feat(optimization): model battery LCOS and probabilistic bypass

CHANGELOG.md

@@ -32,6 +32,14 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
     resolution, so both the hourly and the 15-minute optimizer are fed the correct
     grid. The seasonal price extrapolation is resolution-agnostic and stays identical
     at the default hourly resolution.
+- Separate battery economics into two independent settings: battery
+  `levelized_cost_of_storage_kwh` is now charged once on delivered DC energy, while
+  `optimization.terminal_value_euro_per_kwh` values usable battery energy left at the end of the
+  optimization horizon. LCOS applies to both local battery supply and battery-to-grid export and is
+  included in hourly and total costs.
+- Model direct PV-to-load consumption probabilistically from the bundled conditional minute-load
+  table. The expected direct flow is used consistently for PV bypass, residual load, battery
+  charging, and grid export on hourly and 15-minute optimization grids.
 
 ## 0.3.0 (2026-03-17)
 

docs/_generated/configdevices.md

@@ -456,7 +456,7 @@ as a cohesive unit for scheduling and availability checking.
 | charging_efficiency | `float` | `rw` | `0.88` | Charging efficiency [0.01 ... 1.00]. |
 | device_id | `str` | `rw` | `<unknown>` | ID of device |
 | discharging_efficiency | `float` | `rw` | `0.88` | Discharge efficiency [0.01 ... 1.00]. |
-| levelized_cost_of_storage_kwh | `float` | `rw` | `0.0` | Levelized cost of storage (LCOS), the average lifetime cost of delivering one kWh [€/kWh]. |
+| levelized_cost_of_storage_kwh | `float` | `rw` | `0.0` | Levelized cost of storage (LCOS), applied once to each kWh delivered by the battery [€/kWh]. |
 | max_charge_power_w | `Optional[float]` | `rw` | `5000` | Maximum charging power [W]. |
 | max_soc_percentage | `int` | `rw` | `100` | Maximum state of charge (SOC) as percentage of capacity [%]. |
 | measurement_key_power_3_phase_sym_w | `str` | `ro` | `N/A` | Measurement key for the symmetric 3 phase power the battery is charged or discharged with [W]. |

docs/_generated/configelecprice.md

@@ -99,7 +99,7 @@
 | Name | Type | Read-Only | Default | Description |
 | ---- | ---- | --------- | ------- | ----------- |
 | access_token | `Optional[str]` | `rw` | `None` | Tibber API access token. |
-| home_id | `Optional[str]` | `rw` | `None` | Tibber home id to read prices from. |
+| home_id | `Optional[str]` | `rw` | `None` | Optional Tibber home id. If omitted, the first home with a subscription is used. |
 :::
 <!-- pyml enable line-length -->
 

docs/_generated/configexample.md

@@ -176,6 +176,8 @@
            "horizon_hours": 24,
            "interval": 3600,
            "algorithm": "GENETIC",
+           "visualize_pdf": true,
+           "terminal_value_euro_per_kwh": 0.0,
            "genetic": {
                "individuals": 400,
                "generations": 400,

docs/_generated/configoptimization.md

@@ -13,6 +13,8 @@
 | 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 (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. |
+| terminal_value_euro_per_kwh | `EOS_OPTIMIZATION__TERMINAL_VALUE_EURO_PER_KWH` | `float` | `rw` | `0.0` | Value assigned to usable battery energy remaining at the end of the optimization horizon [EUR/kWh]. This terminal value is independent of the battery LCOS. Defaults to 0 EUR/kWh. |
+| visualize_pdf | `EOS_OPTIMIZATION__VISUALIZE_PDF` | `bool` | `rw` | `True` | Generate the PDF visualization after each optimization run. Disable for headless setups (e.g. Node-RED integration) to save several seconds per run. Defaults to True. |
 :::
 <!-- pyml enable line-length -->
 
@@ -27,6 +29,8 @@
            "horizon_hours": 24,
            "interval": 3600,
            "algorithm": "GENETIC",
+           "visualize_pdf": true,
+           "terminal_value_euro_per_kwh": 0.0,
            "genetic": {
                "individuals": 400,
                "generations": 400,
@@ -51,6 +55,8 @@
            "horizon_hours": 24,
            "interval": 3600,
            "algorithm": "GENETIC",
+           "visualize_pdf": true,
+           "terminal_value_euro_per_kwh": 0.0,
            "genetic": {
                "individuals": 400,
                "generations": 400,

docs/akkudoktoreos/optimauto.md

@@ -145,6 +145,11 @@ The energy management can be run in three modes:
     `prediction.hours * (3600 / interval)`, and device power caps as well as the solution
     and energy-management-plan serializers are slot-aware.
 
+- **terminal_value_euro_per_kwh** (`float`, default: `0.0`): Monetary value assigned to usable
+    battery energy remaining at the end of the optimization horizon. This terminal value influences
+    whether the optimizer preserves or depletes the battery near the horizon. It is independent of
+    the battery's `levelized_cost_of_storage_kwh`, which prices actual discharge throughput.
+
 :::{note}
 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
@@ -224,8 +229,9 @@ The behavior of the genetic algorithm can be customized using the following conf
 ```json
 {
     "optimization": {
-        "hours": 24,
+        "horizon_hours": 24,
         "interval": 3600,
+        "terminal_value_euro_per_kwh": 0.20,
         "genetic" : {
             "individuals": 300,
             "generations": 400,
@@ -342,6 +348,19 @@ The inverter supports separate AC↔DC conversion efficiencies:
 }
 ```
 
+`levelized_cost_of_storage_kwh` is an optional variable cost in EUR/kWh (default `0.0`). EOS applies
+it exactly once to the DC energy actually delivered by the battery, whether that energy supplies
+the local load or is exported to the grid:
+
+```{math}
+C_{LCOS} = \frac{E_{bat,out,Wh}}{1000}\,c_{LCOS,EUR/kWh}
+```
+
+Charging energy, battery-internal discharge losses, and the subsequent DC-to-AC inverter loss do
+not receive another LCOS charge. LCOS is included in the hourly and total simulation costs and is
+separate from `optimization.terminal_value_euro_per_kwh`, which values only the usable energy left
+at the end of the optimization horizon.
+
 #### Home appliance simulation configuration
 
 **Example:**

docs/akkudoktoreos/optimpost.md

@@ -70,6 +70,7 @@ to `DISABLED` in the configuration.
     "pv_akku": {
         "device_id": "battery1",
         "capacity_wh": 26400,
+        "levelized_cost_of_storage_kwh": 0.12,
         "max_charge_power_w": 5000,
         "initial_soc_percentage": 80,
         "min_soc_percentage": 15
@@ -112,12 +113,15 @@ to `DISABLED` in the configuration.
 
 ### Energy Management System (EMS)
 
-#### Battery Cost (`preis_euro_pro_wh_akku`)
+#### Battery Terminal Value (`preis_euro_pro_wh_akku`)
 
 - Unit: €/Wh
 - Purpose: Represents the residual value of energy stored in the battery
 - Impact: Lower values encourage battery depletion, higher values preserve charge at the end of the
   simulation.
+- Separation from LCOS: This value is only applied to usable battery energy remaining at the end of
+  the optimization horizon. Battery discharge throughput is priced separately with
+  `pv_akku.levelized_cost_of_storage_kwh`.
 
 #### Feed-in Tariff (`einspeiseverguetung_euro_pro_wh`)
 
@@ -145,6 +149,51 @@ to `DISABLED` in the configuration.
 - Format: Array of hourly values
 - Data Source: `GET /v1/prediction/series?key=pvforecast_ac_power`
 
+#### Probabilistic Direct PV Consumption and Bypass
+
+Hourly or 15-minute mean values alone would optimistically assume that the smaller of mean PV
+generation and mean load is consumed directly. Real household load varies within the interval. EOS
+therefore uses a conditional probability table derived from one-minute load samples. For a forecast
+mean load \(\mu_L\), the table contains load-bin powers \(L_i\) and their conditional probabilities
+\(p_i = P(L=L_i\mid\mu_L)\), with \(\sum_i p_i=1\).
+
+Because the finite 50 W table grid can deviate slightly from the requested forecast mean, the load
+bins are first normalized without changing the shape of the distribution:
+
+```{math}
+\widetilde{L}_i = L_i \frac{\mu_L}{\sum_j p_j L_j}
+```
+
+For mean PV power \(P_{PV}\), the expected power flowing directly from PV to the load is:
+
+```{math}
+P_{direct} = \sum_i p_i \min\left(\widetilde{L}_i, P_{PV}\right)
+```
+
+For a slot of duration \(\Delta t\), EOS converts this power into energy and derives both residual
+flows from the same direct-consumption value:
+
+```{math}
+\begin{aligned}
+E_{direct} &= \Delta t\,P_{direct} \\
+E_{load,residual} &= E_{load}-E_{direct} \\
+E_{PV,surplus} &= E_{PV}-E_{direct}
+\end{aligned}
+```
+
+The residual load is supplied by the battery and then the grid. The PV surplus charges the battery;
+any remainder bypasses the battery and is exported. Both residual load and PV surplus may be
+positive in the same coarse slot because they occur during different sub-intervals. This is expected
+and preserves the energy balances
+\(E_{direct}+E_{load,residual}=E_{load}\) and
+\(E_{direct}+E_{PV,surplus}=E_{PV}\).
+
+The bundled table is conditioned on a one-hour mean load and models load variation only; mean PV is
+treated as constant inside the slot. For a 15-minute grid produced by splitting hourly energy, the
+power lookup retains the original hourly mean. A native 15-minute load forecast uses the same table
+as an approximation until a separately calibrated 15-minute distribution is available. Fast PV
+variability, for example from clouds, is not represented by this table.
+
 #### Electricity Price Forecast (`strompreis_euro_pro_wh`)
 
