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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_optimization_interval.py
+30 -24
View File
@@ -28,6 +28,11 @@ ems_eos = get_ems(init=True) # init once
DIR_TESTDATA = Path(__file__).parent / "testdata"
@pytest.fixture(autouse=True)
def start_at_midnight(config_eos):
ems_eos.set_start_datetime(to_datetime().set(hour=0, minute=0))
def load_hourly_parameters() -> GeneticOptimizationParameters:
"""Load the legacy 48-value API example used by hourly clients."""
with (DIR_TESTDATA / "optimize_input_1.json").open("r") as f_in:
@@ -51,7 +56,7 @@ def test_slot_helpers(
config_eos.merge_settings_from_dict(
{
"prediction": {"hours": 48},
"optimization": {"horizon_hours": 48, "interval": interval},
"optimization": {"tail_horizon_hours": 0, "horizon_hours": 48, "interval": interval},
}
)
ems_eos.set_start_datetime(to_datetime().set(hour=10, minute=0))
@@ -60,7 +65,7 @@ def test_slot_helpers(
assert opt.slots_per_hour == exp_slots_per_hour
assert opt.slot_duration_h == exp_slot_duration_h
assert opt.total_slots == 48 * exp_slots_per_hour
assert opt.control_slots == 48 * exp_slots_per_hour
# At minute 0 the start slot is the hour scaled by the slot count.
assert opt._start_day_slot() == 10 * exp_slots_per_hour
@@ -70,7 +75,7 @@ def test_start_day_slot_includes_minute_offset(config_eos: ConfigEOS):
config_eos.merge_settings_from_dict(
{
"prediction": {"hours": 48},
"optimization": {"horizon_hours": 48, "interval": 900},
"optimization": {"tail_horizon_hours": 0, "horizon_hours": 48, "interval": 900},
}
)
ems_eos.set_start_datetime(to_datetime().set(hour=10, minute=30))
@@ -88,7 +93,7 @@ def test_ems_start_is_floored_to_quarter_hour(config_eos: ConfigEOS):
config_eos.merge_settings_from_dict(
{
"prediction": {"hours": 48},
"optimization": {"horizon_hours": 48, "interval": 900},
"optimization": {"tail_horizon_hours": 0, "horizon_hours": 48, "interval": 900},
}
)
@@ -101,7 +106,7 @@ def test_ems_start_is_floored_to_quarter_hour(config_eos: ConfigEOS):
def test_unsupported_interval_falls_back_to_hourly(config_eos: ConfigEOS):
"""The genetic optimizer falls back without restricting interval-aware providers."""
config_eos.merge_settings_from_dict({"optimization": {"interval": 1800}})
config_eos.merge_settings_from_dict({"optimization": {"tail_horizon_hours": 0, "interval": 1800}})
assert config_eos.optimization.interval == 1800
GeneticOptimization(fixed_seed=42)
@@ -113,7 +118,7 @@ def test_hourly_api_input_is_normalized_to_quarter_hour_slots(config_eos: Config
config_eos.merge_settings_from_dict(
{
"prediction": {"hours": 48},
"optimization": {"horizon_hours": 48, "interval": 900},
"optimization": {"tail_horizon_hours": 0, "horizon_hours": 48, "interval": 900},
}
)
parameters = load_hourly_parameters()
@@ -141,7 +146,7 @@ def test_native_quarter_hour_input_is_not_resampled(config_eos: ConfigEOS):
config_eos.merge_settings_from_dict(
{
"prediction": {"hours": 48},
"optimization": {"horizon_hours": 48, "interval": 900},
"optimization": {"tail_horizon_hours": 0, "horizon_hours": 48, "interval": 900},
}
)
parameters = load_hourly_parameters()
@@ -170,7 +175,7 @@ def test_scalar_feed_in_tariff_fills_quarter_hour_grid(config_eos: ConfigEOS):
config_eos.merge_settings_from_dict(
{
"prediction": {"hours": 48},
"optimization": {"horizon_hours": 48, "interval": 900},
"optimization": {"tail_horizon_hours": 0, "horizon_hours": 48, "interval": 900},
}
)
parameters = load_hourly_parameters()
@@ -190,7 +195,7 @@ def test_ambiguous_input_length_is_rejected(config_eos: ConfigEOS):
config_eos.merge_settings_from_dict(
{
"prediction": {"hours": 48},
"optimization": {"horizon_hours": 48, "interval": 900},
"optimization": {"tail_horizon_hours": 0, "horizon_hours": 48, "interval": 900},
