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EOS/tests/test_genticinverterefficiencycurve.py
T
Bobby NoelteandClaude Opus 5.5 faf37abfe7 feat(devices): add inverter profiles 
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)
2026-11-11 22:08:18 +01:00

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"""Tests for the load-dependent efficiency curves of the GENETIC inverter.
``dc_to_ac_efficiency_curve`` replaces the constant ``dc_to_ac_efficiency`` by a
curve over the load fraction (AC power / rated power). Real inverters are much
less efficient at low load, which matters for night-time discharge into a small
base load. ``ac_to_dc_efficiency_curve`` does the same for charging the battery
from the grid. Without a curve the behaviour must stay exactly the legacy one.
"""
from types import SimpleNamespace
from unittest.mock import Mock, patch
import numpy as np
import pytest
from pydantic import ValidationError
from akkudoktoreos.devices.devicesabc import (
interpolate_efficiency_curve,
solve_ac_for_charge_dc_energy,
solve_ac_for_dc_energy,
validate_efficiency_curve,
)
from akkudoktoreos.devices.genetic.battery import Battery, SolarPanelBatteryParameters
from akkudoktoreos.devices.genetic.inverter import Inverter, InverterParameters
from akkudoktoreos.devices.settings.invertersettings import (
InverterCommonSettings,
InverterProfileAdjustment,
)
from akkudoktoreos.optimization.genetic.genetic import GeneticSimulation
from akkudoktoreos.optimization.genetic.geneticparams import (
GeneticEnergyManagementParameters,
)
from akkudoktoreos.optimization.genetic.tailvalue import build_tail_value_curve
from akkudoktoreos.optimization.genetic.terminalvalue import TerminalValueCurve
CURVE = [(0.05, 0.80), (0.20, 0.90), (0.50, 0.95), (1.0, 0.93)]
def _make_inverter(
curve=None,
max_power_wh: float = 1000.0,
battery=None,
dc_to_ac_efficiency: float = 1.0,
**kwargs,
) -> Inverter:
predictor = Mock()
predictor.calculate_expected_direct_consumption.side_effect = min
with patch(
"akkudoktoreos.devices.genetic.inverter.get_eos_load_interpolator",
return_value=predictor,
):
return Inverter(
InverterParameters(
device_id="inverter1",
max_power_wh=max_power_wh,
battery_id=battery.parameters.device_id if battery else None,
dc_to_ac_efficiency=dc_to_ac_efficiency,
dc_to_ac_efficiency_curve=curve,
**kwargs,
),
battery=battery,
)
def _passthrough_battery() -> Mock:
"""Battery mock that delivers exactly the requested DC energy."""
battery = Mock()
battery.parameters.device_id = "battery1"
battery.charge_energy = Mock(return_value=(0.0, 0.0))
battery.discharge_energy = Mock(side_effect=lambda dc, hour, **kw: (dc, 0.0))
return battery
# ---------------------------------------------------------------------------
# Validation
# ---------------------------------------------------------------------------
class TestCurveValidation:
def test_none_is_allowed(self):
assert validate_efficiency_curve(None) is None
params = InverterParameters(device_id="inverter1", max_power_wh=1000)
assert params.dc_to_ac_efficiency_curve is None
