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AndreasandClaude Opus 4.8
67cf6f7d8a
Replace the single hourly "dishwasher" home appliance with a list of flexible consumers (home_appliances). Each consumer defines its load either as an explicit power profile (energy-preservingly resampled onto the optimization slot grid, incl. 15-min and non-integer interval ratios) or the flat consumption_wh/duration_h fallback, and runs ONCE or DAILY within its time windows and the optimization horizon. - ConsumerScheduleMode + shared load-definition validation (XOR of profile/fallback, reject negative/NaN/inf, unique device_id) - ApplianceGeneLayout: variable appliance gene block (index into allowed_start_slots), ONCE/DAILY calendar-day based, no snapping - per-device output: result.home_appliance_energy_wh, appliance_starts (absolute local times), per-device solution columns and DDBC RUN/OFF instructions on state transitions only - deprecate dishwasher/washingstart/Home_appliance_wh_per_hour with backward-compatible mapping and explicit conflict rejection - max_home_appliances is now an upper bound only; no demo appliance and no on/off behaviour - docs, openapi.json, CHANGELOG and optimize_result_2* fixtures updated; new tests/test_homeappliance.py covers the mandatory test matrix Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
987 lines
39 KiB
Python
987 lines
39 KiB
Python
"""Tests for inverter AC/DC efficiency separation and AC charging break-even penalty.
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Tests the new inverter parameters:
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- dc_to_ac_efficiency: DC→AC conversion loss on battery discharge
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- ac_to_dc_efficiency: AC→DC conversion loss on grid-to-battery charging
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- max_ac_charge_power_w: Maximum AC charging power limit
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And the economic break-even penalty in GeneticOptimization.evaluate():
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- Penalises AC grid charging that cannot be recovered given round-trip losses and future prices
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- Respects free PV-charged energy already in battery when ranking future discharge hours
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"""
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from types import SimpleNamespace
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from typing import cast
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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
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from akkudoktoreos.devices.genetic.inverter import Inverter
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from akkudoktoreos.optimization.genetic.geneticdevices import (
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InverterParameters,
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SolarPanelBatteryParameters,
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)
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# ---------------------------------------------------------------------------
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# Helpers / Fixtures
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# ---------------------------------------------------------------------------
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def _make_inverter(
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dc_to_ac_efficiency: float = 1.0,
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ac_to_dc_efficiency: float = 1.0,
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max_ac_charge_power_w=None,
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max_power_wh: float = 10000.0,
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mock_battery=None,
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) -> Inverter:
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"""Create an Inverter with custom efficiency parameters and a mock battery."""
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mock_self_consumption_predictor = Mock()
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mock_self_consumption_predictor.calculate_expected_direct_consumption.side_effect = min
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params = InverterParameters(
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device_id="inv1",
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max_power_wh=max_power_wh,
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battery_id=mock_battery.parameters.device_id if mock_battery else None,
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dc_to_ac_efficiency=dc_to_ac_efficiency,
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ac_to_dc_efficiency=ac_to_dc_efficiency,
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max_ac_charge_power_w=max_ac_charge_power_w,
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)
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with patch(
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"akkudoktoreos.devices.genetic.inverter.get_eos_load_interpolator",
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return_value=mock_self_consumption_predictor,
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):
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return Inverter(params, battery=mock_battery)
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@pytest.fixture
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def mock_battery() -> Mock:
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mock_bat = Mock()
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mock_bat.charge_energy = Mock(return_value=(0.0, 0.0))
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mock_bat.discharge_energy = Mock(return_value=(0.0, 0.0))
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mock_bat.parameters.device_id = "battery1"
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return mock_bat
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# ===================================================================
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# 1. InverterParameters – new fields and defaults
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# ===================================================================
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class TestInverterParametersDefaults:
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"""Verify backward-compatible defaults for new parameters."""
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def test_defaults(self):
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params = InverterParameters(device_id="inv1", max_power_wh=5000)
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assert params.dc_to_ac_efficiency == 1.0
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assert params.ac_to_dc_efficiency == 1.0
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assert params.max_ac_charge_power_w is None
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def test_custom_values(self):
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params = InverterParameters(
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device_id="inv1",
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max_power_wh=5000,
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dc_to_ac_efficiency=0.95,
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ac_to_dc_efficiency=0.93,
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max_ac_charge_power_w=3000,
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)
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assert params.dc_to_ac_efficiency == 0.95
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assert params.ac_to_dc_efficiency == 0.93
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assert params.max_ac_charge_power_w == 3000
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def test_ac_to_dc_zero_disables_ac_charging(self):
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params = InverterParameters(
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device_id="inv1", max_power_wh=5000, ac_to_dc_efficiency=0.0
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)
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assert params.ac_to_dc_efficiency == 0.0
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def test_dc_to_ac_must_be_positive(self):
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with pytest.raises(Exception):
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InverterParameters(device_id="inv1", max_power_wh=5000, dc_to_ac_efficiency=0.0)
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def test_max_ac_charge_power_zero(self):
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params = InverterParameters(
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device_id="inv1", max_power_wh=5000, max_ac_charge_power_w=0
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)
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assert params.max_ac_charge_power_w == 0
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# ===================================================================
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# 2. dc_to_ac_efficiency – battery discharge through inverter
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# ===================================================================
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class TestDcToAcEfficiency:
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"""Battery discharge energy is reduced by dc_to_ac_efficiency."""
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def test_discharge_shortfall_with_95_percent_efficiency(self, mock_battery):
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"""With 0.95 efficiency, 100 Wh DC from battery → 95 Wh AC delivered."""
