diff --git a/docs/_generated/configdevices.md b/docs/_generated/configdevices.md
index ede7c4ff..ce09b543 100644
--- a/docs/_generated/configdevices.md
+++ b/docs/_generated/configdevices.md
@@ -57,7 +57,13 @@ config path from ``self.device_id`` without needing an external index.
                        1.0
                    ],
                    "min_soc_percentage": 0,
-                   "max_soc_percentage": 100
+                   "max_soc_percentage": 100,
+                   "grid_export_rates": [
+                       0.25,
+                       0.5,
+                       0.75,
+                       1.0
+                   ]
                }
            },
            "max_batteries": 1,
@@ -86,7 +92,13 @@ config path from ``self.device_id`` without needing an external index.
                        1.0
                    ],
                    "min_soc_percentage": 0,
-                   "max_soc_percentage": 100
+                   "max_soc_percentage": 100,
+                   "grid_export_rates": [
+                       0.25,
+                       0.5,
+                       0.75,
+                       1.0
+                   ]
                }
            },
            "max_electric_vehicles": 1,
@@ -143,6 +155,12 @@ config path from ``self.device_id`` without needing an external index.
                    ],
                    "min_soc_percentage": 0,
                    "max_soc_percentage": 100,
+                   "grid_export_rates": [
+                       0.25,
+                       0.5,
+                       0.75,
+                       1.0
+                   ],
                    "measurement_key_soc_factor": "bat0-soc-factor",
                    "measurement_key_power_l1_w": "bat0-power-l1-w",
                    "measurement_key_power_l2_w": "bat0-power-l2-w",
@@ -184,6 +202,12 @@ config path from ``self.device_id`` without needing an external index.
                    ],
                    "min_soc_percentage": 0,
                    "max_soc_percentage": 100,
+                   "grid_export_rates": [
+                       0.25,
+                       0.5,
+                       0.75,
+                       1.0
+                   ],
                    "measurement_key_soc_factor": "ev0-soc-factor",
                    "measurement_key_power_l1_w": "ev0-power-l1-w",
                    "measurement_key_power_l2_w": "ev0-power-l2-w",
diff --git a/docs/_generated/configexample.md b/docs/_generated/configexample.md
index 6579a832..d926c3c4 100644
--- a/docs/_generated/configexample.md
+++ b/docs/_generated/configexample.md
@@ -61,7 +61,13 @@
                        1.0
                    ],
                    "min_soc_percentage": 0,
-                   "max_soc_percentage": 100
+                   "max_soc_percentage": 100,
+                   "grid_export_rates": [
+                       0.25,
+                       0.5,
+                       0.75,
+                       1.0
+                   ]
                }
            },
            "max_batteries": 1,
@@ -90,7 +96,13 @@
                        1.0
                    ],
                    "min_soc_percentage": 0,
-                   "max_soc_percentage": 100
+                   "max_soc_percentage": 100,
+                   "grid_export_rates": [
+                       0.25,
+                       0.5,
+                       0.75,
+                       1.0
+                   ]
                }
            },
            "max_electric_vehicles": 1,
diff --git a/docs/_generated/openapi.md b/docs/_generated/openapi.md
index 6e171f97..056e6de5 100644
--- a/docs/_generated/openapi.md
+++ b/docs/_generated/openapi.md
@@ -1,6 +1,6 @@
 # Akkudoktor-EOS
 
-**Version**: `v0.3.0.dev2609171537448705`
+**Version**: `v0.3.0.dev2609171651094774`
 