assert params.dc_to_ac_efficiency_reference_load_fraction == pytest.approx(0.06)
def test_valid_curve_is_accepted(self):
params = InverterParameters(
device_id="inverter1", max_power_wh=1000, dc_to_ac_efficiency_curve=CURVE
)
assert params.dc_to_ac_efficiency_curve == CURVE
def test_json_lists_are_accepted(self):
params = InverterParameters.model_validate(
{
"device_id": "inverter1",
"max_power_wh": 1000,
"dc_to_ac_efficiency_curve": [[0.0, 0.8], [1.0, 0.95]],
}
)
assert params.dc_to_ac_efficiency_curve == [(0.0, 0.8), (1.0, 0.95)]
@pytest.mark.parametrize(
"curve",
[
[],
[(0.5, 0.9)], # fewer than two points
[(0.5, 0.9), (0.2, 0.8)], # not sorted
[(0.2, 0.9), (0.2, 0.8)], # duplicate load fraction
[(-0.1, 0.9), (1.0, 0.9)], # load fraction below 0
[(0.0, 0.9), (1.1, 0.9)], # load fraction above 1
[(0.0, 0.0), (1.0, 0.9)], # efficiency 0
[(0.0, 0.9), (1.0, 1.2)], # efficiency above 1
[(0.0, float("nan")), (1.0, 0.9)], # not finite
],
)
def test_invalid_curve_is_rejected(self, curve):
with pytest.raises(ValidationError):
InverterParameters(
device_id="inverter1", max_power_wh=1000, dc_to_ac_efficiency_curve=curve
)
@pytest.mark.parametrize("fraction", [-0.1, 1.1])
def test_reference_load_fraction_range(self, fraction):
with pytest.raises(ValidationError):
InverterParameters(
device_id="inverter1",
max_power_wh=1000,
dc_to_ac_efficiency_reference_load_fraction=fraction,
)
# ---------------------------------------------------------------------------
# Interpolation
# ---------------------------------------------------------------------------
class TestInterpolation:
def test_points_midpoints_and_clamping(self):
assert interpolate_efficiency_curve(CURVE, 0.05) == pytest.approx(0.80)
assert interpolate_efficiency_curve(CURVE, 0.20) == pytest.approx(0.90)
assert interpolate_efficiency_curve(CURVE, 1.0) == pytest.approx(0.93)
assert interpolate_efficiency_curve(CURVE, 0.125) == pytest.approx(0.85)
assert interpolate_efficiency_curve(CURVE, 0.75) == pytest.approx(0.94)
# Clamped to the first and the last point.
assert interpolate_efficiency_curve(CURVE, 0.0) == pytest.approx(0.80)
assert interpolate_efficiency_curve(CURVE, 5.0) == pytest.approx(0.93)
def test_inverter_evaluates_slot_load_fraction(self):
inverter = _make_inverter(CURVE, max_power_wh=1000.0)
assert inverter.dc_to_ac_efficiency_at(50.0) == pytest.approx(0.80)
assert inverter.dc_to_ac_efficiency_at(125.0) == pytest.approx(0.85)
assert inverter.dc_to_ac_efficiency_at(10.0) == pytest.approx(0.80)
assert inverter.dc_to_ac_efficiency_at(5000.0) == pytest.approx(0.93)
def test_curve_scales_with_device_size(self):
"""The same relative load gives the same efficiency on any device size."""
small = _make_inverter(CURVE, max_power_wh=5000.0)
large = _make_inverter(CURVE, max_power_wh=10000.0)
assert small.dc_to_ac_efficiency_at(1000.0) == pytest.approx(
large.dc_to_ac_efficiency_at(2000.0)
)
assert small.dc_to_ac_efficiency_at(1000.0) == pytest.approx(0.90)
def test_load_fraction_uses_slot_duration(self):
"""max_power_wh is scaled to the slot, so 250 Wh in 15 min is 1 kW."""
