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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
@@ -54,7 +54,7 @@ def genetic_simulation(config_eos) -> GeneticSimulation:
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battery=akku,
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)
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# Household device (currently not used, set to None)
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# Flexible consumer (fixed start at slot 2 for this deterministic test)
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home_appliance = HomeAppliance(
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HomeApplianceParameters(
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device_id="dishwasher1",
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@@ -65,6 +65,7 @@ def genetic_simulation(config_eos) -> GeneticSimulation:
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optimization_hours=config_eos.optimization.horizon_hours,
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prediction_hours=config_eos.prediction.hours,
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)
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home_appliance.build_load_curve([2])
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# Example initialization of electric car battery
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eauto = Battery(
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@@ -246,7 +247,7 @@ def genetic_simulation(config_eos) -> GeneticSimulation:
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prediction_hours=config_eos.prediction.hours,
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inverter=inverter,
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ev=eauto,
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home_appliance=home_appliance,
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home_appliances=[home_appliance],
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)
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# Init for test
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@@ -259,7 +260,6 @@ def genetic_simulation(config_eos) -> GeneticSimulation:
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simulation.dc_charge_hours[start_hour] = 1.0
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simulation.bat_discharge_hours[start_hour] = 1.0
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simulation.ev_charge_hours[start_hour] = 1.0
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simulation.home_appliance_start_hour = 2
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return simulation
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@@ -362,8 +362,8 @@ def test_simulation(genetic_simulation):
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# Check home appliances
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assert (
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sum(simulation.home_appliance.get_load_curve()) == 2000
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), "The sum of 'simulation.home_appliance.get_load_curve()' should be 2000."
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sum(simulation.home_appliances[0].get_load_curve()) == 2000
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), "The sum of 'simulation.home_appliances[0].get_load_curve()' should be 2000."
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assert (
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np.nansum(
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@@ -50,7 +50,7 @@ def genetic_simulation_2(config_eos) -> GeneticSimulation:
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battery = akku,
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)
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# Household device (currently not used, set to None)
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# Flexible consumer (fixed start at slot 2 for this deterministic test)
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home_appliance = HomeAppliance(
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HomeApplianceParameters(
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device_id="dishwasher1",
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@@ -61,6 +61,7 @@ def genetic_simulation_2(config_eos) -> GeneticSimulation:
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optimization_hours = config_eos.optimization.horizon_hours,
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prediction_hours = config_eos.prediction.hours,
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)
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home_appliance.build_load_curve([2])
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# Example initialization of electric car battery
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eauto = Battery(
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@@ -148,7 +149,7 @@ def genetic_simulation_2(config_eos) -> GeneticSimulation:
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prediction_hours = config_eos.prediction.hours,
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inverter=inverter,