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mock_battery.discharge_energy.return_value = (100.0, 10.0)
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inv = _make_inverter(dc_to_ac_efficiency=0.95, mock_battery=mock_battery)
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generation = 0.0
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consumption = 200.0
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hour = 5
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grid_export, grid_import, losses, self_consumption = inv.process_energy(
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generation, consumption, hour
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)
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# Battery delivers 100 Wh DC → 95 Wh AC after inverter
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# Inverter loss = 100 - 95 = 5 Wh
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battery_discharge_ac = 100.0 * 0.95 # 95 Wh
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assert self_consumption == pytest.approx(generation + battery_discharge_ac, rel=1e-5)
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assert grid_import == pytest.approx(consumption - battery_discharge_ac, rel=1e-5)
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# Total losses = battery internal (10) + inverter DC→AC (5)
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expected_losses = 10.0 + (100.0 * 0.05)
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assert losses == pytest.approx(expected_losses, rel=1e-5)
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# Battery was asked for more DC to compensate for inverter loss
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# ac_needed = min(200, max_power_wh - 0) = 200
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# dc_request = 200 / 0.95 ≈ 210.526
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expected_dc_request = 200.0 / 0.95
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mock_battery.discharge_energy.assert_called_once_with(
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pytest.approx(expected_dc_request, rel=1e-3), hour
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)
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def test_discharge_with_100_percent_efficiency_unchanged(self, mock_battery):
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"""With 1.0 efficiency, behavior is identical to the legacy model."""
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mock_battery.discharge_energy.return_value = (100.0, 10.0)
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inv = _make_inverter(dc_to_ac_efficiency=1.0, mock_battery=mock_battery)
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generation = 100.0
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consumption = 300.0
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hour = 5
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grid_export, grid_import, losses, self_consumption = inv.process_energy(
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generation, consumption, hour
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)
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# No inverter loss: battery_discharge_ac = 100 Wh
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assert self_consumption == pytest.approx(200.0, rel=1e-5)
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assert grid_import == pytest.approx(100.0, rel=1e-5)
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assert losses == pytest.approx(10.0, rel=1e-5) # Only battery losses
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def test_discharge_surplus_path_with_efficiency(self, mock_battery):
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"""A probabilistic load gap discharges through the inverter."""
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mock_battery.discharge_energy.return_value = (30.0 / 0.90, 5.0)
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mock_battery.charge_energy.return_value = (100.0, 10.0)
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inv = _make_inverter(dc_to_ac_efficiency=0.90, mock_battery=mock_battery)
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predictor = cast(Mock, inv.self_consumption_predictor)
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predictor.calculate_expected_direct_consumption.side_effect = None
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predictor.calculate_expected_direct_consumption.return_value = 170.0
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generation = 500.0
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consumption = 200.0
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hour = 5
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grid_export, grid_import, losses, self_consumption = inv.process_energy(
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generation, consumption, hour
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)
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# Expected direct PV is 170 Wh, leaving 30 Wh of load gap and
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# 330 Wh of PV surplus within different sub-periods of the slot.
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# DC request for discharge = 30 / 0.90 = 33.333 Wh.
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expected_dc_request = 30.0 / 0.90
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mock_battery.discharge_energy.assert_called_once_with(
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pytest.approx(expected_dc_request, rel=1e-3), hour
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)
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# Battery delivers 33.333 Wh DC -> 30 Wh AC.
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from_battery_dc = 30.0 / 0.90
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from_battery_ac = from_battery_dc * 0.90
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inverter_discharge_loss = from_battery_dc - from_battery_ac
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assert self_consumption == pytest.approx(170.0 + from_battery_ac, rel=1e-5)
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assert grid_import == pytest.approx(0.0)
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assert grid_export == pytest.approx(220.0)
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assert losses == pytest.approx(5.0 + inverter_discharge_loss + 10.0)
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# ===================================================================
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# 3. ac_to_dc_efficiency + max_ac_charge_power_w – in simulation
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# ===================================================================
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class TestAcChargingInSimulation:
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"""Test AC charging logic with inverter efficiency in GeneticSimulation.
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These tests use a real Battery object (not a mock) and directly exercise
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the AC charging path in GeneticSimulation.simulate().
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"""
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@pytest.fixture
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def simulation_setup(self, config_eos):
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"""Set up a minimal GeneticSimulation with battery and inverter."""
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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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prediction_hours = config_eos.prediction.hours
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def _build(
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ac_to_dc_efficiency: float = 1.0,
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dc_to_ac_efficiency: float = 1.0,
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max_ac_charge_power_w=None,
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battery_capacity_wh: int = 10000,
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battery_charging_efficiency: float = 0.90,
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battery_discharging_efficiency: float = 0.90,
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battery_initial_soc_pct: int = 50,
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battery_max_charge_power_w: int = 5000,
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):
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akku = Battery(
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SolarPanelBatteryParameters(
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device_id="battery1",
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capacity_wh=battery_capacity_wh,
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initial_soc_percentage=battery_initial_soc_pct,
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charging_efficiency=battery_charging_efficiency,
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discharging_efficiency=battery_discharging_efficiency,
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min_soc_percentage=0,
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max_soc_percentage=100,
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max_charge_power_w=battery_max_charge_power_w,
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),
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prediction_hours=prediction_hours,
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)
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akku.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_to_dc_efficiency=ac_to_dc_efficiency,
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dc_to_ac_efficiency=dc_to_ac_efficiency,
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max_ac_charge_power_w=max_ac_charge_power_w,
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),
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battery=akku,
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)
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sim = GeneticSimulation()
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sim.prepare(
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GeneticEnergyManagementParameters(
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pv_prognose_wh=[0.0] * prediction_hours, # No PV
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strompreis_euro_pro_wh=[0.0003] * prediction_hours, # ~30ct/kWh
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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=[1000.0] * prediction_hours, # 1 kW constant load
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),
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optimization_hours=config_eos.optimization.horizon_hours,
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prediction_hours=prediction_hours,
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inverter=inverter,
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ev=None,
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home_appliances=None,
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)
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return sim, akku, inverter
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return _build
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def test_ac_charge_with_unity_efficiency_backward_compat(self, simulation_setup):
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"""With ac_to_dc_efficiency=1.0, behavior matches legacy model."""
