mirror of
https://github.com/Akkudoktor-EOS/EOS.git
synced 2026-10-09 07:56:40 +00:00
Andreas
a2f4ef6f54
The optimizer treated the end of `optimization.horizon_hours` as the end of the world: energy left in the battery there was worth a single configured price per kWh, so it either dumped the battery into the last hours or hoarded it, depending on that one number. The horizon is now two spans. `horizon_hours` still receives every control command. The new `optimization.tail_horizon_hours` (default 48 h) is a pure lookahead that never produces a command. In AUTO terminal-value mode a deterministic dynamic program solves that tail backwards on a 101-point SoC grid using the production battery and inverter models - SoC bounds, power caps, conversion losses, configured charge and export rates, direct-marketing permission and LCOS on delivered DC energy - and the existing AUTO proxy supplies the continuation value at the tail end. Genetic fitness reads the resulting curve. `tail_horizon_hours: 0` restores the plain proxy at the control end, FIXED is unchanged. The forecast budget is reported, never enforced by refusal: a tail that does not fit is shortened to what the forecast covers and reported as `effective_tail_hours`, and a control horizon that does not fit is warned about at configuration time and rejected by the optimizer at run time, which knows which series ran out. `prediction.hours` defaults to 72 so the new defaults fit out of the box; existing shorter configurations keep starting. Control arrays and warm-start genomes now begin at the run timestamp rather than midnight, flagged by `controls_start_at_now` so the adapters still read older solutions. `forecast_interval_seconds` declares the resolution of shortened native quarter-hour inputs. Required forecasts are no longer silently replaced by demo providers. A missing PV, price, load, feed-in or weather forecast used to rewrite the configured provider and retry, so a run could quietly optimize against invented data. Missing values now stay missing, and provider values are held only within their own source interval instead of being extended indefinitely. Also fixes a config update that could leave EOS half-updated: the merged candidate is validated before the singleton is reinitialized. Four provider tests that hard-coded the old 48 h prediction default are rewritten to derive their expectations from the configured horizon.
413 lines
16 KiB
Python
413 lines
16 KiB
Python
from types import SimpleNamespace
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from unittest.mock import MagicMock, patch
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import numpy as np
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import pytest
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from deap import creator, tools
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from akkudoktoreos.config.config import ConfigEOS
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from akkudoktoreos.core.coreabc import get_ems
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from akkudoktoreos.core.emsettings import EnergyManagementMode
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from akkudoktoreos.optimization.genetic.genetic import GeneticOptimization
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from akkudoktoreos.utils.datetimeutil import to_datetime
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def _configure_hourly_grid(config_eos: ConfigEOS, *, start_hour: int = 0) -> None:
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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": {"tail_horizon_hours": 0, "horizon_hours": 48, "interval": 3600},
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}
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)
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get_ems(init=True).set_start_datetime(to_datetime().set(hour=start_hour, minute=0))
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def test_energy_management_forwards_individuals_and_generations_separately(
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config_eos: ConfigEOS,
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):
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_configure_hourly_grid(config_eos)
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config_eos.optimization.genetic.individuals = 100
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config_eos.optimization.genetic.generations = 80
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ems = get_ems(init=True)
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parameters = MagicMock()
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solution = MagicMock()
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optimizer = MagicMock()
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optimizer.optimierung_ems.return_value = solution
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with (
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patch("akkudoktoreos.adapter.adapterabc.AdapterContainer.update_data"),
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patch("akkudoktoreos.core.ems.GeneticOptimization", return_value=optimizer),
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):
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ems._run(
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start_datetime=to_datetime().set(hour=0, minute=0),
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mode=EnergyManagementMode.OPTIMIZATION,
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genetic_parameters=parameters,
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)
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optimizer.optimierung_ems.assert_called_once_with(
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start_hour=0,
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parameters=parameters,
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ngen=80,
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individuals=100,
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)
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def test_ev_repair_is_resimulated_before_fitness_assignment(config_eos: ConfigEOS):
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_configure_hourly_grid(config_eos)
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opt = GeneticOptimization(fixed_seed=42)
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opt.optimize_ev = True
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opt.ev_possible_charge_values = [0.0, 1.0]
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opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
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individual = creator.Individual([0] * opt.control_slots + [1] * opt.control_slots)
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first_result = {
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"Gesamtbilanz_Euro": 10.0,
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"Gesamt_Verluste": 0.0,
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"EAuto_SoC_pro_Stunde": np.full(opt.control_slots, 100.0),
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}
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repaired_result = {
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"Gesamtbilanz_Euro": 1.0,
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"Gesamt_Verluste": 0.0,
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"EAuto_SoC_pro_Stunde": np.full(opt.control_slots, 100.0),
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}
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parameters = SimpleNamespace(
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ems=SimpleNamespace(preis_euro_pro_wh_akku=0.0),
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eauto=None,
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)
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with patch.object(
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opt, "evaluate_inner", side_effect=[first_result, repaired_result]
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) as evaluate:
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fitness = opt.evaluate(individual, parameters, start_hour=0, worst_case=False) # type: ignore[arg-type]
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assert evaluate.call_count == 2
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assert fitness == pytest.approx((1.0,))
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assert individual[opt.control_slots :] == [0] * opt.control_slots
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def test_fitness_cache_restores_canonical_ev_genome(config_eos: ConfigEOS):