predictor = Mock()
with patch(
"akkudoktoreos.devices.genetic.inverter.get_eos_load_interpolator",
return_value=predictor,
):
inverter = Inverter(
InverterParameters(
device_id="inverter1",
max_power_wh=5000.0,
dc_to_ac_efficiency_curve=CURVE,
),
slot_duration_h=0.25,
)
# 1 kW of 5 kW -> load fraction 0.2
assert inverter.dc_to_ac_efficiency_at(250.0) == pytest.approx(0.90)
# ---------------------------------------------------------------------------
# Without curve: unchanged behaviour
# ---------------------------------------------------------------------------
class TestWithoutCurve:
@pytest.mark.parametrize("efficiency", [1.0, 0.95, 0.8])
def test_efficiency_is_the_constant(self, efficiency):
inverter = _make_inverter(None, dc_to_ac_efficiency=efficiency)
for ac_wh in (0.0, 10.0, 500.0, 1000.0, 5000.0):
assert inverter.dc_to_ac_efficiency_at(ac_wh) == efficiency
assert inverter.reference_dc_to_ac_efficiency == efficiency
def test_reference_load_fraction_is_ignored(self):
inverter = _make_inverter(
None, dc_to_ac_efficiency=0.9, dc_to_ac_efficiency_reference_load_fraction=0.5
)
assert inverter.reference_dc_to_ac_efficiency == 0.9
def test_discharge_matches_legacy_formula(self):
battery = _passthrough_battery()
inverter = _make_inverter(None, battery=battery, dc_to_ac_efficiency=0.93)
grid_export, grid_import, losses, self_consumption = inverter.process_energy(
0.0, 100.0, 0
)
requested_dc = battery.discharge_energy.call_args[0][0]
assert requested_dc == 100.0 / 0.93
assert self_consumption == requested_dc * 0.93
assert losses == requested_dc - requested_dc * 0.93
assert grid_import == pytest.approx(0.0, abs=1e-9)
# ---------------------------------------------------------------------------
# With curve: process_energy
# ---------------------------------------------------------------------------
class TestProcessEnergyWithCurve:
def test_partial_load_draws_more_dc(self):
"""100 Wh at 10 % load (efficiency 0.8333) needs 120 Wh DC."""
battery = _passthrough_battery()
inverter = _make_inverter(CURVE, max_power_wh=1000.0, battery=battery)
grid_export, grid_import, losses, self_consumption = inverter.process_energy(
0.0, 100.0, 0
)
efficiency = 0.80 + (0.05 / 0.15) * 0.10
requested_dc = battery.discharge_energy.call_args[0][0]
assert requested_dc == pytest.approx(100.0 / efficiency)
assert grid_import == pytest.approx(0.0, abs=1e-9)
assert self_consumption == pytest.approx(100.0)
assert losses == pytest.approx(requested_dc - 100.0)
def test_grid_export_uses_efficiency_at_export_level(self, config_eos):
"""A full-power export runs at the efficiency of the rated load."""
battery = Battery(
SolarPanelBatteryParameters(
device_id="battery1",
capacity_wh=10000,
initial_soc_percentage=100,
min_soc_percentage=0,
charging_efficiency=1.0,
discharging_efficiency=1.0,
max_charge_power_w=1000,
),
prediction_hours=1,
)
battery.reset()
battery.discharge_array = np.ones(1)
inverter = _make_inverter(CURVE, max_power_wh=1000.0, battery=battery)
grid_export, _, losses, _ = inverter.process_energy(
0.0, 0.0, 0, allow_battery_grid_export=True, battery_grid_export_factor=1.0
)
# The export bound is estimated at the rated DC energy (load fraction
# 1.0, efficiency 0.93). The conversion itself uses the efficiency at
# the exported AC energy and never draws more than the rated DC energy.
assert grid_export == pytest.approx(930.0)
efficiency = inverter.dc_to_ac_efficiency_at(930.0)
assert 0.93 < efficiency < 0.95
assert losses == pytest.approx(930.0 / efficiency - 930.0)
assert grid_export + losses <= 1000.0 + 1e-9
# ---------------------------------------------------------------------------
# Reference efficiency for valuing stored energy
# ---------------------------------------------------------------------------
class TestReferenceEfficiency:
def test_default_reference_load(self):
inverter = _make_inverter(CURVE, max_power_wh=1000.0)
# 6 % load -> between 0.05:0.80 and 0.20:0.90
expected = 0.80 + (0.06 - 0.05) / (0.20 - 0.05) * (0.90 - 0.80)
assert inverter.reference_dc_to_ac_efficiency == pytest.approx(expected)
def test_configured_reference_load(self):
inverter = _make_inverter(
CURVE, max_power_wh=1000.0, dc_to_ac_efficiency_reference_load_fraction=0.5
)
assert inverter.reference_dc_to_ac_efficiency == pytest.approx(0.95)
def test_tail_value_uses_reference_efficiency(self):
"""Stored energy is valued with the efficiency at the reference load."""
