mirror of
https://github.com/Akkudoktor-EOS/EOS.git
synced 2026-11-13 01:03:45 +01:00
Bobby NoelteandClaude Opus 5.5
faf37abfe7
An inverter profile describes the technical data of an inverter model: its nominal AC power, the DC→AC and AC→DC efficiency curves over the load fraction (AC power relative to the nominal power), the AC charge power limit of the model, whether that limit also limits PV charging, and an optional power derating over the ambient temperature. Curves are read linearly and clamped at their ends, so every lookup yields a value. The packaged profile generic-hybrid has typical hybrid inverter efficiencies, no nominal power (the inverter device has to provide it) and full power up to 45 degC. Inverter settings name their profile (inverter_profile, default generic-hybrid) and hold the differences of the unit in profile_adjustment: nominal_power_w (replaces the profile value, required for a generic profile), max_power_w and max_ac_charge_power_w (installation limits that can only lower; an AC charge limit of 0 disables AC charging). The effective parameters are resolved at the ambient temperature of the inverter, given by the new measurement key <device_id>-temperature-degc (45 degC without a fresh value); the power derating lowers the maximum power and the AC charge limit. Optimization parameters are always valid: the GENETIC and GENETIC0 parameter preparation resolve the inverter with resolve_current_operating_parameters_or_defaults, which never raises. An unusable profile gives the generic-hybrid profile, a missing nominal power the fallback of 10000 W, an inverter derated to zero power the parameters at 45 degC, and no usable profile at all the profile adjustment with constant efficiency; each is logged as error. The GENETIC inverter takes the AC→DC efficiency curve of the profile, solved exactly on each linear piece of the curve (about 2 % longer runs); GENETIC0 keeps constant efficiencies and no longer replaces the configured inverters with the demo inverter; a battery, electric vehicle or inverter that cannot be prepared is left out of the GENETIC0 run, never replaced by a demo device. Configurations migrate: max_power_w becomes profile_adjustment.nominal_power_w, max_ac_charge_power_w moves into the adjustment, an ac_to_dc_efficiency of 0 keeps AC charging disabled, and the efficiency fields are dropped with an error log, as for batteries, so the profile efficiencies apply. Home Assistant config paths follow. Additional changes - Move the curve algorithms of the battery profile into profilecurve, so battery and inverter profiles share them; the battery profile keeps its document format and classes. - Move ProfileBaseModel, the strict base of profile documents, into profilestore. - Serve inverter profiles through the profile REST API. Refuse to delete a profile that a configured device uses and that has no packaged profile to revert to (409). - Share the measurement access of the device settings in devicemeasurement. - Rename check_battery_profiles to check_device_profiles; the device profile check at configuration setup covers inverters, too. - Document inverter profiles, their configuration, defaults and sharing; regenerate the configuration and OpenAPI documentation. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Assisted-by: Claude Code (claude-opus-5-5)
469 lines
18 KiB
Python
469 lines
18 KiB
Python
"""Real small optimizer runs covering device contracts across the public result."""
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from collections.abc import AsyncGenerator, Callable
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from typing import Any
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import numpy as np
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import pytest
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import pytest_asyncio
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from akkudoktoreos.config.config import ConfigEOS
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from akkudoktoreos.core.coreabc import get_ems, get_measurement
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from akkudoktoreos.core.emplan import DDBCInstruction
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from akkudoktoreos.measurement.measurement import Measurement
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from akkudoktoreos.optimization.genetic.configrequest import ConfigOptimizationRequest
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from akkudoktoreos.optimization.genetic.genetic import GeneticOptimization
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from akkudoktoreos.optimization.genetic.geneticparams import (
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GeneticOptimizationParameters,
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)
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from akkudoktoreos.optimization.genetic.geneticsolution import GeneticSolution
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from akkudoktoreos.utils.datetimeutil import DateTime, to_datetime
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@pytest.fixture(autouse=True, params=["UTC", "Europe/Berlin"])
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def local_clock(request: pytest.FixtureRequest, set_other_timezone: Callable[[str], str]) -> None:
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"""Run the same local-wall-clock schedules in UTC and a DST-observing zone.
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Scenario dates intentionally have no fixed offset: each names local midnight,
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a local time window or a local departure in the selected timezone.
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"""
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set_other_timezone(request.param)
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@pytest_asyncio.fixture
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async def isolated_measurement(config_eos: ConfigEOS) -> AsyncGenerator[Measurement, None]:
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"""Keep synthetic records out of the process-wide measurement singleton."""