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sim, akku, inverter = simulation_setup(ac_to_dc_efficiency=1.0)
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# Enable AC charging for hour 1 at 50% power
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sim.ac_charge_hours[1] = 0.5
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sim.dc_charge_hours[:] = 0
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sim.bat_discharge_hours[:] = 0
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result = sim.simulate(start_hour=0)
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# At hour 1: AC charge at 50% of 5000W = 2500W DC requested
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# With efficiency 1.0, AC consumed from grid = DC = 2500W
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# Battery stores: 2500 * 0.90 (battery eff) = 2250 Wh
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# Battery loss: 2500 - 2250 = 250 Wh
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# Total grid consumption for that hour = 1000 (load) + 2500 (AC charge)
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hour_idx = 1
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assert result["Netzbezug_Wh_pro_Stunde"][hour_idx] == pytest.approx(3500.0, rel=1e-3)
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assert result["Verluste_Pro_Stunde"][hour_idx] == pytest.approx(250.0, rel=1e-3)
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def test_ac_charge_with_95_percent_efficiency(self, simulation_setup):
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"""With ac_to_dc_efficiency=0.95, more AC energy is consumed for same DC charge."""
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sim, akku, inverter = simulation_setup(ac_to_dc_efficiency=0.95)
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sim.ac_charge_hours[1] = 0.5
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sim.dc_charge_hours[:] = 0
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sim.bat_discharge_hours[:] = 0
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result = sim.simulate(start_hour=0)
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# At hour 1: AC charge at 50% of 5000W = 2500W DC requested
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# With ac_to_dc_efficiency=0.95:
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# AC consumed = 2500 / 0.95 ≈ 2631.58 Wh
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# Inverter loss = 2631.58 - 2500 = 131.58 Wh
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# Battery stores: 2500 * 0.90 = 2250 Wh
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# Battery loss: 2500 - 2250 = 250 Wh
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# Total losses: 250 + 131.58 = 381.58 Wh
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hour_idx = 1
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dc_energy = 2500.0
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ac_energy = dc_energy / 0.95
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inverter_loss = ac_energy - dc_energy
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battery_loss = dc_energy * (1 - 0.90)
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total_loss = battery_loss + inverter_loss
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expected_grid = 1000.0 + ac_energy
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assert result["Netzbezug_Wh_pro_Stunde"][hour_idx] == pytest.approx(
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expected_grid, rel=1e-3
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)
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assert result["Verluste_Pro_Stunde"][hour_idx] == pytest.approx(total_loss, rel=1e-3)
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def test_ac_charge_disabled_by_zero_efficiency(self, simulation_setup):
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"""With ac_to_dc_efficiency=0.0, AC charging is completely disabled."""
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sim, akku, inverter = simulation_setup(ac_to_dc_efficiency=0.0)
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sim.ac_charge_hours[1] = 1.0 # Try to AC charge
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sim.dc_charge_hours[:] = 0
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sim.bat_discharge_hours[:] = 0
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initial_soc = akku.soc_wh
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result = sim.simulate(start_hour=0)
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# Battery should not charge at all (AC charging disabled)
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# Grid consumption = only load
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hour_idx = 1
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assert result["Netzbezug_Wh_pro_Stunde"][hour_idx] == pytest.approx(1000.0, rel=1e-3)
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# Battery SoC should not change (no ac charge, no dc charge, no discharge)
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assert result["akku_soc_pro_stunde"][hour_idx] == pytest.approx(50.0, rel=1e-3)
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def test_ac_charge_disabled_by_zero_max_power(self, simulation_setup):
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"""With max_ac_charge_power_w=0, AC charging is disabled."""
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sim, akku, inverter = simulation_setup(
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ac_to_dc_efficiency=0.95, max_ac_charge_power_w=0
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)
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sim.ac_charge_hours[1] = 1.0
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sim.dc_charge_hours[:] = 0
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sim.bat_discharge_hours[:] = 0
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result = sim.simulate(start_hour=0)
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hour_idx = 1
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assert result["Netzbezug_Wh_pro_Stunde"][hour_idx] == pytest.approx(1000.0, rel=1e-3)
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assert result["akku_soc_pro_stunde"][hour_idx] == pytest.approx(50.0, rel=1e-3)
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def test_ac_charge_limited_by_max_ac_power(self, simulation_setup):
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"""max_ac_charge_power_w limits the effective charge factor."""
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# battery max_charge_power_w = 5000, ac_to_dc_efficiency = 0.95
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# max_ac_charge_power_w = 2000
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# max_dc_factor = (2000 * 0.95) / 5000 = 0.38
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sim, akku, inverter = simulation_setup(
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ac_to_dc_efficiency=0.95, max_ac_charge_power_w=2000
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)
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sim.ac_charge_hours[1] = 1.0 # Request full power
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sim.dc_charge_hours[:] = 0
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sim.bat_discharge_hours[:] = 0
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result = sim.simulate(start_hour=0)
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# Effective charge factor is capped at 0.38
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# DC energy = 5000 * 0.38 = 1900 W
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# AC energy = 1900 / 0.95 = 2000 W (respects limit)
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hour_idx = 1
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max_dc_factor = (2000 * 0.95) / 5000
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dc_energy = 5000 * max_dc_factor
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ac_energy = dc_energy / 0.95
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expected_grid = 1000.0 + ac_energy # load + AC charge
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assert result["Netzbezug_Wh_pro_Stunde"][hour_idx] == pytest.approx(
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expected_grid, rel=1e-2
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)
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def test_discharge_with_dc_to_ac_efficiency(self, simulation_setup):
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"""dc_to_ac_efficiency affects how much AC energy is delivered from battery."""
