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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)
255 lines
10 KiB
Python
255 lines
10 KiB
Python
"""Tests for inverters whose AC charge setpoint caps the total battery charge.
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With ``ac_charge_limits_total_charge`` an AC slot charges the battery with at
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most ``ac_charge x max_charge_power_w``, PV included (e.g. Deye time-of-use grid
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charging). PV surplus above that cap is exported; the grid only fills what PV
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leaves of the cap. Without the flag the default model applies: PV charges first
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and the grid adds ``ac_charge`` of the remaining charge power on top.
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"""
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import dataclasses
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from unittest.mock import Mock, patch
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import numpy as np
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import pytest
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from akkudoktoreos.devices.genetic.battery import Battery, SolarPanelBatteryParameters
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from akkudoktoreos.devices.genetic.inverter import Inverter, InverterParameters
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from akkudoktoreos.devices.profile.inverterprofile import InverterProfileSettings
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from akkudoktoreos.devices.profile.profilestore import get_profile_store
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from akkudoktoreos.devices.settings.invertersettings import (
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InverterCommonSettings,
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InverterProfileAdjustment,
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)
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MAX_CHARGE_W = 5000
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def _build(limits_total: bool, max_ac_charge_power_w=None) -> tuple[Inverter, Battery]:
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battery = Battery(
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SolarPanelBatteryParameters(
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device_id="battery1",
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capacity_wh=20000,
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initial_soc_percentage=20,
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charging_efficiency=0.9,
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discharging_efficiency=0.9,
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min_soc_percentage=0,
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max_soc_percentage=100,
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max_charge_power_w=MAX_CHARGE_W,
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),
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prediction_hours=4,
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)
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battery.reset()
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# reset() creates an integer array; charge factors are fractions.
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battery.charge_array = np.zeros(4)
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predictor = Mock()
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predictor.calculate_expected_direct_consumption.side_effect = min
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with patch(
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"akkudoktoreos.devices.genetic.inverter.get_eos_load_interpolator",
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return_value=predictor,
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):
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inverter = Inverter(
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InverterParameters(
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device_id="inverter1",
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max_power_wh=10000,
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battery_id="battery1",
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ac_to_dc_efficiency=1.0,
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max_ac_charge_power_w=max_ac_charge_power_w,
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ac_charge_limits_total_charge=limits_total,
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),
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battery=battery,
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)
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return inverter, battery
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def _run_ac_slot(inverter: Inverter, battery: Battery, pv_wh: float, factor: float):
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"""Run one AC slot the way GeneticSimulation.simulate() does."""
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battery.charge_array[0] = factor
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battery.discharge_array[0] = 0
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rate = inverter.ac_charge_factor(factor)
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inverter.begin_ac_charge_slot(0, rate)
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export, grid_import, _, _ = inverter.process_energy(pv_wh, 0.0, 0)
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ac_wh, _ = inverter.charge_battery_from_grid(0, rate)
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return export, grid_import + ac_wh, battery._charged_raw_wh_per_slot[0]
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class TestPvSurplusAboveCap:
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"""PV alone exceeds the AC setpoint - the Deye situation that exported PV."""
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def test_total_limit_exports_pv_above_the_cap(self):
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inverter, battery = _build(limits_total=True)
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export, grid, charged_raw = _run_ac_slot(inverter, battery, pv_wh=6000, factor=0.5)
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assert charged_raw == pytest.approx(0.5 * MAX_CHARGE_W)
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assert export == pytest.approx(6000 - 0.5 * MAX_CHARGE_W)
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assert grid == pytest.approx(0.0)
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def test_default_model_stores_pv_up_to_full_charge_power(self):
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inverter, battery = _build(limits_total=False)
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export, grid, charged_raw = _run_ac_slot(inverter, battery, pv_wh=6000, factor=0.5)
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assert charged_raw == pytest.approx(MAX_CHARGE_W)
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assert export == pytest.approx(6000 - MAX_CHARGE_W)
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assert grid == pytest.approx(0.0)
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class TestPvSurplusBelowCap:
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"""PV does not reach the setpoint - the grid tops up."""
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def test_total_limit_grid_fills_up_to_the_cap(self):
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inverter, battery = _build(limits_total=True)
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export, grid, charged_raw = _run_ac_slot(inverter, battery, pv_wh=1000, factor=0.5)
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assert charged_raw == pytest.approx(0.5 * MAX_CHARGE_W)
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assert grid == pytest.approx(0.5 * MAX_CHARGE_W - 1000)
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assert export == pytest.approx(0.0)
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def test_default_model_grid_adds_factor_of_remaining_power(self):
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inverter, battery = _build(limits_total=False)
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export, grid, charged_raw = _run_ac_slot(inverter, battery, pv_wh=1000, factor=0.5)
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assert grid == pytest.approx(0.5 * (MAX_CHARGE_W - 1000))
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assert charged_raw == pytest.approx(1000 + 0.5 * (MAX_CHARGE_W - 1000))
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assert export == pytest.approx(0.0)
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def test_max_ac_charge_power_caps_the_total_charge():
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"""max_ac_charge_power_w lowers the setpoint and with it the total cap."""
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inverter, battery = _build(limits_total=True, max_ac_charge_power_w=2000)
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export, grid, charged_raw = _run_ac_slot(inverter, battery, pv_wh=6000, factor=1.0)
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assert charged_raw == pytest.approx(2000)
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assert export == pytest.approx(4000)
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assert grid == pytest.approx(0.0)
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def test_dc_slot_is_not_capped():
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"""Only AC slots are capped: a DC slot takes PV up to max_charge_power_w."""