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measurement = get_measurement()
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await measurement.delete_by_datetime(None, None)
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try:
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yield measurement
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finally:
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await measurement.delete_by_datetime(None, None)
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def configure(
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config: ConfigEOS,
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*,
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hours: int = 4,
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start: str = "2026-09-16T00:00:00",
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marketing: bool = False,
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) -> None:
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config.merge_settings_from_dict(
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{
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"prediction": {"hours": max(48, hours)},
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"optimization": {
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"algorithm": "GENETIC",
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"genetic": {
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"horizon_hours": hours,
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"tail_horizon_hours": 0,
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"interval_sec": 900,
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"individuals": 12,
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"generations": 10,
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"terminal_value_mode": "FIXED",
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},
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},
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"feedintariff": {"direct_marketing_enabled": marketing},
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}
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)
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get_ems(init=True).set_start_datetime(to_datetime(start))
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def parameters(
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*, hours: int = 4, consumers: list[dict[str, Any]] | None = None, **kwargs: Any
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) -> GeneticOptimizationParameters:
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slots = hours * 4 + get_ems().start_datetime.hour * 4 + get_ems().start_datetime.minute // 15
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return GeneticOptimizationParameters.model_validate(
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{
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"ems": {
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"pv_prognose_wh": [0.0] * slots,
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"gesamtlast": [0.0] * slots,
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"strompreis_euro_pro_wh": [0.0003] * slots,
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"einspeiseverguetung_euro_pro_wh": [0.0001] * slots,
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"preis_euro_pro_wh_akku": 0.0,
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},
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"inverter": {"device_id": "inv", "max_power_wh": 10000},
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"forecast_interval_seconds": 900,
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"home_appliances": consumers,
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"pv_battery": None,
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"ev": None,
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**kwargs,
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}
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)
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def run(params: GeneticOptimizationParameters) -> tuple[GeneticOptimization, GeneticSolution]:
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optimizer = GeneticOptimization(fixed_seed=42)
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solution = optimizer.optimize_ems(params, ngen=1, individuals=12)
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return optimizer, solution
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def run_local_starts(solution: GeneticSolution) -> list[DateTime]:
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"""Compare scheduled instants in the run zone, independently of output zone."""
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timezone = get_ems().start_datetime.timezone_name
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assert timezone is not None
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return [moment.in_timezone(timezone) for moment in solution.appliance_starts["washer"]]
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def profile(**kwargs: Any) -> dict[str, Any]:
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return {
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"device_id": "washer",
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"load_profile_power_w": [1200.0, 600.0, 300.0],
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"load_profile_interval_seconds": 600,
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**kwargs,
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}
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def test_real_optimizer_keeps_reverse_cycle_window_identity(config_eos: ConfigEOS) -> None:
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configure(config_eos)
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consumer = profile(
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num_cycles=2,
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time_windows={
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"windows": [
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{"start_time": "02:00", "duration": "30 minutes", "value": 0},
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{"start_time": "01:00", "duration": "30 minutes", "value": 1},
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]
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},
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)
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_, solution = run(parameters(consumers=[consumer]))
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assert [value.hour for value in run_local_starts(solution)] == [1, 2]
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assert sum(solution.result.home_appliance_energy_wh["washer"]) == pytest.approx(700.0)
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assert sum(solution.result.grid_consumption_wh_per_hour) == pytest.approx(700.0)
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assert solution.result.total_costs == pytest.approx(0.21)
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def test_real_daily_optimizer_skips_completed_cycles_only_on_first_day(
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config_eos: ConfigEOS,
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) -> None:
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configure(config_eos, hours=28)
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consumer = profile(
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num_cycles=2,
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completed_cycles=1,
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schedule_mode="DAILY",
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time_windows={
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"windows": [
