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

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.

CHANGELOG.md

@@ -13,6 +13,13 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
   - `PVForecastPVNode` — native 15-minute forecasts from the pvnode.com API.
   - `PVForecastForecastSolar` — forecasts from the free Forecast.Solar API.
   - `PVForecastSolcast` — forecasts from the Solcast rooftop-site API.
+- 15-minute optimization interval for the genetic optimizer. `optimization.interval`
+  now accepts 900 (15 min) in addition to the default 3600 (1 hour), letting the
+  optimizer schedule on a quarter-hour grid for 15-minute dynamic electricity
+  tariffs. Device power caps and the solution/plan serializers are slot-aware; the
+  default 3600 s interval keeps the previous hourly behaviour. The new sub-hourly PV
+  providers (pvnode, Forecast.Solar, Solcast) feed their native resolution straight
+  into the quarter-hour grid.
 
 ## 0.3.0 (2026-03-17)
 

docs/_generated/configoptimization.md

@@ -11,7 +11,7 @@
 | genetic | `EOS_OPTIMIZATION__GENETIC` | `GeneticCommonSettings` | `rw` | `required` | Genetic optimization algorithm configuration. |
 | horizon | | `int` | `ro` | `N/A` | Number of optimization steps. |
 | horizon_hours | `EOS_OPTIMIZATION__HORIZON_HOURS` | `int` | `rw` | `24` | The general time window within which the energy optimization goal shall be achieved [h]. Defaults to 24 hours. |
-| interval | `EOS_OPTIMIZATION__INTERVAL` | `int` | `rw` | `3600` | The optimization interval [sec]. Defaults to 3600 seconds (1 hour) |
+| interval | `EOS_OPTIMIZATION__INTERVAL` | `int` | `rw` | `3600` | The optimization interval (slot length) [sec]. The genetic optimizer supports 3600 (1 hour) and 900 (15 min); other values fall back to 3600. Defaults to 3600 seconds (1 hour). |
 | keys | | `list[str]` | `ro` | `N/A` | The keys of the solution. |
 :::
 <!-- pyml enable line-length -->

docs/akkudoktoreos/optimauto.md

@@ -139,17 +139,16 @@ The energy management can be run in three modes:
     Each device simulation run must ensure that all tasks or appliance cycles (e.g., running a
     dishwasher) are completed within the configured time windows.
 
-- **interval**: Defines the time step in seconds between control actions
+- **interval**: Defines the time step (slot length) in seconds between control actions.
-    (e.g. `3600` for one hour, `900` for 15 minutes).
+    The genetic algorithm supports `3600` (one hour, the default) and `900` (15 minutes);
+    any other value falls back to `3600`. The number of optimization slots is
+    `prediction.hours * (3600 / interval)`, and device power caps as well as the solution
+    and energy-management-plan serializers are slot-aware.
 
-:::{warning}
+:::{note}
-**Current Limitation**
+Use `900` together with a 15-minute electricity price source (for example a dynamic or
-
+exchange-priced tariff) to let the optimizer schedule on a quarter-hour grid. Keeping the
-At present, the `interval` setting is **not used** by the genetic algorithm. Instead:
+default `3600` preserves the previous hourly behaviour.
-
-- The control interval is fixed to **1 hour**.
-
-Support for configurable intervals (e.g. 15-minute steps) may be added in a future release.
 :::
 
 #### Genetic Algorithm Parameters

src/akkudoktoreos/devices/genetic/battery.py

@@ -12,9 +12,20 @@ from akkudoktoreos.optimization.genetic.geneticdevices import (
 class Battery:
     """Represents a battery device with methods to simulate energy charging and discharging."""
 