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ev=eauto,
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home_appliance=home_appliance,
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home_appliances=[home_appliance],
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)
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ac = np.full(config_eos.prediction.hours, 0.0)
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@@ -157,7 +158,6 @@ def genetic_simulation_2(config_eos) -> GeneticSimulation:
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dc = np.full(config_eos.prediction.hours, 0.0)
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dc[11] = 1
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simulation.dc_charge_hours = dc
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simulation.home_appliance_start_hour = 2
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return simulation
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@@ -0,0 +1,355 @@
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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,
|
||||
inverter=None,
|
||||
eauto=None,
|
||||
dishwasher=HomeApplianceParameters(device_id="d", consumption_wh=1000, duration_h=1),
|
||||
home_appliances=[
|
||||
HomeApplianceParameters(device_id="e", consumption_wh=1000, duration_h=1)
|
||||
],
|
||||
)
|
||||
|
||||
|
||||
def test_deprecated_dishwasher_maps_to_list():
|
||||
parameters = GeneticOptimizationParameters(
|
||||
ems=_ems(2),
|
||||
pv_akku=None,
|
||||
inverter=None,
|
||||
eauto=None,
|
||||
dishwasher=HomeApplianceParameters(device_id="d", consumption_wh=1000, duration_h=1),
|
||||
)
|
||||
resolved = parameters.resolved_home_appliances()
|
||||
assert [appliance.device_id for appliance in resolved] == ["d"]
|
||||
|
||||
|
||||
def test_max_home_appliances_is_upper_bound():
|
||||
with pytest.raises(ValidationError, match="exceeds max_home_appliances"):
|
||||
DevicesCommonSettings(
|
||||
max_home_appliances=1,
|
||||
home_appliances=[
|
||||
{"device_id": "a", "consumption_wh": 1000, "duration_h": 1},
|
||||
{"device_id": "b", "consumption_wh": 1000, "duration_h": 1},
|
||||
],
|
||||
)
|
||||
|
||||
|
||||
def test_start_solution_layout_mismatch_is_ignored(config_eos):
|
||||
opt = _optimizer(config_eos, prediction_hours=48, horizon_hours=48, interval=3600, hour=10)
|
||||
slot0 = opt.ems.start_datetime.set(hour=0, minute=0, second=0, microsecond=0)
|
||||
appliance = _appliance(48, 1.0, device_id="d", consumption_wh=1000, duration_h=1)
|
||||
opt.appliance_layout = opt._build_appliance_layout([appliance], slot0)
|
||||
opt.optimize_ev = False
|
||||
valid_index_count = len(opt.appliance_layout.genes[0].allowed_start_slots)
|
||||
# A tail index beyond the allowed range must be rejected.
|
||||
bad_solution = [0] * opt.total_slots + [valid_index_count + 5]
|
||||
assert opt._start_solution_matches_layout(bad_solution) is False
|
||||
good_solution = [0] * opt.total_slots + [0]
|
||||
assert opt._start_solution_matches_layout(good_solution) is True
|
||||
@@ -277,7 +277,7 @@ class TestAcChargingInSimulation:
|
||||
prediction_hours=prediction_hours,
|
||||
inverter=inverter,
|
||||
ev=None,
|
||||
home_appliance=None,
|
||||
home_appliances=None,
|
||||
)
|
||||
return sim, akku, inverter
|
||||
|
||||
@@ -553,7 +553,10 @@ def _run_evaluate_with_mocked_sim(
|
||||
- self.simulation is replaced by mock_sim
|
||||
Then call evaluate() and return the fitness tuple.
|
||||
"""
|
||||
from akkudoktoreos.optimization.genetic.genetic import GeneticOptimization
|
||||
from akkudoktoreos.optimization.genetic.genetic import (
|
||||
ApplianceGeneLayout,
|
||||
GeneticOptimization,
|
||||
)
|
||||
|
||||
config_eos.merge_settings_from_dict(
|
||||
{
|
||||
@@ -572,6 +575,7 @@ def _run_evaluate_with_mocked_sim(
|
||||
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
|
||||
@@ -602,7 +606,10 @@ def _run_evaluate_with_mocked_sim(
|
||||
|
||||
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 GeneticOptimization
|
||||
from akkudoktoreos.optimization.genetic.genetic import (
|
||||
ApplianceGeneLayout,
|
||||
GeneticOptimization,
|
||||
)
|
||||
|
||||
config_eos.merge_settings_from_dict(
|
||||
{
|
||||
@@ -620,6 +627,7 @@ def _run_evaluate_with_mocked_ev_soc(config_eos, ev_soc_percentage: float) -> fl
|
||||
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
|
||||
|
||||
@@ -221,7 +221,7 @@ def test_hourly_start_solution_is_expanded_to_slots(config_eos: ConfigEOS):
|
||||
opt.optimize_ev = False
|
||||
hourly = list(range(48))
|
||||
|
||||
migrated = opt._start_solution_for_slot_grid(hourly, has_appliance=False)
|
||||
migrated = opt._start_solution_for_slot_grid(hourly)
|
||||
|
||||
assert len(migrated) == 192
|
||||
assert migrated[:8] == [0, 0, 0, 0, 1, 1, 1, 1]
|
||||
@@ -242,8 +242,8 @@ def test_quarter_hour_mutation_probability_preserves_hourly_rate(config_eos: Con
|
||||
assert opt.toolbox.mutate_charge_discharge.keywords["indpb"] == pytest.approx(0.05)
|
||||
|
||||
|
||||
def test_sub_hourly_home_appliance_is_rejected(config_eos: ConfigEOS):
|
||||
"""An hourly appliance model must not silently run on slot indices."""