 <!-- pyml disable line-length -->
 **Description**: This project provides a comprehensive solution for simulating and optimizing an energy system based on renewable energy sources. With a focus on photovoltaic (PV) systems, battery storage (batteries), load management (consumer requirements), heat pumps, electric vehicles, and consideration of electricity price data, this system enables forecasting and optimization of energy flow and costs over a specified period.
diff --git a/docs/development/device-physics.md b/docs/development/device-physics.md
new file mode 100644
index 00000000..93a3da1f
--- /dev/null
+++ b/docs/development/device-physics.md
@@ -0,0 +1,41 @@
+# Slot-aware GENETIC device physics
+
+This package depends on the device-map and algorithm-converter configuration work
+from PR #1256 and the runtime settings foundation from PR #1305. Parameter classes
+remain in `devices/genetic`; settings remain in `devices/settings`.
+
+Battery and inverter models accept a slot duration, apply power limits as energy
+per slot, and preserve the total battery charge/discharge budget across multiple
+calls within that slot. Battery export is an explicit device-simulation operation;
+setting export rates does not by itself activate optimizer export states. The
+converter carries stable device IDs, charge/export levels and LCOS unchanged.
+Configuration LCOS uses amount/kWh; no Wh conversion is applied by the converter.
+
+The GENETIC inverter now computes expected direct PV-to-load power from the
+minute-load distribution, then converts power to interval energy. This changes
+GENETIC simulation economics even at the default hourly interval. The probability
+table was calibrated with hourly mean loads: use with quarter-hour means remains
+an approximation, not a separately calibrated quarter-hour model. Its legacy
+cumulative-probability API now clamps values at the table boundary and to [0, 1].
+The GENETIC0 inverter uses a separate implementation and separate interpolation
+data file; this package does not change either. Existing GENETIC0 optimization
+and PDF golden tests remain required.
+
+The shared in-memory cache must include callable identity in each key. Otherwise
+the legacy cumulative probability and new direct-power method on the same object
+can return each other's cached results for identical arguments. This package
+includes that prerequisite fix and verifies both call orders and cache reuse.
+
+This package alone does **not** make an Optimize mode quarter-hour capable.
+GENETIC parameter preparation still forces the interval to 3600 seconds, and
+`/optimize` still selects the separate GENETIC0 algorithm. Quarter-hour scheduling,
+warm-start alignment, forecast/control horizons, terminal values, export states,
+EV deadlines and flexible-consumer planning require the later optimizer port.
+No inactive EV-deadline fields are introduced here.
+
+Validation covers charge/discharge budgets, inverter/export limits, efficiencies,
+energy conservation, interpolation boundaries, converter fields, both algorithms'
+short optimization/PDF runs and unchanged GENETIC0 monetary goldens. GENETIC runs
+use independent repricing of grid energy, schema checks and physical bounds rather
+than requiring the previous direct-consumption model's monetary golden. Long
+`--finalize` optimization runs and pinned CI environments remain separate checks.
diff --git a/openapi.json b/openapi.json
index 726f455b..7eabfef0 100644
--- a/openapi.json
+++ b/openapi.json
@@ -8,7 +8,7 @@
       "name": "Apache 2.0",
       "url": "https://www.apache.org/licenses/LICENSE-2.0.html"
     },
-    "version": "v0.3.0.dev2609171537448705"
+    "version": "v0.3.0.dev2609171651094774"
   },
   "paths": {
     "/v1/measurement/battery-capacity/{battery_id}": {
@@ -3360,6 +3360,39 @@
             "examples": [
               100
             ]
+          },
+          "grid_export_rates": {
+            "anyOf": [
+              {
+                "items": {
+                  "type": "number"
+                },
+                "type": "array"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Grid Export Rates",
+            "description": "Battery-to-grid export rates as factor of maximum discharge power ]0.00 ... 1.00]. Available to algorithms that explicitly enable battery-to-grid export; configuring rates alone does not enable export. [1.0] selects full-power export. None uses the default export rates.",
+            "default": [
+              0.25,
+              0.5,
+              0.75,
+              1.0
+            ],
+            "examples": [
+              [
+                0.25,
+                0.5,
+                0.75,
+                1.0
+              ],
+              [
+                1.0
+              ],
+              null
+            ]
           }
         },
         "type": "object",
@@ -3535,6 +3568,39 @@
               100
             ]
           },
+          "grid_export_rates": {
+            "anyOf": [
+              {
+                "items": {
+                  "type": "number"
+                },
+                "type": "array"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Grid Export Rates",
+            "description": "Battery-to-grid export rates as factor of maximum discharge power ]0.00 ... 1.00]. Available to algorithms that explicitly enable battery-to-grid export; configuring rates alone does not enable export. [1.0] selects full-power export. None uses the default export rates.",
+            "default": [
+              0.25,
+              0.5,
+              0.75,
+              1.0
+            ],
+            "examples": [
+              [
+                0.25,
+                0.5,
+                0.75,
+                1.0
+              ],
+              [
+                1.0
+              ],
+              null
+            ]
+          },
           "measurement_key_soc_factor": {
             "type": "string",
             "title": "Measurement Key Soc Factor",
@@ -5378,6 +5444,33 @@
               ],
               null
             ]
+          },
+          "grid_export_rates": {
+            "anyOf": [
+              {
+                "items": {
+                  "type": "number"
+                },
+                "type": "array"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Grid Export Rates",
+            "description": "Battery-to-grid export rates as factor of maximum discharge power ]0.00 ... 1.00]. These levels are available to algorithms that explicitly enable battery-to-grid export. None leaves the choice of export levels to the caller.",
+            "examples": [
+              [
+                0.25,
+                0.5,
+                0.75,
+                1.0
+              ],
+              [
+                1.0
+              ],
+              null
+            ]
           }
         },
         "additionalProperties": false,
@@ -14327,6 +14420,43 @@
               ],
               null
             ]
+          },
+          "grid_export_rates": {
+            "anyOf": [
+              {
+                "items": {
+                  "type": "number"
+                },
+                "type": "array"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Grid Export Rates",
+            "description": "Battery-to-grid export rates as factor of maximum discharge power ]0.00 ... 1.00]. These levels are available to algorithms that explicitly enable battery-to-grid export. None leaves the choice of export levels to the caller.",
+            "examples": [
+              [
+                0.25,
+                0.5,
+                0.75,
+                1.0
+              ],
+              [
+                1.0
+              ],
+              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,
diff --git a/src/akkudoktoreos/core/cache.py b/src/akkudoktoreos/core/cache.py
index 4103e1b8..1ad3fc8e 100644
--- a/src/akkudoktoreos/core/cache.py
+++ b/src/akkudoktoreos/core/cache.py
@@ -228,6 +228,9 @@ def cache_energy_management(
 
     cached_wrapper = cachebox.cached(
         cache=CacheEnergyManagementStore().cache,
+        # This store is shared by all decorated callables, including methods
+        # on the same instance. Arguments alone cannot identify their results.
+        key_maker=lambda *args, **kwargs: cachebox.make_key(func, *args, **kwargs),
         callback=cache_energy_management_store_callback,
     )(wrapper)
 
diff --git a/src/akkudoktoreos/devices/genetic/battery.py b/src/akkudoktoreos/devices/genetic/battery.py
index fe7009c1..bb45e52c 100644
--- a/src/akkudoktoreos/devices/genetic/battery.py
+++ b/src/akkudoktoreos/devices/genetic/battery.py
@@ -80,11 +80,34 @@ class BaseBatteryParameters(DeviceParameters):
             "examples": [[0.0, 0.25, 0.5, 0.75, 1.0], None],
         },
     )
+    grid_export_rates: Optional[list[float]] = Field(
+        default=None,
+        json_schema_extra={
+            "description": (
+                "Battery-to-grid export rates as factor of maximum discharge "
+                "power ]0.00 ... 1.00]. These levels are available to algorithms "
+                "that explicitly enable battery-to-grid export. None leaves the "
+                "choice of export levels to the caller."
+            ),
+            "examples": [[0.25, 0.5, 0.75, 1.0], [1.0], None],
+        },
+    )
 
 
 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()
 
 
@@ -103,9 +126,20 @@ class ElectricVehicleParameters(BaseBatteryParameters):
 class Battery:
     """Represents a battery device with methods to simulate energy charging and discharging."""
 
