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feat(optimization): schedule any number of flexible consumers
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>
This commit is contained in:
Andreas
co-authored by
Claude Opus 4.8
Claude Opus 4.8
parent
c59bf1b486
commit
67cf6f7d8a
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"""Tests for flexible consumers (home appliances).
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Covers the energy-preserving load profile, allowed start computation, the
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appliance genome layout (ONCE/DAILY), multi-device scheduling and the
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deprecated single-appliance compatibility path.
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"""
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import numpy as np
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import pytest
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from pydantic import ValidationError
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from akkudoktoreos.config.configabc import TimeWindow, TimeWindowSequence
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from akkudoktoreos.core.cache import CacheEnergyManagementStore
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from akkudoktoreos.core.coreabc import get_ems
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from akkudoktoreos.devices.devices import DevicesCommonSettings
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from akkudoktoreos.devices.genetic.homeappliance import (
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HomeAppliance,
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resample_power_to_slot_energy,
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)
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from akkudoktoreos.optimization.genetic.genetic import GeneticOptimization
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from akkudoktoreos.optimization.genetic.geneticdevices import HomeApplianceParameters
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from akkudoktoreos.optimization.genetic.geneticparams import GeneticOptimizationParameters
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from akkudoktoreos.utils.datetimeutil import to_datetime, to_duration, to_time
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ems_eos = get_ems(init=True)
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def _appliance(prediction_hours: int, slot_duration_h: float, **params) -> HomeAppliance:
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return HomeAppliance(
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HomeApplianceParameters(**params),
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optimization_hours=prediction_hours,
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prediction_hours=prediction_hours,
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slot_duration_h=slot_duration_h,
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)
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def _ems(n: int, load: float = 500.0) -> dict:
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return {
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"pv_prognose_wh": [0.0] * n,
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"strompreis_euro_pro_wh": [0.0003] * n,
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"einspeiseverguetung_euro_pro_wh": 0.00007,
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"preis_euro_pro_wh_akku": 0.0001,
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"gesamtlast": [load] * n,
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}
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# --------------------------------------------------------------------------- #
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# Energy-preserving resampling
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# --------------------------------------------------------------------------- #
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@pytest.mark.parametrize(
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"input_interval, slot_interval",
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[(3600, 900), (900, 3600), (600, 900), (1200, 900), (1800, 900), (3600, 3600)],
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)
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def test_resample_conserves_energy(input_interval: int, slot_interval: int):
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"""Energy is conserved for integer and non-integer interval ratios."""
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power = [1000.0, 0.0, 500.0, 2500.0, 750.0]
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energy = resample_power_to_slot_energy(power, input_interval, slot_interval)
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expected = sum(p * input_interval / 3600 for p in power)
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assert energy.sum() == pytest.approx(expected)
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assert (energy >= 0).all()
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def test_flat_fallback_hourly_matches_legacy():
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"""The flat consumption_wh/duration_h fallback reproduces the legacy curve."""
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appliance = _appliance(48, 1.0, device_id="dw", consumption_wh=2000, duration_h=2)
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assert appliance.run_slots == 2
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assert list(appliance.run_energy_wh) == [1000.0, 1000.0]
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appliance.build_load_curve([5])
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curve = appliance.get_load_curve()
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assert curve[5] == 1000.0 and curve[6] == 1000.0
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assert curve.sum() == 2000.0
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def test_flat_fallback_15min_grid():
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"""The flat fallback resamples onto the quarter-hour grid, conserving energy."""
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appliance = _appliance(192, 0.25, device_id="dw", consumption_wh=2000, duration_h=2)
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assert appliance.run_slots == 8 # 2 h -> 8 quarter-hours
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assert appliance.run_energy_wh.sum() == pytest.approx(2000.0)
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assert all(value == pytest.approx(250.0) for value in appliance.run_energy_wh)
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def test_build_load_curve_overlapping_runs_add():
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"""Overlapping runs of one appliance sum their per-slot energy."""
