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feat(optimization): split the control horizon from the forecast tail

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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.
This commit is contained in:
Andreas
2026-09-09 07:56:38 +02:00
parent 88150d46e3
commit a2f4ef6f54
37 changed files with 3589 additions and 502 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tests/test_genetic_seeding.py
+31 -30
View File
@@ -16,7 +16,7 @@ def _configure_hourly_grid(config_eos: ConfigEOS, *, start_hour: int = 0) -> Non
config_eos.merge_settings_from_dict(
{
"prediction": {"hours": 48},
"optimization": {"horizon_hours": 48, "interval": 3600},
"optimization": {"tail_horizon_hours": 0, "horizon_hours": 48, "interval": 3600},
}
)
get_ems(init=True).set_start_datetime(to_datetime().set(hour=start_hour, minute=0))
@@ -58,17 +58,17 @@ def test_ev_repair_is_resimulated_before_fitness_assignment(config_eos: ConfigEO
opt.optimize_ev = True
opt.ev_possible_charge_values = [0.0, 1.0]
opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
individual = creator.Individual([0] * opt.total_slots + [1] * opt.total_slots)
individual = creator.Individual([0] * opt.control_slots + [1] * opt.control_slots)
first_result = {
"Gesamtbilanz_Euro": 10.0,
"Gesamt_Verluste": 0.0,
"EAuto_SoC_pro_Stunde": np.full(opt.total_slots, 100.0),
"EAuto_SoC_pro_Stunde": np.full(opt.control_slots, 100.0),
}
repaired_result = {
"Gesamtbilanz_Euro": 1.0,
"Gesamt_Verluste": 0.0,
"EAuto_SoC_pro_Stunde": np.full(opt.total_slots, 100.0),
"EAuto_SoC_pro_Stunde": np.full(opt.control_slots, 100.0),
}
parameters = SimpleNamespace(
ems=SimpleNamespace(preis_euro_pro_wh_akku=0.0),
@@ -82,7 +82,7 @@ def test_ev_repair_is_resimulated_before_fitness_assignment(config_eos: ConfigEO
assert evaluate.call_count == 2
assert fitness == pytest.approx((1.0,))
assert individual[opt.total_slots :] == [0] * opt.total_slots
assert individual[opt.control_slots :] == [0] * opt.control_slots
def test_fitness_cache_restores_canonical_ev_genome(config_eos: ConfigEOS):
@@ -98,9 +98,9 @@ def test_fitness_cache_restores_canonical_ev_genome(config_eos: ConfigEOS):
result = {
"Gesamtbilanz_Euro": 1.0,
"Gesamt_Verluste": 0.0,
"EAuto_SoC_pro_Stunde": np.full(opt.total_slots, 100.0),
"EAuto_SoC_pro_Stunde": np.full(opt.control_slots, 100.0),
}
first = creator.Individual([0] * opt.total_slots + [1] * opt.total_slots)
first = creator.Individual([0] * opt.control_slots + [1] * opt.control_slots)
duplicate = creator.Individual(first)
opt._fitness_cache_enabled = True
@@ -113,7 +113,7 @@ def test_fitness_cache_restores_canonical_ev_genome(config_eos: ConfigEOS):
assert evaluate.call_count == 2
assert first_fitness == duplicate_fitness
assert duplicate == first
assert duplicate[opt.total_slots :] == [0] * opt.total_slots
assert duplicate[opt.control_slots :] == [0] * opt.control_slots
assert duplicate.extra_data == first.extra_data
assert opt._fitness_cache_hits == 1
assert opt._fitness_cache_misses == 1
@@ -128,7 +128,7 @@ def test_fitness_cache_never_stores_failed_evaluations(config_eos: ConfigEOS):
ems=SimpleNamespace(preis_euro_pro_wh_akku=0.0),
eauto=None,
)
first = creator.Individual([0] * opt.total_slots)
first = creator.Individual([0] * opt.control_slots)
duplicate = creator.Individual(first)
opt._fitness_cache_enabled = True
@@ -142,7 +142,7 @@ def test_fitness_cache_never_stores_failed_evaluations(config_eos: ConfigEOS):
assert opt._fitness_cache == {}
def test_fitness_cache_ignores_elapsed_control_slots(config_eos: ConfigEOS):
def test_fitness_cache_includes_first_run_relative_control(config_eos: ConfigEOS):
_configure_hourly_grid(config_eos, start_hour=10)
opt = GeneticOptimization(fixed_seed=42)
opt.optimize_ev = False
@@ -154,9 +154,9 @@ def test_fitness_cache_ignores_elapsed_control_slots(config_eos: ConfigEOS):
result = {
"Gesamtbilanz_Euro": 1.0,
"Gesamt_Verluste": 0.0,
"EAuto_SoC_pro_Stunde": np.zeros(opt.total_slots),
"EAuto_SoC_pro_Stunde": np.zeros(opt.control_slots),
}
first = creator.Individual([0] * opt.total_slots)
first = creator.Individual([0] * opt.control_slots)
elapsed_variant = creator.Individual(first)
elapsed_variant[0] = 1
opt._fitness_cache_enabled = True
@@ -165,23 +165,23 @@ def test_fitness_cache_ignores_elapsed_control_slots(config_eos: ConfigEOS):
first_fitness = opt.evaluate(first, parameters, 10, False) # type: ignore[arg-type]
variant_fitness = opt.evaluate(elapsed_variant, parameters, 10, False) # type: ignore[arg-type]
assert evaluate.call_count == 1
assert evaluate.call_count == 2
assert first_fitness == variant_fitness
assert opt._fitness_cache_hits == 1
assert opt._fitness_cache_hits == 0
def test_mutated_warm_start_neighbors_keep_elapsed_slots(config_eos: ConfigEOS):
def test_mutated_warm_start_neighbors_stay_within_control_horizon(config_eos: ConfigEOS):
_configure_hourly_grid(config_eos, start_hour=10)
opt = GeneticOptimization(fixed_seed=42)
opt.optimize_ev = False