def tail_curve(efficiency_curve):
battery = Battery(
SolarPanelBatteryParameters(
device_id="battery1",
capacity_wh=1000,
max_charge_power_w=1000,
charging_efficiency=1.0,
discharging_efficiency=1.0,
initial_soc_percentage=50,
charge_rates=[0, 0.5, 1],
),
prediction_hours=1,
)
inverter = Inverter(
InverterParameters(
device_id="inverter1",
battery_id="battery1",
max_power_wh=5000,
dc_to_ac_efficiency_curve=efficiency_curve,
),
battery=battery,
)
return build_tail_value_curve(
battery=battery,
inverter=inverter,
# Grid charging is never worth it at this price.
prices_euro_per_wh=np.ones(1),
feed_in_euro_per_wh=np.zeros(1),
load_wh=np.zeros(1),
pv_wh=np.zeros(1),
continuation=TerminalValueCurve(energy_wh=[0.0, 2000.0], value_euro=[0.0, 2.0]),
charge_rates=[0.5, 1],
export_rates=[1],
direct_marketing=False,
)
flat = tail_curve([(0.0, 1.0), (1.0, 1.0)])
lossy = tail_curve([(0.0, 0.8), (1.0, 0.8)])
# The same AC energy needs more stored DC energy with lossy conversion.
assert lossy.value(400.0) == pytest.approx(flat.value(400.0))
assert lossy.energy_wh[-1] == pytest.approx(0.8 * flat.energy_wh[-1])
# ---------------------------------------------------------------------------
# Whole-simulation equivalence
# ---------------------------------------------------------------------------
def _simulate(config_eos, efficiency_curve, dc_to_ac_efficiency=0.95):
hours = 24
config_eos.merge_settings_from_dict(
{
"prediction": {"hours": hours},
"optimization": {"genetic": {"tail_horizon_hours": 0, "horizon_hours": hours}},
}
)
battery = Battery(
SolarPanelBatteryParameters(
device_id="battery1",
capacity_wh=10000,
initial_soc_percentage=60,
min_soc_percentage=5,
charging_efficiency=0.95,
discharging_efficiency=0.95,
max_charge_power_w=4000,
),
prediction_hours=hours,
)
battery.reset()
inverter = Inverter(
InverterParameters(
device_id="inverter1",
max_power_wh=5000,
battery_id="battery1",
dc_to_ac_efficiency=dc_to_ac_efficiency,
ac_to_dc_efficiency=0.95,
dc_to_ac_efficiency_curve=efficiency_curve,
),
battery=battery,
)
rng = np.random.default_rng(42)
simulation = GeneticSimulation()
simulation.prepare(
GeneticEnergyManagementParameters.model_validate(
dict(
pv_prognose_wh=list(np.maximum(rng.normal(1500, 1500, hours), 0.0)),
strompreis_euro_pro_wh=list(rng.uniform(0.0001, 0.0004, hours)),
einspeiseverguetung_euro_pro_wh=list(rng.uniform(0.00005, 0.0002, hours)),
preis_euro_pro_wh_akku=0.0001,
gesamtlast=list(rng.uniform(100, 3000, hours)),
)
),
optimization_hours=hours,
prediction_hours=hours,
inverter=inverter,
direct_marketing_enabled=True,
)
assert simulation.bat_discharge_hours is not None
assert simulation.bat_grid_export_hours is not None
assert simulation.ac_charge_hours is not None
simulation.bat_discharge_hours[:] = 1
simulation.bat_grid_export_hours[18:22] = [1.0, 0.5, 0.25, 1.0]
simulation.ac_charge_hours[2:4] = 0.5
return simulation.simulate(start_hour=0)
def test_flat_curve_equals_constant_efficiency(config_eos):
"""A flat curve at the constant efficiency reproduces the result exactly."""