 - Unit: €/Wh
@@ -162,8 +211,27 @@ Verify prices against your local tariffs.
 - `capacity_wh`: Total battery capacity in Wh
 - `charging_efficiency`: Charging efficiency (0-1)
 - `discharging_efficiency`: Discharging efficiency (0-1)
+- `levelized_cost_of_storage_kwh`: LCOS in EUR/kWh, charged once for every kWh of DC energy
+  delivered by the battery. Default: `0.0`.
 - `max_charge_power_w`: Maximum charging power in W
 
+#### Battery LCOS (`levelized_cost_of_storage_kwh`)
+
+LCOS and terminal value have different purposes. LCOS is a variable battery-use cost and is added
+once when the battery delivers energy, both for local load coverage and battery-to-grid export. It
+is not charged when the battery is charged and is not charged again on battery-internal or
+DC-to-AC inverter losses.
+
+For battery-delivered DC energy `E_bat,out` in one slot:
+
+```{math}
+C_{LCOS} = \frac{E_{bat,out}}{1000}\,c_{LCOS}
+```
+
+where `E_bat,out` is in Wh and `c_LCOS` is in EUR/kWh. This cost is included in
+`Kosten_Euro_pro_Stunde`, `Gesamtkosten_Euro`, and therefore `Gesamtbilanz_Euro`. The terminal value
+`preis_euro_pro_wh_akku`, by contrast, applies only to usable energy remaining after the last slot.
+
 #### State of Charge (SoC)
 
 - `initial_soc_percentage`: Current battery level (%)
@@ -198,7 +266,7 @@ Round-trip efficiency for AC charging and discharging:
 `η_round_trip = ac_to_dc_efficiency × charging_efficiency × discharging_efficiency × dc_to_ac_efficiency`
 
 For profitability, the discharge electricity price must exceed:
-`buy_price / η_round_trip`
+`buy_price / η_round_trip + LCOS / dc_to_ac_efficiency`
 