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sim, akku, inverter = simulation_setup(
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dc_to_ac_efficiency=0.90, battery_initial_soc_pct=80
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)
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# No PV, no AC charge, discharge only
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sim.ac_charge_hours[:] = 0
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sim.dc_charge_hours[:] = 1
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sim.bat_discharge_hours[:] = 1
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result = sim.simulate(start_hour=0)
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# With dc_to_ac_efficiency=0.90, battery discharge delivers less AC
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# This means more grid import compared to efficiency=1.0
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# At hour 0: load=1000, PV=0
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# Shortfall = 1000
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# DC request = 1000 / 0.90 ≈ 1111 Wh
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# Battery delivers (limited by capacity and efficiency):
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# max raw = min(soc - min_soc, max_charge_power) = min(8000, 5000) = 5000
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# max deliverable DC = 5000 * 0.90 (battery eff) = 4500
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# delivered DC = min(1111, 4500) = 1111 Wh
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# delivered AC = 1111 * 0.90 = 1000 Wh → covers full load
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||
# So grid_import should be ≈ 0 for first hours while battery has charge
|
||
hour_idx = 0
|
||
assert result["Netzbezug_Wh_pro_Stunde"][hour_idx] == pytest.approx(0.0, abs=5.0)
|
||
|
||
# But losses should be higher due to inverter DC→AC conversion
|
||
# Inverter loss = 1111 * 0.10 = 111 Wh
|
||
# Battery loss = raw_used - delivered = delivered/bat_eff - delivered
|
||
# = 1111/0.90 - 1111 ≈ 123.5 Wh
|
||
assert result["Verluste_Pro_Stunde"][hour_idx] > 200.0 # Significant losses
|
||
|
||
def test_round_trip_efficiency_cost_impact(self, simulation_setup):
|
||
"""Verify AC charge + discharge round-trip losses increase total cost."""
|
||
# Reference run: no inverter losses
|
||
sim_ref, akku_ref, inv_ref = simulation_setup(
|
||
ac_to_dc_efficiency=1.0, dc_to_ac_efficiency=1.0
|
||
)
|
||
sim_ref.ac_charge_hours[1] = 0.5
|
||
sim_ref.dc_charge_hours[:] = 0
|
||
sim_ref.bat_discharge_hours[:] = 1
|
||
sim_ref.bat_discharge_hours[1] = 0 # Don't discharge while charging
|
||
result_ref = sim_ref.simulate(start_hour=0)
|
||
|
||
# Test run: with inverter losses
|
||
sim_test, akku_test, inv_test = simulation_setup(
|
||
ac_to_dc_efficiency=0.93, dc_to_ac_efficiency=0.93
|
||
)
|
||
sim_test.ac_charge_hours[1] = 0.5
|
||
sim_test.dc_charge_hours[:] = 0
|
||
sim_test.bat_discharge_hours[:] = 1
|
||
sim_test.bat_discharge_hours[1] = 0
|
||
result_test = sim_test.simulate(start_hour=0)
|
||
|
||
# With inverter losses, total cost should be HIGHER
|
||
assert result_test["Gesamtkosten_Euro"] > result_ref["Gesamtkosten_Euro"]
|
||
# And total losses should be HIGHER
|
||
assert result_test["Gesamt_Verluste"] > result_ref["Gesamt_Verluste"]
|
||
|
||
|
||
# ===================================================================
|
||
# 4. Integration with optimizer residual battery value
|
||
# ===================================================================
|
||
|
||
|
||
class TestResidualBatteryValue:
|
||
"""Verify that dc_to_ac_efficiency affects residual battery value calculation."""
|
||
|
||
def test_current_energy_content_unaffected(self):
|
||
"""Battery.current_energy_content() is DC-only; inverter eff applied in optimizer."""
|
||
akku = Battery(
|
||
SolarPanelBatteryParameters(
|
||
device_id="bat1",
|
||
capacity_wh=10000,
|
||
initial_soc_percentage=50,
|
||
discharging_efficiency=0.90,
|
||
min_soc_percentage=0,
|
||
),
|
||
prediction_hours=24,
|
||
)
|
||
akku.reset()
|
||
|
||
# DC energy content: (5000 - 0) * 0.90 = 4500
|
||
assert akku.current_energy_content() == pytest.approx(4500.0, rel=1e-5)
|
||
|
||
|
||
# ===================================================================
|
||
# 5. AC charging break-even penalty in GeneticOptimization.evaluate()
|
||
# ===================================================================
|
||
|
||
|
||
def _make_mock_simulation(
|
||
*,
|
||
# Inverter properties
|
||
ac_to_dc_efficiency: float = 0.93,
|
||
dc_to_ac_efficiency: float = 0.95,
|
||
# Battery properties
|
||
charging_efficiency: float = 0.95,
|
||
discharging_efficiency: float = 0.95,
|
||
capacity_wh: float = 10_000.0,
|
||
initial_soc_percentage: float = 0.0, # fraction of capacity already stored (0 = empty)
|
||
min_soc_wh: float = 0.0,
|
||
max_charge_power_w: float = 5_000.0,
|
||
levelized_cost_of_storage_kwh: float = 0.0,
|
||
# Arrays (must be same length)
|
||
ac_charge_hours: list | None = None,
|
||
elect_price_hourly: list | None = None,
|
||
load_energy_array: list | None = None,
|
||
):
|
||
"""Return a mock GeneticSimulation with configurable properties for penalty tests."""