-    def __init__(self, parameters: BaseBatteryParameters, prediction_hours: int):
+    def __init__(
+        self,
+        parameters: BaseBatteryParameters,
+        prediction_hours: int,
+        slot_duration_h: float = 1.0,
+    ):
+        # `prediction_hours` is the number of optimization slots, not hours. At
+        # the default optimization interval of 3600 s, slot_duration_h is 1.0 and
+        # the slot count equals the hour count, so existing callers are
+        # unaffected. At 900 s (15 min) slot_duration_h is 0.25 and there are 4x
+        # as many slots, each able to move a quarter of the hourly energy.
         self.parameters = parameters
         self.prediction_hours = prediction_hours
+        self.slot_duration_h = slot_duration_h
         self._setup()
 
     def _setup(self) -> None:
@@ -114,6 +148,11 @@ 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)
@@ -138,6 +177,8 @@ class Battery:
             self.max_charge_power_w = self.capacity_wh  # TODO this should not be equal capacity_wh
         self.discharge_array = np.full(self.prediction_hours, 0)
         self.charge_array = np.full(self.prediction_hours, 0)
+        self._discharged_raw_wh_per_slot = np.zeros(self.prediction_hours, dtype=float)
+        self._charged_raw_wh_per_slot = np.zeros(self.prediction_hours, dtype=float)
         self.soc_wh = (self.initial_soc_percentage / 100) * self.capacity_wh
         self.min_soc_wh = (self.min_soc_percentage / 100) * self.capacity_wh
         self.max_soc_wh = (self.max_soc_percentage / 100) * self.capacity_wh
@@ -181,6 +222,30 @@ class Battery:
         self.soc_wh = min(self.soc_wh, self.max_soc_wh)  # Only clamp to max
         self.discharge_array = np.full(self.prediction_hours, 0)
         self.charge_array = np.full(self.prediction_hours, 0)
+        self._discharged_raw_wh_per_slot = np.zeros(self.prediction_hours, dtype=float)
+        self._charged_raw_wh_per_slot = np.zeros(self.prediction_hours, dtype=float)
+
+    def rated_discharge_energy_wh(self) -> float:
+        """Return the DC energy one full-power discharge slot delivers.
+
+        This is the reference a grid-export rate is applied to: a rate of 0.5
+        exports at most half the battery's rated discharge power, independent of
+        how much of the slot budget self-consumption already used.
+        """
+        return self.max_charge_power_w * self.slot_duration_h * self.discharging_efficiency
+
+    def remaining_discharge_energy_wh(self, hour: int) -> float:
+        """Return DC energy still deliverable within one optimization slot."""
+        raw_power_budget_wh = self.max_charge_power_w * self.slot_duration_h
+        raw_power_remaining_wh = max(
+            raw_power_budget_wh - self._discharged_raw_wh_per_slot[hour], 0.0
+        )
+        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."""
@@ -228,8 +293,13 @@ class Battery:
         # Raw extractable energy above minimum SoC
         raw_available_wh = max(self.soc_wh - self.min_soc_wh, 0.0)
 
-        # Maximum raw discharge due to power limit
-        max_raw_wh = self.max_charge_power_w  # TODO rename to max_discharge_power_w
+        # Maximum raw discharge due to power limit, scaled to the slot duration.
+        # max_charge_power_w is a power [W]; energy movable in one slot is
+        # power x slot_duration_h.
+        max_raw_wh = max(
+            self.max_charge_power_w * self.slot_duration_h - self._discharged_raw_wh_per_slot[hour],
+            0.0,
+        )  # TODO rename to max_discharge_power_w
 
         # Actual raw withdrawal (internal)
         raw_withdrawal_wh = min(raw_available_wh, max_raw_wh)
@@ -246,6 +316,7 @@ class Battery:
         # Update SoC
         self.soc_wh -= raw_used_wh
         self.soc_wh = max(self.soc_wh, self.min_soc_wh)
+        self._discharged_raw_wh_per_slot[hour] += raw_used_wh
 
         # Losses
         losses_wh = raw_used_wh - delivered_wh
@@ -320,7 +391,12 @@ class Battery:
 
         # Provide fast (3x..5x) local read access (vs. self.xxx) for repetitive read access
         soc_wh_fast = self.soc_wh
-        max_charge_power_w_fast = self.max_charge_power_w
+        # Scale the power cap [W] to a per-slot energy cap [Wh] (W x slot hours).
+        # At slot_duration_h=1.0 (hourly) this equals the legacy power value.
+        max_charge_per_slot_wh_fast = max(
+            self.max_charge_power_w * self.slot_duration_h - self._charged_raw_wh_per_slot[hour],
+            0.0,
+        )
         charging_efficiency_fast = self.charging_efficiency
 
         # Decide mode & determine raw_request_wh and raw_charge_wh
@@ -328,13 +404,13 @@ class Battery:
             raw_request_wh = wh
             raw_charge_wh = max(self.max_soc_wh - soc_wh_fast, 0.0) / charging_efficiency_fast
         elif wh is None and charge_factor > 0.0:  # mode 2
-            raw_request_wh = max_charge_power_w_fast * charge_factor
+            raw_request_wh = max_charge_per_slot_wh_fast * charge_factor
             raw_charge_wh = max(self.max_soc_wh - soc_wh_fast, 0.0) / charging_efficiency_fast
             if raw_request_wh > raw_charge_wh:
                 # Use a lower charge factor
                 lower_charge_factors = self._lower_charge_rates_desc(charge_factor)
                 for charge_factor in lower_charge_factors:
-                    raw_request_wh = max_charge_power_w_fast * charge_factor
+                    raw_request_wh = max_charge_per_slot_wh_fast * charge_factor
                     if raw_request_wh <= raw_charge_wh:
                         self.charge_array[hour] = charge_factor
                         break
@@ -349,7 +425,7 @@ class Battery:
             )
 
         # Remaining capacity
-        max_raw_wh = min(raw_charge_wh, max_charge_power_w_fast)
+        max_raw_wh = min(raw_charge_wh, max_charge_per_slot_wh_fast)
 