constant = _simulate(config_eos, None)
flat = _simulate(config_eos, [(0.0, 0.95), (1.0, 0.95)])
assert constant.keys() == flat.keys()
for key, value in constant.items():
if isinstance(value, np.ndarray):
np.testing.assert_array_equal(value, flat[key], err_msg=key)
else:
assert value == flat[key], key
def test_low_load_curve_increases_losses(config_eos):
constant = _simulate(config_eos, None, dc_to_ac_efficiency=0.95)
curve = _simulate(config_eos, [(0.02, 0.80), (0.07, 0.93), (0.3, 0.96), (1.0, 0.95)])
assert curve["Gesamt_Verluste"] > constant["Gesamt_Verluste"]
# ---------------------------------------------------------------------------
# Configuration
# ---------------------------------------------------------------------------
class TestSettings:
"""The DC→AC curve of the GENETIC parameters comes from the inverter profile."""
def test_settings_pass_profile_curve_to_genetic_parameters(self, config_eos):
settings = InverterCommonSettings(
device_id="inverter1",
profile_adjustment=InverterProfileAdjustment(nominal_power_w=5000),
dc_to_ac_efficiency_reference_load_fraction=0.1,
)
profile = settings.get_inverter_profile()
params = settings.to_genetic_param()
assert params.dc_to_ac_efficiency_curve is not None
# Not derated at 45 degC: the curve is the profile curve over [0, 1].
expected = list(zip(profile.dc_to_ac_efficiency.x, profile.dc_to_ac_efficiency.y))
assert [x for x, _ in params.dc_to_ac_efficiency_curve] == pytest.approx(
[x for x, _ in expected]
)
assert [y for _, y in params.dc_to_ac_efficiency_curve] == pytest.approx(
[y for _, y in expected]
)
assert params.dc_to_ac_efficiency_reference_load_fraction == 0.1
assert params.dc_to_ac_efficiency == pytest.approx(profile.dc_to_ac_efficiency_at(0.1))
def test_genetic0_has_no_curve(self, config_eos):
settings = InverterCommonSettings(
device_id="inverter1",
profile_adjustment=InverterProfileAdjustment(nominal_power_w=5000),
)
params = settings.to_genetic0_param()
assert not hasattr(params, "dc_to_ac_efficiency_curve")