|
||
n = 24
|
||
if ac_charge_hours is None:
|
||
ac_charge_hours = [0.0] * n
|
||
if elect_price_hourly is None:
|
||
elect_price_hourly = [0.0003] * n # 30 ct/kWh flat
|
||
if load_energy_array is None:
|
||
load_energy_array = [1000.0] * n # 1 kWh constant load
|
||
|
||
inv = SimpleNamespace(
|
||
ac_to_dc_efficiency=ac_to_dc_efficiency,
|
||
dc_to_ac_efficiency=dc_to_ac_efficiency,
|
||
)
|
||
bat = SimpleNamespace(
|
||
charging_efficiency=charging_efficiency,
|
||
discharging_efficiency=discharging_efficiency,
|
||
capacity_wh=capacity_wh,
|
||
initial_soc_percentage=initial_soc_percentage,
|
||
min_soc_wh=min_soc_wh,
|
||
max_charge_power_w=max_charge_power_w,
|
||
levelized_cost_of_storage_kwh=levelized_cost_of_storage_kwh,
|
||
current_energy_content=Mock(return_value=0.0),
|
||
)
|
||
|
||
sim = Mock()
|
||
sim.battery = bat
|
||
sim.inverter = inv
|
||
sim.ev = None
|
||
sim.ac_charge_hours = np.array(ac_charge_hours, dtype=float)
|
||
sim.elect_price_hourly = np.array(elect_price_hourly, dtype=float)
|
||
sim.load_energy_array = np.array(load_energy_array, dtype=float)
|
||
return sim
|
||
|
||
|
||
def _run_evaluate_with_mocked_sim(
|
||
config_eos,
|
||
mock_sim,
|
||
*,
|
||
ac_charge_break_even: float = 1.0,
|
||
start_hour: int = 0,
|
||
base_gesamtbilanz: float = 0.0,
|
||
):
|
||
"""
|
||
Patch a GeneticOptimization so that:
|
||
- evaluate_inner() returns a controlled base Gesamtbilanz_Euro
|
||
- self.simulation is replaced by mock_sim
|
||
Then call evaluate() and return the fitness tuple.
|
||
"""
|
||
from akkudoktoreos.optimization.genetic.genetic import (
|
||
ApplianceGeneLayout,
|
||
GeneticOptimization,
|
||
)
|
||
|
||
config_eos.merge_settings_from_dict(
|
||
{
|
||
"prediction": {"hours": 48},
|
||
"optimization": {"hours": 24},
|
||
}
|
||
)
|
||
config_eos.optimization.genetic.penalties = {
|
||
"ev_soc_miss": 10,
|
||
"ac_charge_break_even": ac_charge_break_even,
|
||
}
|
||
|
||
optim = GeneticOptimization.__new__(GeneticOptimization)
|
||
# Minimal __init__ state expected by evaluate()
|
||
optim.config = config_eos
|
||
optim.optimize_ev = False
|
||
optim.verbose = False
|
||
optim.opti_param = {"home_appliance": 0}
|
||
optim.appliance_layout = ApplianceGeneLayout([])
|
||
optim.simulation = mock_sim
|
||
|
||
# evaluate_inner() just returns the base balance; we test the *additional* penalty
|
||
dummy_result = {
|
||
"Gesamtbilanz_Euro": base_gesamtbilanz,
|
||
"Gesamt_Verluste": 0.0,
|
||
"EAuto_SoC_pro_Stunde": np.zeros(48),
|
||
}
|
||
|
||
# DEAP individuals are lists that accept attribute assignment; use a trivial subclass
|
||
class _Ind(list): # noqa: N801
|
||
pass
|
||
|
||
fake_individual = _Ind([0] * 48)
|
||
|
||
with patch.object(optim, "evaluate_inner", return_value=dummy_result):
|
||
fitness = optim.evaluate(
|
||
fake_individual,
|
||
parameters=Mock(
|
||
ems=Mock(preis_euro_pro_wh_akku=0.0),
|
||
eauto=None,
|
||
),
|
||
start_hour=start_hour,
|
||
worst_case=False,
|
||
)
|
||
return fitness[0]
|
||
|
||
|
||
def _run_evaluate_with_mocked_ev_soc(config_eos, ev_soc_percentage: float) -> float:
|
||
"""Return fitness for a mocked EV SoC while EV optimization is active."""
|
||
from akkudoktoreos.optimization.genetic.genetic import (
|
||
ApplianceGeneLayout,
|
||
GeneticOptimization,
|
||
)
|
||
|
||
config_eos.merge_settings_from_dict(
|
||
{
|
||
"prediction": {"hours": 48},
|
||
"optimization": {"hours": 48},
|
||
}
|
||
)
|
||
config_eos.optimization.genetic.penalties = {
|
||
"ev_soc_miss": 10,
|
||
"ac_charge_break_even": 1.0,
|
||
}
|
||
|
||
optim = GeneticOptimization.__new__(GeneticOptimization)
|
||
optim.config = config_eos
|
||
optim.optimize_ev = True
|
||
optim.verbose = False
|
||
optim.opti_param = {"home_appliance": 0}
|
||
optim.appliance_layout = ApplianceGeneLayout([])
|
||
|
||
mock_ev = Mock()
|
||
mock_ev.current_soc_percentage.return_value = ev_soc_percentage
|
||
|
||
sim = Mock()
|
||
sim.battery = None
|
||
sim.inverter = None
|
||
sim.ev = mock_ev
|
||
sim.ac_charge_hours = None
|
||
sim.elect_price_hourly = None
|
||
sim.load_energy_array = None
|
||
optim.simulation = sim
|
||
|
||
dummy_result = {
|
||
"Gesamtbilanz_Euro": 1.0,
|
||
"Gesamt_Verluste": 0.0,
|
||
"EAuto_SoC_pro_Stunde": np.zeros(48),
|
||
}
|
||
|
||
class _Ind(list): # noqa: N801
|
||
pass
|
||
|
||
fake_individual = _Ind([0] * 96)
|
||
|
||
with patch.object(optim, "evaluate_inner", return_value=dummy_result):
|
||
fitness = optim.evaluate(
|
||
fake_individual,
|
||
parameters=Mock(
|
||
ems=Mock(preis_euro_pro_wh_akku=0.0),
|
||
eauto=Mock(min_soc_percentage=70, max_soc_percentage=100),
|
||
),
|
||
start_hour=0,
|
||
worst_case=False,
|
||
)
|
||
return fitness[0]
|
||
|
||
|
||
class TestEvSocPenalty:
|
||
"""EV SoC target is a minimum-only penalty."""