 **Backward compatibility**: With default values (`ac_to_dc_efficiency=1.0`,
 `dc_to_ac_efficiency=1.0`, `max_ac_charge_power_w=null`), existing configurations work identically.
@@ -223,7 +291,7 @@ penalty = ac_wh_charged × (break_even_price − best_uncovered_price) × factor
 ```
 
 where:
-- `break_even_price = charge_price / η_round_trip`
+- `break_even_price = charge_price / η_round_trip + LCOS / dc_to_ac_efficiency`
 - `best_uncovered_price` = highest future price not already covered by free PV battery energy
 - `factor` = `optimization.genetic.penalties.ac_charge_break_even` (default `1.0`)
 

openapi.json

@@ -2213,8 +2213,9 @@
           },
           "levelized_cost_of_storage_kwh": {
             "type": "number",
+            "minimum": 0.0,
             "title": "Levelized Cost Of Storage Kwh",
-            "description": "Levelized cost of storage (LCOS), the average lifetime cost of delivering one kWh [\u20ac/kWh].",
+            "description": "Levelized cost of storage (LCOS), applied once to each kWh delivered by the battery [\u20ac/kWh].",
             "default": 0.0,
             "examples": [
               0.12
@@ -2367,8 +2368,9 @@
           },
           "levelized_cost_of_storage_kwh": {
             "type": "number",
+            "minimum": 0.0,
             "title": "Levelized Cost Of Storage Kwh",
-            "description": "Levelized cost of storage (LCOS), the average lifetime cost of delivering one kWh [\u20ac/kWh].",
+            "description": "Levelized cost of storage (LCOS), applied once to each kWh delivered by the battery [\u20ac/kWh].",
             "default": 0.0,
             "examples": [
               0.12
@@ -4886,7 +4888,7 @@
           "preis_euro_pro_wh_akku": {
             "type": "number",
             "title": "Preis Euro Pro Wh Akku",
-            "description": "A float representing the cost of battery energy per watt-hour."
+            "description": "Terminal value of usable battery energy remaining at the end of the optimization horizon [EUR/Wh]. This is not the battery LCOS."
           },
           "gesamtlast": {
             "items": {
@@ -5111,6 +5113,14 @@
             "type": "array",
             "title": "Electricity Price",
             "description": "Used Electricity Price, including predictions"
+          },
+          "Feed_in_tariff": {
+            "items": {
+              "type": "number"
+            },
+            "type": "array",
+            "title": "Feed In Tariff",
+            "description": "Used feed-in tariff in \u20ac/Wh per hour, including predictions"
           }
         },
         "additionalProperties": false,
@@ -6860,7 +6870,7 @@
             "maximum": 3600.0,
             "minimum": 900.0,
             "title": "Interval",
-            "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).",
             "default": 3600,
             "examples": [
               3600,
@@ -6876,6 +6886,26 @@
               "GENETIC"
             ]
           },
+          "visualize_pdf": {
+            "type": "boolean",
+            "title": "Visualize Pdf",
+            "description": "Generate the PDF visualization after each optimization run. Disable for headless setups (e.g. Node-RED integration) to save several seconds per run. Defaults to True.",
+            "default": true,
+            "examples": [
+              true,
+              false
+            ]
+          },
+          "terminal_value_euro_per_kwh": {
+            "type": "number",
+            "title": "Terminal Value Euro Per Kwh",
+            "description": "Value assigned to usable battery energy remaining at the end of the optimization horizon [EUR/kWh]. This terminal value is independent of the battery LCOS. Defaults to 0 EUR/kWh.",
+            "default": 0.0,
+            "examples": [
+              0.0,
+              0.2
+            ]
+          },
           "genetic": {
             "$ref": "#/components/schemas/GeneticCommonSettings",
             "description": "Genetic optimization algorithm configuration.",
@@ -6910,7 +6940,7 @@
             "maximum": 3600.0,
             "minimum": 900.0,
             "title": "Interval",
-            "description": "The optimization interval [sec]. Defaults to 3600 seconds (1 hour)",
+            "description": "The optimization interval (slot length) [sec]. The genetic optimizer supports 3600 (1 hour) and 900 (15 min); other values fall back to 3600. Defaults to 3600 seconds (1 hour).",
             "default": 3600,
             "examples": [
               3600,
@@ -6926,6 +6956,26 @@
               "GENETIC"
             ]
           },
+          "visualize_pdf": {
+            "type": "boolean",
+            "title": "Visualize Pdf",
+            "description": "Generate the PDF visualization after each optimization run. Disable for headless setups (e.g. Node-RED integration) to save several seconds per run. Defaults to True.",
+            "default": true,
+            "examples": [
+              true,
+              false
+            ]
+          },
+          "terminal_value_euro_per_kwh": {
+            "type": "number",
+            "title": "Terminal Value Euro Per Kwh",
+            "description": "Value assigned to usable battery energy remaining at the end of the optimization horizon [EUR/kWh]. This terminal value is independent of the battery LCOS. Defaults to 0 EUR/kWh.",
+            "default": 0.0,
+            "examples": [
+              0.0,
+              0.2
+            ]
+          },
           "genetic": {
             "$ref": "#/components/schemas/GeneticCommonSettings",
             "description": "Genetic optimization algorithm configuration.",
@@ -8877,6 +8927,16 @@
               ],
               null
             ]
+          },
+          "levelized_cost_of_storage_kwh": {
+            "type": "number",
+            "minimum": 0.0,
+            "title": "Levelized Cost Of Storage Kwh",
+            "description": "Levelized cost of storage applied once to each kWh delivered by the battery [EUR/kWh].",
+            "default": 0.0,
+            "examples": [
+              0.12
+            ]
           }
         },
         "additionalProperties": false,

src/akkudoktoreos/devices/devices.py

@@ -50,8 +50,12 @@ class BatteriesCommonSettings(DevicesBaseSettings):
 
     levelized_cost_of_storage_kwh: float = Field(
         default=0.0,
+        ge=0.0,
         json_schema_extra={
-            "description": "Levelized cost of storage (LCOS), the average lifetime cost of delivering one kWh [€/kWh].",
+            "description": (
+                "Levelized cost of storage (LCOS), applied once to each kWh delivered "
+                "by the battery [€/kWh]."
+            ),
             "examples": [0.12],
         },
     )

src/akkudoktoreos/devices/genetic/battery.py

@@ -34,6 +34,11 @@ class Battery:
         self.initial_soc_percentage = self.parameters.initial_soc_percentage
         self.charging_efficiency = self.parameters.charging_efficiency
         self.discharging_efficiency = self.parameters.discharging_efficiency
+        self.levelized_cost_of_storage_kwh = (
+            self.parameters.levelized_cost_of_storage_kwh
+            if isinstance(self.parameters, SolarPanelBatteryParameters)
+            else 0.0
+        )
 
         # Charge rates, in case of None use default
         self.charge_rates = np.array(BATTERY_DEFAULT_CHARGE_RATES, dtype=float)
@@ -115,6 +120,10 @@ class Battery:
         raw_soc_available_wh = max(self.soc_wh - self.min_soc_wh, 0.0)
         return min(raw_power_remaining_wh, raw_soc_available_wh) * self.discharging_efficiency
 
+    def discharged_energy_wh(self, hour: int) -> float:
+        """Return DC energy delivered by the battery in one optimization slot."""
+        return self._discharged_raw_wh_per_slot[hour] * self.discharging_efficiency
+
     def set_discharge_per_hour(self, discharge_array: np.ndarray) -> None:
         """Sets the discharge values for each hour."""
         if len(discharge_array) != self.prediction_hours:

src/akkudoktoreos/devices/genetic/inverter.py

@@ -14,9 +14,6 @@ class Inverter:
         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
@@ -28,16 +25,13 @@ 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.slot_duration_h
-        )  # Maximum energy the inverter can move in one optimization slot
+        # max_power_wh is supplied as power [W] but used as the maximum energy
+        # the inverter can move during one optimization slot.
+        self.max_power_wh = self.parameters.max_power_wh * self.slot_duration_h
         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().
+        # This value remains a power [W]. GeneticSimulation converts it into a
+        # slot-independent charge-factor limit.
         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]:
@@ -58,128 +52,86 @@ class Inverter:
         hour: int,
         allow_battery_grid_export: bool = False,
     ) -> tuple[float, float, float, float]:
+        """Process one slot using probabilistic direct PV-to-load overlap.
+
+        ``generation`` and ``consumption`` are interval energies. The load
+        probability table is evaluated in watts and yields the expected direct
+        PV-to-load power. The remaining load and PV surplus are then handled
+        independently, because both can occur during different sub-intervals of
+        the same hourly or 15-minute slot.
+        """
         losses = 0.0
         grid_export = 0.0
-        grid_import = 0.0
-        self_consumption = 0.0
+        generation = max(float(generation), 0.0)
+        consumption = max(float(consumption), 0.0)
 
-        # Cache inverter DC→AC efficiency for discharge path
-        dc_to_ac_eff = self.dc_to_ac_efficiency
-
-        if generation >= consumption:
-            if consumption > self.max_power_wh:
-                # If consumption exceeds maximum inverter power
-                losses += generation - self.max_power_wh
-                remaining_power = self.max_power_wh - consumption
-                grid_import = -remaining_power  # Negative indicates feeding into the grid
-                self_consumption = self.max_power_wh
