fix: genetic optimizer charge rates, SoC accuracy, and minor bugfixes (#949)

* record battery SOC at the start of the interval for accurate display

* map battery charge rates to GeneticOptimizationParameters and update related logic

  Instead of using the EV charge rates, the battery now has its own charge rate defined
  in the GeneticOptimizationParameters to better separate those entities.

* separate raw gene values from SOC-clamped op factors

  The genetic_*_factor columns in the OptimizationSolution dataframe now
  always carry the raw gene values (optimizer intent), while the
  battery1_*_op_mode / battery1_*_op_factor columns and FRBCInstruction
  operation_mode_factor reflect SOC-clamped effective values that can
  actually be executed given the battery's state of charge at each hour.

  Adds GeneticSolution._soc_clamped_operation_factors():
    - AC charge factor: proportionally scaled down when battery headroom
      (max_soc - current_soc) is less than what the commanded factor
      would store in one hour; zeroed when battery is full.
    - DC charge factor: zeroed when battery is at or above max SOC.
    - Discharge: blocked when SOC is at or below min SOC.

  Both optimization_solution() and energy_management_plan() pass the
  clamped values to _battery_operation_from_solution(), so the plan
  instructions the HEMS receives reflect physically achievable targets.

* ensure max AC charge power is only used if defined

* update fixtures for PV suffix + SOC-clamp algorithm changes

* handle None case for home appliance start hour in simulation

  if the hour is zero, the the home appliance wont start

* update handling of invalid charge indices in autocharge hours

  Seems to be a bug, since all invalid indexes needs to be invalidated (also the first one)

* remove double code in simulation preparation and improve comments

* update version

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
Co-authored-by: Christopher Nadler <christopher.nadler@gmail.com>

src/akkudoktoreos/optimization/genetic/genetic.py

@@ -113,8 +113,14 @@ class GeneticSimulation(PydanticBaseModel):
         home_appliance: Optional[HomeAppliance] = None,
         inverter: Optional[Inverter] = None,
     ) -> None:
+        """Prepare simulation runs.
+
+        Populate internal arrays and device references used during simulation.
+        """
         self.optimization_hours = optimization_hours
         self.prediction_hours = prediction_hours
+
+        # Load arrays from provided EMS parameters
         self.load_energy_array = np.array(parameters.gesamtlast, float)
         self.pv_prediction_wh = np.array(parameters.pv_prognose_wh, float)
         self.elect_price_hourly = np.array(parameters.strompreis_euro_pro_wh, float)
@@ -125,6 +131,8 @@ class GeneticSimulation(PydanticBaseModel):
                 len(self.load_energy_array), parameters.einspeiseverguetung_euro_pro_wh, float
             )
         )
+
+        # Associate devices
         if inverter:
             self.battery = inverter.battery
         else:
@@ -132,23 +140,14 @@ class GeneticSimulation(PydanticBaseModel):
         self.ev = ev
         self.home_appliance = home_appliance
         self.inverter = inverter
+
+        # Initialize per-hour action arrays for the prediction horizon
         self.ac_charge_hours = np.full(self.prediction_hours, 0.0)
         self.dc_charge_hours = np.full(self.prediction_hours, 0.0)
         self.bat_discharge_hours = np.full(self.prediction_hours, 0.0)
         self.ev_charge_hours = np.full(self.prediction_hours, 0.0)
         self.ev_discharge_hours = np.full(self.prediction_hours, 0.0)
         self.home_appliance_start_hour = None
-        """Prepare simulation runs."""
-        self.load_energy_array = np.array(parameters.gesamtlast, float)
-        self.pv_prediction_wh = np.array(parameters.pv_prognose_wh, float)
-        self.elect_price_hourly = np.array(parameters.strompreis_euro_pro_wh, float)
-        self.elect_revenue_per_hour_arr = (
-            parameters.einspeiseverguetung_euro_pro_wh
-            if isinstance(parameters.einspeiseverguetung_euro_pro_wh, list)
-            else np.full(
-                len(self.load_energy_array), parameters.einspeiseverguetung_euro_pro_wh, float
-            )
-        )
 
     def reset(self) -> None:
         if self.ev:
@@ -299,7 +298,7 @@ class GeneticSimulation(PydanticBaseModel):
             # Default return if no electric vehicle is available
             soc_ev_per_hour = np.full((total_hours), 0)
 