|
||
|
||
def test_ev_soc_below_min_adds_penalty(self, config_eos):
|
||
assert _run_evaluate_with_mocked_ev_soc(config_eos, 65) == pytest.approx(51.0)
|
||
|
||
def test_ev_soc_above_max_does_not_add_min_soc_penalty(self, config_eos):
|
||
assert _run_evaluate_with_mocked_ev_soc(config_eos, 105) == pytest.approx(1.0)
|
||
|
||
|
||
class TestAcChargeBreakEvenPenalty:
|
||
"""Break-even penalty in GeneticOptimization.evaluate().
|
||
|
||
The penalty adds a positive (bad) contribution to the fitness score whenever
|
||
AC grid charging is scheduled at an hour where the round-trip loss means the
|
||
stored energy can never be discharged at a price sufficient to recover costs,
|
||
taking into account that free PV-charged energy already in the battery covers
|
||
the most expensive future hours first.
|
||
"""
|
||
|
||
# -----------------------------------------------------------------
|
||
# 5a. No AC charging → no penalty
|
||
# -----------------------------------------------------------------
|
||
|
||
def test_no_ac_charging_no_penalty(self, config_eos):
|
||
"""When no AC charging is scheduled, fitness equals the base balance."""
|
||
n = 24
|
||
sim = _make_mock_simulation(
|
||
ac_charge_hours=[0.0] * n,
|
||
elect_price_hourly=[0.0003] * n,
|
||
load_energy_array=[1000.0] * n,
|
||
)
|
||
base = 1.5
|
||
fitness = _run_evaluate_with_mocked_sim(config_eos, sim, base_gesamtbilanz=base)
|
||
assert fitness == pytest.approx(base, rel=1e-9)
|
||
|
||
# -----------------------------------------------------------------
|
||
# 5b. AC charging profitable → no penalty
|
||
# -----------------------------------------------------------------
|
||
|
||
def test_profitable_ac_charging_no_penalty(self, config_eos):
|
||
"""When future discharge price > P_charge / η, charging is justified → no penalty."""
|
||
n = 24
|
||
# Charge at hour 0: 0.0001 €/Wh
|
||
# Round-trip: 0.93 * 0.95 * 0.95 * 0.95 ≈ 0.7975
|
||
# break-even price ≈ 0.0001 / 0.7975 ≈ 0.0001254 €/Wh
|
||
# Future hour 1 price: 0.0003 > break-even → profitable
|
||
prices = [0.0001] + [0.0003] * (n - 1)
|
||
ac_charge = [1.0] + [0.0] * (n - 1)
|
||
loads = [1000.0] * n
|
||
|
||
sim = _make_mock_simulation(
|
||
ac_to_dc_efficiency=0.93,
|
||
dc_to_ac_efficiency=0.95,
|
||
charging_efficiency=0.95,
|
||
discharging_efficiency=0.95,
|
||
ac_charge_hours=ac_charge,
|
||
elect_price_hourly=prices,
|
||
load_energy_array=loads,
|
||
initial_soc_percentage=0.0, # empty battery → no free PV energy
|
||
)
|
||
base = 0.0
|
||
fitness = _run_evaluate_with_mocked_sim(config_eos, sim, base_gesamtbilanz=base)
|
||
# penalty should be 0 (or very small due to floating-point rounding)
|
||
assert fitness == pytest.approx(base, abs=1e-9)
|
||
|
||
# -----------------------------------------------------------------
|
||
# 5c. AC charging unprofitable → penalty fires
|
||
# -----------------------------------------------------------------
|
||
|
||
def test_unprofitable_ac_charging_adds_penalty(self, config_eos):
|
||
"""When future discharge prices are too low to justify AC charging, penalty is added."""
|
||
n = 24
|
||
# Charge at hour 0: 0.0004 €/Wh
|
||
# Round-trip: 0.93 * 0.95 * 0.95 * 0.95 ≈ 0.7975
|
||
# break-even price ≈ 0.0004 / 0.7975 ≈ 0.000501 €/Wh
|
||
# All future prices: 0.0003 < break-even → unprofitable
|
||
prices = [0.0004] + [0.0003] * (n - 1)
|
||
ac_charge = [1.0] + [0.0] * (n - 1)
|
||
loads = [1000.0] * n
|
||
|
||
sim = _make_mock_simulation(
|
||
ac_to_dc_efficiency=0.93,
|
||
dc_to_ac_efficiency=0.95,
|
||
charging_efficiency=0.95,
|
||
discharging_efficiency=0.95,
|
||
ac_charge_hours=ac_charge,
|
||
elect_price_hourly=prices,
|
||
load_energy_array=loads,
|
||
initial_soc_percentage=0.0,
|
||
)
|
||
base = 0.0
|
||
fitness = _run_evaluate_with_mocked_sim(config_eos, sim, base_gesamtbilanz=base)
|
||
# Fitness must be worse (higher) than base
|
||
assert fitness > base + 1e-6
|
||
|
||
def test_lcos_is_included_in_ac_charge_break_even_price(self, config_eos):
|
||
"""LCOS can make an otherwise profitable price spread unprofitable."""