         # Actual raw intake
         raw_input_wh = raw_request_wh if raw_request_wh < max_raw_wh else max_raw_wh
@@ -364,6 +440,7 @@ class Battery:
             )
 
         self.soc_wh = new_soc
+        self._charged_raw_wh_per_slot[hour] += raw_input_wh
         losses_wh = raw_input_wh - stored_wh
 
         return stored_wh, losses_wh
diff --git a/src/akkudoktoreos/devices/genetic/inverter.py b/src/akkudoktoreos/devices/genetic/inverter.py
index 977aa3df..ea71c9c5 100644
--- a/src/akkudoktoreos/devices/genetic/inverter.py
+++ b/src/akkudoktoreos/devices/genetic/inverter.py
@@ -63,9 +63,11 @@ class Inverter:
         self,
         parameters: InverterParameters,
         battery: Optional[Battery] = None,
+        slot_duration_h: float = 1.0,
     ):
         self.parameters: InverterParameters = parameters
         self.battery: Optional[Battery] = battery
+        self.slot_duration_h: float = slot_duration_h
         self._setup()
 
     def _setup(self) -> None:
@@ -74,110 +76,135 @@ class Inverter:
             logger.error(error_msg)
             raise ValueError(error_msg)
         self.self_consumption_predictor = get_eos_load_interpolator()
-        self.max_power_wh = (
-            self.parameters.max_power_wh
-        )  # Maximum power that the inverter can handle
+        # 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
+        # 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]:
+        """Discharge battery energy and convert it to AC energy."""
+        if not self.battery or requested_ac_wh <= 0.0:
+            return 0.0, 0.0
+
+        dc_request = requested_ac_wh / self.dc_to_ac_efficiency
+        battery_discharge_dc, discharge_losses = self.battery.discharge_energy(dc_request, hour)
+        battery_discharge_ac = battery_discharge_dc * self.dc_to_ac_efficiency
+        inverter_discharge_losses = battery_discharge_dc - battery_discharge_ac
+        return battery_discharge_ac, discharge_losses + inverter_discharge_losses
+
     def process_energy(
-        self, generation: float, consumption: float, hour: int
+        self,
+        generation: float,
+        consumption: float,
+        hour: int,
+        allow_battery_grid_export: bool = False,
+        battery_grid_export_factor: float = 1.0,
     ) -> 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.
+
+        Args:
+            generation: PV energy of the slot [Wh].
+            consumption: Load energy of the slot [Wh].
+            hour: Slot index.
+            allow_battery_grid_export: Whether the battery may discharge into the
+                grid in this slot (direct marketing).
+            battery_grid_export_factor: Export level as a factor of the battery's
+                rated discharge power [0.0 ... 1.0]. 1.0 exports as much as the
+                battery and the inverter allow, which is the behaviour when no
+                export rates are configured.
+        """
         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
-                scr = self.self_consumption_predictor.calculate_self_consumption(
-                    consumption, generation
+        # 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)
-
-                if remaining_load_evq > 0:
-                    # The battery must cover the remaining consumption
-                    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
-
-                    # If the battery cannot fully cover the remaining consumption, the rest is drawn from the grid
-                    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
-
+            )
+            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
 
+        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:
+            export_factor = min(max(float(battery_grid_export_factor), 0.0), 1.0)
+            remaining_battery_ac = (
+                self.battery.remaining_discharge_energy_wh(hour) * self.dc_to_ac_efficiency
+            )
+            # The rate caps the export against the battery's *rated* discharge
+            # power, so it stays a plain power setpoint ("export at 50 %") that
+            # does not silently grow when self-consumption used less of the slot.
+            # At factor 1.0 this bound never binds; behaviour is unchanged.
+            rated_export_ac = (
+                self.battery.rated_discharge_energy_wh() * export_factor * self.dc_to_ac_efficiency
+            )
+            export_capacity = min(
+                remaining_inverter_ac_capacity, remaining_battery_ac, rated_export_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
diff --git a/src/akkudoktoreos/devices/settings/batterysettings.py b/src/akkudoktoreos/devices/settings/batterysettings.py
index 15541d33..84bd2f15 100644
--- a/src/akkudoktoreos/devices/settings/batterysettings.py
+++ b/src/akkudoktoreos/devices/settings/batterysettings.py
@@ -31,6 +31,9 @@ if TYPE_CHECKING:
 BATTERY_DEFAULT_CHARGE_RATES: list[float] = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
 
 
+BATTERY_DEFAULT_GRID_EXPORT_RATES: list[float] = [0.25, 0.5, 0.75, 1.0]
+
+
 class BatteriesCommonSettings(DevicesBaseSettings):
     """Battery and electric vehicle device settings.
 
@@ -130,6 +133,48 @@ class BatteriesCommonSettings(DevicesBaseSettings):
     # GENETIC domain conversion
     # ------------------------------------------------------------------
 
+    grid_export_rates: Optional[list[float]] = Field(
+        default=BATTERY_DEFAULT_GRID_EXPORT_RATES,
+        json_schema_extra={
+            "description": (
+                "Battery-to-grid export rates as factor of maximum discharge "
+                "power ]0.00 ... 1.00]. Available to algorithms that explicitly "
+                "enable battery-to-grid export; configuring rates alone does not "
+                "enable export. [1.0] selects full-power export. None uses the "
+                "default export rates."
+            ),
+            "examples": [[0.25, 0.5, 0.75, 1.0], [1.0], None],
+        },
+    )
+
+    @field_validator("grid_export_rates", mode="before")
+    def validate_and_sort_grid_export_rates(cls, v: Any) -> list[float]:
+        """Normalize export rates to a finite, sorted, duplicate-free list in ]0, 1]."""
+        # None means fallback to default values
+        if v is None:
+            return BATTERY_DEFAULT_GRID_EXPORT_RATES.copy()
+
+        if isinstance(v, str):
+            numbers = re.split(r"[,\s]+", v.strip("[]"))
+            arr = np.array([float(x) for x in numbers if x])
+        else:
+            arr = np.array(v, dtype=float)
+
+        if arr.ndim != 1 or arr.size == 0:
+            raise ValueError("grid_export_rates must be a nonempty one-dimensional list.")
+        if not np.isfinite(arr).all():
+            raise ValueError("grid_export_rates must contain finite values.")
+
+        # A rate of 0.0 is not an export level - "no export" is expressed by the
+        # other battery states - so the lower bound is exclusive.
+        if (arr <= 0.0).any() or (arr > 1.0).any():
+            raise ValueError("grid_export_rates must be within ]0.0, 1.0].")
+
+        arr = np.unique(arr)
+        arr.sort()
+
+        return arr.tolist()
+
     def to_genetic_pv_bat_param(self) -> "SolarPanelBatteryParameters":
         """Return SolarPanelBatteryParameters for the GENETIC optimizer."""
         from akkudoktoreos.devices.genetic.battery import SolarPanelBatteryParameters
@@ -142,6 +187,9 @@ class BatteriesCommonSettings(DevicesBaseSettings):
             max_charge_power_w=self.max_charge_power_w,
             min_soc_percentage=self.min_soc_percentage,
             max_soc_percentage=self.max_soc_percentage,
+            charge_rates=self.charge_rates,
+            grid_export_rates=self.grid_export_rates,
+            levelized_cost_of_storage_kwh=self.levelized_cost_of_storage_amt_kwh,
         )
 
