fix(devices): constrain the physics port and validate export levels

Defer inactive EV deadline fields to the optimizer port, reject nonfinite export rates, and document the hourly Optimize boundary. Verify converter IDs, rates and LCOS, separate GENETIC0 interpolation, physical boundary flows and independent GENETIC repricing.

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 = genetic_solution.parameters.ems.feed_in_tariff_per_wh
+    if isinstance(tariffs, list):
+        tariffs = np.asarray(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()