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.
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>
The genetic optimizer was hard-wired to an hourly grid and forced
optimization.interval to 3600 s. Generalize it to a configurable slot grid
of length prediction.hours * (3600 / interval), accepting 900 (15 min) in
addition to the default 3600 (1 hour) so the optimizer can schedule on a
quarter-hour grid for 15-minute dynamic electricity tariffs.
- genetic.py: slot_duration_h / slots_per_hour / total_slots helpers; all GA
vectors sized by total_slots; simulate()/evaluate() indexed by start slot.
- geneticparams.py: allow {900, 3600}; scale the load power series to per-slot
energy, mirroring the PV series.
- battery.py / inverter.py: scale power caps to per-slot energy caps via
slot_duration_h; homeappliance.py carries the hook.
- geneticsolution.py: serialize solution and plan on the slot grid (interval
freq, start-slot offset, second-based instruction instants).
The default 3600 s interval keeps the previous hourly behaviour; the genetic
regression suite is unchanged. Adds tests for the 15-minute slot grid.
Andreas
ChristinandAndreas