opt.setup_deap_environment({"home_appliance": 0}, start_hour=10)
start_solution = [0] * opt.total_slots
start_solution = [0] * opt.control_slots
neighbors = opt._mutated_warm_start_neighbors(start_solution, count=5)
assert len(neighbors) == 5
assert len({tuple(neighbor) for neighbor in neighbors}) == 5
assert all(neighbor[:10] == start_solution[:10] for neighbor in neighbors)
assert all(len(neighbor) == opt.control_slots for neighbor in neighbors)
assert all(neighbor != start_solution for neighbor in neighbors)
@@ -193,9 +193,9 @@ def test_initial_population_uses_fixed_seed_budget_and_configured_population(
opt = GeneticOptimization(fixed_seed=42)
opt.optimize_ev = False
opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
start_solution = [5] * opt.total_slots
warm_neighbors = [[6] * opt.total_slots for _ in range(50)]
educated = [[7] * opt.total_slots for _ in range(100)]
start_solution = [5] * opt.control_slots
warm_neighbors = [[6] * opt.control_slots for _ in range(50)]
educated = [[7] * opt.control_slots for _ in range(100)]
captured: dict[str, object] = {}
def fake_evolution(population, **kwargs):
@@ -214,7 +214,7 @@ def test_initial_population_uses_fixed_seed_budget_and_configured_population(
patch.object(
opt.toolbox,
"population",
side_effect=lambda n: [creator.Individual([9] * opt.total_slots) for _ in range(n)],
side_effect=lambda n: [creator.Individual([9] * opt.control_slots) for _ in range(n)],
),
patch.object(opt, "_evolve_population_adaptive", side_effect=fake_evolution),
):
@@ -237,16 +237,16 @@ def test_small_population_scales_warm_and_educated_seed_families(config_eos: Con
opt = GeneticOptimization(fixed_seed=42)
opt.optimize_ev = False
opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
start_solution = [5] * opt.total_slots
start_solution = [5] * opt.control_slots
captured: dict[str, object] = {}
def warm_neighbors(_solution, count):
captured["warm_count"] = count
return [[6] * opt.total_slots for _ in range(count)]
return [[6] * opt.control_slots for _ in range(count)]
def educated(count):
captured["educated_count"] = count
return [[7] * opt.total_slots for _ in range(count)]
return [[7] * opt.control_slots for _ in range(count)]
def fake_evolution(population, **kwargs):
captured["population"] = list(population)
@@ -264,7 +264,7 @@ def test_small_population_scales_warm_and_educated_seed_families(config_eos: Con
patch.object(
opt.toolbox,
"population",
side_effect=lambda n: [creator.Individual([9] * opt.total_slots) for _ in range(n)],
side_effect=lambda n: [creator.Individual([9] * opt.control_slots) for _ in range(n)],
),
patch.object(opt, "_evolve_population_adaptive", side_effect=fake_evolution),
):
@@ -289,14 +289,14 @@ def test_adaptive_evolution_soft_restarts_collapsed_population(config_eos: Confi
opt.optimize_ev = False
opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
opt.toolbox.register("evaluate", lambda individual: (float(sum(individual)),))
population = [creator.Individual([0] * opt.total_slots) for _ in range(20)]
population = [creator.Individual([0] * opt.control_slots) for _ in range(20)]
stats = tools.Statistics(lambda individual: individual.fitness.values)
stats.register("min", np.min)
stats.register("avg", np.mean)
stats.register("max", np.max)
halloffame = tools.HallOfFame(1)
fresh = [creator.Individual([value] + [0] * (opt.total_slots - 1)) for value in range(1, 20)]
fresh = [creator.Individual([value] + [0] * (opt.control_slots - 1)) for value in range(1, 20)]
with patch.object(opt, "_fresh_population", return_value=fresh) as create_fresh:
evolved, log = opt._evolve_population_adaptive(
population,
@@ -324,7 +324,7 @@ def test_local_search_moves_weak_export_to_later_expensive_import(config_eos: Co
opt.bat_possible_charge_values = [1.0]
opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
slots = opt.total_slots
slots = opt.control_slots
export_state = 5
self_consumption_state = 6
discharge_state = 1
@@ -372,7 +372,7 @@ def test_educated_guesses_encode_high_price_direct_marketing(config_eos: ConfigE
opt.bat_possible_charge_values = [1.0]
opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
slots = opt.total_slots
slots = opt.control_slots
opt.simulation.elect_price_hourly = np.linspace(0.0001, 0.0004, slots)
opt.simulation.elect_revenue_per_hour_arr = np.linspace(0.00001, 0.0003, slots)
opt.simulation.pv_prediction_wh = np.full(slots, 1000.0)
@@ -399,7 +399,7 @@ def test_flat_feed_in_tariff_does_not_seed_direct_marketing(config_eos: ConfigEO
opt.bat_possible_charge_values = [1.0]
opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
slots = opt.total_slots
slots = opt.control_slots
opt.simulation.elect_price_hourly = np.linspace(0.0001, 0.0004, slots)
opt.simulation.elect_revenue_per_hour_arr = np.full(slots, 0.00005)
opt.simulation.pv_prediction_wh = np.full(slots, 1000.0)
@@ -409,3 +409,4 @@ def test_flat_feed_in_tariff_does_not_seed_direct_marketing(config_eos: ConfigEO
export_state = 5
assert all(export_state not in guess for guess in guesses)