-            else:
-                # Calculate scr using cached results per energy management/optimization run.
-                # The interpolator expects power levels [W]; consumption/generation are
-                # energy per slot [Wh], so convert via the slot duration (identical at
-                # the hourly default, ×4 on the 15-minute grid).
-                scr = self.self_consumption_predictor.calculate_self_consumption(
-                    consumption / self.slot_duration_h, generation / self.slot_duration_h
+        # Convert interval energy [Wh] to mean power [W] for the probability
+        # lookup, then convert its expected direct power back to slot energy.
+        if generation > 0.0 and consumption > 0.0:
+            expected_direct_power_w = (
+                self.self_consumption_predictor.calculate_expected_direct_consumption(
+                    consumption / self.slot_duration_h,
+                    generation / self.slot_duration_h,
                 )
-
-                # Remaining power after consumption
-                remaining_power = (generation - consumption) * scr  # EVQ
-                # Remaining load Self Consumption not perfect
-                remaining_load_evq = (generation - consumption) * (1.0 - scr)
-
-                from_battery_dc = 0.0
-                if remaining_load_evq > 0:
-                    # Akku muss den Restverbrauch decken
-                    if self.battery:
-                        # Request more DC from battery to account for DC→AC conversion loss
-                        dc_request = remaining_load_evq / dc_to_ac_eff
-                        from_battery_dc, discharge_losses = self.battery.discharge_energy(
-                            dc_request, hour
-                        )
-                        # Convert DC output to AC
-                        from_battery_ac = from_battery_dc * dc_to_ac_eff
-                        inverter_discharge_losses = from_battery_dc - from_battery_ac
-                        remaining_load_evq -= from_battery_ac
-                        losses += discharge_losses + inverter_discharge_losses
-                    else:
-                        from_battery_ac = 0.0
-
-                    # Wenn der Akku den Restverbrauch nicht vollständig decken kann, wird der Rest ins Netz gezogen
-                    if remaining_load_evq > 0:
-                        grid_import += remaining_load_evq
-                        remaining_load_evq = 0
-                else:
-                    from_battery_ac = 0.0
-
-                if remaining_power > 0:
-                    # Load battery with excess energy (DC path, no inverter conversion needed)
-                    charge_losses = 0.0
-                    if self.battery:
-                        charged_energie, charge_losses = self.battery.charge_energy(
-                            remaining_power, hour
-                        )
-                        remaining_surplus = remaining_power - (charged_energie + charge_losses)
-                    else:
-                        remaining_surplus = remaining_power
-
-                    # Feed-in to the grid based on remaining capacity
-                    if remaining_surplus > self.max_power_wh - consumption:
-                        grid_export = self.max_power_wh - consumption
-                        losses += remaining_surplus - grid_export
-                    else:
-                        grid_export = remaining_surplus
-
-                    losses += charge_losses
-                self_consumption = (
-                    consumption + from_battery_ac
-                )  # Self-consumption is equal to the load
-
-                if allow_battery_grid_export and self.battery:
-                    export_capacity = max(self.max_power_wh - consumption - grid_export, 0.0)
-                    remaining_battery_ac = (
-                        self.battery.remaining_discharge_energy_wh(hour) * dc_to_ac_eff
-                    )
-                    export_capacity = min(export_capacity, remaining_battery_ac)
-                    battery_export_ac, battery_export_losses = self._discharge_battery_to_ac(
-                        export_capacity, hour
-                    )
-                    grid_export += battery_export_ac
-                    losses += battery_export_losses
-
+            )
+            direct_pv_energy = expected_direct_power_w * self.slot_duration_h
         else:
-            # Case 2: Insufficient generation, cover shortfall
-            shortfall = consumption - generation
-            available_ac_power = max(self.max_power_wh - generation, 0)
+            direct_pv_energy = 0.0
 
-            # Discharge battery to cover shortfall, if possible
-            if self.battery:
-                # Need shortfall in AC, request more DC from battery for DC→AC conversion
-                ac_needed = min(shortfall, available_ac_power)
-                dc_request = ac_needed / dc_to_ac_eff
-                battery_discharge_dc, discharge_losses = self.battery.discharge_energy(
-                    dc_request, hour
-                )
-                # Convert DC output to AC
-                battery_discharge_ac = battery_discharge_dc * dc_to_ac_eff
-                inverter_discharge_losses = battery_discharge_dc - battery_discharge_ac
-                losses += discharge_losses + inverter_discharge_losses
-            else:
-                battery_discharge_ac = 0
+        # Direct PV is bounded by both input energies and by the AC energy the
+        # inverter can move during this slot.
+        direct_pv_energy = min(
+            max(direct_pv_energy, 0.0),
+            generation,
+            consumption,
+            self.max_power_wh,
+        )
+        remaining_load = max(consumption - direct_pv_energy, 0.0)
+        pv_surplus = max(generation - direct_pv_energy, 0.0)
+        remaining_inverter_ac_capacity = max(self.max_power_wh - direct_pv_energy, 0.0)
 
-            # Draw remaining required power from the grid (discharge_losses are already subtracted in the battery)
-            grid_import = shortfall - battery_discharge_ac
-            self_consumption = generation + battery_discharge_ac
+        # Load gaps and PV surplus may both occur within the same coarse slot.
+        # Cover the load gap first; this preserves the existing chronological
+        # approximation and can create headroom for later PV charging.
+        battery_discharge_ac = 0.0
+        if remaining_load > 0.0 and self.battery and remaining_inverter_ac_capacity > 0.0:
+            requested_ac_wh = min(remaining_load, remaining_inverter_ac_capacity)
+            battery_discharge_ac, battery_discharge_losses = self._discharge_battery_to_ac(
+                requested_ac_wh, hour
+            )
+            remaining_load = max(remaining_load - battery_discharge_ac, 0.0)
+            remaining_inverter_ac_capacity = max(
+                remaining_inverter_ac_capacity - battery_discharge_ac, 0.0
+            )
+            losses += battery_discharge_losses
 
-            if allow_battery_grid_export and self.battery and grid_import <= 0.0:
-                export_capacity = max(self.max_power_wh - consumption, 0.0)
-                remaining_battery_ac = (
-                    self.battery.remaining_discharge_energy_wh(hour) * dc_to_ac_eff
-                )
-                export_capacity = min(export_capacity, remaining_battery_ac)
-                battery_export_ac, battery_export_losses = self._discharge_battery_to_ac(
-                    export_capacity, hour
-                )
-                grid_export += battery_export_ac
-                losses += battery_export_losses
+        grid_import = remaining_load
 
+        # Charge from the probabilistic PV surplus on the DC path. Stored energy
+        # plus charge losses equals the PV energy accepted by the battery.
+        remaining_surplus = pv_surplus
+        if remaining_surplus > 0.0 and self.battery:
+            charged_energy, charge_losses = self.battery.charge_energy(remaining_surplus, hour)
+            remaining_surplus = max(remaining_surplus - charged_energy - charge_losses, 0.0)
+            losses += charge_losses
+
+        pv_grid_export = min(remaining_surplus, remaining_inverter_ac_capacity)
+        grid_export += pv_grid_export
+        remaining_inverter_ac_capacity = max(remaining_inverter_ac_capacity - pv_grid_export, 0.0)
+        # PV which can neither charge the battery nor pass through the inverter
+        # is curtailed and reported as a loss.
+        losses += max(remaining_surplus - pv_grid_export, 0.0)
+
+        if allow_battery_grid_export and self.battery and remaining_inverter_ac_capacity > 0.0:
+            remaining_battery_ac = (
+                self.battery.remaining_discharge_energy_wh(hour) * self.dc_to_ac_efficiency
+            )
+            export_capacity = min(remaining_inverter_ac_capacity, remaining_battery_ac)
+            battery_export_ac, battery_export_losses = self._discharge_battery_to_ac(
+                export_capacity, hour
+            )
+            grid_export += battery_export_ac
+            losses += battery_export_losses
+
+        self_consumption = direct_pv_energy + battery_discharge_ac
         return grid_export, grid_import, losses, self_consumption

src/akkudoktoreos/optimization/genetic/genetic.py

@@ -450,7 +450,18 @@ class GeneticSimulation(PydanticBaseModel):
             feed_in_tariff_per_hour[hour_idx] = hourly_feed_in_tariff
 
             # Financial calculations
-            costs_per_hour[hour_idx] = energy_consumption_grid_actual * hourly_electricity_price
+            grid_cost = energy_consumption_grid_actual * hourly_electricity_price
+            # LCOS is charged exactly once on battery-delivered DC energy. It is
+            # not charged on input energy, internal discharge losses, or the
+            # downstream DC-to-AC inverter loss.
+            battery_lcos_cost = 0.0
+            if battery_fast:
+                battery_lcos_cost = (
+                    battery_fast.discharged_energy_wh(hour)
+                    * battery_fast.levelized_cost_of_storage_kwh
