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>

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]