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feat(optimization): support a 15-minute optimization interval

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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.
This commit is contained in:
Christin
2026-07-12 09:08:39 +02:00
committed by Andreas
parent 7f2ac9098c
commit 3098605b0f
11 changed files with 357 additions and 84 deletions
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src/akkudoktoreos/optimization/genetic/geneticsolution.py
+40 -24
View File
@@ -391,20 +391,28 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
- GRID_SUPPORT_IMPORT: ac_charge > 0 and discharge_allowed == 0 or 1
"""
start_datetime = get_ems().start_datetime
start_day_hour = start_datetime.in_timezone(self.config.general.timezone).hour
interval_hours = 1
power_to_energy_per_interval_factor = 1.0
# The genetic core emits total_slots = prediction.hours * slots_per_hour
# entries indexed by slot (slot 0 == 00:00 local). Index this serializer
# by slot too. At the default interval of 3600 s slots_per_hour == 1 and
# this is the established hourly behaviour.
interval_s = int(self.config.optimization.interval or 3600)
slots_per_hour = max(1, 3600 // interval_s)
slot_minutes = max(1, interval_s // 60)
start_local = start_datetime.in_timezone(self.config.general.timezone)
start_day_slot = start_local.hour * slots_per_hour + start_local.minute // slot_minutes
# power [W] -> energy per slot [Wh]: multiply by the slot duration in hours.
power_to_energy_per_interval_factor = interval_s / 3600.0
# --- Create index based on list length and interval ---
# Ensure we only use the minimum of results and commands if differing
periods = min(len(self.result.Kosten_Euro_pro_Stunde), len(self.ac_charge) - start_day_hour)
periods = min(len(self.result.Kosten_Euro_pro_Stunde), len(self.ac_charge) - start_day_slot)
time_index = pd.date_range(
start=start_datetime,
periods=periods,
freq=f"{interval_hours}h",
freq=f"{interval_s}s",
)
n_points = len(time_index)
end_datetime = start_datetime.add(hours=n_points)
end_datetime = start_datetime.add(seconds=interval_s * n_points)
# Fill solution into dataframe with correct column names
# - load_energy_wh: Load of all energy consumers in wh"
@@ -420,7 +428,7 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
solution = pd.DataFrame(
{
"date_time": time_index,
# result starts at start_day_hour
# result starts at start_day_slot
"load_energy_wh": self.result.Last_Wh_pro_Stunde[:n_points],
"grid_feedin_energy_wh": self.result.Netzeinspeisung_Wh_pro_Stunde[:n_points],
"grid_consumption_energy_wh": self.result.Netzbezug_Wh_pro_Stunde[:n_points],
@@ -435,7 +443,7 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
battery_device_id = self._battery_device_id()
solution[f"{battery_device_id}_soc_factor"] = [
v / 100
for v in self.result.akku_soc_pro_stunde[:n_points] # result starts at start_day_hour
for v in self.result.akku_soc_pro_stunde[:n_points] # result starts at start_day_slot
]
operation: dict[str, list[float]] = {
"genetic_ac_charge_factor": [],
@@ -445,9 +453,9 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
}
# ac_charge, dc_charge, discharge_allowed start at hour 0 of start day
for hour_idx, rate in enumerate(self.ac_charge):
if hour_idx < start_day_hour:
if hour_idx < start_day_slot:
continue
if hour_idx >= start_day_hour + n_points:
if hour_idx >= start_day_slot + n_points:
break
ac_charge_hour = self.ac_charge[hour_idx]
dc_charge_hour = self.dc_charge[hour_idx]
@@ -468,7 +476,7 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
# SOC-clamped effective values — what can physically be executed at
# this hour given the expected battery state of charge.
result_idx = hour_idx - start_day_hour
result_idx = hour_idx - start_day_slot
soc_h_pct = (
self.result.akku_soc_pro_stunde[result_idx]
if result_idx < len(self.result.akku_soc_pro_stunde)
@@ -533,9 +541,9 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
"genetic_ev_charge_factor": [],
}
for hour_idx, rate in enumerate(self.eautocharge_hours_float):
if hour_idx < start_day_hour:
if hour_idx < start_day_slot:
continue
if hour_idx >= start_day_hour + n_points:
if hour_idx >= start_day_slot + n_points:
break
operation["genetic_ev_charge_factor"].append(rate)