+                    / 1000.0
+                )
+            costs_per_hour[hour_idx] = grid_cost + battery_lcos_cost
             revenue_per_hour[hour_idx] = energy_feedin_grid_actual * hourly_feed_in_tariff
 
         total_cost = np.nansum(costs_per_hour)
@@ -1244,8 +1255,14 @@ class GeneticOptimization(OptimizationBase):
                     if charge_price <= 0:
                         continue
 
-                    # Price that a future discharge hour must reach to break even
-                    break_even_price = charge_price / round_trip_eff
+                    # Price that a future AC discharge hour must reach to break
+                    # even. LCOS is defined per DC Wh delivered by the battery;
+                    # dividing it by DC-to-AC efficiency converts it to the
+                    # corresponding cost per useful/exported AC Wh.
+                    lcos_per_wh_dc = getattr(bat, "levelized_cost_of_storage_kwh", 0.0) / 1000.0
+                    break_even_price = (
+                        charge_price / round_trip_eff + lcos_per_wh_dc / inv.dc_to_ac_efficiency
+                    )
 
                     best_uncovered_price = best_prices[hour]
 

src/akkudoktoreos/optimization/genetic/geneticdevices.py

@@ -98,6 +98,17 @@ class BaseBatteryParameters(DeviceParameters):
 class SolarPanelBatteryParameters(BaseBatteryParameters):
     """PV battery device simulation configuration."""
 
+    levelized_cost_of_storage_kwh: float = Field(
+        default=0.0,
+        ge=0.0,
+        json_schema_extra={
+            "description": (
+                "Levelized cost of storage applied once to each kWh delivered "
+                "by the battery [EUR/kWh]."
+            ),
+            "examples": [0.12],
+        },
+    )
     max_charge_power_w: Optional[float] = max_charging_power_field()
 
 

src/akkudoktoreos/optimization/genetic/geneticparams.py

@@ -54,7 +54,10 @@ class GeneticEnergyManagementParameters(GeneticParametersBaseModel):
     )
     preis_euro_pro_wh_akku: float = Field(
         json_schema_extra={
-            "description": "A float representing the cost of battery energy per watt-hour."
+            "description": (
+                "Terminal value of usable battery energy remaining at the end of the "
+                "optimization horizon [EUR/Wh]. This is not the battery LCOS."
+            )
         }
     )
     gesamtlast: list[float] = Field(
@@ -416,7 +419,6 @@ class GeneticOptimizationParameters(
                 cls.config.devices.max_batteries = 1
             if cls.config.devices.max_batteries == 0:
                 battery_params = None
-                battery_lcos_kwh = 0
             else:
                 if cls.config.devices.batteries is None:
                     logger.info("No battery device data available - defaulting to demo data.")
@@ -428,6 +430,9 @@ class GeneticOptimizationParameters(
                         capacity_wh=battery_config.capacity_wh,
                         charging_efficiency=battery_config.charging_efficiency,
                         discharging_efficiency=battery_config.discharging_efficiency,
+                        levelized_cost_of_storage_kwh=(
+                            battery_config.levelized_cost_of_storage_kwh
+                        ),
                         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,
@@ -441,14 +446,6 @@ class GeneticOptimizationParameters(
                     cls.config.devices.batteries = [{"device_id": "battery1", "capacity_wh": 8000}]
                     # Retry
                     continue
-                # Levelized cost of ownership
-                if battery_config.levelized_cost_of_storage_kwh is None:
-                    logger.info(
-                        "No battery device LCOS data available - defaulting to 0 €/kWh. Parameter preparation attempt {}.",
-                        attempt,
-                    )
-                    battery_config.levelized_cost_of_storage_kwh = 0
-                battery_lcos_kwh = battery_config.levelized_cost_of_storage_kwh
                 # Initial SOC
                 try:
                     initial_soc_factor = cls.measurement.key_to_value(
@@ -654,7 +651,9 @@ class GeneticOptimizationParameters(
                         strompreis_euro_pro_wh=elecprice_marketprice_wh,
                         einspeiseverguetung_euro_pro_wh=feed_in_tariff_wh,
                         gesamtlast=loadforecast_power_w,
-                        preis_euro_pro_wh_akku=battery_lcos_kwh / 1000,
+                        preis_euro_pro_wh_akku=(
+                            cls.config.optimization.terminal_value_euro_per_kwh / 1000
+                        ),
                     ),
                     temperature_forecast=weather_temp_air,
                     pv_akku=battery_params,

src/akkudoktoreos/optimization/optimization.py

@@ -101,6 +101,18 @@ class OptimizationCommonSettings(SettingsBaseModel):
         },
     )
 
+    terminal_value_euro_per_kwh: float = Field(
+        default=0.0,
+        json_schema_extra={
+            "description": (
+                "Value assigned to usable battery energy remaining at the end of the "
+                "optimization horizon [EUR/kWh]. This terminal value is independent "
+                "of the battery LCOS. Defaults to 0 EUR/kWh."
+            ),
+            "examples": [0.0, 0.20],
+        },
+    )
+
     genetic: GeneticCommonSettings = Field(
         default_factory=GeneticCommonSettings,
         json_schema_extra={

src/akkudoktoreos/prediction/interpolator.py

@@ -17,8 +17,32 @@ class SelfConsumptionProbabilityInterpolator:
             self.interpolator: RegularGridInterpolator = pickle.load(file)  # noqa: S301
         self.load_power_min_w = float(self.interpolator.grid[0][0])
         self.load_power_max_w = float(self.interpolator.grid[0][-1])
+        self.minute_load_levels_w = np.asarray(self.interpolator.grid[1], dtype=float)
         self.minute_load_max_w = float(self.interpolator.grid[1][-1])
 
+    def _load_distribution(self, mean_load_power_w: float) -> tuple[np.ndarray, np.ndarray]:
+        """Return the conditional minute-load distribution for a mean load.
+
+        The table stores one probability mass for each 50 W minute-load bin.
+        Linear interpolation between its mean-load rows can introduce very small
+        numerical deviations, so negative masses are removed and the result is
+        normalized explicitly.
+        """
+        bounded_mean_load_w = float(
+            np.clip(mean_load_power_w, self.load_power_min_w, self.load_power_max_w)
+        )
+        points = np.column_stack(
+            (
+                np.full(self.minute_load_levels_w.shape, bounded_mean_load_w),
+                self.minute_load_levels_w,
+            )
+        )
+        probabilities = np.maximum(np.asarray(self.interpolator(points), dtype=float), 0.0)
+        probability_sum = float(probabilities.sum())
+        if probability_sum <= 0.0:
+            return self.minute_load_levels_w, probabilities
+        return self.minute_load_levels_w, probabilities / probability_sum
+
     def _generate_points(
         self, mean_load_power_w: float, pv_power_w: float
     ) -> tuple[np.ndarray, np.ndarray]:
@@ -39,7 +63,12 @@ class SelfConsumptionProbabilityInterpolator:
 
     @cache_energy_management
     def calculate_self_consumption(self, mean_load_power_w: float, pv_power_w: float) -> float:
-        """Calculate the PV self-consumption rate using RegularGridInterpolator.
+        """Return the legacy cumulative minute-load probability.
+
+        This method is retained for API compatibility. Its result is the
+        probability that the minute load is no greater than ``pv_power_w``;
+        it is not an energy self-consumption ratio. New energy-flow code must
+        use :meth:`calculate_expected_direct_consumption`.
 
         The results are cached until the start of the next energy management run/ optimization.
 
@@ -54,6 +83,46 @@ class SelfConsumptionProbabilityInterpolator:
         probabilities = self.interpolator(points)
         return float(np.clip(probabilities.sum(), 0.0, 1.0))
 
+    @cache_energy_management
+    def calculate_expected_direct_consumption(
+        self, mean_load_power_w: float, pv_power_w: float
+    ) -> float:
+        """Calculate expected direct PV-to-load power in watts.
+
+        For conditional minute-load probabilities ``p_i`` and load-bin powers
+        ``L_i``, the expected direct consumption is
+
+        ``sum(p_i * min(L_i, pv_power_w))``.
+
+        The tabulated load-bin powers are rescaled to preserve the supplied
+        forecast mean exactly. This compensates for discretization and the
+        finite upper table boundary while retaining the distribution shape.
+
+        Args:
+            mean_load_power_w: Mean load power of the forecast interval [W].
+            pv_power_w: Mean PV power of the forecast interval [W].
+
+        Returns:
+            Expected direct PV-to-load power [W].
+        """
+        mean_load_power_w = max(float(mean_load_power_w), 0.0)
+        pv_power_w = max(float(pv_power_w), 0.0)
+        if mean_load_power_w == 0.0 or pv_power_w == 0.0:
+            return 0.0
+
+        load_levels_w, probabilities = self._load_distribution(mean_load_power_w)
+        modeled_mean_load_w = float(np.dot(probabilities, load_levels_w))
+        if modeled_mean_load_w <= 0.0:
+            return 0.0
+
+        # Preserve the requested mean load while keeping the conditional shape
+        # from the probability table.
+        normalized_load_levels_w = load_levels_w * (mean_load_power_w / modeled_mean_load_w)
+        expected_direct_power_w = float(
+            np.dot(probabilities, np.minimum(normalized_load_levels_w, pv_power_w))
+        )
+        return float(np.clip(expected_direct_power_w, 0.0, min(mean_load_power_w, pv_power_w)))
+
     # def calculate_self_consumption(self, load_1h_power: float, pv_power: float) -> float:
     #     """Calculate the PV self-consumption rate using RegularGridInterpolator.
 

tests/test_battery.py

@@ -337,4 +337,9 @@ def test_quarter_hour_discharge_calls_share_one_power_budget():
 
