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EOS/tests/test_inverter_efficiency_curve.py
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ChristinGitHubChristinClaude Opus 5.5Andreas
 7f26ea3f3f feat(devices): add load-dependent DC-to-AC efficiency curve for GENETIC inverters (#1375) 
* feat(devices): add load-dependent DC-to-AC efficiency curve for GENETIC inverters

Real inverters are much less efficient at low load than at rated power
(e.g. 0.80 at 2 % vs. 0.93 at 7 % load). A constant dc_to_ac_efficiency
overrates night-time battery discharge into a small base load and, with
it, arbitrage and grid charging.

Add two optional inverter settings for the GENETIC optimizer:

- dc_to_ac_efficiency_curve: list of (load_fraction, efficiency) points,
  load fraction = AC power / max_power_w. Points must be strictly
  increasing in [0, 1] with efficiencies in (0, 1]; at least two points.
  Linear interpolation, clamped at both ends. When set, the curve
  replaces dc_to_ac_efficiency.
- dc_to_ac_efficiency_reference_load_fraction (default 0.06): load at
  which the curve values stored energy without a specific conversion.

Battery discharges in Inverter.process_energy() use the efficiency at
the AC energy of each conversion. The end-of-horizon value, the terminal
and tail value curves and the AC charge break-even penalty use
Inverter.reference_dc_to_ac_efficiency.

Without a curve, dc_to_ac_efficiency_at() and the reference efficiency
return dc_to_ac_efficiency itself, so results are bit-identical to the
previous model. GENETIC0 is unchanged.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

* fix(devices): evaluate the efficiency curve at the delivered AC load

When the battery limits a discharge (low state of charge or its power
limit), the conversion ran with the efficiency of the requested AC energy.
With a 1000 Wh battery at 10 % SoC, a 1000 W inverter and the curve
[(0, .8), (.5, .95), (1, .95)], a request of 500 Wh AC returned 95 Wh AC
from the available 100 Wh DC, although the curve gives 82.5 % at that load.

The delivered AC energy now solves a = dc * efficiency(a / rated) exactly on
the piecewise-linear curve (solve_ac_for_dc_energy), bounded by the request.
Unlimited discharges and inverters without a curve are unchanged.

Regression tests use a real Battery: low SoC and a power-limited discharge,
each with 60- and 15-minute slots, battery discharge losses, the load path
through process_energy, and constant efficiency.

* docs: regenerate OpenAPI for the efficiency-curve fix

---------

Co-authored-by: Christin <christin@BigBrother.bikini.bottom.zone>
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
Co-authored-by: Andreas <drbacke@gmx.de>
2026-10-11 10:45:44 +02:00

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"""Tests for the load-dependent DC-to-AC efficiency curve 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. 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_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
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
)
with pytest.raises(ValidationError):
InverterCommonSettings(
device_id="inverter1", max_power_w=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:
def test_settings_pass_curve_to_genetic_parameters(self):
settings = InverterCommonSettings.model_validate(
{
"device_id": "inverter1",
"max_power_w": 5000,
"dc_to_ac_efficiency_curve": [[0.02, 0.8], [0.07, 0.93], [1.0, 0.95]],
"dc_to_ac_efficiency_reference_load_fraction": 0.1,
}
)
params = settings.to_genetic_param()
assert params.dc_to_ac_efficiency_curve == [(0.02, 0.8), (0.07, 0.93), (1.0, 0.95)]
assert params.dc_to_ac_efficiency_reference_load_fraction == 0.1
def test_defaults_keep_constant_efficiency(self):
params = InverterCommonSettings(device_id="inverter1", max_power_w=5000).to_genetic_param()
assert params.dc_to_ac_efficiency_curve is None
assert params.dc_to_ac_efficiency_reference_load_fraction == pytest.approx(0.06)
def test_genetic0_is_unaffected(self):
settings = InverterCommonSettings(
device_id="inverter1",
max_power_w=5000,
dc_to_ac_efficiency_curve=[(0.0, 0.8), (1.0, 0.95)],
)
params = settings.to_genetic0_param()
assert not hasattr(params, "dc_to_ac_efficiency_curve")
def test_config_accepts_curve(self, config_eos):
config_eos.merge_settings_from_dict(
{
"devices": {
"inverters": {
"inverter1": {
"max_power_w": 5000,
"dc_to_ac_efficiency_curve": [[0.02, 0.8], [0.07, 0.93], [1.0, 0.95]],
}
}
}
}
)
inverters = config_eos.devices.inverters
assert inverters is not None
assert inverters["inverter1"].dc_to_ac_efficiency_curve == [
(0.02, 0.8),
(0.07, 0.93),
(1.0, 0.95),
]
config_eos.set_nested_value(
"devices/inverters/inverter1/dc_to_ac_efficiency_curve", [[0.0, 0.85], [1.0, 0.95]]
)
assert inverters["inverter1"].dc_to_ac_efficiency_curve == [(0.0, 0.85), (1.0, 0.95)]
with pytest.raises(ValueError, match="at least two points"):
config_eos.set_nested_value(
"devices/inverters/inverter1/dc_to_ac_efficiency_curve", [[0.5, 0.9]]
)
# ---------------------------------------------------------------------------
# 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)
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