operation_mode, operation_mode_factor = self._battery_operation_from_solution(
@@ -565,7 +573,7 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
# Use config and not self.washingstart as washingstart may be None (no start)
# even if configured to be started.
homeappliance_device_id = self._homeappliance_device_id()
# result starts at start_day_hour
# result starts at start_day_slot
solution[f"{homeappliance_device_id}_energy_wh"] = (
self.result.Home_appliance_wh_per_hour[:n_points]
)
@@ -663,7 +671,7 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
key=pred_key,
start_datetime=start_datetime,
end_datetime=end_datetime,
interval=to_duration(f"{interval_hours} hours"),
interval=to_duration(f"{interval_s} seconds"),
fill_method=pred_fill_method,
)
# 'key_to_array()' creates None values array if no data records are available.
@@ -691,7 +699,13 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
def energy_management_plan(self) -> EnergyManagementPlan:
"""Provide the genetic solution as an energy management plan."""
start_datetime = get_ems().start_datetime
start_day_hour = start_datetime.in_timezone(self.config.general.timezone).hour
# Index by slot, not hour (mirrors optimization_solution). At the default
# interval of 3600 s this reduces to the start hour-of-day.
interval_s = int(self.config.optimization.interval or 3600)
slots_per_hour = max(1, 3600 // interval_s)
slot_minutes = max(1, interval_s // 60)
start_local = start_datetime.in_timezone(self.config.general.timezone)
start_day_slot = start_local.hour * slots_per_hour + start_local.minute // slot_minutes
plan = EnergyManagementPlan(
id=f"plan-genetic@{to_datetime(as_string=True)}",
generated_at=to_datetime(),
@@ -704,15 +718,15 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
last_operation_mode_factor: Optional[float] = None
resource_id = self._battery_device_id()
# ac_charge, dc_charge, discharge_allowed start at hour 0 of start day
logger.debug("BAT: {} - {}", resource_id, self.ac_charge[start_day_hour:])
logger.debug("BAT: {} - {}", resource_id, self.ac_charge[start_day_slot:])
for hour_idx, rate in enumerate(self.ac_charge):
if hour_idx < start_day_hour:
if hour_idx < start_day_slot:
continue
# Derive SOC-clamped effective factors so that FRBCInstruction
# operation_mode_factor reflects what can physically be executed,
# while the raw genetic gene values are preserved in the solution
# dataframe (genetic_*_factor columns).
result_idx = hour_idx - start_day_hour
result_idx = hour_idx - start_day_slot
soc_h_pct = (
self.result.akku_soc_pro_stunde[result_idx]
if result_idx < len(self.result.akku_soc_pro_stunde)
@@ -741,7 +755,7 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
continue
last_operation_mode = operation_mode
last_operation_mode_factor = operation_mode_factor
execution_time = start_datetime.add(hours=hour_idx - start_day_hour)
execution_time = start_datetime.add(seconds=interval_s * (hour_idx - start_day_slot))
plan.add_instruction(
FRBCInstruction(
resource_id=resource_id,
@@ -772,10 +786,10 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
last_operation_mode = None
last_operation_mode_factor = None
logger.debug(
"EV: {} - {}", resource_id, self.eautocharge_hours_float[start_day_hour:]
"EV: {} - {}", resource_id, self.eautocharge_hours_float[start_day_slot:]
)
for hour_idx, rate in enumerate(self.eautocharge_hours_float):
if hour_idx < start_day_hour:
if hour_idx < start_day_slot:
continue
operation_mode, operation_mode_factor = self._battery_operation_from_solution(
rate, 0.0, False
@@ -788,7 +802,9 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
continue
last_operation_mode = operation_mode
last_operation_mode_factor = operation_mode_factor
execution_time = start_datetime.add(hours=hour_idx - start_day_hour)
execution_time = start_datetime.add(
seconds=interval_s * (hour_idx - start_day_slot)
)
plan.add_instruction(
FRBCInstruction(
resource_id=resource_id,
@@ -817,7 +833,7 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
else:
operation_mode = ApplianceOperationMode.OFF # type: ignore[assignment]
operation_mode_factor = 1.0
execution_time = start_datetime.add(hours=hours)
execution_time = start_datetime.add(seconds=interval_s * hours)
plan.add_instruction(
DDBCInstruction(
resource_id=resource_id,