     assert first_delivered == pytest.approx(200.0)
     assert second_delivered == pytest.approx(50.0)
+    assert battery.discharged_energy_wh(0) == pytest.approx(250.0)
     assert battery.soc_wh == pytest.approx(9_750.0)
+
+    battery.reset()
+
+    assert battery.discharged_energy_wh(0) == 0.0

tests/test_geneticsimulation.py

@@ -338,15 +338,15 @@ def test_simulation(genetic_simulation):
 
     # Verify the total balance
     assert (
-        abs(result["Gesamtbilanz_Euro"] - 6.62818441758576) < 1e-5
+        abs(result["Gesamtbilanz_Euro"] - 7.025236588371921) < 1e-5
     ), "Total balance should reflect the shared per-slot battery power limit."
 
     # Check total revenue and total costs
     assert (
-        abs(result["Gesamteinnahmen_Euro"] - 1.9606946615517515) < 1e-5
+        abs(result["Gesamteinnahmen_Euro"] - 2.3247787887715) < 1e-5
     ), "Total revenue should respect the shared per-slot battery power limit."
     assert (
-        abs(result["Gesamtkosten_Euro"] - 8.588879079137512) < 1e-5
+        abs(result["Gesamtkosten_Euro"] - 9.350015377143421) < 1e-5
     ), "Total costs should respect the shared per-slot battery power limit."
 
     # Check the losses
@@ -387,8 +387,8 @@ def test_direct_marketing_curtails_negative_feed_in(config_eos, monkeypatch):
     inverter = Inverter(InverterParameters(device_id="inverter1", max_power_wh=1000.0))
     monkeypatch.setattr(
         inverter.self_consumption_predictor,
-        "calculate_self_consumption",
-        Mock(return_value=1.0),
+        "calculate_expected_direct_consumption",
+        Mock(side_effect=min),
     )
 
     simulation = GeneticSimulation()
@@ -413,7 +413,11 @@ def test_direct_marketing_curtails_negative_feed_in(config_eos, monkeypatch):
     assert result["Verluste_Pro_Stunde"][0] == pytest.approx(500.0)
 
 
-def _direct_marketing_battery_export_simulation(config_eos) -> GeneticSimulation:
+def _direct_marketing_battery_export_simulation(
+    config_eos,
+    levelized_cost_of_storage_kwh: float = 0.0,
+    dc_to_ac_efficiency: float = 1.0,
+) -> GeneticSimulation:
     config_eos.merge_settings_from_dict(
         {"prediction": {"hours": 2}, "optimization": {"horizon_hours": 2}}
     )
@@ -426,6 +430,7 @@ def _direct_marketing_battery_export_simulation(config_eos) -> GeneticSimulation
             min_soc_percentage=0,
             charging_efficiency=1.0,
             discharging_efficiency=1.0,
+            levelized_cost_of_storage_kwh=levelized_cost_of_storage_kwh,
             max_charge_power_w=500,
         ),
         prediction_hours=config_eos.prediction.hours,
@@ -435,6 +440,7 @@ def _direct_marketing_battery_export_simulation(config_eos) -> GeneticSimulation
             device_id="inverter1",
             max_power_wh=500.0,
             battery_id=battery.parameters.device_id,
+            dc_to_ac_efficiency=dc_to_ac_efficiency,
         ),
         battery=battery,
     )
@@ -479,3 +485,23 @@ def test_direct_marketing_battery_grid_export_uses_separate_signal(config_eos):
     assert result["Einnahmen_Euro_pro_Stunde"][0] == pytest.approx(0.1)
     assert simulation.battery is not None
     assert simulation.battery.current_soc_percentage() == 50.0
+
+
+def test_battery_lcos_is_charged_once_on_delivered_energy(config_eos):
+    simulation = _direct_marketing_battery_export_simulation(
+        config_eos,
+        levelized_cost_of_storage_kwh=0.12,
+        dc_to_ac_efficiency=0.8,
+    )
+    assert simulation.bat_grid_export_hours is not None
+    simulation.bat_grid_export_hours[0] = 1
+
+    result = simulation.simulate(start_hour=0)
+
+    # The battery delivers 500 Wh DC, so LCOS is 0.5 kWh * 0.12 EUR/kWh
+    # = 0.06 EUR exactly once. After the 80% inverter, 400 Wh AC reaches
+    # the grid and earns 400 Wh * 0.0002 EUR/Wh = 0.08 EUR.
+    assert result["Kosten_Euro_pro_Stunde"][0] == pytest.approx(0.06)
+    assert result["Gesamtkosten_Euro"] == pytest.approx(0.06)
+    assert result["Einnahmen_Euro_pro_Stunde"][0] == pytest.approx(0.08)
+    assert result["Gesamtbilanz_Euro"] == pytest.approx(-0.02)

tests/test_interpolator.py

@@ -10,8 +10,8 @@ def test_quarter_hour_energy_is_converted_back_to_same_mean_power():
     hourly_pv_wh = 1200.0
     slot_duration_h = 0.25
 
-    hourly = interpolator.calculate_self_consumption(hourly_load_wh, hourly_pv_wh)
-    quarter_hour = interpolator.calculate_self_consumption(
+    hourly = interpolator.calculate_expected_direct_consumption(hourly_load_wh, hourly_pv_wh)
+    quarter_hour = interpolator.calculate_expected_direct_consumption(
         (hourly_load_wh / 4) / slot_duration_h,
         (hourly_pv_wh / 4) / slot_duration_h,
     )
@@ -28,3 +28,43 @@ def test_load_above_probability_grid_uses_highest_supported_distribution():
 
     assert above_boundary == pytest.approx(at_boundary)
     assert above_boundary > 0.99
+
+
+def test_expected_direct_consumption_accounts_for_subhourly_load_variation():
+    """Expected overlap must be below the optimistic overlap of interval means."""
+    interpolator = get_eos_load_interpolator()
+
+    direct_power_w = interpolator.calculate_expected_direct_consumption(800.0, 1200.0)
+
+    assert direct_power_w == pytest.approx(621.0, abs=2.0)
+    assert 0.0 < direct_power_w < 800.0
+
+
+@pytest.mark.parametrize(
+    ("mean_load_power_w", "pv_power_w"),
+    [(800.0, 1200.0), (1000.0, 500.0), (1500.0, 1500.0)],
+)
+def test_expected_direct_consumption_produces_conservative_energy_balance(
+    mean_load_power_w, pv_power_w
+):
+    """Direct use, residual load and surplus must conserve both mean powers."""
+    interpolator = get_eos_load_interpolator()
+
+    direct_power_w = interpolator.calculate_expected_direct_consumption(
+        mean_load_power_w, pv_power_w
+    )
+    residual_load_w = mean_load_power_w - direct_power_w
+    pv_surplus_w = pv_power_w - direct_power_w
+
+    assert 0.0 <= direct_power_w <= min(mean_load_power_w, pv_power_w)
+    assert direct_power_w + residual_load_w == pytest.approx(mean_load_power_w)
+    assert direct_power_w + pv_surplus_w == pytest.approx(pv_power_w)
+
+
+def test_expected_direct_consumption_preserves_forecast_mean_at_high_pv():
+    """A PV level above every normalized load bin covers the complete mean load."""
+    interpolator = get_eos_load_interpolator()
+
+    direct_power_w = interpolator.calculate_expected_direct_consumption(3000.0, 10000.0)
+
+    assert direct_power_w == pytest.approx(3000.0)

tests/test_inverter.py

@@ -5,7 +5,9 @@ import pytest
 
 from akkudoktoreos.devices.genetic.battery import Battery
 from akkudoktoreos.devices.genetic.inverter import Inverter, InverterParameters
-from akkudoktoreos.optimization.genetic.geneticdevices import SolarPanelBatteryParameters
+from akkudoktoreos.optimization.genetic.geneticdevices import (
+    SolarPanelBatteryParameters,
+)
 
 
 @pytest.fixture
@@ -20,7 +22,7 @@ def mock_battery() -> Mock:
 @pytest.fixture
 def inverter(mock_battery) -> Inverter:
     mock_self_consumption_predictor = Mock()
-    mock_self_consumption_predictor.calculate_self_consumption.return_value = 1.0
+    mock_self_consumption_predictor.calculate_expected_direct_consumption.side_effect = min
     with patch(
         "akkudoktoreos.devices.genetic.inverter.get_eos_load_interpolator",
         return_value=mock_self_consumption_predictor,
@@ -91,7 +93,7 @@ def test_process_energy_excess_generation(inverter, mock_battery):
     assert self_consumption == 200.0  # All consumption is met
     mock_battery.charge_energy.assert_called_once_with(400.0, hour)
     mock_battery.discharge_energy.assert_not_called()
-    inverter.self_consumption_predictor.calculate_self_consumption.assert_called_once_with(
+    inverter.self_consumption_predictor.calculate_expected_direct_consumption.assert_called_once_with(
         consumption, generation
     )
 
@@ -100,7 +102,8 @@ def test_process_energy_excess_generation_interpolator(inverter, mock_battery):
     # Battery charges 100 Wh with 10 Wh loss
     mock_battery.charge_energy.return_value = (100.0, 10.0)
     mock_battery.discharge_energy.return_value = (20.0, 2.0)
-    inverter.self_consumption_predictor.calculate_self_consumption.return_value = 0.95
+    inverter.self_consumption_predictor.calculate_expected_direct_consumption.side_effect = None
+    inverter.self_consumption_predictor.calculate_expected_direct_consumption.return_value = 180.0
 
     generation = 600.0
     consumption = 200.0
@@ -110,19 +113,71 @@ def test_process_energy_excess_generation_interpolator(inverter, mock_battery):
         generation, consumption, hour
     )
 
-    assert grid_export == pytest.approx(
-        270.0, rel=1e-2
-    )  # 290 Wh feed-in - 5% of generation-consumption self consumption after battery charges
+    assert grid_export == pytest.approx(300.0, rel=1e-2)
     assert grid_import == pytest.approx(0.0, rel=1e-2)  # No grid draw
-    assert losses == 12.0  # Battery charging losses
-    assert self_consumption == 220.0  # All consumption is met
-    mock_battery.charge_energy.assert_called_once_with(pytest.approx(380.0, rel=1e-2), hour)
+    assert losses == 22.0  # Battery/inverter losses plus curtailed PV
+    assert self_consumption == 200.0  # 180 Wh direct PV + 20 Wh battery
+    mock_battery.charge_energy.assert_called_once_with(pytest.approx(420.0, rel=1e-2), hour)
     mock_battery.discharge_energy.assert_called_once_with(pytest.approx(20.0, rel=1e-2), hour)
-    inverter.self_consumption_predictor.calculate_self_consumption.assert_called_once_with(
+    inverter.self_consumption_predictor.calculate_expected_direct_consumption.assert_called_once_with(
         consumption, generation
     )
 
 
+def test_probabilistic_bypass_conserves_energy_without_battery():
+    predictor = Mock()
+    predictor.calculate_expected_direct_consumption.return_value = 150.0
+    with patch(
+        "akkudoktoreos.devices.genetic.inverter.get_eos_load_interpolator",
+        return_value=predictor,
+    ):
+        inverter_without_battery = Inverter(
+            InverterParameters(device_id="inverter", max_power_wh=1000.0)
+        )
+
+    generation = 600.0
+    consumption = 200.0