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{"start_time": "01:00", "duration": "30 minutes", "value": 0},
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{"start_time": "02:00", "duration": "30 minutes", "value": 1},
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]
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},
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)
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_, solution = run(parameters(hours=28, consumers=[consumer]))
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assert [(value.day, value.hour) for value in run_local_starts(solution)] == [
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(16, 2),
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(17, 1),
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(17, 2),
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]
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assert sum(solution.result.home_appliance_energy_wh["washer"]) == pytest.approx(1050.0)
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def test_real_best_effort_multicycle_prioritizes_delay_over_cheaper_prices(
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config_eos: ConfigEOS,
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) -> None:
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configure(config_eos)
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consumer = profile(num_cycles=2, min_cycle_gap_h=1, deadline_datetime="2026-09-15T23:00:00")
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params = parameters(consumers=[consumer])
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params.ems.electricity_price_per_wh = [0.001] * 8 + [-0.001] * 8
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_, solution = run(params)
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assert [(value.hour, value.minute) for value in run_local_starts(solution)] == [
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(0, 0),
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(1, 30),
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]
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assert solution.appliance_deadline_missed["washer"]
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assert sum(solution.result.home_appliance_energy_wh["washer"]) == pytest.approx(700.0)
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def test_real_mixed_best_effort_cycle_status_survives_later_strict_cycle(
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config_eos: ConfigEOS,
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) -> None:
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configure(config_eos)
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consumer = profile(
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num_cycles=2,
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deadline_datetime="2026-09-16T01:00:00",
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time_windows={
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"windows": [
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{"start_time": "02:00", "duration": "2 hours", "value": 0},
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{"start_time": "00:00", "duration": "30 minutes", "value": 1},
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]
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},
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)
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params = parameters(consumers=[consumer])
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params.ems.electricity_price_per_wh = [0.001] * 12 + [-0.001] * 4
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_, solution = run(params)
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assert [(value.hour, value.minute) for value in run_local_starts(solution)] == [
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(0, 0),
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(2, 0),
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]
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assert solution.appliance_deadline_missed["washer"]
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def test_real_warm_start_handles_changed_completed_cycle_layout(config_eos: ConfigEOS) -> None:
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configure(config_eos)
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consumer = profile(
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num_cycles=2,
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time_windows={
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"windows": [
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{"start_time": "01:00", "duration": "30 minutes", "value": 0},
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{"start_time": "02:00", "duration": "30 minutes", "value": 1},
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]
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},
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)
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optimizer, first = run(parameters(consumers=[consumer]))
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consumer["completed_cycles"] = 1
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followup = parameters(
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consumers=[consumer],
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start_solution=first.start_solution,
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start_solution_datetime=first.start_solution_datetime,
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)
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second = optimizer.optimize_ems(followup, ngen=1, individuals=12)
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assert len(second.appliance_starts["washer"]) == 1
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assert run_local_starts(second)[0].hour == 2
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assert sum(second.result.home_appliance_energy_wh["washer"]) == pytest.approx(350.0)
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assert first.start_solution is not None and second.start_solution is not None
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assert len(second.start_solution) == len(first.start_solution) - 1
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@pytest.mark.parametrize("deadline", ["2026-09-15T23:00:00", "2026-09-16T00:01:00"])
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def test_real_ev_does_not_credit_energy_delivered_after_departure(
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config_eos: ConfigEOS, deadline: str
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) -> None:
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configure(config_eos)
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params = parameters(
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ev={
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"device_id": "car",
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"capacity_wh": 4000,
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"initial_soc_percentage": 0,
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"min_soc_percentage": 25,
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"charging_efficiency": 1.0,
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"max_charge_power_w": 4000,
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"charge_rates": [0.0, 1.0],
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"min_soc_deadline_datetime": deadline,
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}
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)
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optimizer, solution = run(params)
