc_percentage: int = Field(
default=0,
ge=0,
le=100,
json_schema_extra={
"description": "An integer representing the minimum state of charge (SOC) of the battery in percentage.",
"examples": [10],
},
)
max_soc_percentage: int = Field(
default=100,
ge=0,
le=100,
json_schema_extra={
"description": "An integer representing the maximum state of charge (SOC) of the battery in percentage."
},
)
charge_rates: Optional[list[float]] = Field(
default=None,
json_schema_extra={
"description": "Charge rates as factor of maximum charging power [0.00 ... 1.00]. None denotes all charge rates are available.",
"examples": [[0.0, 0.25, 0.5, 0.75, 1.0], None],
},
)
grid_export_rates: Optional[list[float]] = Field(
default=None,
json_schema_extra={
"description": (
"Battery-to-grid export rates as factor of maximum discharge "
"power ]0.00 ... 1.00]. Only used with direct marketing. None "
"falls back to the configured devices.batteries[0]."
"grid_export_rates."
),
"examples": [[0.25, 0.5, 0.75, 1.0], [1.0], None],
},
)
class SolarPanelBatteryParameters(BaseBatteryParameters):
"""PV battery device simulation configuration."""
levelized_cost_of_storage_kwh: float = Field(
default=0.0,
ge=0.0,
json_schema_extra={
"description": (
"Levelized cost of storage applied once to each kWh delivered "
"by the battery [EUR/kWh]."
),
"examples": [0.12],
},
)
max_charge_power_w: Optional[float] = max_charging_power_field()
class ElectricVehicleParameters(BaseBatteryParameters):
"""Battery Electric Vehicle Device Simulation Configuration.
``min_soc_percentage`` is the charging target. By default it only has to be
reached by the end of the optimization horizon; a deadline
(``min_soc_deadline_datetime`` and/or ``min_soc_max_duration_h``) moves that
requirement forward, for example to the next departure.
"""
device_id: str = Field(
json_schema_extra={"description": "ID of electric vehicle", "examples": ["ev1"]}
)
discharging_efficiency: float = discharging_efficiency_field(1.0)
initial_soc_percentage: int = initial_soc_percentage_field(
"An integer representing the current state of charge (SOC) of the battery in percentage."
)
min_soc_deadline_datetime: Optional[DateTime] = Field(
default=None,
json_schema_extra={
"description": (
"Absolute moment by which 'min_soc_percentage' has to be "
"reached (departure time). A date time without timezone is read "
"as local time. None means end of the optimization horizon."
),
"examples": [None, "2026-07-16T07:00:00+02:00"],
},
)
min_soc_max_duration_h: Optional[float] = Field(
default=None,
gt=0,
json_schema_extra={
"description": (
"Maximum time from the start of the optimization until "
"'min_soc_percentage' has to be reached [h]. Combined with "
"'min_soc_deadline_datetime' the earlier of the two applies."
),
"examples": [None, 6.0],
},
)
@field_validator("min_soc_deadline_datetime", mode="before")
@classmethod
def transform_deadline_to_datetime(cls, value: Any) -> Optional[DateTime]:
"""Accept the usual date time representations, naive input is local time."""
if value is None:
return None
return to_datetime(value)
class HomeApplianceParameters(DeviceParameters):
"""Flexible consumer (home appliance) device simulation configuration.
A consumer's load is defined **either** by an explicit power profile
(``load_profile_power_w`` with an optional ``load_profile_interval_seconds``)
**or** by the flat fallback ``consumption_wh`` + ``duration_h``. Exactly one
of the two must be provided.
*When* the run may happen is constrained by three independent mechanisms that
all have to hold at once:
- ``time_windows``: recurring wall-clock windows ("only between 10:00 and 13:00").
- ``earliest_start_datetime``: absolute lower bound ("not before I get home").
- ``deadline_datetime``: absolute upper bound; the run must be *finished*
before that moment ("clean dishes by 03:00 tonight").
"""
device_id: str = Field(
json_schema_extra={"description": "ID of home appliance", "examples": ["dishwasher1"]}
)
load_profile_power_w: Optional[list[float]] = Field(
default=None,
json_schema_extra={
"description": (
"Explicit load profile describing a single complete run as a "
"sequence of non-negative power values in watts. Each value "
"covers 'load_profile_interval_seconds'. Mutually exclusive with "
"consumption_wh/duration_h."
),
"examples": [[200.0, 2000.0, 1800.0, 100.0]],
},
)
load_profile_interval_seconds: Optional[int] = Field(
default=None,
gt=0,
json_schema_extra={
"description": (
"Duration of one 'load_profile_power_w' step in seconds. Defaults "
"to the configured optimization interval when a profile is given."