     def to_genetic_ev_bat_param(self) -> "ElectricVehicleParameters":
diff --git a/src/akkudoktoreos/prediction/interpolator.py b/src/akkudoktoreos/prediction/interpolator.py
index ae7818d6..9a204c51 100644
--- a/src/akkudoktoreos/prediction/interpolator.py
+++ b/src/akkudoktoreos/prediction/interpolator.py
@@ -15,31 +15,113 @@ class SelfConsumptionProbabilityInterpolator:
         # Load the RegularGridInterpolator
         with open(self.filepath, "rb") as file:
             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, load_1h_power: float, pv_power: float
+        self, mean_load_power_w: float, pv_power_w: float
     ) -> tuple[np.ndarray, np.ndarray]:
-        """Generate the grid points for interpolation."""
-        partial_loads = np.arange(0, pv_power + 50, 50)
-        points = np.array([np.full_like(partial_loads, load_1h_power), partial_loads]).T
+        """Generate in-bounds grid points for interpolation.
+
+        The bundled probability table was calibrated from a one-hour mean load
+        and one-minute samples. Sub-hourly optimization still passes *power* in
+        watts here; a native 15-minute mean is therefore a documented
+        approximation until a separately calibrated table is available.
+        """
+        bounded_mean_load_w = float(
+            np.clip(mean_load_power_w, self.load_power_min_w, self.load_power_max_w)
+        )
+        bounded_pv_power_w = float(np.clip(pv_power_w, 0.0, self.minute_load_max_w))
+        partial_loads = np.arange(0.0, bounded_pv_power_w + 1.0, 50.0)
+        points = np.column_stack((np.full(partial_loads.shape, bounded_mean_load_w), partial_loads))
         return points, partial_loads
 
     @cache_energy_management
-    def calculate_self_consumption(self, load_1h_power: float, pv_power: float) -> float:
-        """Calculate the PV self-consumption rate using RegularGridInterpolator.
+    def calculate_self_consumption(self, mean_load_power_w: float, pv_power_w: float) -> float:
+        """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 ``calculate_expected_direct_consumption``.
 
         The results are cached until the start of the next energy management run/ optimization.
 
         Args:
-         - last_1h_power: 1h power levels (W).
-         - pv_power: Current PV power output (W).
+         - mean_load_power_w: Mean load power for the current forecast interval (W).
+         - pv_power_w: Current PV power output (W).
 
         Returns:
          - Self-consumption rate as a float.
         """
-        points, partial_loads = self._generate_points(load_1h_power, pv_power)
+        points, _ = self._generate_points(mean_load_power_w, pv_power_w)
         probabilities = self.interpolator(points)
-        return probabilities.sum()
+        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.
diff --git a/tests/test_battery.py b/tests/test_battery.py
index 0021a620..27d06491 100644
--- a/tests/test_battery.py
+++ b/tests/test_battery.py
@@ -1,6 +1,8 @@
 import numpy as np
 import pytest
+from pydantic import ValidationError
 
+from akkudoktoreos.devices.devices import BatteriesCommonSettings
 from akkudoktoreos.devices.genetic.battery import Battery, SolarPanelBatteryParameters
 