|
||||
def test_sub_hourly_home_appliance_is_scheduled(config_eos: ConfigEOS):
|
||||
"""A home appliance is scheduled on the 15-min slot grid and delivers its energy."""
|
||||
config_eos.merge_settings_from_dict(
|
||||
{
|
||||
"prediction": {"hours": 48},
|
||||
@@ -252,18 +252,28 @@ def test_sub_hourly_home_appliance_is_rejected(config_eos: ConfigEOS):
|
||||
)
|
||||
parameters = load_hourly_parameters().model_copy(
|
||||
update={
|
||||
"dishwasher": HomeApplianceParameters(
|
||||
device_id="dishwasher", consumption_wh=1200, duration_h=2
|
||||
)
|
||||
"home_appliances": [
|
||||
HomeApplianceParameters(
|
||||
device_id="dishwasher1", consumption_wh=1200, duration_h=2
|
||||
)
|
||||
]
|
||||
},
|
||||
deep=True,
|
||||
)
|
||||
ems_eos.set_start_datetime(to_datetime().set(hour=10, minute=0))
|
||||
CacheEnergyManagementStore().clear()
|
||||
|
||||
with pytest.raises(ValueError, match="Home-appliance scheduling"):
|
||||
GeneticOptimization(fixed_seed=42).optimierung_ems(
|
||||
parameters=parameters, start_hour=10, ngen=1
|
||||
)
|
||||
genetic_solution = GeneticOptimization(fixed_seed=42).optimierung_ems(
|
||||
parameters=parameters, start_hour=10, ngen=3
|
||||
)
|
||||
|
||||
# The appliance runs exactly once and delivers its full energy on the 15-min grid.
|
||||
energy = genetic_solution.result.home_appliance_energy_wh["dishwasher1"]
|
||||
assert sum(energy) == pytest.approx(1200.0)
|
||||
# The run occupies 2 h = 8 quarter-hour slots at 1200/2 = 600 W -> 150 Wh/slot.
|
||||
assert max(energy) == pytest.approx(150.0)
|
||||
# A single start time is reported as an absolute datetime.
|
||||
assert len(genetic_solution.appliance_starts["dishwasher1"]) == 1
|
||||
|
||||
|
||||
def test_optimize_15min_slot_grid(config_eos: ConfigEOS):
|
||||
|
||||
+338
-291
@@ -10,12 +10,24 @@
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
1.0,
|
||||
0.0,
|
||||
0.0,
|
||||
1.0,
|
||||
1.0,
|
||||
1.0,
|
||||
1.0,
|
||||
1.0,
|
||||
1.0,
|
||||
1.0,
|
||||
1.0,
|
||||
0.0,
|
||||
1.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
1.0,
|
||||
1.0,
|
||||
0.0,
|
||||
@@ -25,7 +37,6 @@
|
||||
0.0,
|
||||
1.0,
|
||||
1.0,
|
||||
1.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
@@ -36,17 +47,6 @@
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
1.0,
|
||||
0.0,
|
||||
1.0,
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"dc_charge": [
|
||||
@@ -110,7 +110,11 @@
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
1,
|
||||
1,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
@@ -121,19 +125,18 @@
|
||||
0,
|
||||
0,
|
||||
1,
|
||||
1,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
1,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
0,
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
1,
|
||||
@@ -142,9 +145,6 @@
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
1,
|
||||
0,
|
||||
0,
|
||||
0,
|
||||
1,
|
||||
1
|
||||
@@ -161,15 +161,15 @@
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.875,
|
||||
0.75,
|
||||
0.0,
|
||||
0.875,
|
||||
0.75,
|
||||
1.0,
|
||||
0.625,
|
||||
0.375,
|
||||
1.0,
|
||||
0.75,
|
||||
0.875,
|
||||
0.1,
|
||||
0.75,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
@@ -202,28 +202,28 @@
|
||||
],
|
||||
"result": {
|
||||
"Last_Wh_pro_Stunde": [
|
||||
1053.07,
|
||||
10240.91,
|
||||
14186.477801352086,
|
||||
11620.03,
|
||||
10686.11504084929,
|
||||
7609.82,
|
||||
9082.22,
|
||||
10280.78,
|
||||
2177.92,
|
||||
15230.07,
|
||||
8929.91,
|
||||
1320.56,
|
||||
10061.61912107894,
|
||||
13077.21553917656,
|
||||
9042.82,