-    def __init__(self, parameters: BaseBatteryParameters, prediction_hours: int):
+    def __init__(
+        self,
+        parameters: BaseBatteryParameters,
+        prediction_hours: int,
+        slot_duration_h: float = 1.0,
+    ):
+        # `prediction_hours` is the number of optimization slots, not hours. At
+        # the default optimization interval of 3600 s, slot_duration_h is 1.0 and
+        # the slot count equals the hour count, so existing callers are
+        # unaffected. At 900 s (15 min) slot_duration_h is 0.25 and there are 4x
+        # as many slots, each able to move a quarter of the hourly energy.
         self.parameters = parameters
         self.prediction_hours = prediction_hours
+        self.slot_duration_h = slot_duration_h
         self._setup()
 
     def _setup(self) -> None:
@@ -137,8 +148,12 @@ class Battery:
         # Raw extractable energy above minimum SoC
         raw_available_wh = max(self.soc_wh - self.min_soc_wh, 0.0)
 
-        # Maximum raw discharge due to power limit
+        # Maximum raw discharge due to power limit, scaled to the slot duration.
-        max_raw_wh = self.max_charge_power_w  # TODO rename to max_discharge_power_w
+        # max_charge_power_w is a power [W]; energy movable in one slot is
+        # power x slot_duration_h.
+        max_raw_wh = (
+            self.max_charge_power_w * self.slot_duration_h
+        )  # TODO rename to max_discharge_power_w
 
         # Actual raw withdrawal (internal)
         raw_withdrawal_wh = min(raw_available_wh, max_raw_wh)
@@ -229,7 +244,9 @@ class Battery:
 
         # Provide fast (3x..5x) local read access (vs. self.xxx) for repetitive read access
         soc_wh_fast = self.soc_wh
-        max_charge_power_w_fast = self.max_charge_power_w
+        # Scale the power cap [W] to a per-slot energy cap [Wh] (W x slot hours).
+        # At slot_duration_h=1.0 (hourly) this equals the legacy power value.
+        max_charge_per_slot_wh_fast = self.max_charge_power_w * self.slot_duration_h
         charging_efficiency_fast = self.charging_efficiency
 
         # Decide mode & determine raw_request_wh and raw_charge_wh
@@ -237,13 +254,13 @@ class Battery:
             raw_request_wh = wh
             raw_charge_wh = max(self.max_soc_wh - soc_wh_fast, 0.0) / charging_efficiency_fast
         elif wh is None and charge_factor > 0.0:  # mode 2
-            raw_request_wh = max_charge_power_w_fast * charge_factor
+            raw_request_wh = max_charge_per_slot_wh_fast * charge_factor
             raw_charge_wh = max(self.max_soc_wh - soc_wh_fast, 0.0) / charging_efficiency_fast
             if raw_request_wh > raw_charge_wh:
                 # Use a lower charge factor
                 lower_charge_factors = self._lower_charge_rates_desc(charge_factor)
                 for charge_factor in lower_charge_factors:
-                    raw_request_wh = max_charge_power_w_fast * charge_factor
+                    raw_request_wh = max_charge_per_slot_wh_fast * charge_factor
                     if raw_request_wh <= raw_charge_wh:
                         self.charge_array[hour] = charge_factor
                         break
@@ -258,7 +275,7 @@ class Battery:
             )
 
         # Remaining capacity
-        max_raw_wh = min(raw_charge_wh, max_charge_power_w_fast)
+        max_raw_wh = min(raw_charge_wh, max_charge_per_slot_wh_fast)
 
         # Actual raw intake
         raw_input_wh = raw_request_wh if raw_request_wh < max_raw_wh else max_raw_wh

src/akkudoktoreos/devices/genetic/homeappliance.py

@@ -11,9 +11,15 @@ class HomeAppliance:
         parameters: HomeApplianceParameters,
         optimization_hours: int,
         prediction_hours: int,
+        slot_duration_h: float = 1.0,
     ):
+        # slot_duration_h is a forward-compatibility hook. Full sub-hourly home
+        # appliance scheduling additionally requires converting the start hour to
+        # a slot index and the duration to a slot count; the default of 1.0 keeps
+        # the hourly behaviour for the default optimization interval of 3600 s.
         self.parameters: HomeApplianceParameters = parameters
         self.prediction_hours = prediction_hours
+        self.slot_duration_h = slot_duration_h
         self._setup()
 
     def _setup(self) -> None:

src/akkudoktoreos/devices/genetic/inverter.py

@@ -12,9 +12,14 @@ class Inverter:
         self,
         parameters: InverterParameters,
         battery: Optional[Battery] = None,
+        slot_duration_h: float = 1.0,
     ):
+        # slot_duration_h scales the per-slot energy cap (max_power_wh). It
+        # defaults to 1.0, which keeps the hourly behaviour for the default
+        # optimization interval of 3600 s.
         self.parameters: InverterParameters = parameters
         self.battery: Optional[Battery] = battery
+        self.slot_duration_h: float = slot_duration_h
         self._setup()
 
