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https://github.com/Akkudoktor-EOS/EOS.git
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Andreas
38eb4ad450
Defer inactive EV deadline fields to the optimizer port, reject nonfinite export rates, and document the hourly Optimize boundary. Verify converter IDs, rates and LCOS, separate GENETIC0 interpolation, physical boundary flows and independent GENETIC repricing.
106 lines
4.2 KiB
Python
106 lines
4.2 KiB
Python
import numpy as np
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import pytest
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from akkudoktoreos.prediction.interpolator import get_eos_load_interpolator
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def test_quarter_hour_energy_is_converted_back_to_same_mean_power():
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"""Splitting hourly energy must not change the minute-load probability lookup."""
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interpolator = get_eos_load_interpolator()
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hourly_load_wh = 800.0
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hourly_pv_wh = 1200.0
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slot_duration_h = 0.25
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hourly = interpolator.calculate_expected_direct_consumption(hourly_load_wh, hourly_pv_wh)
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quarter_hour = interpolator.calculate_expected_direct_consumption(
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(hourly_load_wh / 4) / slot_duration_h,
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(hourly_pv_wh / 4) / slot_duration_h,
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)
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assert quarter_hour == pytest.approx(hourly)
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def test_load_above_probability_grid_uses_highest_supported_distribution():
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"""Out-of-range household load must not make self-consumption jump to zero."""
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interpolator = get_eos_load_interpolator()
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at_boundary = interpolator.calculate_self_consumption(3450.0, 5000.0)
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above_boundary = interpolator.calculate_self_consumption(4000.0, 5000.0)
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assert above_boundary == pytest.approx(at_boundary)
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assert above_boundary > 0.99
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def test_expected_direct_consumption_accounts_for_subhourly_load_variation():
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"""Expected overlap must be below the optimistic overlap of interval means."""
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interpolator = get_eos_load_interpolator()
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direct_power_w = interpolator.calculate_expected_direct_consumption(800.0, 1200.0)
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assert direct_power_w == pytest.approx(621.0, abs=2.0)
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assert 0.0 < direct_power_w < 800.0
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@pytest.mark.parametrize(
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("mean_load_power_w", "pv_power_w"),
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[(800.0, 1200.0), (1000.0, 500.0), (1500.0, 1500.0)],
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)
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def test_expected_direct_consumption_produces_conservative_energy_balance(
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mean_load_power_w, pv_power_w
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):
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"""Direct use, residual load and surplus must conserve both mean powers."""
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interpolator = get_eos_load_interpolator()
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direct_power_w = interpolator.calculate_expected_direct_consumption(
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mean_load_power_w, pv_power_w
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)
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residual_load_w = mean_load_power_w - direct_power_w
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pv_surplus_w = pv_power_w - direct_power_w
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assert 0.0 <= direct_power_w <= min(mean_load_power_w, pv_power_w)
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assert direct_power_w + residual_load_w == pytest.approx(mean_load_power_w)
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assert direct_power_w + pv_surplus_w == pytest.approx(pv_power_w)
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def test_expected_direct_consumption_preserves_forecast_mean_at_high_pv():
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"""A PV level above every normalized load bin covers the complete mean load."""
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interpolator = get_eos_load_interpolator()
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direct_power_w = interpolator.calculate_expected_direct_consumption(3000.0, 10000.0)
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assert direct_power_w == pytest.approx(3000.0)
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@pytest.mark.parametrize("load,pv", [(4000.0, 5000.0), (10000.0, 20000.0), (0.0, 0.0)])
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def test_genetic_interpolator_boundaries_are_finite_and_physical(load, pv):
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interpolator = get_eos_load_interpolator()
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fraction = interpolator.calculate_self_consumption(load, pv)
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direct = interpolator.calculate_expected_direct_consumption(load, pv)
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assert np.isfinite(fraction)
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assert 0.0 <= fraction <= 1.0
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assert np.isfinite(direct)
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assert 0.0 <= direct <= min(load, pv)
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def test_genetic0_inverter_keeps_its_independent_interpolator():
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from akkudoktoreos.devices.genetic0.genetic0inverter import (
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Genetic0Inverter,
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Genetic0InverterParameters,
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)
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from akkudoktoreos.optimization.genetic0.genetic0loadinterpolator import (
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get_genetic0_load_interpolator,
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)
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inverter = Genetic0Inverter(Genetic0InverterParameters(device_id="legacy", max_power_wh=10000))
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assert inverter.self_consumption_predictor is get_genetic0_load_interpolator()
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assert inverter.self_consumption_predictor is not get_eos_load_interpolator()
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@pytest.mark.parametrize("load,pv", [(4000.0, 5000.0), (10000.0, 20000.0)])
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def test_genetic_inverter_boundary_flows_remain_nonnegative(load, pv):
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from akkudoktoreos.devices.genetic.inverter import Inverter, InverterParameters
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inverter = Inverter(InverterParameters(device_id="boundary", max_power_wh=25000))
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flows = inverter.process_energy(generation=pv, consumption=load, hour=0)
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assert all(np.isfinite(value) and value >= 0.0 for value in flows)
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