# ---------------------------------------------------------------------------
# Discharge limited by the battery (low state of charge, power limit)
# ---------------------------------------------------------------------------
# Rising up to half load, flat above: efficiency(x) = 0.8 + 0.3 * x for x <= 0.5.
RISING_CURVE = [(0.0, 0.80), (0.5, 0.95), (1.0, 0.95)]
def _real_battery(
*,
capacity_wh: int = 1000,
soc_percentage: int = 100,
max_power_w: float = 1000.0,
discharging_efficiency: float = 1.0,
slot_duration_h: float = 1.0,
) -> Battery:
battery = Battery(
SolarPanelBatteryParameters(
device_id="battery1",
capacity_wh=capacity_wh,
initial_soc_percentage=soc_percentage,
min_soc_percentage=0,
charging_efficiency=1.0,
discharging_efficiency=discharging_efficiency,
max_charge_power_w=max_power_w,
),
prediction_hours=1,
slot_duration_h=slot_duration_h,
)
battery.reset()
battery.discharge_array = np.ones(1)
return battery
def _real_inverter(battery: Battery, curve=None, slot_duration_h: float = 1.0, **kwargs) -> Inverter:
predictor = Mock()
predictor.calculate_expected_direct_consumption.side_effect = min
with patch(
"akkudoktoreos.devices.genetic.inverter.get_eos_load_interpolator",
return_value=predictor,
):
return Inverter(
InverterParameters(
device_id="inverter1",
max_power_wh=1000.0,
battery_id="battery1",
dc_to_ac_efficiency_curve=curve,
**kwargs,
),
battery=battery,
slot_duration_h=slot_duration_h,
)
class TestSolveAcForDcEnergy:
def test_rising_piece(self):
# a = 100 * (0.8 + 0.3 * a / 1000) => a = 80 / 0.97
assert solve_ac_for_dc_energy(RISING_CURVE, 100.0, 1000.0, 500.0) == pytest.approx(
80.0 / 0.97
)
def test_clamped_ends(self):
curve = [(0.2, 0.90), (0.6, 0.96)]
# Below the first point the efficiency is constant.
assert solve_ac_for_dc_energy(curve, 100.0, 1000.0, 1000.0) == pytest.approx(90.0)
# Above the last point as well.
assert solve_ac_for_dc_energy(curve, 800.0, 1000.0, 1000.0) == pytest.approx(768.0)
def test_falling_piece(self):
curve = [(0.0, 0.90), (0.5, 0.96), (1.0, 0.90)]
ac = solve_ac_for_dc_energy(curve, 900.0, 1000.0, 1000.0)
assert ac == pytest.approx(900.0 * interpolate_efficiency_curve(curve, ac / 1000.0))
assert 0.5 <= ac / 1000.0 <= 1.0
def test_never_exceeds_the_bound(self):
assert solve_ac_for_dc_energy(RISING_CURVE, 900.0, 1000.0, 300.0) <= 300.0 + 1e-6
@pytest.mark.parametrize("dc_wh", [0.0, -5.0])
def test_nothing_from_nothing(self, dc_wh):
assert solve_ac_for_dc_energy(RISING_CURVE, dc_wh, 1000.0, 500.0) == 0.0
@pytest.mark.parametrize("curve", [CURVE, RISING_CURVE, [(0.1, 0.7), (0.3, 0.97), (1.0, 0.9)]])
@pytest.mark.parametrize("dc_wh", [1.0, 37.0, 180.0, 420.0, 760.0, 1050.0])
def test_result_is_consistent_with_the_curve(self, curve, dc_wh):
ac = solve_ac_for_dc_energy(curve, dc_wh, 1000.0, 1000.0)
if ac < 1000.0 - 1e-6:
assert ac == pytest.approx(dc_wh * interpolate_efficiency_curve(curve, ac / 1000.0))
class TestBatteryLimitedDischarge:
"""The efficiency follows the AC load that is delivered, not the request."""
def test_low_soc_hourly_slot(self):
# 100 Wh DC left, 500 Wh AC requested from a 1000 W inverter.
battery = _real_battery(soc_percentage=10)
inverter = _real_inverter(battery, RISING_CURVE)
ac, losses = inverter._discharge_battery_to_ac(500.0, 0)
assert ac == pytest.approx(80.0 / 0.97) # 82.474 Wh
assert ac / 100.0 == pytest.approx(inverter.dc_to_ac_efficiency_at(ac))
assert ac + losses == pytest.approx(100.0)
assert battery.soc_wh == pytest.approx(0.0)
def test_low_soc_quarter_hour_slot(self):