     def _setup(self) -> None:
@@ -23,11 +28,16 @@ class Inverter:
             logger.error(error_msg)
             raise ValueError(error_msg)
         self.self_consumption_predictor = get_eos_load_interpolator()
+        # max_power_wh is supplied as a power [W] that the legacy hourly code
+        # treats as Wh-per-hour. Scale it to the actual slot length so a 15-min
+        # slot can move at most a quarter of that energy.
         self.max_power_wh = (
-            self.parameters.max_power_wh
+            self.parameters.max_power_wh * self.slot_duration_h
-        )  # Maximum power that the inverter can handle
+        )  # Maximum energy the inverter can move in one optimization slot
         self.dc_to_ac_efficiency = self.parameters.dc_to_ac_efficiency
         self.ac_to_dc_efficiency = self.parameters.ac_to_dc_efficiency
+        # max_ac_charge_power_w stays in Watts. It feeds a dimensionless,
+        # slot-agnostic power-ratio cap in genetic.py simulate().
         self.max_ac_charge_power_w = self.parameters.max_ac_charge_power_w
 
     def _discharge_battery_to_ac(self, requested_ac_wh: float, hour: int) -> tuple[float, float]:

src/akkudoktoreos/optimization/genetic/genetic.py

@@ -484,6 +484,45 @@ class GeneticSimulation(PydanticBaseModel):
 class GeneticOptimization(OptimizationBase):
     """GENETIC algorithm to solve energy optimization."""
 
+    # Slot-math helpers — single source of truth for the optimization grid.
+    # At the default optimization interval of 3600 s, slot_duration_h is 1.0 and
+    # total_slots equals prediction.hours, so the established hourly behaviour is
+    # preserved. At 900 s (15 min) slot_duration_h is 0.25 and there are 4x as
+    # many slots.
+    @property
+    def slot_duration_h(self) -> float:
+        """Length of one optimization slot in hours (1.0 hourly, 0.25 at 15 min)."""
+        interval = self.config.optimization.interval or 3600
+        return interval / 3600
+
+    @property
+    def slots_per_hour(self) -> int:
+        """Number of optimization slots per hour (1 hourly, 4 at 15 min)."""
+        interval = self.config.optimization.interval or 3600
+        return 3600 // interval
+
+    @property
+    def total_slots(self) -> int:
+        """Total number of optimization slots = prediction.hours * slots_per_hour."""
+        # Read prediction.hours directly to avoid recursing through total_slots.
+        return int(self.config.prediction.hours * self.slots_per_hour)
+
+    def _start_day_slot(self) -> int:
+        """Slot index of ems.start_datetime counted from the start day's midnight.
+
+        simulate()/evaluate() use the simulation start position as a slot index
+        into the prediction/charge arrays. Those arrays begin at the midnight of
+        ``ems.start_datetime`` (geneticparams sets ``start_datetime.set(hour=0)``),
+        so the index is computed from the same datetime — no timezone conversion —
+        keeping it consistent with how the arrays are built. At interval=3600 s
+        slots_per_hour == 1 and minute // 60 == 0, so this reduces to
+        ``start_datetime.hour`` (the previous hourly behaviour).
+        """
+        sd = self.ems.start_datetime
+        sph = self.slots_per_hour
+        slot_minutes = max(1, 60 // sph)
+        return sd.hour * sph + sd.minute // slot_minutes
+
     def __init__(
         self,
         verbose: bool = False,
@@ -491,8 +530,11 @@ class GeneticOptimization(OptimizationBase):
     ):
         """Initialize the optimization problem with the required parameters."""
         self.opti_param: dict[str, Any] = {}
-        self.fixed_eauto_hours = (
+        # Number of slots at the tail of the optimization window where EV
-            self.config.prediction.hours - self.config.optimization.horizon_hours
+        # charging is fixed to 0. Slot-counted so 15-min runs reserve the right
+        # tail length (at interval=3600 s this equals prediction.hours - horizon).
+        self.fixed_eauto_hours = self.total_slots - (
+            self.config.optimization.horizon_hours * self.slots_per_hour
         )
         self.ev_possible_charge_values: list[float] = [1.0]
         # Separate charge-level list for battery AC charging (independent of EV rates).
@@ -610,25 +652,21 @@ class GeneticOptimization(OptimizationBase):
             total_states += 1
 