 
@@ -294,3 +296,105 @@ def test_car_and_pv_battery_discharge_and_max_charge_power(setup_pv_battery, set
     assert car_battery.parameters.max_charge_power_w == 7000, (
         "Car battery max charge power should remain as defined"
     )
+
+
+def test_quarter_hour_charge_calls_share_one_power_budget():
+    params = SolarPanelBatteryParameters(
+        device_id="battery1",
+        capacity_wh=10_000,
+        initial_soc_percentage=0,
+        min_soc_percentage=0,
+        max_soc_percentage=100,
+        max_charge_power_w=1_000,
+        charging_efficiency=1.0,
+        discharging_efficiency=1.0,
+    )
+    battery = Battery(params, prediction_hours=4, slot_duration_h=0.25)
+    battery.set_charge_per_hour(np.ones(4))
+
+    first_stored, _ = battery.charge_energy(200.0, 0)
+    second_stored, _ = battery.charge_energy(200.0, 0)
+
+    assert first_stored == pytest.approx(200.0)
+    assert second_stored == pytest.approx(50.0)
+    assert battery.soc_wh == pytest.approx(250.0)
+
+
+def test_quarter_hour_discharge_calls_share_one_power_budget():
+    params = SolarPanelBatteryParameters(
+        device_id="battery1",
+        capacity_wh=10_000,
+        initial_soc_percentage=100,
+        min_soc_percentage=0,
+        max_soc_percentage=100,
+        max_charge_power_w=1_000,
+        charging_efficiency=1.0,
+        discharging_efficiency=1.0,
+    )
+    battery = Battery(params, prediction_hours=4, slot_duration_h=0.25)
+    battery.set_discharge_per_hour(np.ones(4))
+
+    first_delivered, _ = battery.discharge_energy(200.0, 0)
+    second_delivered, _ = battery.discharge_energy(200.0, 0)
+
+    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
+
+
+def test_grid_export_rates_are_sorted_and_deduplicated():
+    """Export rates are normalized like the charge rates."""
+    settings = BatteriesCommonSettings(
+        device_id="battery1", grid_export_rates=[1.0, 0.5, 0.5, 0.25]
+    )
+    assert settings.grid_export_rates == [0.25, 0.5, 1.0]
+
+
+def test_grid_export_rates_default_and_override():
+    """None falls back to the defaults; [1.0] restores all-or-nothing export."""
+    assert BatteriesCommonSettings(device_id="battery1").grid_export_rates == [
+        0.25,
+        0.5,
+        0.75,
+        1.0,
+    ]
+    fallback = BatteriesCommonSettings(device_id="battery1", grid_export_rates=None)
+    assert fallback.grid_export_rates == [0.25, 0.5, 0.75, 1.0]
+    full_export = BatteriesCommonSettings(device_id="battery1", grid_export_rates=[1.0])
+    assert full_export.grid_export_rates == [1.0]
+
+
+@pytest.mark.parametrize("rates", [[0.0, 0.5], [1.5], [-0.25], [], [np.nan], [np.inf], [[0.5]], 0.5])
+def test_grid_export_rates_reject_invalid_values(rates):
+    """0.0 is not an export level, and rates above the rated power are rejected."""
+    with pytest.raises(ValidationError):
+        BatteriesCommonSettings(device_id="battery1", grid_export_rates=rates)
+
+
+def test_rated_discharge_energy_scales_with_slot_duration(setup_pv_battery):
+    """The rate reference is the rated discharge energy of one slot."""
+    battery = setup_pv_battery
+    expected = battery.max_charge_power_w * battery.slot_duration_h * battery.discharging_efficiency
+    assert battery.rated_discharge_energy_wh() == pytest.approx(expected)
+
+
+def test_pv_converter_preserves_id_lcos_charge_and_export_rates():
+    settings = BatteriesCommonSettings(
+        device_id="house",
+        charge_rates=[0.0, 0.5, 1.0],
+        grid_export_rates=[1.0, 0.25],
+        levelized_cost_of_storage_amt_kwh=0.123,
+    )
+    assert isinstance(BatteriesCommonSettings.validate_and_sort_charge_rates(None), np.ndarray)
+    parameters = settings.to_genetic_pv_bat_param()
+    assert parameters.device_id == "house"
+    assert parameters.charge_rates == [0.0, 0.5, 1.0]
+    assert parameters.grid_export_rates == [0.25, 1.0]
+    assert parameters.levelized_cost_of_storage_kwh == pytest.approx(0.123)
+    battery = Battery(parameters, prediction_hours=4, slot_duration_h=0.25)
+    assert battery.levelized_cost_of_storage_kwh == pytest.approx(0.123)
diff --git a/tests/test_cache.py b/tests/test_cache.py
index 0680eaa8..1e91c0c6 100644
--- a/tests/test_cache.py
+++ b/tests/test_cache.py
@@ -88,6 +88,55 @@ class TestCacheUntilUpdateDecorators:
         assert CacheEnergyManagementStore.hit_count == 1
         assert result1 == result2
 
+    @pytest.mark.parametrize("reverse", [False, True])
+    @pytest.mark.parametrize("use_kwargs", [False, True])
+    def test_methods_with_identical_arguments_keep_separate_results(
+        self, cache_energy_management_store, reverse, use_kwargs
+    ):
+        calls = []
+
+        class Model:
+            @cache_energy_management
+            def fraction(self, value):
+                calls.append("fraction")
+                return value / 1000
+
+            @cache_energy_management
+            def energy(self, value):
+                calls.append("energy")
+                return value * 1000
+
+        model = Model()
+        cases = [(model.fraction, 0.005), (model.energy, 5000)]
+        if reverse:
+            cases.reverse()
+        for _ in range(2):
+            for method, expected in cases:
+                result = method(value=5) if use_kwargs else method(5)
+                assert result == expected
+        assert sorted(calls) == ["energy", "fraction"]
+        assert CacheEnergyManagementStore.miss_count == 2
+        assert CacheEnergyManagementStore.hit_count == 2
+
+    def test_distinct_closures_with_same_name_do_not_share_results(
+        self, cache_energy_management_store
+    ):
+        def make_function(factor):
+            @cache_energy_management
+            def compute(value):
+                return value * factor
+            return compute
+
+        double = make_function(2)
+        triple = make_function(3)
+        assert double.__qualname__ == triple.__qualname__
+        assert double(4) == 8
+        assert triple(4) == 12
+        assert double(4) == 8
+        assert triple(4) == 12
+        assert CacheEnergyManagementStore.miss_count == 2
+        assert CacheEnergyManagementStore.hit_count == 2
+
     def test_cache_energy_management(self, cache_energy_management_store):
         """Test that cache_energy_management caches function results."""
 
diff --git a/tests/test_geneticoptimize.py b/tests/test_geneticoptimize.py
index 63e62bc8..b9a72b7d 100644
--- a/tests/test_geneticoptimize.py
+++ b/tests/test_geneticoptimize.py
@@ -4,6 +4,7 @@ from pathlib import Path
 from typing import Any
 from unittest.mock import patch
 
+import numpy as np
 import pytest
 from pydantic import ValidationError
 from pypdf import PdfReader
@@ -140,14 +141,29 @@ async def test_optimize(
             f"cp {TESTDATA_FILE} {solution_file}\n"
         )
 
-    assert genetic_solution.result.Gesamtbilanz_Euro == pytest.approx(
-        expected_result.result.Gesamtbilanz_Euro
-    )
-
-    # Assert that the output contains all expected entries.
-    # This does not assert that the optimization always gives the same result!
-    # Reproducibility and mathematical accuracy should be tested on the level of individual components.
-    compare_dict(genetic_solution.model_dump(), expected_result.model_dump())
+    # Keep the output contract, but do not demand an identical stochastic
+    # schedule or monetary golden from the previous direct-consumption model.
+    assert set(genetic_solution.model_dump()) == set(expected_result.model_dump())
+    result = genetic_solution.result
+    expected_slots = len(input_data.ems.pv_forecast_wh) - fixed_start_hour
+    assert len(result.grid_consumption_wh_per_hour) == expected_slots
+    assert len(result.grid_feed_in_wh_per_hour) == expected_slots
+    prices = np.asarray(genetic_solution.parameters.ems.electricity_price_per_wh)[fixed_start_hour:]
+    tariffs = np.asarray(genetic_solution.parameters.ems.feed_in_tariff_per_wh)
+    if tariffs.ndim > 0:
+        tariffs = tariffs[fixed_start_hour:]
+    expected_costs = np.asarray(result.grid_consumption_wh_per_hour) * prices
+    expected_revenues = np.asarray(result.grid_feed_in_wh_per_hour) * tariffs
+    np.testing.assert_allclose(result.costs_per_hour, expected_costs)
+    np.testing.assert_allclose(result.revenue_per_hour, expected_revenues)
+    assert result.total_costs == pytest.approx(sum(expected_costs))
+    assert result.total_revenue == pytest.approx(sum(expected_revenues))
+    assert result.total_balance == pytest.approx(sum(expected_costs) - sum(expected_revenues))
+    assert result.total_losses == pytest.approx(sum(result.losses_per_hour))
+    assert all(value >= 0 for value in result.grid_consumption_wh_per_hour)
+    assert all(value >= 0 for value in result.grid_feed_in_wh_per_hour)
+    assert all(0 <= value <= 100 for value in result.battery_soc_per_hour)
+    assert all(0 <= value <= 100 for value in result.ev_soc_per_hour)
 