+    grid_export, grid_import, losses, self_consumption = (
+        inverter_without_battery.process_energy(generation, consumption, hour=0)
+    )
+
+    assert self_consumption == pytest.approx(150.0)
+    assert grid_import == pytest.approx(50.0)
+    assert grid_export == pytest.approx(450.0)
+    assert losses == 0.0
+    assert generation + grid_import == pytest.approx(
+        consumption + grid_export + losses
+    )
+
+
+def test_probabilistic_bypass_conserves_energy_on_quarter_hour_grid():
+    predictor = Mock()
+    predictor.calculate_expected_direct_consumption.return_value = 600.0
+    with patch(
+        "akkudoktoreos.devices.genetic.inverter.get_eos_load_interpolator",
+        return_value=predictor,
+    ):
+        inverter_without_battery = Inverter(
+            InverterParameters(device_id="inverter", max_power_wh=2000.0),
+            slot_duration_h=0.25,
+        )
+
+    generation = 300.0  # 1200 W over 15 minutes
+    consumption = 200.0  # 800 W over 15 minutes
+    grid_export, grid_import, losses, self_consumption = (
+        inverter_without_battery.process_energy(generation, consumption, hour=0)
+    )
+
+    predictor.calculate_expected_direct_consumption.assert_called_once_with(800.0, 1200.0)
+    assert self_consumption == pytest.approx(150.0)
+    assert grid_import == pytest.approx(50.0)
+    assert grid_export == pytest.approx(150.0)
+    assert losses == 0.0
+    assert generation + grid_import == pytest.approx(
+        consumption + grid_export + losses
+    )
+
+
 def test_process_energy_generation_equals_consumption(inverter, mock_battery):
     generation = 300.0
     consumption = 300.0
@@ -139,7 +194,7 @@ def test_process_energy_generation_equals_consumption(inverter, mock_battery):
 
     mock_battery.charge_energy.assert_not_called()
     mock_battery.discharge_energy.assert_not_called()
-    inverter.self_consumption_predictor.calculate_self_consumption.assert_called_once_with(
+    inverter.self_consumption_predictor.calculate_expected_direct_consumption.assert_called_once_with(
         consumption, generation
     )
 
@@ -163,7 +218,9 @@ def test_process_energy_battery_discharges(inverter, mock_battery):
     assert self_consumption == 200.0  # Generation + battery discharge
     mock_battery.charge_energy.assert_not_called()
     mock_battery.discharge_energy.assert_called_once_with(150.0, hour)
-    inverter.self_consumption_predictor.calculate_self_consumption.assert_not_called()
+    inverter.self_consumption_predictor.calculate_expected_direct_consumption.assert_called_once_with(
+        consumption, generation
+    )
 
 
 def test_process_energy_allows_battery_grid_export(inverter, mock_battery):
@@ -183,7 +240,7 @@ def test_process_energy_allows_battery_grid_export(inverter, mock_battery):
     assert losses == 0.0
     assert self_consumption == 100.0
     mock_battery.discharge_energy.assert_has_calls([call(100.0, 12), call(200.0, 12)])
-    inverter.self_consumption_predictor.calculate_self_consumption.assert_not_called()
+    inverter.self_consumption_predictor.calculate_expected_direct_consumption.assert_not_called()
 
 
 def test_process_energy_battery_empty(inverter, mock_battery):
@@ -203,7 +260,9 @@ def test_process_energy_battery_empty(inverter, mock_battery):
     assert self_consumption == 100.0  # Only generation is consumed
     mock_battery.charge_energy.assert_not_called()
     mock_battery.discharge_energy.assert_called_once_with(200.0, hour)
-    inverter.self_consumption_predictor.calculate_self_consumption.assert_not_called()
+    inverter.self_consumption_predictor.calculate_expected_direct_consumption.assert_called_once_with(
+        consumption, generation
+    )
 
 
 def test_process_energy_battery_full_at_start(inverter, mock_battery):
@@ -225,7 +284,7 @@ def test_process_energy_battery_full_at_start(inverter, mock_battery):
     assert self_consumption == 200.0  # Only consumption is met
     mock_battery.charge_energy.assert_called_once_with(300.0, hour)
     mock_battery.discharge_energy.assert_not_called()
-    inverter.self_consumption_predictor.calculate_self_consumption.assert_called_once_with(
+    inverter.self_consumption_predictor.calculate_expected_direct_consumption.assert_called_once_with(
         consumption, generation
     )
 
@@ -247,7 +306,9 @@ def test_process_energy_insufficient_generation_no_battery(inverter, mock_batter
     assert self_consumption == 100.0  # Only generation is consumed
     mock_battery.charge_energy.assert_not_called()
     mock_battery.discharge_energy.assert_called_once_with(400.0, hour)
-    inverter.self_consumption_predictor.calculate_self_consumption.assert_not_called()
+    inverter.self_consumption_predictor.calculate_expected_direct_consumption.assert_called_once_with(
+        consumption, generation
+    )
 
 
 def test_process_energy_insufficient_generation_battery_assists(inverter, mock_battery):
@@ -272,7 +333,9 @@ def test_process_energy_insufficient_generation_battery_assists(inverter, mock_b
     assert self_consumption == 250.0  # Generation + battery discharge
     mock_battery.charge_energy.assert_not_called()
     mock_battery.discharge_energy.assert_called_once_with(200.0, hour)
-    inverter.self_consumption_predictor.calculate_self_consumption.assert_not_called()
+    inverter.self_consumption_predictor.calculate_expected_direct_consumption.assert_called_once_with(
+        consumption, generation
+    )
 
 
 def test_process_energy_zero_generation(inverter, mock_battery):
@@ -295,7 +358,7 @@ def test_process_energy_zero_generation(inverter, mock_battery):
     assert self_consumption == 100.0  # Only battery discharge is consumed
     mock_battery.charge_energy.assert_not_called()
     mock_battery.discharge_energy.assert_called_once_with(300.0, hour)
-    inverter.self_consumption_predictor.calculate_self_consumption.assert_not_called()
+    inverter.self_consumption_predictor.calculate_expected_direct_consumption.assert_not_called()
 
 
 def test_process_energy_zero_consumption(inverter, mock_battery):
@@ -315,9 +378,7 @@ def test_process_energy_zero_consumption(inverter, mock_battery):
     assert self_consumption == 0.0  # Zero consumption
     mock_battery.charge_energy.assert_called_once_with(500.0, hour)
     mock_battery.discharge_energy.assert_not_called()
-    inverter.self_consumption_predictor.calculate_self_consumption.assert_called_once_with(
-        consumption, generation
-    )
+    inverter.self_consumption_predictor.calculate_expected_direct_consumption.assert_not_called()
 
 
 def test_process_energy_zero_generation_zero_consumption(inverter, mock_battery):
@@ -335,9 +396,7 @@ def test_process_energy_zero_generation_zero_consumption(inverter, mock_battery)
     assert self_consumption == 0.0  # No consumption
     mock_battery.charge_energy.assert_not_called()
     mock_battery.discharge_energy.assert_not_called()
-    inverter.self_consumption_predictor.calculate_self_consumption.assert_called_once_with(
-        consumption, generation
-    )
+    inverter.self_consumption_predictor.calculate_expected_direct_consumption.assert_not_called()
 
 
 def test_process_energy_partial_battery_discharge(inverter, mock_battery):
@@ -358,7 +417,9 @@ def test_process_energy_partial_battery_discharge(inverter, mock_battery):
     assert self_consumption == 250.0  # Generation + battery discharge
     mock_battery.charge_energy.assert_not_called()
     mock_battery.discharge_energy.assert_called_once_with(200.0, 12)
-    inverter.self_consumption_predictor.calculate_self_consumption.assert_not_called()
+    inverter.self_consumption_predictor.calculate_expected_direct_consumption.assert_called_once_with(
+        consumption, generation
+    )
 
 
 def test_process_energy_consumption_exceeds_max_no_battery(inverter, mock_battery):
@@ -378,7 +439,9 @@ def test_process_energy_consumption_exceeds_max_no_battery(inverter, mock_batter
     assert self_consumption == 100.0  # Only the generation is consumed, maxing out the inverter
     mock_battery.charge_energy.assert_not_called()
     mock_battery.discharge_energy.assert_called_once_with(400.0, hour)
-    inverter.self_consumption_predictor.calculate_self_consumption.assert_not_called()
+    inverter.self_consumption_predictor.calculate_expected_direct_consumption.assert_called_once_with(
+        consumption, generation
+    )
 
 
 def test_process_energy_zero_generation_full_battery_high_consumption(inverter, mock_battery):
@@ -400,4 +463,4 @@ def test_process_energy_zero_generation_full_battery_high_consumption(inverter,
     assert self_consumption == 500.0  # Battery fully discharges to meet consumption
     mock_battery.charge_energy.assert_not_called()
     mock_battery.discharge_energy.assert_called_once_with(500.0, hour)
-    inverter.self_consumption_predictor.calculate_self_consumption.assert_not_called()
+    inverter.self_consumption_predictor.calculate_expected_direct_consumption.assert_not_called()

tests/test_inverter_efficiency.py

@@ -38,7 +38,7 @@ def _make_inverter(
 ) -> Inverter:
     """Create an Inverter with custom efficiency parameters and a mock battery."""
     mock_self_consumption_predictor = Mock()
-    mock_self_consumption_predictor.calculate_self_consumption.return_value = 1.0
+    mock_self_consumption_predictor.calculate_expected_direct_consumption.side_effect = min
 
     params = InverterParameters(
         device_id="inv1",
@@ -165,12 +165,14 @@ class TestDcToAcEfficiency:
         assert losses == pytest.approx(10.0, rel=1e-5)  # Only battery losses
 
     def test_discharge_surplus_path_with_efficiency(self, mock_battery):
-        """When generation > consumption but SCR < 1, discharge goes through inverter."""
-        mock_battery.discharge_energy.return_value = (50.0, 5.0)
+        """A probabilistic load gap discharges through the inverter."""
+        mock_battery.discharge_energy.return_value = (30.0 / 0.90, 5.0)
         mock_battery.charge_energy.return_value = (100.0, 10.0)
 
         inv = _make_inverter(dc_to_ac_efficiency=0.90, mock_battery=mock_battery)
-        cast(Mock, inv.self_consumption_predictor).calculate_self_consumption.return_value = 0.90