-        if home_appliance_fast and self.home_appliance_start_hour:
+        if home_appliance_fast and self.home_appliance_start_hour is not None:
             home_appliance_enabled = True
             # Pre-allocate arrays for the results, optimized for speed
             home_appliance_wh_per_hour = np.full((total_hours), np.nan)
@@ -335,6 +334,13 @@ class GeneticSimulation(PydanticBaseModel):
                     consumption += loaded_energy_ev
                     losses_wh_per_hour[hour_idx] += verluste_eauto
 
+            # Save battery SOC before inverter processing = true begin-of-interval state.
+            # Must be recorded here (before DC charge/discharge) so the displayed SOC at
+            # timestamp T reflects what the battery actually had at the START of interval T,
+            # not the post-DC result. Consistent with the EV SOC convention above.
+            if battery_fast:
+                soc_per_hour[hour_idx] = battery_fast.current_soc_percentage()
+
             # Process inverter logic
             energy_feedin_grid_actual = energy_consumption_grid_actual = losses = eigenverbrauch = (
                 0.0
@@ -351,7 +357,6 @@ class GeneticSimulation(PydanticBaseModel):
 
             # AC PV Battery Charge
             if battery_fast:
-                soc_per_hour[hour_idx] = battery_fast.current_soc_percentage()  # save begin state
                 hour_ac_charge = ac_charge_hours_fast[hour]
                 if hour_ac_charge > 0.0 and ac_charging_possible:
                     # Cap charge factor by max_ac_charge_power_w if set
@@ -436,6 +441,9 @@ class GeneticOptimization(OptimizationBase):
             self.config.prediction.hours - self.config.optimization.horizon_hours
         )
         self.ev_possible_charge_values: list[float] = [1.0]
+        # Separate charge-level list for battery AC charging (independent of EV rates).
+        # Populated from parameters.pv_akku.charge_rates in optimierung_ems.
+        self.bat_possible_charge_values: list[float] = [1.0]
         self.verbose = verbose
         self.fix_seed = fixed_seed
         self.optimize_ev = True
@@ -457,29 +465,31 @@ class GeneticOptimization(OptimizationBase):
     ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
         """Decode the input array into ac_charge, dc_charge, and discharge arrays."""
         discharge_hours_bin_np = np.array(discharge_hours_bin)
-        len_ac = len(self.ev_possible_charge_values)
+        # Battery AC charge uses its own charge-level list (bat_possible_charge_values).
+        len_bat = len(self.bat_possible_charge_values)
 
-        # Categorization:
-        # Idle:       0 .. len_ac-1
-        # Discharge:  len_ac .. 2*len_ac - 1
-        # AC Charge:  2*len_ac .. 3*len_ac - 1
-        # DC optional: 3*len_ac (not allowed), 3*len_ac + 1 (allowed)
+        # Categorization (using battery charge levels):
+        # Idle:       0 .. len_bat-1
+        # Discharge:  len_bat .. 2*len_bat - 1
+        # AC Charge:  2*len_bat .. 3*len_bat - 1  (maps to bat_possible_charge_values)
+        # DC optional: 3*len_bat (not allowed), 3*len_bat + 1 (allowed)
 
-        # Idle has no charge, Discharge has binary 1, AC Charge has corresponding values
         # Idle states
-        idle_mask = (discharge_hours_bin_np >= 0) & (discharge_hours_bin_np < len_ac)
+        idle_mask = (discharge_hours_bin_np >= 0) & (discharge_hours_bin_np < len_bat)
 
         # Discharge states
-        discharge_mask = (discharge_hours_bin_np >= len_ac) & (discharge_hours_bin_np < 2 * len_ac)
+        discharge_mask = (discharge_hours_bin_np >= len_bat) & (
+            discharge_hours_bin_np < 2 * len_bat
+        )
 
