fix(optimization): clear disabled AC charge in the reported plan

With grid charging switched off (inverter.max_ac_charge_power_w = 0) the
simulation zeroes the AC charge array before using it, but it did so by
rebinding a local name. The solution is read back from the original array,
so it still carried the optimizer's AC charge genes - genes the fitness
never evaluated, because the simulation ignored them, and which are
therefore arbitrary.

The effect was visible as a slot with ac_charge = 0.8 where the battery SoC
does not move. Harmless inside EOS, but a controller that follows the plan
would grid-charge the battery at a time nobody planned or paid for.

Zero the array in place so the reported plan matches what was simulated.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

CHANGELOG.md

@@ -49,6 +49,10 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
   two applies. The `ev_soc_miss` penalty is then evaluated at that slot instead of at the end
   of the horizon, and the seeding heuristics only propose charge slots before it. Without a
   deadline the behaviour is unchanged.
+- Fix: with grid charging disabled (`inverter.max_ac_charge_power_w = 0`) the returned
+  `ac_charge` array kept the optimizer's unused gene values. The simulation ignored them, so
+  they were never costed - but a controller acting on the plan would grid-charge the battery
+  anyway. The disabled AC charge is now cleared in the reported plan as well.
 - EV Bug (wrong output in genetic.py / no senseful results)
 - Direktvermarktung active / Battery discharge into grid (new state / action battery_grid_export_allowed) + (new simulation output Feed_in_tariff)
 - New PV forecast providers giving operators more cloud forecast sources to choose from in

src/akkudoktoreos/optimization/genetic/genetic.py

@@ -350,9 +350,13 @@ class GeneticSimulation(PydanticBaseModel):
                 max_ac_charge_w_fast is None or max_ac_charge_w_fast > 0
             )
 
-            # If AC charging is disabled via inverter, zero out AC charge hours
+            # If AC charging is disabled via inverter, zero out AC charge hours.
+            # In place, not by rebinding: the reported plan is read back from
+            # this very array, so a rebind would leave AC charge values in the
+            # solution that the simulation never executed - and a controller
+            # acting on them would grid-charge the battery unplanned.
             if not ac_charging_possible:
-                ac_charge_hours_fast = np.zeros_like(ac_charge_hours_fast)
+                ac_charge_hours_fast[:] = 0.0
 
             # Fill the charge array of the battery
             dc_charge_hours_fast[0:start_hour] = 0

tests/test_geneticsimulation.py

@@ -560,3 +560,62 @@ def test_battery_lcos_is_charged_once_on_delivered_energy(config_eos):
     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)
+
+
+def test_disabled_ac_charging_clears_the_reported_plan(config_eos):
+    """With AC charging off the reported plan must not keep charge commands.
+
+    The simulation ignores the AC charge genes when the inverter forbids grid
+    charging. The solution is read back from the same array, so a controller
+    acting on it would grid-charge the battery although no such charge was ever
+    simulated or paid for.
+    """
+    config_eos.merge_settings_from_dict(
+        {"prediction": {"hours": 2}, "optimization": {"horizon_hours": 2}}
+    )
+
+    battery = Battery(
+        SolarPanelBatteryParameters(
+            device_id="battery1",
+            capacity_wh=10000,
+            initial_soc_percentage=50,
+            min_soc_percentage=0,
+            charging_efficiency=1.0,
+            discharging_efficiency=1.0,
+            max_charge_power_w=5000,
+        ),
+        prediction_hours=config_eos.prediction.hours,
+    )
+    inverter = Inverter(
+        InverterParameters(
+            device_id="inverter1",
+            max_power_wh=5000.0,
+            battery_id=battery.parameters.device_id,
+            max_ac_charge_power_w=0,  # Netzladen deaktiviert
+        ),
+        battery=battery,
+    )
+
+    simulation = GeneticSimulation()
+    simulation.prepare(
+        GeneticEnergyManagementParameters(
+            pv_prognose_wh=[0.0, 0.0],
+            strompreis_euro_pro_wh=[0.0003, 0.0003],
+            einspeiseverguetung_euro_pro_wh=[0.0001, 0.0001],
+            preis_euro_pro_wh_akku=0.0,
+            gesamtlast=[0.0, 0.0],
+        ),
+        optimization_hours=config_eos.optimization.horizon_hours,
+        prediction_hours=config_eos.prediction.hours,
+        inverter=inverter,
+    )
+    simulation.ac_charge_hours = np.array([0.8, 0.0])
+
+    soc_before = battery.current_soc_percentage()
+    simulation.simulate(start_hour=0)
+
+    # Nothing was charged ...
+    assert battery.current_soc_percentage() == pytest.approx(soc_before)
+    # ... and the plan says so.
+    assert simulation.ac_charge_hours is not None
+    assert list(simulation.ac_charge_hours) == [0.0, 0.0]