         # 1. Mutating the charge_discharge part
-        charge_discharge_part = individual[: self.config.prediction.hours]
+        charge_discharge_part = individual[: self.total_slots]
         (charge_discharge_mutated,) = self.toolbox.mutate_charge_discharge(charge_discharge_part)
 
         # Instead of a fixed clamping to 0..8 or 0..6 dynamically:
         charge_discharge_mutated = np.clip(charge_discharge_mutated, 0, total_states - 1)
-        individual[: self.config.prediction.hours] = charge_discharge_mutated
+        individual[: self.total_slots] = charge_discharge_mutated
 
         # 2. Mutating the EV charge part, if active
         if self.optimize_ev:
-            ev_charge_part = individual[
+            ev_charge_part = individual[self.total_slots : self.total_slots * 2]
-                self.config.prediction.hours : self.config.prediction.hours * 2
-            ]
             (ev_charge_part_mutated,) = self.toolbox.mutate_ev_charge_index(ev_charge_part)
-            ev_charge_part_mutated[self.config.prediction.hours - self.fixed_eauto_hours :] = [
+            ev_charge_part_mutated[self.total_slots - self.fixed_eauto_hours :] = [
                 0
             ] * self.fixed_eauto_hours
-            individual[self.config.prediction.hours : self.config.prediction.hours * 2] = (
+            individual[self.total_slots : self.total_slots * 2] = ev_charge_part_mutated
-                ev_charge_part_mutated
-            )
 
         # 3. Mutating the appliance start time, if applicable
         if self.opti_param["home_appliance"] > 0:
@@ -642,13 +680,13 @@ class GeneticOptimization(OptimizationBase):
     def create_individual(self) -> list[int]:
         # Start with discharge states for the individual
         individual_components = [
-            self.toolbox.attr_discharge_state() for _ in range(self.config.prediction.hours)
+            self.toolbox.attr_discharge_state() for _ in range(self.total_slots)
         ]
 
         # Add EV charge index values if optimize_ev is True
         if self.optimize_ev:
             individual_components += [
-                self.toolbox.attr_ev_charge_index() for _ in range(self.config.prediction.hours)
+                self.toolbox.attr_ev_charge_index() for _ in range(self.total_slots)
             ]
 
         # Add the start time of the household appliance if it's being optimized
@@ -681,7 +719,7 @@ class GeneticOptimization(OptimizationBase):
             individual.extend(eautocharge_hours_index.tolist())
         elif self.optimize_ev:
             # Falls optimize_ev aktiv ist, aber keine EV-Daten vorhanden sind, fügen wir Nullen hinzu
-            individual.extend([0] * self.config.prediction.hours)
+            individual.extend([0] * self.total_slots)
 
         # Add dishwasher start time if applicable
         if self.opti_param.get("home_appliance", 0) > 0 and washingstart_int is not None:
@@ -703,13 +741,13 @@ class GeneticOptimization(OptimizationBase):
         3. Dishwasher start time (integer if applicable).
         """
         # Discharge hours as a NumPy array of ints
-        discharge_hours_bin = np.array(individual[: self.config.prediction.hours], dtype=int)
+        discharge_hours_bin = np.array(individual[: self.total_slots], dtype=int)
 
         # EV charge hours as a NumPy array of ints (if optimize_ev is True)
         eautocharge_hours_index = (
             # append ev charging states to individual
             np.array(
-                individual[self.config.prediction.hours : self.config.prediction.hours * 2],
+                individual[self.total_slots : self.total_slots * 2],
                 dtype=int,
             )
             if self.optimize_ev
@@ -819,7 +857,7 @@ class GeneticOptimization(OptimizationBase):
         if self.optimize_dc_charge:
             self.simulation.dc_charge_hours = dc_charge_hours
         else:
-            self.simulation.dc_charge_hours = np.full(self.config.prediction.hours, 1)
+            self.simulation.dc_charge_hours = np.full(self.total_slots, 1)
         self.simulation.ac_charge_hours = ac_charge_hours
 
         if eautocharge_hours_index is not None:
@@ -831,10 +869,12 @@ class GeneticOptimization(OptimizationBase):
             self.simulation.ev_charge_hours = eautocharge_hours_float
         else:
             # discharge is set to 0 by default
-            self.simulation.ev_charge_hours = np.full(self.config.prediction.hours, 0)
+            self.simulation.ev_charge_hours = np.full(self.total_slots, 0)
 