),
"examples": [900, 3600],
},
)
schedule_mode: ConsumerScheduleMode = Field(
default=ConsumerScheduleMode.ONCE,
json_schema_extra={
"description": (
"Scheduling mode: ONCE (a single run within the horizon) or DAILY "
"(one run per local calendar day with a feasible full run)."
),
"examples": ["ONCE", "DAILY"],
},
)
consumption_wh: Optional[int] = Field(
default=None,
gt=0,
json_schema_extra={
"description": (
"Flat fallback: total energy consumption of one run in watt-hours. "
"Used only when no load_profile_power_w is given."
),
"examples": [2000],
},
)
duration_h: Optional[int] = Field(
default=None,
gt=0,
json_schema_extra={
"description": (
"Flat fallback: run duration in hours. Used only when no "
"load_profile_power_w is given."
),
"examples": [3],
},
)
time_windows: Optional[TimeWindowSequence] = Field(
default=None,
json_schema_extra={
"description": "List of allowed time windows. Defaults to optimization general time window.",
"examples": [
[
{"start_time": "10:00", "duration": "3 hours"},
],
],
},
)
earliest_start_datetime: Optional[DateTime] = Field(
default=None,
json_schema_extra={
"description": (
"Absolute earliest moment the run may start. Starts before it are "
"dropped, in addition to 'time_windows' and the horizon. A date "
"time without timezone is read as local time. This bound is never "
"relaxed."
),
"examples": [None, "2026-07-15T20:00:00+02:00"],
},
)
deadline_datetime: Optional[DateTime] = Field(
default=None,
json_schema_extra={
"description": (
"Absolute deadline: the complete run must have *finished* at or "
"before this moment (e.g. end of the day, or 03:00 tonight). A "
"date time without timezone is read as local time. See "
"'deadline_policy' for what happens when no start can meet it."
),
"examples": [None, "2026-07-16T03:00:00+02:00"],
},
)
deadline_policy: ConsumerDeadlinePolicy = Field(
default=ConsumerDeadlinePolicy.BEST_EFFORT,
json_schema_extra={
"description": (
"What to do when 'deadline_datetime' cannot be met: BEST_EFFORT "
"runs as early as possible instead (warning logged), STRICT keeps "
"the deadline (a ONCE consumer then fails the optimization)."
),
"examples": ["BEST_EFFORT", "STRICT"],
},
)
@field_validator("earliest_start_datetime", "deadline_datetime", mode="before")
@classmethod
def transform_to_datetime(cls, value: Any) -> Optional[DateTime]:
"""Accept the usual date time representations, naive input is local time."""
if value is None:
return None
return to_datetime(value)
@model_validator(mode="after")
def validate_load_definition(self) -> Self:
"""Ensure exactly one complete, valid load definition is provided."""
validate_home_appliance_load_definition(
load_profile_power_w=self.load_profile_power_w,
load_profile_interval_seconds=self.load_profile_interval_seconds,
consumption_wh=self.consumption_wh,
duration_h=self.duration_h,
)
return self
@model_validator(mode="after")
def validate_schedule_bounds(self) -> Self:
"""Reject an empty scheduling interval."""
if self.earliest_start_datetime is not None and self.deadline_datetime is not None:
if compare_datetimes(self.deadline_datetime, self.earliest_start_datetime).le:
raise ValueError(
f"deadline_datetime {self.deadline_datetime} must be after "
f"earliest_start_datetime {self.earliest_start_datetime}."
)
return self
class InverterParameters(DeviceParameters):
"""Inverter Device Simulation Configuration."""
device_id: str = Field(
json_schema_extra={"description": "ID of inverter", "examples": ["inverter1"]}
)
max_power_wh: float = Field(gt=0, json_schema_extra={"examples": [10000]})
battery_id: Optional[str] = Field(
default=None,
json_schema_extra={"description": "ID of battery", "examples": [None, "battery1"]},
)
ac_to_dc_efficiency: float = Field(
default=1.0,
ge=0,
le=1,
json_schema_extra={
"description": (
"Efficiency of AC to DC conversion (for AC/grid charging of battery). "
"Set to 0 to disable AC charging via inverter. "
"Default 1.0 for backward compatibility (no additional inverter loss)."
),
"examples": [0.95, 1.0, 0.0],
},
)
dc_to_ac_efficiency: float = Field(
default=1.0,
gt=0,
le=1,
json_schema_extra={
"description": (
"Efficiency of DC to AC conversion (for battery discharging to AC load/grid). "
"Default 1.0 for backward compatibility (no additional inverter loss)."
),
"examples": [0.95, 1.0],
},
)
max_ac_charge_power_w: Optional[float] = Field(
default=None,
ge=0,
json_schema_extra={
"description": (
"Maximum AC charging power in watts. "
"None means no additional limit (battery's own max_charge_power_w applies). "
"Set to 0 to disable AC charging."
),
"examples": [None, 0, 5000],
},
)