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inverter, battery = _build(limits_total=True)
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battery.charge_array[0] = 1
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battery.discharge_array[0] = 0
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export, _, _, _ = inverter.process_energy(6000, 0.0, 0)
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assert battery._charged_raw_wh_per_slot[0] == pytest.approx(MAX_CHARGE_W)
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assert export == pytest.approx(1000)
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def test_reset_lifts_the_slot_cap():
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inverter, battery = _build(limits_total=True)
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_run_ac_slot(inverter, battery, pv_wh=6000, factor=0.5)
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battery.reset()
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battery.charge_array = np.zeros(4)
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battery.charge_array[0] = 1
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inverter.process_energy(6000, 0.0, 0)
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assert battery._charged_raw_wh_per_slot[0] == pytest.approx(MAX_CHARGE_W)
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class TestGeneticSimulation:
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"""The flag changes the simulated plan, so the optimizer can prefer DC."""
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@pytest.fixture
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def simulate(self, config_eos):
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from akkudoktoreos.optimization.genetic.genetic import GeneticSimulation
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from akkudoktoreos.optimization.genetic.geneticparams import (
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GeneticEnergyManagementParameters,
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)
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config_eos.merge_settings_from_dict(
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{"prediction": {"hours": 48}, "optimization": {"hours": 24}}
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)
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hours = config_eos.prediction.hours
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def _simulate(limits_total: bool, ac_factor: float, dc_factor: float):
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battery = Battery(
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SolarPanelBatteryParameters(
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device_id="battery1",
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capacity_wh=30000,
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initial_soc_percentage=20,
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charging_efficiency=0.9,
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discharging_efficiency=0.9,
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min_soc_percentage=0,
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max_soc_percentage=100,
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max_charge_power_w=MAX_CHARGE_W,
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),
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prediction_hours=hours,
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)
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battery.reset()
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inverter = Inverter(
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InverterParameters(
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device_id="inverter1",
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max_power_wh=10000,
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battery_id="battery1",
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ac_charge_limits_total_charge=limits_total,
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),
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battery=battery,
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)
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sim = GeneticSimulation()
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sim.prepare(
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GeneticEnergyManagementParameters.model_validate(
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dict(
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pv_prognose_wh=[8000.0] * hours,
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strompreis_euro_pro_wh=[0.0003] * hours,
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einspeiseverguetung_euro_pro_wh=0.00008,
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preis_euro_pro_wh_akku=0.0001,
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gesamtlast=[0.0] * hours,
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)
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),
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optimization_hours=config_eos.optimization.genetic.horizon_hours,
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prediction_hours=hours,
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inverter=inverter,
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ev=None,
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home_appliance=None,
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)
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ac_hours, dc_hours = sim.ac_charge_hours, sim.dc_charge_hours
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discharge_hours = sim.bat_discharge_hours
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assert ac_hours is not None and dc_hours is not None and discharge_hours is not None
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ac_hours[:] = 0
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dc_hours[:] = 0
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discharge_hours[:] = 0
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ac_hours[1] = ac_factor
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dc_hours[1] = dc_factor
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return sim.simulate(start_hour=0)
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return _simulate
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def test_ac_slot_under_pv_surplus_exports_more_than_dc(self, simulate):
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ac = simulate(limits_total=True, ac_factor=0.5, dc_factor=0)
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dc = simulate(limits_total=True, ac_factor=0, dc_factor=1)
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assert ac["Netzeinspeisung_Wh_pro_Stunde"][1] == pytest.approx(
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dc["Netzeinspeisung_Wh_pro_Stunde"][1] + 0.5 * MAX_CHARGE_W
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)
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assert ac["akku_soc_pro_stunde"][2] < dc["akku_soc_pro_stunde"][2]
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def test_default_model_treats_ac_slot_like_dc_under_pv_surplus(self, simulate):
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ac = simulate(limits_total=False, ac_factor=0.5, dc_factor=0)
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dc = simulate(limits_total=False, ac_factor=0, dc_factor=1)
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assert ac["Netzeinspeisung_Wh_pro_Stunde"][1] == pytest.approx(
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dc["Netzeinspeisung_Wh_pro_Stunde"][1]
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)
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class TestConfigOption:
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"""The flag is a property of the inverter profile that reaches the GENETIC optimizer."""
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@pytest.mark.parametrize("enabled", [False, True])
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def test_profile_passes_the_flag_to_the_optimizer(self, config_eos, monkeypatch, enabled):
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generic = get_profile_store().get(InverterProfileSettings, "generic-hybrid")
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profile = dataclasses.replace(generic, ac_charge_limits_total_charge=enabled)
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monkeypatch.setattr(InverterCommonSettings, "get_inverter_profile", lambda self: profile)
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settings = InverterCommonSettings(
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device_id="inverter",
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battery_id="battery1",
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profile_adjustment=InverterProfileAdjustment(nominal_power_w=10000),
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)
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assert settings.to_genetic_param().ac_charge_limits_total_charge is enabled
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def test_default_profile_keeps_the_existing_model(self, config_eos):
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settings = InverterCommonSettings(
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device_id="inverter",
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profile_adjustment=InverterProfileAdjustment(nominal_power_w=10000),
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)
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assert settings.to_genetic_param().ac_charge_limits_total_charge is False
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