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_configure_hourly_grid(config_eos)
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opt = GeneticOptimization(fixed_seed=42)
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opt.optimize_ev = True
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opt.ev_possible_charge_values = [0.0, 1.0]
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opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
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parameters = SimpleNamespace(
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ems=SimpleNamespace(preis_euro_pro_wh_akku=0.0),
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eauto=None,
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)
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result = {
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"Gesamtbilanz_Euro": 1.0,
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"Gesamt_Verluste": 0.0,
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"EAuto_SoC_pro_Stunde": np.full(opt.control_slots, 100.0),
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}
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first = creator.Individual([0] * opt.control_slots + [1] * opt.control_slots)
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duplicate = creator.Individual(first)
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opt._fitness_cache_enabled = True
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with patch.object(opt, "evaluate_inner", return_value=result) as evaluate:
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first_fitness = opt.evaluate(first, parameters, 0, False) # type: ignore[arg-type]
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duplicate_fitness = opt.evaluate(duplicate, parameters, 0, False) # type: ignore[arg-type]
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# The miss evaluates and then re-evaluates the repaired EV plan. The duplicate
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# is served directly from the original-key alias and receives the canonical genome.
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assert evaluate.call_count == 2
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assert first_fitness == duplicate_fitness
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assert duplicate == first
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assert duplicate[opt.control_slots :] == [0] * opt.control_slots
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assert duplicate.extra_data == first.extra_data
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assert opt._fitness_cache_hits == 1
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assert opt._fitness_cache_misses == 1
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def test_fitness_cache_never_stores_failed_evaluations(config_eos: ConfigEOS):
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_configure_hourly_grid(config_eos)
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opt = GeneticOptimization(fixed_seed=42)
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opt.optimize_ev = False
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opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
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parameters = SimpleNamespace(
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ems=SimpleNamespace(preis_euro_pro_wh_akku=0.0),
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eauto=None,
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)
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first = creator.Individual([0] * opt.control_slots)
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duplicate = creator.Individual(first)
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opt._fitness_cache_enabled = True
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with patch.object(opt, "evaluate_inner", side_effect=RuntimeError("transient")) as evaluate:
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assert opt.evaluate(first, parameters, 0, False) == (100000.0,) # type: ignore[arg-type]
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assert opt.evaluate(duplicate, parameters, 0, False) == (100000.0,) # type: ignore[arg-type]
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assert evaluate.call_count == 2
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assert opt._fitness_cache_hits == 0
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assert opt._fitness_cache_misses == 2
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assert opt._fitness_cache == {}
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def test_fitness_cache_includes_first_run_relative_control(config_eos: ConfigEOS):
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_configure_hourly_grid(config_eos, start_hour=10)
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opt = GeneticOptimization(fixed_seed=42)
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opt.optimize_ev = False
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opt.setup_deap_environment({"home_appliance": 0}, start_hour=10)
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parameters = SimpleNamespace(
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ems=SimpleNamespace(preis_euro_pro_wh_akku=0.0),
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eauto=None,
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)
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result = {
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"Gesamtbilanz_Euro": 1.0,
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"Gesamt_Verluste": 0.0,
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"EAuto_SoC_pro_Stunde": np.zeros(opt.control_slots),
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}
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first = creator.Individual([0] * opt.control_slots)
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elapsed_variant = creator.Individual(first)
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elapsed_variant[0] = 1
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opt._fitness_cache_enabled = True
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with patch.object(opt, "evaluate_inner", return_value=result) as evaluate:
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first_fitness = opt.evaluate(first, parameters, 10, False) # type: ignore[arg-type]
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variant_fitness = opt.evaluate(elapsed_variant, parameters, 10, False) # type: ignore[arg-type]
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assert evaluate.call_count == 2
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assert first_fitness == variant_fitness
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assert opt._fitness_cache_hits == 0
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def test_mutated_warm_start_neighbors_stay_within_control_horizon(config_eos: ConfigEOS):
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_configure_hourly_grid(config_eos, start_hour=10)
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opt = GeneticOptimization(fixed_seed=42)
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opt.optimize_ev = False
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opt.setup_deap_environment({"home_appliance": 0}, start_hour=10)
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start_solution = [0] * opt.control_slots
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neighbors = opt._mutated_warm_start_neighbors(start_solution, count=5)
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assert len(neighbors) == 5
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assert len({tuple(neighbor) for neighbor in neighbors}) == 5
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assert all(len(neighbor) == opt.control_slots for neighbor in neighbors)
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assert all(neighbor != start_solution for neighbor in neighbors)
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def test_initial_population_uses_fixed_seed_budget_and_configured_population(
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config_eos: ConfigEOS,
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):
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_configure_hourly_grid(config_eos)
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config_eos.optimization.genetic.individuals = 300
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opt = GeneticOptimization(fixed_seed=42)
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opt.optimize_ev = False
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opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