|
||
n = 24
|
||
prices = [0.0001] + [0.00015] * (n - 1)
|
||
sim = _make_mock_simulation(
|
||
ac_to_dc_efficiency=1.0,
|
||
dc_to_ac_efficiency=1.0,
|
||
charging_efficiency=1.0,
|
||
discharging_efficiency=1.0,
|
||
levelized_cost_of_storage_kwh=0.10,
|
||
ac_charge_hours=[1.0] + [0.0] * (n - 1),
|
||
elect_price_hourly=prices,
|
||
load_energy_array=[1000.0] * n,
|
||
initial_soc_percentage=0.0,
|
||
)
|
||
|
||
fitness = _run_evaluate_with_mocked_sim(config_eos, sim)
|
||
|
||
# Break-even is 0.0001 + 0.0001 = 0.0002 EUR/Wh.
|
||
# The 0.00005 EUR/Wh gap on a 5000 Wh charge adds 0.25 EUR.
|
||
assert fitness == pytest.approx(0.25)
|
||
|
||
# -----------------------------------------------------------------
|
||
# 5d. Free PV energy covers expensive hours → penalty reduced/eliminated
|
||
# -----------------------------------------------------------------
|
||
|
||
def test_free_pv_energy_eliminates_penalty(self, config_eos):
|
||
"""PV energy covers the best future hour; penalty is larger than without PV energy."""
|
||
n = 24
|
||
# Charge at hour 0: 0.0005 €/Wh
|
||
# Round-trip: 0.93*0.95*0.95*0.95 ≈ 0.7974
|
||
# break-even ≈ 0.0005 / 0.7974 ≈ 0.000627 €/Wh
|
||
#
|
||
# Future: one expensive hour at 0.0006 (< break-even!) and rest at 0.0003
|
||
# → even the best future price 0.0006 < 0.000627 → AC charging is never profitable
|
||
#
|
||
# Empty battery: best_uncovered = 0.0006 < 0.000627 → penalty fires
|
||
# With PV (50% SoC → ~4512 Wh deliverable free AC): covers the 0.0006 hour (1000 Wh)
|
||
# → best_uncovered drops to 0.0003 → penalty fires with LARGER excess
|
||
prices = [0.0005] + [0.0003] * (n - 2) + [0.0006] # expensive hour at end
|
||
ac_charge = [1.0] + [0.0] * (n - 1)
|
||
loads = [1000.0] * n
|
||
|
||
sim_empty = _make_mock_simulation(
|
||
ac_to_dc_efficiency=0.93,
|
||
dc_to_ac_efficiency=0.95,
|
||
charging_efficiency=0.95,
|
||
discharging_efficiency=0.95,
|
||
ac_charge_hours=list(ac_charge),
|
||
elect_price_hourly=list(prices),
|
||
load_energy_array=list(loads),
|
||
initial_soc_percentage=0.0, # no free PV energy
|
||
)
|
||
sim_with_pv = _make_mock_simulation(
|
||
ac_to_dc_efficiency=0.93,
|
||
dc_to_ac_efficiency=0.95,
|
||
charging_efficiency=0.95,
|
||
discharging_efficiency=0.95,
|
||
ac_charge_hours=list(ac_charge),
|
||
elect_price_hourly=list(prices),
|
||
load_energy_array=list(loads),
|
||
capacity_wh=10_000.0,
|
||
initial_soc_percentage=50.0, # 5000 Wh free PV energy → ~4512 Wh deliverable AC
|
||
)
|
||
|
||
fitness_empty = _run_evaluate_with_mocked_sim(config_eos, sim_empty, base_gesamtbilanz=0.0)
|
||
fitness_pv = _run_evaluate_with_mocked_sim(config_eos, sim_with_pv, base_gesamtbilanz=0.0)
|
||
|
||
# Both are penalised (break-even > max future price)
|
||
assert fitness_empty > 1e-6, "Empty battery: best_uncovered=0.0006 < break_even→ penalty"
|
||
assert fitness_pv > 1e-6, "With PV: free energy covers 0.0006 hour, best drops to 0.0003"
|
||
|
||
# With PV the expensive hour is covered for free → uncovered best price is lower
|
||
# → excess_cost_per_wh = break_even - best_uncovered is larger → penalty is BIGGER
|
||
assert fitness_pv > fitness_empty, "PV covers expensive hour → uncovered best is cheaper"
|
||
|
||
|
||
def test_free_pv_energy_exposes_only_cheap_future_prices(self, config_eos):
|
||
"""When PV covers ALL expensive hours, best_uncovered_price = 0 → max penalty."""
|
||
n = 5
|
||
capacity_wh = 10_000.0
|
||
# Free PV: initial 80% → (8000 - 0) * 0.95 * 0.95 = 7220 Wh deliverable AC
|
||
# Future loads: 2 expensive hours × 1000 Wh = 2000 Wh → all covered by free PV
|
||
prices = [0.0010, 0.0008, 0.0008, 0.0002, 0.0002]
|
||
ac_charge = [1.0, 0.0, 0.0, 0.0, 0.0]
|
||
loads = [1000.0] * n
|
||
|
||
sim = _make_mock_simulation(
|
||
ac_to_dc_efficiency=0.93,
|
||
dc_to_ac_efficiency=0.95,
|
||
charging_efficiency=0.95,
|
||
discharging_efficiency=0.95,
|
||
capacity_wh=capacity_wh,
|
||
initial_soc_percentage=80.0,
|
||
ac_charge_hours=ac_charge,
|
||
elect_price_hourly=prices,
|
||
load_energy_array=loads,
|
||
)
|
||
|
||
# break_even = 0.001 / (0.93*0.95*0.95*0.95) ≈ 0.001254
|
||
# PV covers both 0.0008 hours → best_uncovered = 0.0002 → penalty fires
|
||
fitness = _run_evaluate_with_mocked_sim(config_eos, sim, base_gesamtbilanz=0.0)
|
||
assert fitness > 1e-6
|
||
|
||
# -----------------------------------------------------------------
|
||
# 5e. Penalty factor scales the penalty
|
||
# -----------------------------------------------------------------
|
||
|
||
def test_penalty_factor_scales_linearly(self, config_eos):
|
||
"""The ac_charge_break_even factor doubles the penalty when doubled."""