     # Check the correct generic optimization solution is created
     optimization_solution = await genetic_solution.optimization_solution()
diff --git a/tests/test_geneticsimulation.py b/tests/test_geneticsimulation.py
index e348790b..2b3eba84 100644
--- a/tests/test_geneticsimulation.py
+++ b/tests/test_geneticsimulation.py
@@ -334,18 +334,13 @@ def test_simulation(genetic_simulation):
         "The value at index 1 of 'Netzbezug_Wh_pro_Stunde' should be 1527.13."
     )
 
-    # Verify the total balance
-    assert abs(result["Gesamtbilanz_Euro"] - 6.612835813556755) < 1e-5, (
-        "Total balance should be 6.612835813556755."
-    )
-
-    # Check total revenue and total costs
-    assert abs(result["Gesamteinnahmen_Euro"] - 1.964301131937134) < 1e-5, (
-        "Total revenue should be 1.964301131937134."
-    )
-    assert abs(result["Gesamtkosten_Euro"] - 8.577136945493889) < 1e-5, (
-        "Total costs should be 8.577136945493889 ."
-    )
+    # Reprice the physical grid flows independently. The new direct-use
+    # probability model changes the old aggregate monetary golden values.
+    costs = np.dot(result["Netzbezug_Wh_pro_Stunde"], simulation.elect_price_hourly[start_hour:])
+    revenues = np.dot(result["Netzeinspeisung_Wh_pro_Stunde"], simulation.elect_revenue_per_hour_arr[start_hour:])
+    assert result["Gesamtkosten_Euro"] == pytest.approx(costs)
+    assert result["Gesamteinnahmen_Euro"] == pytest.approx(revenues)
+    assert result["Gesamtbilanz_Euro"] == pytest.approx(costs - revenues)
 
     # Check the losses
     assert abs(result["Gesamt_Verluste"] - 1620.0) < 1e-5, (
diff --git a/tests/test_interpolator.py b/tests/test_interpolator.py
new file mode 100644
index 00000000..df3cbfb5
--- /dev/null
+++ b/tests/test_interpolator.py
@@ -0,0 +1,105 @@
+import numpy as np
+import pytest
+
+from akkudoktoreos.prediction.interpolator import get_eos_load_interpolator
+
+
+def test_quarter_hour_energy_is_converted_back_to_same_mean_power():
+    """Splitting hourly energy must not change the minute-load probability lookup."""
+    interpolator = get_eos_load_interpolator()
+    hourly_load_wh = 800.0
+    hourly_pv_wh = 1200.0
+    slot_duration_h = 0.25
+
+    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,
+    )
+
+    assert quarter_hour == pytest.approx(hourly)
+
+
+def test_load_above_probability_grid_uses_highest_supported_distribution():
+    """Out-of-range household load must not make self-consumption jump to zero."""
+    interpolator = get_eos_load_interpolator()
+
+    at_boundary = interpolator.calculate_self_consumption(3450.0, 5000.0)
+    above_boundary = interpolator.calculate_self_consumption(4000.0, 5000.0)
+
+    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)
+
+
+@pytest.mark.parametrize("load,pv", [(4000.0, 5000.0), (10000.0, 20000.0), (0.0, 0.0)])
+def test_genetic_interpolator_boundaries_are_finite_and_physical(load, pv):
+    interpolator = get_eos_load_interpolator()
+    fraction = interpolator.calculate_self_consumption(load, pv)
+    direct = interpolator.calculate_expected_direct_consumption(load, pv)
+    assert np.isfinite(fraction)
+    assert 0.0 <= fraction <= 1.0
+    assert np.isfinite(direct)
+    assert 0.0 <= direct <= min(load, pv)
+
+
+def test_genetic0_inverter_keeps_its_independent_interpolator():
+    from akkudoktoreos.devices.genetic0.genetic0inverter import (
+        Genetic0Inverter,
+        Genetic0InverterParameters,
+    )
+    from akkudoktoreos.optimization.genetic0.genetic0loadinterpolator import (
+        get_genetic0_load_interpolator,
+    )
+
+    inverter = Genetic0Inverter(Genetic0InverterParameters(device_id="legacy", max_power_wh=10000))
+    assert inverter.self_consumption_predictor is get_genetic0_load_interpolator()
+    assert inverter.self_consumption_predictor is not get_eos_load_interpolator()
+
+
+@pytest.mark.parametrize("load,pv", [(4000.0, 5000.0), (10000.0, 20000.0)])
+def test_genetic_inverter_boundary_flows_remain_nonnegative(load, pv):
+    from akkudoktoreos.devices.genetic.inverter import Inverter, InverterParameters
+
+    inverter = Inverter(InverterParameters(device_id="boundary", max_power_wh=25000))
+    flows = inverter.process_energy(generation=pv, consumption=load, hour=0)
+    assert all(np.isfinite(value) and value >= 0.0 for value in flows)
diff --git a/tests/test_inverter.py b/tests/test_inverter.py
index ff82d435..11ab11ea 100644
--- a/tests/test_inverter.py
+++ b/tests/test_inverter.py
@@ -1,7 +1,12 @@
-from unittest.mock import Mock, patch
+from unittest.mock import Mock, call, patch
 