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assert optimizer._ev_soc_deadline_slot == 0
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assert (
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optimizer._ev_soc_at_deadline({"EAuto_SoC_pro_Stunde": solution.result.ev_soc_per_hour}, 0)
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== 0.0
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)
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def test_real_ev_target_across_midnight_uses_elapsed_slots(config_eos: ConfigEOS) -> None:
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configure(config_eos, start="2026-09-16T23:30:00")
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params = parameters(
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ev={
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"device_id": "car",
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"capacity_wh": 4000,
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"initial_soc_percentage": 0,
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"min_soc_percentage": 50,
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"charging_efficiency": 1.0,
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"max_charge_power_w": 4000,
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"charge_rates": [0.0, 1.0],
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"min_soc_deadline_datetime": "2026-09-17T00:00:00",
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}
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)
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params.ems.electricity_price_per_wh[-16:] = [0.001] * 2 + [0.00001] * 14
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optimizer, solution = run(params)
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assert optimizer._ev_soc_deadline_slot == 2
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assert solution.result.ev_soc_per_hour[2] >= 50
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assert solution.ev_charge_hours_float is not None
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assert solution.ev_charge_hours_float[:2] == [1.0, 1.0]
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@pytest.mark.parametrize(
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"marketing,lcos,export_expected",
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[(False, 0.0, False), (True, 0.0, True), (True, 0.2, True), (True, 2.0, False)],
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)
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def test_real_export_respects_marketing_gate_and_storage_cost(
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config_eos: ConfigEOS, marketing: bool, lcos: float, export_expected: bool
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) -> None:
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configure(config_eos, marketing=marketing)
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params = parameters(
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pv_battery={
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"device_id": "battery",
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"capacity_wh": 1000,
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"initial_soc_percentage": 100,
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"charging_efficiency": 1.0,
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"discharging_efficiency": 1.0,
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"min_soc_percentage": 0,
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"max_charge_power_w": 4000,
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"grid_export_rates": [0.5, 1.0],
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"levelized_cost_of_storage_kwh": lcos,
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},
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inverter={"device_id": "inv", "max_power_wh": 10000, "battery_id": "battery"},
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)
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params.ems.feed_in_tariff_per_wh = [0.00001] + [0.001] * 4 + [0.00001] * 11
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_, solution = run(params)
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exported = sum(solution.result.grid_feed_in_wh_per_hour)
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if export_expected:
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assert exported == pytest.approx(1000.0)
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assert solution.result.total_revenue == pytest.approx(1.0)
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assert solution.result.total_costs == pytest.approx(lcos)
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assert any(solution.battery_grid_export_allowed)
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else:
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assert exported == pytest.approx(0.0)
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assert not any(solution.battery_grid_export_allowed)
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assert np.isfinite(solution.result.total_balance)
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@pytest.mark.asyncio
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@pytest.mark.parametrize("custom_key", [None, "washer.completed_today"])
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async def test_real_measurement_completed_cycles_reach_request_and_optimizer(
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config_eos: ConfigEOS, custom_key: str | None, isolated_measurement: Measurement
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) -> None:
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configure(config_eos)
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config_eos.merge_settings_from_dict(
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{
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"devices": {
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"max_batteries": 0,
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"batteries": {},
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"max_electric_vehicles": 0,
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"electric_vehicles": {},
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"max_inverters": 1,
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"inverters": {"inv": {"profile_adjustment": {"nominal_power_w": 10000}}},
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"max_home_appliances": 1,
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"home_appliances": {
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"washer": profile(
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num_cycles=2,
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cycles_completed_measurement_key=custom_key,
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cycle_time_windows={
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"windows": [
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{"start_time": "01:00", "duration": "30 minutes", "value": 0},
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{"start_time": "02:00", "duration": "30 minutes", "value": 1},
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]
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},
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)
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},
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}
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}
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)
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key = custom_key or "washer.cycles_completed"
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assert key in config_eos.devices.measurement_keys
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measurement = isolated_measurement