         # AC states
-        ac_mask = (discharge_hours_bin_np >= 2 * len_ac) & (discharge_hours_bin_np < 3 * len_ac)
-        ac_indices = (discharge_hours_bin_np[ac_mask] - 2 * len_ac).astype(int)
+        ac_mask = (discharge_hours_bin_np >= 2 * len_bat) & (discharge_hours_bin_np < 3 * len_bat)
+        ac_indices = (discharge_hours_bin_np[ac_mask] - 2 * len_bat).astype(int)
 
         # DC states (if enabled)
         if self.optimize_dc_charge:
-            dc_not_allowed_state = 3 * len_ac
-            dc_allowed_state = 3 * len_ac + 1
+            dc_not_allowed_state = 3 * len_bat
+            dc_allowed_state = 3 * len_bat + 1
             dc_charge = np.where(discharge_hours_bin_np == dc_allowed_state, 1, 0)
         else:
             dc_charge = np.ones_like(discharge_hours_bin_np, dtype=float)
@@ -489,7 +499,7 @@ class GeneticOptimization(OptimizationBase):
         discharge[discharge_mask] = 1  # Set Discharge states to 1
 
         ac_charge = np.zeros_like(discharge_hours_bin_np, dtype=float)
-        ac_charge[ac_mask] = [self.ev_possible_charge_values[i] for i in ac_indices]
+        ac_charge[ac_mask] = [self.bat_possible_charge_values[i] for i in ac_indices]
 
         # Idle is just 0, already default.
 
@@ -497,12 +507,12 @@ class GeneticOptimization(OptimizationBase):
 
     def mutate(self, individual: list[int]) -> tuple[list[int]]:
         """Custom mutation function for the individual."""
-        # Calculate the number of states
-        len_ac = len(self.ev_possible_charge_values)
+        # Calculate the number of states using battery charge levels
+        len_bat = len(self.bat_possible_charge_values)
         if self.optimize_dc_charge:
-            total_states = 3 * len_ac + 2
+            total_states = 3 * len_bat + 2
         else:
-            total_states = 3 * len_ac
+            total_states = 3 * len_bat
 
         # 1. Mutating the charge_discharge part
         charge_discharge_part = individual[: self.config.prediction.hours]
@@ -633,30 +643,30 @@ class GeneticOptimization(OptimizationBase):
         creator.create("Individual", list, fitness=creator.FitnessMin)
 
         self.toolbox = base.Toolbox()
-        len_ac = len(self.ev_possible_charge_values)
+        # Battery state space uses bat_possible_charge_values; EV index space uses ev_possible_charge_values.
+        len_bat = len(self.bat_possible_charge_values)
+        len_ev = len(self.ev_possible_charge_values)
 
-        # Total number of states without DC:
-        # Idle: len_ac states
-        # Discharge: len_ac states
-        # AC-Charge: len_ac states
-        # Total without DC: 3 * len_ac
-
-        # With DC: + 2 states
+        # Total battery/discharge states:
+        # Idle:      len_bat states
+        # Discharge: len_bat states
+        # AC-Charge: len_bat states  (maps to bat_possible_charge_values)
+        # With DC: + 2 additional states
         if self.optimize_dc_charge:
-            total_states = 3 * len_ac + 2
+            total_states = 3 * len_bat + 2
         else:
-            total_states = 3 * len_ac
+            total_states = 3 * len_bat
 
         # State space: 0 .. (total_states - 1)
         self.toolbox.register("attr_discharge_state", random.randint, 0, total_states - 1)
 
-        # EV attributes
+        # EV attributes (separate index space)
         if self.optimize_ev:
             self.toolbox.register(
                 "attr_ev_charge_index",
                 random.randint,
                 0,
-                len_ac - 1,
+                len_ev - 1,
             )
 
         # Household appliance start time
@@ -666,17 +676,17 @@ class GeneticOptimization(OptimizationBase):
         self.toolbox.register("population", tools.initRepeat, list, self.toolbox.individual)
         self.toolbox.register("mate", tools.cxTwoPoint)
 