# Same 100 Wh DC, but a 15-minute slot: rated AC energy is 250 Wh, so the
# same energy is a four times higher load.
battery = _real_battery(soc_percentage=10, slot_duration_h=0.25)
inverter = _real_inverter(battery, RISING_CURVE, slot_duration_h=0.25)
ac, losses = inverter._discharge_battery_to_ac(125.0, 0)
# a = 100 * (0.8 + 0.3 * a / 250) => a = 80 / 0.88
assert ac == pytest.approx(80.0 / 0.88)
assert ac / 100.0 == pytest.approx(inverter.dc_to_ac_efficiency_at(ac))
assert ac + losses == pytest.approx(100.0)
def test_power_limited_hourly_slot(self):
# Full battery, but it can only discharge with 200 W.
battery = _real_battery(capacity_wh=10000, max_power_w=200.0)
inverter = _real_inverter(battery, RISING_CURVE)
ac, losses = inverter._discharge_battery_to_ac(500.0, 0)
# a = 200 * (0.8 + 0.3 * a / 1000) => a = 160 / 0.94
assert ac == pytest.approx(160.0 / 0.94)
assert ac / 200.0 == pytest.approx(inverter.dc_to_ac_efficiency_at(ac))
assert ac + losses == pytest.approx(200.0)
def test_power_limited_quarter_hour_slot(self):
battery = _real_battery(capacity_wh=10000, max_power_w=200.0, slot_duration_h=0.25)
inverter = _real_inverter(battery, RISING_CURVE, slot_duration_h=0.25)
ac, losses = inverter._discharge_battery_to_ac(125.0, 0)
# 50 Wh DC per slot: a = 50 * (0.8 + 0.3 * a / 250) => a = 40 / 0.94
assert ac == pytest.approx(40.0 / 0.94)
assert ac / 50.0 == pytest.approx(inverter.dc_to_ac_efficiency_at(ac))
assert ac + losses == pytest.approx(50.0)
def test_battery_discharge_losses_stay_separate(self):
# Battery delivers 90 Wh DC of its 100 Wh; the curve applies to those 90 Wh.
battery = _real_battery(soc_percentage=10, discharging_efficiency=0.9)
inverter = _real_inverter(battery, RISING_CURVE)
ac, losses = inverter._discharge_battery_to_ac(500.0, 0)
assert ac == pytest.approx(90.0 * 0.8 / (1.0 - 90.0 * 0.3 / 1000.0))
assert ac / 90.0 == pytest.approx(inverter.dc_to_ac_efficiency_at(ac))
assert ac + losses == pytest.approx(100.0)
def test_load_is_covered_with_the_delivered_energy(self):
battery = _real_battery(soc_percentage=10)
inverter = _real_inverter(battery, RISING_CURVE)
grid_export, grid_import, losses, self_consumption = inverter.process_energy(0.0, 500.0, 0)
assert self_consumption == pytest.approx(80.0 / 0.97)
assert grid_import == pytest.approx(500.0 - 80.0 / 0.97)
assert grid_export == 0.0
assert self_consumption + losses == pytest.approx(100.0)
def test_unlimited_discharge_is_unchanged(self):
battery = _real_battery(capacity_wh=10000)
inverter = _real_inverter(battery, RISING_CURVE)
ac, losses = inverter._discharge_battery_to_ac(500.0, 0)
assert ac == pytest.approx(500.0)
assert losses == pytest.approx(500.0 / 0.95 - 500.0)
@pytest.mark.parametrize("slot_duration_h", [1.0, 0.25])
@pytest.mark.parametrize("efficiency", [1.0, 0.95, 0.8])
def test_constant_efficiency_is_unchanged(self, slot_duration_h, efficiency):
