feat(optimization): deadlines for consumers and EV, graded grid export

Three related scheduling improvements, all opt-in and behaviour-preserving
when the new fields are not set.

Flexible consumers get absolute time bounds next to the recurring
time_windows: earliest_start_datetime and deadline_datetime, where the
deadline requires the complete run to have *finished* before that moment
("clean dishes by 03:00 tonight"). When no start can meet it,
deadline_policy decides between BEST_EFFORT (run as early as possible, so
the delay rather than the cost is minimized) and STRICT (keep the
deadline; a ONCE consumer then fails the optimization). The solution
reports appliance_deadline_missed per device.

The EV charging target can be given the same kind of deadline, as an
absolute min_soc_deadline_datetime and/or a relative min_soc_max_duration_h
("full in 6 hours"), the earlier of the two winning. The ev_soc_miss
penalty is then evaluated at that slot instead of at the end of the
horizon, and the seeding heuristic only proposes charge slots before it.

Battery-to-grid export under direct marketing is no longer all-or-nothing:
grid_export_rates configures the selectable export levels as a factor of
the rated discharge power (default [0.25, 0.5, 0.75, 1.0]). Each rate is
its own optimizer state, with the full-power state keeping its previous
index so existing seeds and heuristics are unaffected. The chosen level
per slot is reported in battery_grid_export_factor and as the
GRID_SUPPORT_EXPORT operation factor.

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

tests/test_geneticsolution.py

@@ -74,3 +74,55 @@ def test_decode_charge_discharge_has_self_consumption_state_after_legacy_export(
     assert dc_charge.tolist() == [1]
     assert discharge.tolist() == [1]
     assert battery_grid_export.tolist() == [0]
+
+
+def test_graded_grid_export_states_decode_to_rates():
+    """Each configured export rate gets its own state; state 5 stays full power."""
+    optimization = GeneticOptimization()
+    optimization.bat_possible_charge_values = [1.0]
+    optimization.bat_possible_grid_export_values = [1.0, 0.5, 0.25]
+    optimization.optimize_dc_charge = True
+    optimization.optimize_battery_grid_export = True
+
+    layout = optimization._battery_state_layout()
+
+    assert layout.grid_export_states == (5, 6, 7)
+    # The full-power state keeps its index, so existing seeds stay valid.
+    assert layout.grid_export_state == 5
+    assert layout.self_consumption_state == 8
+    assert layout.total_states == 9
+
+    _, _, _, battery_grid_export = optimization.decode_charge_discharge(
+        np.array([0, 5, 6, 7])
+    )
+    assert battery_grid_export.tolist() == [0.0, 1.0, 0.5, 0.25]
+
+
+def test_single_export_rate_keeps_all_or_nothing_layout():
+    """Without configured rates the state space is the one from before grading."""
+    optimization = GeneticOptimization()
+    optimization.bat_possible_charge_values = [1.0]
+    optimization.optimize_dc_charge = True
+    optimization.optimize_battery_grid_export = True
+
+    layout = optimization._battery_state_layout()
+
+    assert layout.grid_export_states == (5,)
+    assert layout.total_states == 7
+
+
+def test_battery_grid_export_factor_becomes_operation_factor(config_eos):
+    """A partial export level is reported as the GRID_SUPPORT_EXPORT factor."""
+    config_eos.merge_settings_from_dict({"feedintariff": {"direct_marketing_enabled": True}})
+    solution = GeneticSolution.model_construct()
+
+    operation_mode, operation_mode_factor = solution._battery_operation_from_solution(
+        ac_charge=0.0,
+        dc_charge=0.0,
+        discharge_allowed=False,
+        battery_grid_export_allowed=True,
+        battery_grid_export_factor=0.25,
+    )
+
+    assert operation_mode == BatteryOperationMode.GRID_SUPPORT_EXPORT
+    assert operation_mode_factor == 0.25