}
)
parameters = load_hourly_parameters()
@@ -214,7 +219,7 @@ def test_hourly_start_solution_is_expanded_to_slots(config_eos: ConfigEOS):
config_eos.merge_settings_from_dict(
{
"prediction": {"hours": 48},
"optimization": {"horizon_hours": 48, "interval": 900},
"optimization": {"tail_horizon_hours": 0, "horizon_hours": 48, "interval": 900},
}
)
opt = GeneticOptimization(fixed_seed=42)
@@ -232,35 +237,36 @@ def test_quarter_hour_mutation_targets_three_future_controls(config_eos: ConfigE
config_eos.merge_settings_from_dict(
{
"prediction": {"hours": 48},
"optimization": {"horizon_hours": 48, "interval": 900},
"optimization": {"tail_horizon_hours": 0, "horizon_hours": 48, "interval": 900},
}
)
opt = GeneticOptimization(fixed_seed=42)
opt.optimize_ev = False
opt.setup_deap_environment({"home_appliance": 0}, start_hour=0)
active_slots = opt.total_slots - opt._start_day_slot()
active_slots = opt.control_slots
expected = min(0.10, opt.POINT_MUTATION_EXPECTED_GENES / active_slots)
assert opt.toolbox.mutate_charge_discharge.keywords["indpb"] == pytest.approx(expected)
def test_point_mutation_keeps_elapsed_slots_unchanged(config_eos: ConfigEOS):
def test_point_mutation_uses_run_relative_controls(config_eos: ConfigEOS):
config_eos.merge_settings_from_dict(
{
"prediction": {"hours": 48},
"optimization": {"horizon_hours": 48, "interval": 900},
"optimization": {"tail_horizon_hours": 0, "horizon_hours": 48, "interval": 900},
}
)
get_ems(init=True).set_start_datetime(to_datetime().set(hour=10, minute=0))
opt = GeneticOptimization(fixed_seed=42)
opt.optimize_ev = False
opt.setup_deap_environment({"home_appliance": 0}, start_hour=10)
individual = [0] * opt.total_slots
individual = [0] * opt.control_slots
changed = opt._mutate_point_controls(individual)
assert changed
assert individual[: opt._start_day_slot()] == [0] * opt._start_day_slot()
assert len(individual) == opt.control_slots
assert any(individual)
def test_sub_hourly_home_appliance_is_scheduled(config_eos: ConfigEOS):
@@ -268,7 +274,7 @@ def test_sub_hourly_home_appliance_is_scheduled(config_eos: ConfigEOS):
config_eos.merge_settings_from_dict(
{
"prediction": {"hours": 48},
"optimization": {"horizon_hours": 48, "interval": 900},
"optimization": {"tail_horizon_hours": 0, "horizon_hours": 38, "interval": 900},
}
)
parameters = load_hourly_parameters().model_copy(
@@ -306,8 +312,8 @@ def test_optimize_15min_slot_grid(config_eos: ConfigEOS):
config_eos.merge_settings_from_dict(
{
"prediction": {"hours": 48},
"optimization": {
"horizon_hours": 48,
"optimization": {"tail_horizon_hours": 0,
"horizon_hours": 38,
"interval": 900,
"genetic": {
"individuals": 300,
@@ -335,7 +341,7 @@ def test_optimize_15min_slot_grid(config_eos: ConfigEOS):
CacheEnergyManagementStore().clear()
opt = GeneticOptimization(fixed_seed=42)
assert opt.total_slots == 192
assert opt.control_slots == 152
assert opt.slot_duration_h == 0.25
visualize_filename = str((DIR_TESTDATA / "new_optimize_15min.json").with_suffix(".pdf"))
@@ -348,10 +354,10 @@ def test_optimize_15min_slot_grid(config_eos: ConfigEOS):
genetic_solution = opt.optimierung_ems(parameters=input_data, start_hour=10, ngen=3)
# The genetic core emitted a full-day grid at 15-min resolution.
assert len(genetic_solution.ac_charge) == 192
assert len(genetic_solution.dc_charge) == 192
assert len(genetic_solution.discharge_allowed) == 192
expected_result_slots = 192 - opt._start_day_slot()
assert len(genetic_solution.ac_charge) == 152
assert len(genetic_solution.dc_charge) == 152
assert len(genetic_solution.discharge_allowed) == 152
expected_result_slots = 152
assert len(genetic_solution.result.Last_Wh_pro_Stunde) == expected_result_slots
assert len(genetic_solution.result.Electricity_price) == expected_result_slots