|
||||
10393.22,
|
||||
8969.78,
|
||||
1129.12,
|
||||
1178.71,
|
||||
1050.98,
|
||||
1488.5587949275546,
|
||||
988.56,
|
||||
912.38,
|
||||
2204.61,
|
||||
704.61,
|
||||
516.37,
|
||||
868.05,
|
||||
694.34,
|
||||
608.79,
|
||||
556.31,
|
||||
2056.31,
|
||||
488.89,
|
||||
506.91,
|
||||
804.89,
|
||||
1304.8889978824886,
|
||||
1141.98,
|
||||
1056.97,
|
||||
992.46,
|
||||
@@ -235,51 +235,51 @@
|
||||
860.88,
|
||||
1158.03,
|
||||
1222.72,
|
||||
1221.04,
|
||||
949.99,
|
||||
3721.04,
|
||||
3449.99,
|
||||
987.01,
|
||||
733.99,
|
||||
592.97
|
||||
],
|
||||
"EAuto_SoC_pro_Stunde": [
|
||||
5.0,
|
||||
5.0,
|
||||
20.294999999999998,
|
||||
33.405,
|
||||
50.885000000000005,
|
||||
61.809999999999995,
|
||||
68.365,
|
||||
81.475,
|
||||
96.77,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518,
|
||||
98.518
|
||||
33.405,
|
||||
39.96,
|
||||
57.440000000000005,
|
||||
70.55,
|
||||
85.845,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955,
|
||||
98.955
|
||||
],
|
||||
"Einnahmen_Euro_pro_Stunde": [
|
||||
0.0,
|
||||
@@ -288,10 +288,12 @@
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.005594705401588846,
|
||||
0.0016437871377312284,
|
||||
0.028240415856282213,
|
||||
9.722458428505041e-05,
|
||||
0.0023429436853348124,
|
||||
0.0692592282596561,
|
||||
0.023370695807696434,
|
||||
0.0019327051509204403,
|
||||
8.435507117427132e-07,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
@@ -305,27 +307,54 @@
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.09023487592359272,
|
||||
0.03692581803776506,
|
||||
0.09634325718089931,
|
||||
0.038455087951794004,
|
||||
0.31400739999999994,
|
||||
0.2577866264025879,
|
||||
0.15146384621553569,
|
||||
0.09798107754792196,
|
||||
0.029150936607659956,
|
||||
0.020928608511559126,
|
||||
0.003524558735906156,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"Gesamt_Verluste": 6008.882879266785,
|
||||
"Gesamtbilanz_Euro": 13.081213953988335,
|
||||
"Gesamteinnahmen_Euro": 0.7099201341480623,
|
||||
"Gesamtkosten_Euro": 13.791134088136397,
|
||||
"Gesamt_Verluste": 6850.393259430652,
|
||||
"Gesamtbilanz_Euro": 13.198417631718888,
|
||||
"Gesamteinnahmen_Euro": 1.1191176909827683,
|
||||
"Gesamtkosten_Euro": 14.317535322701657,
|
||||
"Home_appliance_wh_per_hour": [
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
@@ -334,113 +363,126 @@
|
||||
2500.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
||||
0.0,
|
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|
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16541.07,
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8929.91,
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8375.540038207693,
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6376.03,
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|
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|
||||
22.48,
|
||||
35.589999999999996,
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||||
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||||
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@@ -557,13 +599,13 @@
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"washingstart": 44,
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"appliance_starts": {
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"dishwasher1": [
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||||
"2026-07-16 20:00:00+02:00"
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||||
]
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||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user