# Without a curve a limited discharge converts with the constant, as before.
low = _real_battery(soc_percentage=10, slot_duration_h=slot_duration_h)
inverter = _real_inverter(
low, None, slot_duration_h=slot_duration_h, dc_to_ac_efficiency=efficiency
)
ac, losses = inverter._discharge_battery_to_ac(500.0 * slot_duration_h, 0)
assert ac == pytest.approx(100.0 * efficiency)
assert losses == pytest.approx(100.0 * (1.0 - efficiency))
limited = _real_battery(
capacity_wh=10000, max_power_w=200.0, slot_duration_h=slot_duration_h
)
inverter = _real_inverter(
limited, None, slot_duration_h=slot_duration_h, dc_to_ac_efficiency=efficiency
)
ac, losses = inverter._discharge_battery_to_ac(500.0 * slot_duration_h, 0)
assert ac == pytest.approx(200.0 * slot_duration_h * efficiency)
# ---------------------------------------------------------------------------
# AC→DC curve for charging the battery from the grid
# ---------------------------------------------------------------------------
CHARGE_CURVE = [(0.05, 0.86), (0.3, 0.96), (1.0, 0.95)]
class TestSolveAcForChargeDcEnergy:
@pytest.mark.parametrize("dc_wh", [1.0, 30.0, 100.0, 250.0, 500.0, 900.0, 2000.0])
def test_solution_is_consistent(self, dc_wh):
ac_wh = solve_ac_for_charge_dc_energy(CHARGE_CURVE, dc_wh, 1000.0)
efficiency = interpolate_efficiency_curve(CHARGE_CURVE, ac_wh / 1000.0)
assert ac_wh * efficiency == pytest.approx(dc_wh, rel=1e-9)
def test_clamped_ends(self):
# Below the first point the first efficiency applies, above the last the last.
assert solve_ac_for_charge_dc_energy(CHARGE_CURVE, 8.6, 1000.0) == pytest.approx(10.0)
assert solve_ac_for_charge_dc_energy(CHARGE_CURVE, 1900.0, 1000.0) == pytest.approx(2000.0)
def test_flat_curve_equals_constant_efficiency(self):
flat = [(0.0, 0.9), (1.0, 0.9)]
assert solve_ac_for_charge_dc_energy(flat, 450.0, 1000.0) == pytest.approx(500.0)
def test_no_energy(self):
assert solve_ac_for_charge_dc_energy(CHARGE_CURVE, 0.0, 1000.0) == 0.0
class TestChargeFromGridWithCurve:
def test_curve_is_validated(self):
with pytest.raises(ValidationError):
InverterParameters(
device_id="inverter1", max_power_wh=1000, ac_to_dc_efficiency_curve=[(0.5, 0.9)]
)
@staticmethod
def _empty_battery() -> Battery:
battery = _real_battery(soc_percentage=0)
battery.charge_array = np.ones(1)
return battery
def test_low_load_charge_draws_more_ac(self):
flat = _real_inverter(self._empty_battery(), ac_to_dc_efficiency=0.96)
curved = _real_inverter(
self._empty_battery(),
ac_to_dc_efficiency=0.96,
ac_to_dc_efficiency_curve=CHARGE_CURVE,
)
# 5 % of the 1 kW battery charge power: a low AC load, efficiency ~0.87.
flat_ac, flat_losses = flat.charge_battery_from_grid(0, 0.05)
curved_ac, curved_losses = curved.charge_battery_from_grid(0, 0.05)
assert flat_ac == pytest.approx(50.0 / 0.96)
assert curved_ac > flat_ac
assert curved_ac * interpolate_efficiency_curve(
CHARGE_CURVE, curved_ac / 1000.0
) == pytest.approx(50.0)
assert curved_losses == pytest.approx(curved_ac - 50.0)
def test_settings_pass_the_profile_curve(self, config_eos):
settings = InverterCommonSettings(
device_id="inverter1",
profile_adjustment=InverterProfileAdjustment(nominal_power_w=5000),
)
profile = settings.get_inverter_profile()
params = settings.to_genetic_param()
assert params.ac_to_dc_efficiency_curve is not None
assert [y for _, y in params.ac_to_dc_efficiency_curve] == pytest.approx(
list(profile.ac_to_dc_efficiency.y)
)
# The constant is the efficiency at the AC charge limit, here the nominal power.
assert params.ac_to_dc_efficiency == pytest.approx(profile.ac_to_dc_efficiency_at(1.0))
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