-        # Mutation operator for charge/discharge states
+        # Mutation operator for battery charge/discharge states
         self.toolbox.register(
             "mutate_charge_discharge", tools.mutUniformInt, low=0, up=total_states - 1, indpb=0.2
         )
 
-        # Mutation operator for EV states
+        # Mutation operator for EV states (separate index space)
         self.toolbox.register(
             "mutate_ev_charge_index",
             tools.mutUniformInt,
             low=0,
-            up=len_ac - 1,
+            up=len_ev - 1,
             indpb=0.2,
         )
 
@@ -800,7 +810,7 @@ class GeneticOptimization(OptimizationBase):
             if np.any(invalid_charge_mask):
                 invalid_indices = np.where(invalid_charge_mask)[0]
                 if len(invalid_indices) > 1:
-                    eautocharge_hours_index_tail[invalid_indices[1:]] = 0
+                    eautocharge_hours_index_tail[invalid_indices] = 0
 
                 eautocharge_hours_index[-min_length:] = eautocharge_hours_index_tail.tolist()
 
@@ -1127,6 +1137,25 @@ class GeneticOptimization(OptimizationBase):
         else:
             self.optimize_ev = False
 
+        # Battery AC charge rates — use the battery's configured charge_rates so the
+        # optimizer can select partial AC charge power (e.g. 10 %, 50 %, 100 %) instead
+        # of always forcing full power.  Falls back to [1.0] when not configured.
+        if parameters.pv_akku and parameters.pv_akku.charge_rates:
+            self.bat_possible_charge_values = [
+                r for r in parameters.pv_akku.charge_rates if r > 0.0
+            ] or [1.0]
+        elif (
+            self.config.devices.batteries
+            and self.config.devices.batteries[0]
+            and self.config.devices.batteries[0].charge_rates
+        ):
+            self.bat_possible_charge_values = [
+                r for r in self.config.devices.batteries[0].charge_rates if r > 0.0
+            ] or [1.0]
+        else:
+            self.bat_possible_charge_values = [1.0]
+        logger.debug("Battery AC charge levels: {}", self.bat_possible_charge_values)
+
         # Initialize household appliance if applicable
         dishwasher = (
             HomeAppliance(

src/akkudoktoreos/optimization/genetic/geneticparams.py

@@ -407,6 +407,7 @@ class GeneticOptimizationParameters(
                         max_charge_power_w=battery_config.max_charge_power_w,
                         min_soc_percentage=battery_config.min_soc_percentage,
                         max_soc_percentage=battery_config.max_soc_percentage,
+                        charge_rates=battery_config.charge_rates,
                     )
                 except:
                     logger.info(

src/akkudoktoreos/optimization/genetic/geneticsolution.py

@@ -262,6 +262,67 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
         # Fallback → safe idle
         return BatteryOperationMode.IDLE, 1.0
 