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appliance = _appliance(
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10, 1.0, device_id="d", load_profile_power_w=[3600.0, 3600.0], load_profile_interval_seconds=3600
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)
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appliance.build_load_curve([2, 3]) # runs occupy [2,3] and [3,4] -> overlap at 3
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curve = appliance.get_load_curve()
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assert curve[2] == pytest.approx(3600.0)
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assert curve[3] == pytest.approx(7200.0)
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assert curve[4] == pytest.approx(3600.0)
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# --------------------------------------------------------------------------- #
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# Allowed start slots and time windows
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# --------------------------------------------------------------------------- #
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def test_allowed_start_slots_time_window_and_horizon():
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"""Only starts whose full run fits a window and the horizon are allowed."""
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slot0 = to_datetime("2026-07-15 00:00:00")
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windows = TimeWindowSequence(
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windows=[TimeWindow(start_time=to_time("10:00"), duration=to_duration("3 hours"))]
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)
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appliance = _appliance(
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48, 1.0, device_id="d", consumption_wh=1000, duration_h=1, time_windows=windows
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)
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allowed = appliance.allowed_start_slots(
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slot0_datetime=slot0, earliest_slot=0, horizon_end_slot=48
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)
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# window 10:00-13:00, run 1 h -> starts 10,11,12 each day (+24 on day 1)
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assert allowed == [10, 11, 12, 34, 35, 36]
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def test_allowed_start_slots_window_over_midnight():
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"""A window crossing midnight yields starts on both sides of midnight."""
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slot0 = to_datetime("2026-07-15 00:00:00")
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windows = TimeWindowSequence(
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windows=[TimeWindow(start_time=to_time("23:00"), duration=to_duration("3 hours"))]
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)
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appliance = _appliance(
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48, 1.0, device_id="d", consumption_wh=1000, duration_h=1, time_windows=windows
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)
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allowed = appliance.allowed_start_slots(
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slot0_datetime=slot0, earliest_slot=0, horizon_end_slot=48
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)
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# 23:00-02:00 window: a run starting at 23:00 crosses midnight (ends 00:00).
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# TimeWindow evaluates the window on the start's own calendar day, so the
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# only allowed start per day is 23:00 (slot 23 on day 0, slot 47 on day 1).
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assert allowed == [23, 47]
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def test_allowed_start_slots_weekday_restriction():
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"""A weekday-restricted window only allows starts on that weekday."""
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slot0 = to_datetime("2026-07-15 00:00:00") # Wednesday
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weekday = slot0.day_of_week
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windows = TimeWindowSequence(
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windows=[
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TimeWindow(
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start_time=to_time("10:00"), duration=to_duration("2 hours"), day_of_week=weekday
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)
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]
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)
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appliance = _appliance(
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72, 1.0, device_id="d", consumption_wh=1000, duration_h=1, time_windows=windows
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)
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allowed = appliance.allowed_start_slots(
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slot0_datetime=slot0, earliest_slot=0, horizon_end_slot=72
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)
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# Only day 0 (the Wednesday) matches: starts 10, 11
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assert allowed == [10, 11]
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# --------------------------------------------------------------------------- #
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# Genome layout (ONCE / DAILY)
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# --------------------------------------------------------------------------- #
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def _optimizer(config_eos, *, prediction_hours: int, horizon_hours: int, interval: int, hour: int):
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config_eos.merge_settings_from_dict(
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{
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"prediction": {"hours": prediction_hours},
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"optimization": {"horizon_hours": horizon_hours, "interval": interval},
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}
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)
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ems_eos.set_start_datetime(to_datetime().set(hour=hour, minute=0))
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return GeneticOptimization(fixed_seed=1)
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def test_once_layout_single_gene(config_eos):
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opt = _optimizer(config_eos, prediction_hours=48, horizon_hours=48, interval=3600, hour=10)
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slot0 = opt.ems.start_datetime.set(hour=0, minute=0, second=0, microsecond=0)
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appliance = _appliance(48, 1.0, device_id="d", consumption_wh=1000, duration_h=2)
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layout = opt._build_appliance_layout([appliance], slot0)
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assert layout.n_genes == 1
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assert layout.genes[0].run_date is None
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assert layout.genes[0].allowed_start_slots[0] == opt._start_day_slot()
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def test_once_no_valid_start_raises(config_eos):
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opt = _optimizer(config_eos, prediction_hours=48, horizon_hours=10, interval=3600, hour=10)
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slot0 = opt.ems.start_datetime.set(hour=0, minute=0, second=0, microsecond=0)
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# 02:00 window is in the past (start slot 10) and day 1 is beyond the 10 h horizon.