-        # Do the simulation and return result.
+        # Do the simulation and return result. simulate()'s argument is a slot
-        return self.simulation.simulate(self.ems.start_datetime.hour)
+        # index into the prediction/charge arrays, not an hour-of-day, so pass
+        # the start_day_slot to keep sub-hourly runs aligned.
+        return self.simulation.simulate(self._start_day_slot())
 
     def evaluate(
         self,
@@ -1188,6 +1228,10 @@ class GeneticOptimization(OptimizationBase):
             raise ValueError(
                 f"Start hour not synced. EMS {self.ems.start_datetime.hour} vs. GENETIC {start_hour}."
             )
+        # start_hour stays the hour-of-day for the appliance-start gene bounds
+        # (0..23). Everything that indexes the slot arrays (the simulate offset
+        # and evaluate's break-even loop) uses the slot index instead.
+        start_slot = self._start_day_slot()
 
         # Set the number of generations
         generations = ngen
@@ -1200,22 +1244,25 @@ class GeneticOptimization(OptimizationBase):
 
         self.simulation.reset()
 
-        # Initialize PV and EV batteries
+        # Initialize PV and EV batteries. slot_duration_h lets the Battery scale
+        # its power caps (max_charge_power_w) to a per-slot energy cap.
         akku: Optional[Battery] = None
         if parameters.pv_akku:
             akku = Battery(
                 parameters.pv_akku,
-                prediction_hours=self.config.prediction.hours,
+                prediction_hours=self.total_slots,
+                slot_duration_h=self.slot_duration_h,
             )
-            akku.set_charge_per_hour(np.full(self.config.prediction.hours, 0))
+            akku.set_charge_per_hour(np.full(self.total_slots, 0))
 
         eauto: Optional[Battery] = None
         if parameters.eauto:
             eauto = Battery(
                 parameters.eauto,
-                prediction_hours=self.config.prediction.hours,
+                prediction_hours=self.total_slots,
+                slot_duration_h=self.slot_duration_h,
             )
-            eauto.set_charge_per_hour(np.full(self.config.prediction.hours, 1))
+            eauto.set_charge_per_hour(np.full(self.total_slots, 1))
             self.optimize_ev = (
                 parameters.eauto.min_soc_percentage > parameters.eauto.initial_soc_percentage
             )
@@ -1273,36 +1320,41 @@ class GeneticOptimization(OptimizationBase):
             HomeAppliance(
                 parameters=parameters.dishwasher,
                 optimization_hours=self.config.optimization.horizon_hours,
-                prediction_hours=self.config.prediction.hours,
+                prediction_hours=self.total_slots,
+                slot_duration_h=self.slot_duration_h,
             )
             if parameters.dishwasher is not None
             else None
         )
 
-        # Initialize the inverter and energy management system
+        # Initialize the inverter and energy management system. slot_duration_h
+        # lets the Inverter scale max_power_wh to a per-slot energy cap.
         inverter: Optional[Inverter] = None
         if parameters.inverter:
             inverter = Inverter(
                 parameters.inverter,
                 battery=akku,
+                slot_duration_h=self.slot_duration_h,
             )
 
         # Prepare device simulation
         self.simulation.prepare(
             parameters=parameters.ems,
             optimization_hours=self.config.optimization.horizon_hours,
-            prediction_hours=self.config.prediction.hours,
+            prediction_hours=self.total_slots,
             inverter=inverter,  # battery is part of inverter
             ev=eauto,
             home_appliance=dishwasher,
             direct_marketing_enabled=direct_marketing_enabled,
         )
 