+        predictor = cast(Mock, inv.self_consumption_predictor)
+        predictor.calculate_expected_direct_consumption.side_effect = None
+        predictor.calculate_expected_direct_consumption.return_value = 170.0
 
         generation = 500.0
         consumption = 200.0
@@ -180,18 +182,23 @@ class TestDcToAcEfficiency:
             generation, consumption, hour
         )
 
-        # surplus = 300, remaining_power = 300*0.9 = 270, remaining_load_evq = 300*0.1 = 30
-        # DC request for discharge = 30 / 0.90 = 33.333
+        # Expected direct PV is 170 Wh, leaving 30 Wh of load gap and
+        # 330 Wh of PV surplus within different sub-periods of the slot.
+        # DC request for discharge = 30 / 0.90 = 33.333 Wh.
         expected_dc_request = 30.0 / 0.90
         mock_battery.discharge_energy.assert_called_once_with(
             pytest.approx(expected_dc_request, rel=1e-3), hour
         )
 
-        # Battery delivers 50 Wh DC → 45 Wh AC
-        from_battery_ac = 50.0 * 0.90  # 45 Wh
-        inverter_discharge_loss = 50.0 - from_battery_ac  # 5 Wh
+        # Battery delivers 33.333 Wh DC -> 30 Wh AC.
+        from_battery_dc = 30.0 / 0.90
+        from_battery_ac = from_battery_dc * 0.90
+        inverter_discharge_loss = from_battery_dc - from_battery_ac
 
-        assert self_consumption == pytest.approx(consumption + from_battery_ac, rel=1e-5)
+        assert self_consumption == pytest.approx(170.0 + from_battery_ac, rel=1e-5)
+        assert grid_import == pytest.approx(0.0)
+        assert grid_export == pytest.approx(220.0)
+        assert losses == pytest.approx(5.0 + inverter_discharge_loss + 10.0)
 
 
 # ===================================================================
@@ -492,6 +499,7 @@ def _make_mock_simulation(
     initial_soc_percentage: float = 0.0,   # fraction of capacity already stored (0 = empty)
     min_soc_wh: float = 0.0,
     max_charge_power_w: float = 5_000.0,
+    levelized_cost_of_storage_kwh: float = 0.0,
     # Arrays (must be same length)
     ac_charge_hours: list | None = None,
     elect_price_hourly: list | None = None,
@@ -517,6 +525,7 @@ def _make_mock_simulation(
         initial_soc_percentage=initial_soc_percentage,
         min_soc_wh=min_soc_wh,
         max_charge_power_w=max_charge_power_w,
+        levelized_cost_of_storage_kwh=levelized_cost_of_storage_kwh,
         current_energy_content=Mock(return_value=0.0),
     )
 
@@ -744,6 +753,28 @@ class TestAcChargeBreakEvenPenalty:
         # Fitness must be worse (higher) than base
         assert fitness > base + 1e-6
 
+    def test_lcos_is_included_in_ac_charge_break_even_price(self, config_eos):
+        """LCOS can make an otherwise profitable price spread unprofitable."""
+        n = 24
+        prices = [0.0001] + [0.00015] * (n - 1)
+        sim = _make_mock_simulation(
+            ac_to_dc_efficiency=1.0,
+            dc_to_ac_efficiency=1.0,
+            charging_efficiency=1.0,
+            discharging_efficiency=1.0,
+            levelized_cost_of_storage_kwh=0.10,
+            ac_charge_hours=[1.0] + [0.0] * (n - 1),
+            elect_price_hourly=prices,
+            load_energy_array=[1000.0] * n,
+            initial_soc_percentage=0.0,
+        )
+
+        fitness = _run_evaluate_with_mocked_sim(config_eos, sim)
+
+        # Break-even is 0.0001 + 0.0001 = 0.0002 EUR/Wh.
+        # The 0.00005 EUR/Wh gap on a 5000 Wh charge adds 0.25 EUR.
+        assert fitness == pytest.approx(0.25)
+
     # -----------------------------------------------------------------
     # 5d. Free PV energy covers expensive hours → penalty reduced/eliminated
     # -----------------------------------------------------------------

tests/test_optimization_settings.py

@@ -0,0 +1,30 @@
+from akkudoktoreos.devices.devices import BatteriesCommonSettings
+from akkudoktoreos.optimization.genetic.geneticdevices import (
+    SolarPanelBatteryParameters,
+)
+from akkudoktoreos.optimization.optimization import OptimizationCommonSettings
+
+
+def test_terminal_value_is_independent_from_battery_lcos():
+    battery = BatteriesCommonSettings(
+        device_id="battery1",
+        levelized_cost_of_storage_kwh=0.12,
+    )
+    optimization = OptimizationCommonSettings(terminal_value_euro_per_kwh=0.20)
+
+    assert battery.levelized_cost_of_storage_kwh == 0.12
+    assert optimization.terminal_value_euro_per_kwh == 0.20
+
+
+def test_terminal_value_defaults_to_zero():
+    assert OptimizationCommonSettings().terminal_value_euro_per_kwh == 0.0
+
+
+def test_genetic_battery_lcos_is_independent_from_terminal_value():
+    battery = SolarPanelBatteryParameters(
+        device_id="battery1",
+        capacity_wh=8000,
+        levelized_cost_of_storage_kwh=0.12,
+    )
+
+    assert battery.levelized_cost_of_storage_kwh == 0.12

tests/testdata/optimize_result_1.json

@@ -157,7 +157,7 @@
             1063.91,
             1320.56,
             1132.03,
-            1308.5200000002487,
+            1308.5200000000004,
             1176.82,
             1216.22,
             1103.78,
@@ -238,10 +238,12 @@
             0.0,
             0.0,
             0.0,
-            0.20303854854278033,
-            0.1899652543898917,
-            0.12833851757957424,
-            0.04233866391809883,
+            0.21077535122459587,
+            0.19320652266312205,
+            0.1358062627100041,
+            0.0692592282596561,
+            0.023370695807696434,
+            0.0019327051509204403,
             0.0,
             0.0,
             0.0,
@@ -260,22 +262,20 @@
             0.0,
             0.0,
             0.0,
-            0.0,
-            0.0,
-            0.16357655037574115,
-            0.14900060381217387,
-            0.08949503544160814,
-            0.010110585812541543,
-            0.0,
-            0.0,
+            0.18814608875566813,
+            0.15236390731688437,
+            0.10316291465819699,
+            0.029150936607659956,
+            0.020928608511559126,
+            0.003524558735906156,
             0.0,
             0.0,
             0.0
         ],
-        "Gesamt_Verluste": 2807.7292841655817,
-        "Gesamtbilanz_Euro": 0.8879905947253857,
-        "Gesamteinnahmen_Euro": 0.9758637598724098,
-        "Gesamtkosten_Euro": 1.8638543545977955,
+        "Gesamt_Verluste": 3425.4668727209255,
+        "Gesamtbilanz_Euro": 0.9585224311392879,
+        "Gesamteinnahmen_Euro": 1.1316277804018695,
+        "Gesamtkosten_Euro": 2.0901502115411574,
         "Home_appliance_wh_per_hour": [
             0.0,
             0.0,
@@ -320,14 +320,14 @@
             0.0,
             0.0,
             0.0,
-            0.001880612819166666,
-            0.026623430000091274,
-            4.482711108977355e-14,
-            0.008763569215739961,
-            0.018335381563380656,
-            0.06267084962493971,
-            0.05480703000000003,
-            0.225316611,
+            0.07557452231671152,
+            0.026623430000000066,
+            4.55656845588237e-17,
+            0.001414013162203277,
+            0.005881449073870462,
+            0.05258762370598476,
+            0.1614775630079859,
+            0.2338232746714084,
             0.0,
             0.26650619799999997,
             0.19588158,
@@ -343,15 +343,15 @@
             0.0,
             0.0,
             0.0,
-            0.02844719513362201,
+            0.009619897970888898,
             0.0,
             0.0,
-            0.0004412281431084693,
-            0.008372170029774037,
-            0.03130999506792791,
+            2.3325608707865465e-05,
+            0.0021886029750169123,
+            0.013012984677295973,
             0.0,
-            0.08231598,
-            0.174597189,
+            0.17784012884918773,
+            0.19011028252189552,
             0.0,
             0.0,
             0.16484566
@@ -360,14 +360,14 @@
             0.0,
             0.0,
             0.0,
-            10.008583390988111,
-            144.8500000004966,
-            2.236881790906864e-10,
-            39.870651572975255,
-            80.77260600608219,
-            209.11194402715952,
-            171.54000000000008,
-            731.31,
+            402.20607938643707,
+            144.85000000000036,
+            2.2737367544323206e-13,
+            6.433180901743754,
+            25.909467285772962,
+            175.4675465665157,
+            505.40708296709204,
+            758.9200735845777,
             0.0,
             912.38,
             704.61,
@@ -383,15 +383,15 @@
             0.0,
             0.0,
             0.0,
-            135.91588692604878,
+            45.96224544141853,
             0.0,
             0.0,
-            2.201737241060226,
-            38.089945540373236,
-            137.92949369131236,
+            0.11639525303326081,
+            9.957247384062384,
+            57.32592368852852,
             0.0,
-            257.64,
-            566.69,
+            556.6201215937018,
+            617.0408390843736,
             0.0,
             0.0,
             592.97
@@ -402,10 +402,12 @@
             0.0,
             0.0,
             0.0,
-            2900.550693468291,
-            2713.7893484270244,
-            1833.4073939939178,
-            604.8380559728405,
+            3011.0764460656555,
+            2760.093180901744,
+            1940.089467285773,
+            989.4175465665157,
+            333.86708296709196,
+            27.61007358457772,
             0.0,
             0.0,
             0.0,
@@ -424,31 +426,29 @@
             0.0,
             0.0,
             0.0,
-            0.0,
-            0.0,
-            2336.807862510588,
-            2128.580054459627,
-            1278.5005063086878,
-            144.4369401791649,
-            0.0,
-            0.0,
+            2687.8012679381163,
+            2176.6272473840627,
+            1473.7559236885286,
+            416.4419515379994,
+            298.9801215937018,
+            50.35083908437366,
             0.0,
             0.0,
             0.0
         ],
         "Verluste_Pro_Stunde": [
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tests/testdata/optimize_result_2_be.json

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