+    def _soc_clamped_operation_factors(
+        self,
+        ac_charge: float,
+        dc_charge: float,
+        discharge_allowed: bool,
+        soc_pct: float,
+    ) -> tuple[float, float, bool]:
+        """Clamp raw genetic gene values by the battery's actual SOC at that hour.
+
+        The raw gene values represent the optimizer's *intent* and are stored
+        verbatim in the ``genetic_*`` solution columns.  This method derives
+        the *effective* values that can physically be executed given the
+        battery's state of charge, used for the ``battery1_*_op_*`` columns
+        and for ``energy_management_plan`` instructions.
+
+        Clamping rules:
+          - AC charge factor: scaled down proportionally when the battery
+            headroom (max_soc − current_soc) is smaller than what the
+            commanded factor would store in one hour.  Set to 0 when full.
+          - DC charge factor (PV): zeroed when battery is at or above max SOC
+            (the inverter curtails automatically, but this makes intent clear).
+          - Discharge: blocked when SOC is at or below min SOC.
+        """
+        bat_list = self.config.devices.batteries
+        if not bat_list:
+            return ac_charge, dc_charge, discharge_allowed
+
+        bat = bat_list[0]
+        min_soc = float(bat.min_soc_percentage)
+        max_soc = float(bat.max_soc_percentage)
+        capacity_wh = float(bat.capacity_wh)
+        ch_eff = float(bat.charging_efficiency)
+        headroom_wh = max(0.0, (max_soc - soc_pct) / 100.0 * capacity_wh)
+
+        # --- AC charge: scale to available headroom ---
+        effective_ac = ac_charge
+        if effective_ac > 0.0:
+            if headroom_wh <= 0.0:
+                effective_ac = 0.0
+            else:
+                inv_list = self.config.devices.inverters
+                ac_to_dc_eff = float(inv_list[0].ac_to_dc_efficiency) if inv_list else 1.0
+                max_ac_cp_w = (
+                    float(inv_list[0].max_ac_charge_power_w)
+                    if inv_list and inv_list[0].max_ac_charge_power_w is not None
+                    else float(bat.max_charge_power_w)
+                )
+                max_dc_per_h_wh = effective_ac * max_ac_cp_w * ac_to_dc_eff * ch_eff
+                if max_dc_per_h_wh > headroom_wh:
+                    effective_ac = effective_ac * (headroom_wh / max_dc_per_h_wh)
+
+        # --- DC charge (PV): zero when battery is full ---
+        effective_dc = dc_charge
+        if effective_dc > 0.0 and headroom_wh <= 0.0:
+            effective_dc = 0.0
+
+        # --- Discharge: block at min SOC ---
+        effective_dis = discharge_allowed and (soc_pct > min_soc)
+
+        return effective_ac, effective_dc, effective_dis
+
     def optimization_solution(self) -> OptimizationSolution:
         """Provide the genetic solution as a general optimization solution.
 
@@ -334,12 +395,26 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
             ac_charge_hour = self.ac_charge[hour_idx]
             dc_charge_hour = self.dc_charge[hour_idx]
             discharge_allowed_hour = bool(self.discharge_allowed[hour_idx])
-            operation_mode, operation_mode_factor = self._battery_operation_from_solution(
-                ac_charge_hour, dc_charge_hour, discharge_allowed_hour
-            )
+
+            # Raw genetic gene values — optimizer intent, stored verbatim
             operation["genetic_ac_charge_factor"].append(ac_charge_hour)
             operation["genetic_dc_charge_factor"].append(dc_charge_hour)
-            operation["genetic_discharge_allowed_factor"].append(discharge_allowed_hour)
+            operation["genetic_discharge_allowed_factor"].append(float(discharge_allowed_hour))
+
+            # 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
+            soc_h_pct = (
+                self.result.akku_soc_pro_stunde[result_idx]
+                if result_idx < len(self.result.akku_soc_pro_stunde)
+                else 0.0
+            )
+            eff_ac, eff_dc, eff_dis = self._soc_clamped_operation_factors(
+                ac_charge_hour, dc_charge_hour, discharge_allowed_hour, soc_h_pct
+            )
+            operation_mode, operation_mode_factor = self._battery_operation_from_solution(
+                eff_ac, eff_dc, eff_dis
+            )
             for mode in BatteryOperationMode:
                 mode_key = f"battery1_{mode.lower()}_op_mode"
                 factor_key = f"battery1_{mode.lower()}_op_factor"
@@ -561,10 +636,24 @@ class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
         for hour_idx, rate in enumerate(self.ac_charge):
             if hour_idx < start_day_hour:
                 continue
-            operation_mode, operation_mode_factor = self._battery_operation_from_solution(
+            # 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
+            soc_h_pct = (
+                self.result.akku_soc_pro_stunde[result_idx]
+                if result_idx < len(self.result.akku_soc_pro_stunde)
+                else 0.0
+            )
+            eff_ac, eff_dc, eff_dis = self._soc_clamped_operation_factors(
                 self.ac_charge[hour_idx],
                 self.dc_charge[hour_idx],
                 bool(self.discharge_allowed[hour_idx]),
+                soc_h_pct,
+            )
+            operation_mode, operation_mode_factor = self._battery_operation_from_solution(
+                eff_ac, eff_dc, eff_dis
             )
             if (
                 operation_mode == last_operation_mode