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start_solution = [5] * opt.control_slots
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warm_neighbors = [[6] * opt.control_slots for _ in range(50)]
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educated = [[7] * opt.control_slots for _ in range(100)]
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captured: dict[str, object] = {}
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def fake_evolution(population, **kwargs):
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captured["population"] = list(population)
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captured["mu"] = kwargs["mu"]
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captured["lambda"] = kwargs["lambda_"]
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for individual in population:
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individual.fitness.values = (float(sum(individual)),)
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individual.extra_data = (0.0, 0.0, 0.0)
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kwargs["halloffame"].update(population)
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return population, SimpleNamespace(select=lambda _name: [])
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with (
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patch.object(opt, "_mutated_warm_start_neighbors", return_value=warm_neighbors),
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patch.object(opt, "_educated_guess_individuals", return_value=educated),
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patch.object(
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opt.toolbox,
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"population",
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side_effect=lambda n: [creator.Individual([9] * opt.control_slots) for _ in range(n)],
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),
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patch.object(opt, "_evolve_population_adaptive", side_effect=fake_evolution),
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):
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opt.optimize(start_solution=start_solution, ngen=1)
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population = captured["population"]
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first_genes = [individual[0] for individual in population] # type: ignore[union-attr]
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assert len(population) == 300 # type: ignore[arg-type]
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assert first_genes.count(5) == 10
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assert first_genes.count(6) == 50
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assert first_genes.count(7) == 100
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assert first_genes.count(9) == 140
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assert captured["mu"] == 300
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assert captured["lambda"] == 300
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def test_small_population_scales_warm_and_educated_seed_families(config_eos: ConfigEOS):
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_configure_hourly_grid(config_eos)
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config_eos.optimization.genetic.individuals = 100
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opt = GeneticOptimization(fixed_seed=42)
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opt.optimize_ev = False
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opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
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start_solution = [5] * opt.control_slots
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captured: dict[str, object] = {}
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def warm_neighbors(_solution, count):
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captured["warm_count"] = count
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return [[6] * opt.control_slots for _ in range(count)]
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def educated(count):
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captured["educated_count"] = count
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return [[7] * opt.control_slots for _ in range(count)]
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def fake_evolution(population, **kwargs):
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captured["population"] = list(population)
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captured["mu"] = kwargs["mu"]
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captured["lambda"] = kwargs["lambda_"]
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for individual in population:
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individual.fitness.values = (float(sum(individual)),)
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individual.extra_data = (0.0, 0.0, 0.0)
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kwargs["halloffame"].update(population)
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return population, SimpleNamespace(select=lambda _name: [])
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with (
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patch.object(opt, "_mutated_warm_start_neighbors", side_effect=warm_neighbors),
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patch.object(opt, "_educated_guess_individuals", side_effect=educated),
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patch.object(
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opt.toolbox,
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"population",
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side_effect=lambda n: [creator.Individual([9] * opt.control_slots) for _ in range(n)],
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),
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patch.object(opt, "_evolve_population_adaptive", side_effect=fake_evolution),
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):
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opt.optimize(start_solution=start_solution, ngen=1)
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population = captured["population"]
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first_genes = [individual[0] for individual in population] # type: ignore[union-attr]
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assert len(population) == 100 # type: ignore[arg-type]
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assert first_genes.count(5) == 10
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assert first_genes.count(6) == 20
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assert first_genes.count(7) == 40
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assert first_genes.count(9) == 30
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assert captured["warm_count"] == 20
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assert captured["educated_count"] == 40
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assert captured["mu"] == 100
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assert captured["lambda"] == 100
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def test_adaptive_evolution_soft_restarts_collapsed_population(config_eos: ConfigEOS):
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_configure_hourly_grid(config_eos)
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opt = GeneticOptimization(fixed_seed=42)
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opt.optimize_ev = False
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opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
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opt.toolbox.register("evaluate", lambda individual: (float(sum(individual)),))
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population = [creator.Individual([0] * opt.control_slots) for _ in range(20)]
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stats = tools.Statistics(lambda individual: individual.fitness.values)
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stats.register("min", np.min)
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stats.register("avg", np.mean)
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stats.register("max", np.max)
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halloffame = tools.HallOfFame(1)
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fresh = [creator.Individual([value] + [0] * (opt.control_slots - 1)) for value in range(1, 20)]
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with patch.object(opt, "_fresh_population", return_value=fresh) as create_fresh:
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evolved, log = opt._evolve_population_adaptive(
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population,
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mu=20,
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lambda_=20,
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ngen=1,
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stats=stats,
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halloffame=halloffame,
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)
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create_fresh.assert_called_once_with(19, educated_fraction=0.40)
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assert log.select("restart") == [0, 1]
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assert log.select("immigrants") == [0, 19]
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assert opt._adaptive_evolution_metrics["soft_restarts"] == 1
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assert opt._population_diversity(evolved) == pytest.approx(1.0)
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assert halloffame[0].fitness.values == (0.0,)
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def test_local_search_moves_weak_export_to_later_expensive_import(config_eos: ConfigEOS):
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_configure_hourly_grid(config_eos)
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opt = GeneticOptimization(fixed_seed=42)
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opt.optimize_ev = False
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opt.optimize_dc_charge = True
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opt.optimize_battery_grid_export = True
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opt.bat_possible_charge_values = [1.0]
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opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
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slots = opt.control_slots
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export_state = 5
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self_consumption_state = 6
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discharge_state = 1
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source = 10
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targets = list(range(20, 32))
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base = [self_consumption_state] * slots
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base[source] = export_state
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for slot in targets:
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base[slot] = 0
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opt.simulation.elect_price_hourly = np.full(slots, 0.10)
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opt.simulation.elect_price_hourly[targets] = 0.30
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opt.simulation.elect_revenue_per_hour_arr = np.full(slots, 0.05)
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opt.simulation.elect_revenue_per_hour_arr[source] = 0.20
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opt.simulation.pv_prediction_wh = np.zeros(slots)
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opt.simulation.load_energy_array = np.full(slots, 100.0)
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def evaluate(individual):
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export_value = -0.20 if individual[source] == export_state else 0.0
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avoided_import = -0.05 * sum(individual[slot] == discharge_state for slot in targets)
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return (export_value + avoided_import,)
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opt.toolbox.register("evaluate", evaluate)
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incumbent = creator.Individual(base)
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incumbent.fitness.values = evaluate(incumbent)
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best, evaluations, improvements, initial, final = opt._locally_improve_grid_export(
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incumbent,
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max_evaluations=96,
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)
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assert evaluations > 0
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assert improvements == 1
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assert final < initial
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assert best[source] == self_consumption_state
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assert sum(best[slot] == discharge_state for slot in targets) >= 6
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def test_educated_guesses_encode_high_price_direct_marketing(config_eos: ConfigEOS):
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_configure_hourly_grid(config_eos)
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opt = GeneticOptimization(fixed_seed=42)
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opt.optimize_ev = False
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opt.optimize_dc_charge = True
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opt.optimize_battery_grid_export = True
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opt.bat_possible_charge_values = [1.0]
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opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
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slots = opt.control_slots
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opt.simulation.elect_price_hourly = np.linspace(0.0001, 0.0004, slots)
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opt.simulation.elect_revenue_per_hour_arr = np.linspace(0.00001, 0.0003, slots)
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opt.simulation.pv_prediction_wh = np.full(slots, 1000.0)
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opt.simulation.load_energy_array = np.full(slots, 500.0)
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guesses = opt._educated_guess_individuals()
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dc_allowed_state = 4
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export_state = 5
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self_consumption_state = 6
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assert len(guesses) == opt.EDUCATED_GUESS_TARGET
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assert all(len(guess) == slots for guess in guesses)
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assert any(dc_allowed_state in guess or self_consumption_state in guess for guess in guesses)
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assert any(self_consumption_state in guess for guess in guesses)
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assert any(guess[-1] == export_state for guess in guesses)
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def test_flat_feed_in_tariff_does_not_seed_direct_marketing(config_eos: ConfigEOS):
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_configure_hourly_grid(config_eos)
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opt = GeneticOptimization(fixed_seed=42)
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opt.optimize_ev = False
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opt.optimize_dc_charge = True
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opt.optimize_battery_grid_export = True
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opt.bat_possible_charge_values = [1.0]
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opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
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slots = opt.control_slots
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opt.simulation.elect_price_hourly = np.linspace(0.0001, 0.0004, slots)
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opt.simulation.elect_revenue_per_hour_arr = np.full(slots, 0.00005)
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opt.simulation.pv_prediction_wh = np.full(slots, 1000.0)
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opt.simulation.load_energy_array = np.full(slots, 500.0)
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guesses = opt._educated_guess_individuals()
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export_state = 5
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assert all(export_state not in guess for guess in guesses)
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