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windows = TimeWindowSequence(
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windows=[TimeWindow(start_time=to_time("02:00"), duration=to_duration("1 hours"))]
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)
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appliance = _appliance(
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48, 1.0, device_id="d", consumption_wh=500, duration_h=1, time_windows=windows
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)
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with pytest.raises(ValueError, match="no valid start"):
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opt._build_appliance_layout([appliance], slot0)
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def test_daily_layout_one_gene_per_calendar_day(config_eos):
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opt = _optimizer(config_eos, prediction_hours=72, horizon_hours=72, interval=3600, hour=0)
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slot0 = opt.ems.start_datetime.set(hour=0, minute=0, second=0, microsecond=0)
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windows = TimeWindowSequence(
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windows=[TimeWindow(start_time=to_time("10:00"), duration=to_duration("2 hours"))]
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)
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appliance = _appliance(
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72,
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1.0,
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device_id="d",
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consumption_wh=500,
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duration_h=1,
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schedule_mode="DAILY",
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time_windows=windows,
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)
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layout = opt._build_appliance_layout([appliance], slot0)
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assert layout.n_genes == 3 # 3 calendar days in the 72 h horizon
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assert len({gene.run_date for gene in layout.genes}) == 3
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for gene in layout.genes:
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assert len(gene.allowed_start_slots) == 2 # starts 10 and 11 on each day
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def test_daily_layout_partial_first_day(config_eos):
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"""A partial first day (start after the window) produces no gene for that day."""
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opt = _optimizer(config_eos, prediction_hours=48, horizon_hours=48, interval=3600, hour=14)
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slot0 = opt.ems.start_datetime.set(hour=0, minute=0, second=0, microsecond=0)
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windows = TimeWindowSequence(
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windows=[TimeWindow(start_time=to_time("10:00"), duration=to_duration("2 hours"))]
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)
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appliance = _appliance(
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48,
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1.0,
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device_id="d",
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consumption_wh=500,
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duration_h=1,
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schedule_mode="DAILY",
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time_windows=windows,
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)
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layout = opt._build_appliance_layout([appliance], slot0)
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# Day 0 window (10:00-12:00) is already in the past at start hour 14 -> only day 1.
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assert layout.n_genes == 1
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assert all(slot >= opt._start_day_slot() for slot in layout.genes[0].allowed_start_slots)
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# --------------------------------------------------------------------------- #
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# Multiple devices, aggregate and deprecated compatibility (integration)
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# --------------------------------------------------------------------------- #
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def test_multiple_appliances_scheduled_and_aggregate(config_eos):
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config_eos.merge_settings_from_dict(
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{
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"prediction": {"hours": 48},
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"optimization": {
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"horizon_hours": 48,
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"interval": 3600,
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"genetic": {
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"individuals": 60,
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"generations": 10,
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"penalties": {"ev_soc_miss": 10, "ac_charge_break_even": 0},
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},
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},
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}
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)
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ems_eos.set_start_datetime(to_datetime().set(hour=0, minute=0))
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CacheEnergyManagementStore().clear()
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parameters = GeneticOptimizationParameters(
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ems=_ems(48),
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pv_akku=None,
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inverter=None,
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eauto=None,
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home_appliances=[
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HomeApplianceParameters(device_id="dw", consumption_wh=1000, duration_h=1),
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HomeApplianceParameters(device_id="wm", consumption_wh=2000, duration_h=2),
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],
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)
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solution = GeneticOptimization(fixed_seed=7).optimierung_ems(
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parameters=parameters, start_hour=0, ngen=3
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)
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per_device = solution.result.home_appliance_energy_wh
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assert set(per_device) == {"dw", "wm"}
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assert sum(per_device["dw"]) == pytest.approx(1000.0)
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assert sum(per_device["wm"]) == pytest.approx(2000.0)
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# Per-device energy sums exactly to the deprecated aggregate.