-        # Setup the DEAP environment and optimization process
+        # Setup the DEAP environment and optimization process. setup_deap gets
+        # the hour-of-day (appliance gene bounds); evaluate gets the slot index
+        # (its break-even loop walks the slot arrays from "now").
         self.setup_deap_environment({"home_appliance": 1 if dishwasher else 0}, start_hour)
         self.toolbox.register(
             "evaluate",
-            lambda ind: self.evaluate(ind, parameters, start_hour, worst_case),
+            lambda ind: self.evaluate(ind, parameters, start_slot, worst_case),
         )
 
         start_time = time.time()

src/akkudoktoreos/optimization/genetic/geneticparams.py

@@ -194,9 +194,17 @@ class GeneticOptimizationParameters(
         if cls.config.optimization.interval is None:
             logger.info("Optimization interval unknown - defaulting to 3600 seconds.")
             cls.config.optimization.interval = 3600
-        if cls.config.optimization.interval != 3600:
+        # The genetic optimizer runs on a fixed slot grid whose length is
-            logger.info(
+        # prediction.hours * (3600 / interval). 900 s (15 min) enables a
-                "Optimization interval '{}' seconds not supported - forced to 3600 seconds."
+        # quarter-hour grid for 15-minute electricity tariffs; the default
+        # 3600 s keeps the established hourly resolution. Other values fall back
+        # to 3600 s.
+        allowed_intervals = (3600, 900)
+        if cls.config.optimization.interval not in allowed_intervals:
+            logger.warning(
+                "Optimization interval {} seconds not in {} - forcing 3600 seconds.",
+                cls.config.optimization.interval,
+                allowed_intervals,
             )
             cls.config.optimization.interval = 3600
         # Check genetic algorithm definitions
@@ -306,12 +314,18 @@ class GeneticOptimizationParameters(
                 # Retry
                 continue
             try:
-                loadforecast_power_w = cls.prediction.key_to_array(
+                # Load is a power series [W] that the genetic optimizer consumes
+                # as Wh-per-slot. Scale by interval/3600 (mirrors the PV forecast
+                # above) so a 15-min slot sees a quarter of the hourly energy.
+                loadforecast_power_w = (
+                    cls.prediction.key_to_array(
                         key="loadforecast_power_w",
                         start_datetime=parameter_start_datetime,
                         end_datetime=parameter_end_datetime,
                         interval=interval,
                         fill_method="ffill",
+                    )
+                    * power_to_energy_per_interval_factor
                 ).tolist()
             except:
                 logger.info(

src/akkudoktoreos/optimization/genetic/geneticsolution.py

@@ -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
+        # The genetic core emits total_slots = prediction.hours * slots_per_hour
-        interval_hours = 1
+        # entries indexed by slot (slot 0 == 00:00 local). Index this serializer
-        power_to_energy_per_interval_factor = 1.0
+        # 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,

src/akkudoktoreos/optimization/optimization.py

@@ -72,7 +72,11 @@ class OptimizationCommonSettings(SettingsBaseModel):
         ge=15 * 60,
         le=60 * 60,
         json_schema_extra={
-            "description": "The optimization interval [sec]. Defaults to 3600 seconds (1 hour)",
+            "description": (
+                "The optimization interval (slot length) [sec]. The genetic "
+                "optimizer supports 3600 (1 hour) and 900 (15 min); other values "
+                "fall back to 3600. Defaults to 3600 seconds (1 hour)."
+            ),
             "examples": [60 * 60, 15 * 60],
         },
     )