|
||
n = 24
|
||
prices = [0.0004] + [0.0003] * (n - 1)
|
||
ac_charge = [1.0] + [0.0] * (n - 1)
|
||
loads = [1000.0] * n
|
||
|
||
def _fitness(factor):
|
||
sim = _make_mock_simulation(
|
||
ac_to_dc_efficiency=0.93,
|
||
dc_to_ac_efficiency=0.95,
|
||
charging_efficiency=0.95,
|
||
discharging_efficiency=0.95,
|
||
ac_charge_hours=list(ac_charge),
|
||
elect_price_hourly=list(prices),
|
||
load_energy_array=list(loads),
|
||
initial_soc_percentage=0.0,
|
||
)
|
||
return _run_evaluate_with_mocked_sim(
|
||
config_eos, sim, ac_charge_break_even=factor, base_gesamtbilanz=0.0
|
||
)
|
||
|
||
f1 = _fitness(1.0)
|
||
f2 = _fitness(2.0)
|
||
|
||
# With factor=2 the penalty should be exactly double
|
||
assert f2 == pytest.approx(2.0 * f1, rel=1e-6)
|
||
|
||
# -----------------------------------------------------------------
|
||
# 5f. Zero or negative AC charge factor → no penalty contribution
|
||
# -----------------------------------------------------------------
|
||
|
||
def test_zero_ac_charge_factor_no_penalty(self, config_eos):
|
||
"""ac_charge_hours[h] = 0 means no charging, so no penalty."""
|
||
n = 24
|
||
prices = [0.0010] * n # Expensive, but no AC charging
|
||
ac_charge = [0.0] * n
|
||
loads = [1000.0] * n
|
||
|
||
sim = _make_mock_simulation(
|
||
ac_charge_hours=ac_charge,
|
||
elect_price_hourly=prices,
|
||
load_energy_array=loads,
|
||
)
|
||
base = 3.0
|
||
fitness = _run_evaluate_with_mocked_sim(config_eos, sim, base_gesamtbilanz=base)
|
||
assert fitness == pytest.approx(base, abs=1e-9)
|
||
|
||
# -----------------------------------------------------------------
|
||
# 5g. No battery / inverter → penalty skipped entirely
|
||
# -----------------------------------------------------------------
|
||
|
||
def test_no_battery_skips_penalty(self, config_eos):
|
||
"""When no battery is present, the penalty block is skipped."""
|
||
n = 24
|
||
sim = _make_mock_simulation(
|
||
ac_charge_hours=[1.0] * n,
|
||
elect_price_hourly=[0.001] * n,
|
||
load_energy_array=[1000.0] * n,
|
||
)
|
||
sim.battery = None # no battery → penalty skipped
|
||
|
||
base = 2.5
|
||
fitness = _run_evaluate_with_mocked_sim(config_eos, sim, base_gesamtbilanz=base)
|
||
assert fitness == pytest.approx(base, abs=1e-9)
|
||
|
||
def test_no_inverter_skips_penalty(self, config_eos):
|
||
"""When no inverter is present, the penalty block is skipped."""
|
||
n = 24
|
||
sim = _make_mock_simulation(
|
||
ac_charge_hours=[1.0] * n,
|
||
elect_price_hourly=[0.001] * n,
|
||
load_energy_array=[1000.0] * n,
|
||
)
|
||
sim.inverter = None # no inverter → penalty skipped
|
||
|
||
base = 2.5
|
||
fitness = _run_evaluate_with_mocked_sim(config_eos, sim, base_gesamtbilanz=base)
|
||
assert fitness == pytest.approx(base, abs=1e-9)
|
||
|
||
# -----------------------------------------------------------------
|
||
# 5h. Unit-level break-even maths (no optimizer setup needed)
|
||
# -----------------------------------------------------------------
|
||
|
||
def test_break_even_formula(self):
|
||
"""Verify the break-even formula: P_break_even = P_charge / η_round_trip."""
|
||
ac_to_dc = 0.93
|
||
bat_charge = 0.95
|
||
bat_discharge = 0.95
|
||
dc_to_ac = 0.95
|
||
|
||
eta_rt = ac_to_dc * bat_charge * bat_discharge * dc_to_ac
|
||
p_charge = 0.0004 # 40 ct/kWh
|
||
|
||
break_even = p_charge / eta_rt
|
||
|
||
# 1 Wh drawn from grid at p_charge → η_rt Wh delivered
|
||
# Need discharge price ≥ p_charge / η_rt to break even
|
||
assert break_even == pytest.approx(p_charge / eta_rt, rel=1e-9)
|
||
assert break_even > p_charge # Always worse due to losses
|
||
|
||
def test_free_pv_energy_formula(self):
|
||
"""Verify free pv energy: (initial_soc - min_soc) × η_bat_dis × η_inv_dis."""
|
||
capacity_wh = 10_000.0
|
||
initial_soc_pct = 60.0
|
||
min_soc_wh = 500.0
|
||
bat_dis = 0.95
|
||
inv_dis = 0.95
|
||
|
||
initial_soc_wh = (initial_soc_pct / 100.0) * capacity_wh # 6000
|
||
free_ac_wh = max(0.0, initial_soc_wh - min_soc_wh) * bat_dis * inv_dis
|
||
|
||
# = (6000 - 500) * 0.95 * 0.95 = 5500 * 0.9025 = 4963.75
|
||
expected = 5500.0 * 0.95 * 0.95
|
||
assert free_ac_wh == pytest.approx(expected, rel=1e-9)
|