+import numpy as np
 import pytest
 
+from akkudoktoreos.devices.genetic.battery import (
+    Battery,
+    SolarPanelBatteryParameters,
+)
 from akkudoktoreos.devices.genetic.inverter import Inverter, InverterParameters
 
 
@@ -10,6 +15,9 @@ def mock_battery() -> Mock:
     mock_battery = Mock()
     mock_battery.charge_energy = Mock(return_value=(0.0, 0.0))
     mock_battery.discharge_energy = Mock(return_value=(0.0, 0.0))
+    # Rated discharge energy of one slot - the reference a grid-export rate is
+    # applied to. Large enough to never bind at the default factor of 1.0.
+    mock_battery.rated_discharge_energy_wh = Mock(return_value=1e9)
     mock_battery.parameters.device_id = "battery1"
     return mock_battery
 
@@ -17,7 +25,7 @@ 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,
@@ -26,11 +34,51 @@ def inverter(mock_battery) -> Inverter:
             InverterParameters(
                 device_id="iv1", max_power_wh=500.0, battery_id=mock_battery.parameters.device_id
             ),
-            battery = mock_battery
+            battery=mock_battery,
         )
         return iv
 
 
+def test_quarter_hour_load_and_grid_export_share_discharge_power_limit():
+    """Local supply plus direct export may not exceed one slot's battery budget."""
+    battery = Battery(
+        SolarPanelBatteryParameters(
+            device_id="battery",
+            capacity_wh=10000,
+            charging_efficiency=1.0,
+            discharging_efficiency=1.0,
+            max_charge_power_w=7000,
+            initial_soc_percentage=100,
+        ),
+        prediction_hours=1,
+        slot_duration_h=0.25,
+    )
+    battery.set_discharge_per_hour(np.array([1]))
+    quarter_hour_inverter = Inverter(
+        InverterParameters(
+            device_id="inverter",
+            max_power_wh=10000,
+            battery_id="battery",
+            dc_to_ac_efficiency=1.0,
+            ac_to_dc_efficiency=1.0,
+        ),
+        battery=battery,
+        slot_duration_h=0.25,
+    )
+    initial_soc_wh = battery.soc_wh
+
+    grid_export, grid_import, _, _ = quarter_hour_inverter.process_energy(
+        generation=0.0,
+        consumption=1000.0,
+        hour=0,
+        allow_battery_grid_export=True,
+    )
+
+    assert grid_import == 0.0
+    assert grid_export == pytest.approx(750.0)
+    assert initial_soc_wh - battery.soc_wh == pytest.approx(1750.0)
+
+
 def test_process_energy_excess_generation(inverter, mock_battery):
     # Battery charges 100 Wh with 10 Wh loss
     mock_battery.charge_energy.return_value = (100.0, 10.0)
@@ -48,7 +96,7 @@ 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
     )
 
@@ -57,7 +105,8 @@ 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
@@ -67,19 +116,71 @@ 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
@@ -96,7 +197,7 @@ 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
     )
 
@@ -120,7 +221,49 @@ 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):
+    mock_battery.max_charge_power_w = 300.0
+    mock_battery.remaining_discharge_energy_wh.return_value = 200.0
+    mock_battery.discharge_energy.side_effect = [(100.0, 0.0), (200.0, 0.0)]
+
+    grid_export, grid_import, losses, self_consumption = inverter.process_energy(
+        generation=0.0,
+        consumption=100.0,
+        hour=12,
+        allow_battery_grid_export=True,
+    )
+
+    assert grid_export == pytest.approx(200.0, rel=1e-2)
+    assert grid_import == 0.0
+    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_expected_direct_consumption.assert_not_called()
+
+
+def test_process_energy_grid_export_rate_limits_export(inverter, mock_battery):
+    """An export rate caps the export at that share of the rated discharge power."""
+    mock_battery.max_charge_power_w = 300.0
+    mock_battery.remaining_discharge_energy_wh.return_value = 200.0
+    mock_battery.rated_discharge_energy_wh.return_value = 300.0
+    mock_battery.discharge_energy.side_effect = [(100.0, 0.0), (150.0, 0.0)]
+
+    grid_export, grid_import, losses, self_consumption = inverter.process_energy(
+        generation=0.0,
+        consumption=100.0,
+        hour=12,
+        allow_battery_grid_export=True,
+        battery_grid_export_factor=0.5,
+    )
+
+    # 0.5 * 300 Wh rated = 150 Wh, below the 200 Wh the battery could still give.
+    assert grid_export == pytest.approx(150.0)
+    mock_battery.discharge_energy.assert_has_calls([call(100.0, 12), call(150.0, 12)])
 
 
 def test_process_energy_battery_empty(inverter, mock_battery):
@@ -140,7 +283,9 @@ 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):
@@ -162,7 +307,7 @@ 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
     )
 
@@ -184,7 +329,9 @@ 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):
@@ -209,7 +356,9 @@ 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):
@@ -232,7 +381,7 @@ 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):
@@ -252,9 +401,7 @@ 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):
@@ -272,9 +419,7 @@ 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):
@@ -295,7 +440,9 @@ 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):
@@ -315,7 +462,9 @@ 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):
@@ -337,4 +486,4 @@ 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()
diff --git a/tests/test_inverter_efficiency.py b/tests/test_inverter_efficiency.py
index f3f24f8b..d720236b 100644
--- a/tests/test_inverter_efficiency.py
+++ b/tests/test_inverter_efficiency.py
@@ -21,10 +21,7 @@ from akkudoktoreos.devices.genetic.battery import (
     Battery,
     SolarPanelBatteryParameters,
 )
-from akkudoktoreos.devices.genetic.inverter import (
-    Inverter,
-    InverterParameters,
-)
+from akkudoktoreos.devices.genetic.inverter import Inverter, InverterParameters
 
 # ---------------------------------------------------------------------------
 # Helpers / Fixtures
@@ -40,7 +37,7 @@ 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",
@@ -167,12 +164,14 @@ 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
@@ -182,18 +181,23 @@ 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)
 
 
 # ===================================================================