tests/test_optimization_interval.py

@@ -0,0 +1,148 @@
+"""Tests for the 15-minute optimization interval.
+
+The genetic optimizer runs on a fixed slot grid whose length is
+``prediction.hours * (3600 / interval)``. At the default interval of 3600 s this
+is the established hourly behaviour (covered by ``test_geneticoptimize.py``);
+here we cover the 900 s (15 min) slot grid.
+"""
+
+import json
+from pathlib import Path
+from unittest.mock import patch
+
+import pytest
+
+from akkudoktoreos.config.config import ConfigEOS
+from akkudoktoreos.core.cache import CacheEnergyManagementStore
+from akkudoktoreos.core.coreabc import get_ems
+from akkudoktoreos.optimization.genetic.genetic import GeneticOptimization
+from akkudoktoreos.optimization.genetic.geneticparams import (
+    GeneticOptimizationParameters,
+)
+from akkudoktoreos.utils.datetimeutil import to_datetime
+from akkudoktoreos.utils.visualize import prepare_visualize
+
+ems_eos = get_ems(init=True)  # init once
+
+DIR_TESTDATA = Path(__file__).parent / "testdata"
+
+
+@pytest.mark.parametrize(
+    "interval, exp_slots_per_hour, exp_slot_duration_h",
+    [
+        (3600, 1, 1.0),
+        (900, 4, 0.25),
+    ],
+)
+def test_slot_helpers(
+    config_eos: ConfigEOS,
+    interval: int,
+    exp_slots_per_hour: int,
+    exp_slot_duration_h: float,
+):
+    """slot_duration_h / slots_per_hour / total_slots track the configured interval."""
+    config_eos.merge_settings_from_dict(
+        {
+            "prediction": {"hours": 48},
+            "optimization": {"horizon_hours": 48, "interval": interval},
+        }
+    )
+    ems_eos.set_start_datetime(to_datetime().set(hour=10, minute=0))
+
+    opt = GeneticOptimization(fixed_seed=42)
+
+    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
+    # At minute 0 the start slot is the hour scaled by the slot count.
+    assert opt._start_day_slot() == 10 * exp_slots_per_hour
+
+
+def test_start_day_slot_includes_minute_offset(config_eos: ConfigEOS):
+    """At 15-min resolution the start slot includes the minute offset."""
+    config_eos.merge_settings_from_dict(
+        {
+            "prediction": {"hours": 48},
+            "optimization": {"horizon_hours": 48, "interval": 900},
+        }
+    )
+    ems_eos.set_start_datetime(to_datetime().set(hour=10, minute=30))
+
+    opt = GeneticOptimization(fixed_seed=42)
+
+    # Slot index is derived from the actual EMS start datetime (which may be
+    # floored to the hour by the energy management system): hour*4 + minute//15.
+    sd = opt.ems.start_datetime
+    assert opt._start_day_slot() == sd.hour * 4 + sd.minute // 15
+
+
+def test_optimize_15min_slot_grid(config_eos: ConfigEOS):
+    """An end-to-end optimization at interval=900 runs on a 192-slot day grid.
+
+    This exercises the full path (parameter preparation, GA core, device
+    simulation, solution/plan serialization) at 15-min resolution and asserts the
+    structural properties; the optimization result itself is not pinned because
+    the 15-min grid is a different problem than the hourly one.
+    """
+    config_eos.merge_settings_from_dict(
+        {
+            "prediction": {"hours": 48},
+            "optimization": {
+                "horizon_hours": 48,
+                "interval": 900,
+                "genetic": {
+                    "individuals": 300,
+                    "generations": 10,
+                    "penalties": {
+                        "ev_soc_miss": 10,
+                        "ac_charge_break_even": 0,
+                    },
+                },
+            },
+            "devices": {
+                "max_electric_vehicles": 1,
+                "electric_vehicles": [
+                    {
+                        "charge_rates": [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0],
+                    }
+                ],
+            },
+        }
+    )
+
+    with (DIR_TESTDATA / "optimize_input_1.json").open("r") as f_in:
+        input_data = GeneticOptimizationParameters(**json.load(f_in))
+
+    ems_eos.set_start_datetime(to_datetime().set(hour=10, minute=0))
+    CacheEnergyManagementStore().clear()
+
+    opt = GeneticOptimization(fixed_seed=42)
+    assert opt.total_slots == 192
+    assert opt.slot_duration_h == 0.25
+
+    visualize_filename = str((DIR_TESTDATA / "new_optimize_15min.json").with_suffix(".pdf"))
+    with patch(
+        "akkudoktoreos.utils.visualize.prepare_visualize",
+        side_effect=lambda parameters, results, *args, **kwargs: prepare_visualize(
+            parameters, results, filename=visualize_filename, **kwargs
+        ),
+    ):
+        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
+
+    # The serializers consume the 15-min grid without error and emit a 900 s
+    # spaced solution index.
+    solution = genetic_solution.optimization_solution()
+    df = solution.solution.to_dataframe()
+    assert len(df.index) >= 2
+    delta_seconds = (df.index[1] - df.index[0]).total_seconds()
+    assert delta_seconds == 900
+
+    plan = genetic_solution.energy_management_plan()
+    assert plan is not None