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zero = get_ems().start_datetime
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await measurement.update_value(zero.subtract(days=1), key, 2.0)
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await measurement.update_value(zero, key, 1.0)
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await measurement.update_value(zero.add(seconds=1), key, 2.0)
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dates, counts = await measurement.key_to_lists(
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key=key, start_datetime=zero, end_datetime=zero.add(seconds=1)
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)
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assert len(dates) == 1 and counts == [1.0]
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prepared = await ConfigOptimizationRequest.model_validate(
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{
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"forecasts": {
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"pv_forecast_wh": [0.0] * 16,
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"total_load": [0.0] * 16,
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"electricity_price_per_wh": [0.0003] * 16,
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"feed_in_tariff_per_wh": [0.0001] * 16,
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}
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}
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).resolve()
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assert prepared.home_appliances is not None
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assert prepared.home_appliances[0].completed_cycles == 1
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_, solution = run(prepared)
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assert [start.hour for start in run_local_starts(solution)] == [2]
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assert sum(solution.result.home_appliance_energy_wh["washer"]) == pytest.approx(350.0)
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@pytest.mark.asyncio
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async def test_real_multiple_consumers_keep_separate_solution_channels(
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config_eos: ConfigEOS,
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) -> None:
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configure(config_eos)
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consumers = [
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profile(
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shared_time_windows={"windows": [{"start_time": "01:00", "duration": "30 minutes"}]}
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),
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profile(
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device_id="dryer",
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load_profile_power_w=[2000.0],
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load_profile_interval_seconds=900,
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shared_time_windows={"windows": [{"start_time": "01:00", "duration": "15 minutes"}]},
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),
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]
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_, solution = run(parameters(consumers=consumers))
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assert sum(solution.result.home_appliance_energy_wh["washer"]) == pytest.approx(350.0)
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assert sum(solution.result.home_appliance_energy_wh["dryer"]) == pytest.approx(500.0)
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assert sum(solution.result.grid_consumption_wh_per_hour) == pytest.approx(850.0)
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exported = await solution.optimization_solution()
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table = exported.solution.to_dataframe()
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assert table["washer_energy_wh"].sum() == pytest.approx(350.0)
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assert table["dryer_energy_wh"].sum() == pytest.approx(500.0)
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assert table.index[4].hour == 1
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assert table["washer_run_op_mode"].tolist()[4:6] == [1.0, 1.0]
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assert table["dryer_run_op_mode"].tolist()[4:6] == [1.0, 0.0]
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@pytest.mark.asyncio
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async def test_profile_zero_power_phase_keeps_device_running_until_complete(
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config_eos: ConfigEOS,
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) -> None:
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configure(config_eos)
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consumer = profile(
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load_profile_power_w=[1200.0, 0.0, 1200.0],
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load_profile_interval_seconds=900,
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shared_time_windows={"windows": [{"start_time": "01:00", "duration": "45 minutes"}]},
|
|
)
|
|
_, solution = run(parameters(consumers=[consumer]))
|
|
exported = await solution.optimization_solution()
|
|
table = exported.solution.to_dataframe()
|
|
assert table["washer_energy_wh"].tolist()[4:7] == [300.0, 0.0, 300.0]
|
|
assert table["washer_run_op_mode"].tolist()[4:7] == [1.0, 1.0, 1.0]
|
|
plan = solution.energy_management_plan()
|
|
assert {item.resource_id for item in plan.instructions} == {"washer"}
|
|
commands = [
|
|
(item.execution_time.hour, item.execution_time.minute, str(item.operation_mode_id))
|
|
for item in plan.instructions
|
|
if isinstance(item, DDBCInstruction)
|
|
]
|
|
assert commands == [(0, 0, "OFF"), (1, 0, "RUN"), (1, 45, "OFF")]
|
|
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_meter_only_record_does_not_hide_completed_cycles(
|
|
config_eos: ConfigEOS, isolated_measurement: Measurement
|
|
) -> None:
|
|
"""A newer record without a cycle count (meter reading) must not abort the run (#1447)."""
|
|
configure(config_eos)
|
|
config_eos.merge_settings_from_dict(
|
|
{
|
|
"measurement": {"load_emr_keys": ["load_meter"]},
|
|
"devices": {
|
|
"max_batteries": 0,
|
|
"batteries": {},
|
|
"max_electric_vehicles": 0,
|
|
"electric_vehicles": {},
|
|
"max_inverters": 1,
|
|
"inverters": {"inv": {"profile_adjustment": {"nominal_power_w": 10000}}},
|
|
"max_home_appliances": 1,
|
|
"home_appliances": {
|
|
"washer": profile(
|
|
num_cycles=2,
|
|
cycle_time_windows={
|
|
"windows": [
|
|
{"start_time": "01:00", "duration": "30 minutes", "value": 0},
|
|
{"start_time": "02:00", "duration": "30 minutes", "value": 1},
|
|
]
|
|
},
|
|
)
|
|
},
|
|
},
|
|
}
|
|
)
|
|
run_time = get_ems().start_datetime.add(minutes=26)
|
|
get_ems().set_start_datetime(run_time, interval_seconds=900)
|
|
measurement = isolated_measurement
|
|
# Cycle count through the measurement API, then the adapter's meter reading.
|
|
await measurement.update_value(run_time.subtract(seconds=30), "washer.cycles_completed", 1.0)
|
|
await measurement.update_value(run_time, "load_meter", 1234.5)
|
|
prepared = await ConfigOptimizationRequest.model_validate(
|
|
{
|
|
"forecasts": {
|
|
"pv_forecast_wh": [0.0] * 20,
|
|
"total_load": [0.0] * 20,
|
|
"electricity_price_per_wh": [0.0003] * 20,
|
|
"feed_in_tariff_per_wh": [0.0001] * 20,
|
|
}
|
|
}
|
|
).resolve()
|
|
assert prepared.home_appliances is not None
|
|
assert prepared.home_appliances[0].completed_cycles == 1
|