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aggregate = [
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(per_device["dw"][i] or 0.0) + (per_device["wm"][i] or 0.0)
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for i in range(len(per_device["dw"]))
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]
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reported = [value or 0.0 for value in solution.result.Home_appliance_wh_per_hour]
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assert reported == pytest.approx(aggregate)
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# Each device has an absolute start datetime.
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assert set(solution.appliance_starts) == {"dw", "wm"}
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assert len(solution.appliance_starts["dw"]) == 1
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# DDBC instructions are only emitted on RUN/OFF transitions.
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plan = solution.energy_management_plan()
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dw_instructions = [i for i in plan.instructions if i.resource_id == "dw"]
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modes = [str(i.operation_mode_id) for i in dw_instructions]
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# A single 1 h run yields an OFF/RUN/OFF sequence (no repeated RUN).
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assert modes.count("RUN") == 1
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def test_duplicate_device_id_rejected():
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with pytest.raises(ValidationError, match="unique"):
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GeneticOptimizationParameters(
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ems=_ems(2),
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pv_akku=None,
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inverter=None,
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eauto=None,
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home_appliances=[
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HomeApplianceParameters(device_id="x", consumption_wh=1000, duration_h=1),
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HomeApplianceParameters(device_id="x", consumption_wh=1000, duration_h=1),
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],
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)
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def test_dishwasher_and_home_appliances_conflict_rejected():
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with pytest.raises(ValidationError, match="either"):
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GeneticOptimizationParameters(
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ems=_ems(2),
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pv_akku=None,
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inverter=None,
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eauto=None,
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dishwasher=HomeApplianceParameters(device_id="d", consumption_wh=1000, duration_h=1),
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home_appliances=[
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HomeApplianceParameters(device_id="e", consumption_wh=1000, duration_h=1)
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],
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)
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def test_deprecated_dishwasher_maps_to_list():
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parameters = GeneticOptimizationParameters(
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ems=_ems(2),
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pv_akku=None,
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inverter=None,
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eauto=None,
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dishwasher=HomeApplianceParameters(device_id="d", consumption_wh=1000, duration_h=1),
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)
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resolved = parameters.resolved_home_appliances()
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assert [appliance.device_id for appliance in resolved] == ["d"]
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def test_max_home_appliances_is_upper_bound():
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with pytest.raises(ValidationError, match="exceeds max_home_appliances"):
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DevicesCommonSettings(
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max_home_appliances=1,
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home_appliances=[
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{"device_id": "a", "consumption_wh": 1000, "duration_h": 1},
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{"device_id": "b", "consumption_wh": 1000, "duration_h": 1},
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],
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)
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def test_start_solution_layout_mismatch_is_ignored(config_eos):
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opt = _optimizer(config_eos, prediction_hours=48, horizon_hours=48, interval=3600, hour=10)
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slot0 = opt.ems.start_datetime.set(hour=0, minute=0, second=0, microsecond=0)
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appliance = _appliance(48, 1.0, device_id="d", consumption_wh=1000, duration_h=1)
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opt.appliance_layout = opt._build_appliance_layout([appliance], slot0)
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opt.optimize_ev = False
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valid_index_count = len(opt.appliance_layout.genes[0].allowed_start_slots)
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# A tail index beyond the allowed range must be rejected.
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bad_solution = [0] * opt.total_slots + [valid_index_count + 5]
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assert opt._start_solution_matches_layout(bad_solution) is False
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good_solution = [0] * opt.total_slots + [0]
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assert opt._start_solution_matches_layout(good_solution) is True
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