fix(optimization): add battery self-consumption state

CHANGELOG.md

@@ -80,6 +80,14 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
   are deprecated in favour of `appliance_starts` and `result.home_appliance_energy_wh`.
 
 ### Fixed
+- Allow the direct-marketing optimizer to select a true battery self-consumption state with DC
+  charging and local-load discharge enabled in the same slot. Existing warm-start state numbers
+  remain compatible, and educated guesses now use the combined state for PV/load overlap instead
+  of unnecessarily bypassing PV while serving loads such as EV charging.
+- Account for EV charging losses in the AC load seen by the inverter and grid, so fitness and
+  energy costs use the charger's raw input rather than only the energy stored in the EV battery.
+- Treat fitness memoization as disabled for lightweight optimizer instances constructed without
+  the normal initializer, preserving isolated penalty evaluation and test callers.
 - Re-simulate genetic candidates after removing EV charging genes from slots that begin at full
   SoC, keeping the repaired genome and its assigned fitness consistent.
 - FeedInTariffEnergyCharts no longer aborts the whole prediction/optimization when the

docs/akkudoktoreos/optimpost.md

@@ -357,6 +357,11 @@ smaller values (e.g. `0.0`) disable the penalty entirely.
 - `discharge_allowed`: Battery discharge permission for local self-consumption/load coverage (0 or 1)
 - `battery_grid_export_allowed`: Battery discharge permission for grid export/direct marketing (0 or 1)
 
+With direct marketing enabled, `dc_charge = 1` and `discharge_allowed = 1` may occur together. This
+is the normal self-consumption mode: within a coarse optimization slot, the battery may cover
+probabilistic load gaps and store PV surplus from different sub-intervals. A discharge-only state
+remains available when deliberately bypassing PV charging is economically preferable.
+
 0 (no charge)
 1 (charge with full load)
 

src/akkudoktoreos/optimization/genetic/genetic.py

@@ -85,6 +85,17 @@ class FitnessCacheEntry:
     extra_data: tuple[float, float, float]
 
 
+@dataclass(frozen=True)
+class BatteryStateLayout:
+    """Indices of optional battery states appended to the legacy state ranges."""
+
+    total_states: int
+    dc_not_allowed_state: Optional[int] = None
+    dc_allowed_state: Optional[int] = None
+    grid_export_state: Optional[int] = None
+    self_consumption_state: Optional[int] = None
+
+
 class GeneticSimulation(PydanticBaseModel):
     """Device simulation for GENETIC optimization algorithm."""
 
@@ -411,10 +422,12 @@ class GeneticSimulation(PydanticBaseModel):
             if ev_fast:
                 soc_ev_per_hour[hour_idx] = ev_fast.current_soc_percentage()  # save begin state
                 if ev_charge_hours_fast[hour] > 0:
-                    loaded_energy_ev, verluste_eauto = ev_fast.charge_energy(
+                    stored_energy_ev, verluste_eauto = ev_fast.charge_energy(
                         wh=None, hour=hour, charge_factor=ev_charge_hours_fast[hour]
                     )
-                    consumption += loaded_energy_ev
+                    # The inverter/grid must supply the EV charger's raw input,
+                    # not only the energy stored after charging losses.
+                    consumption += stored_energy_ev + verluste_eauto
                     losses_wh_per_hour[hour_idx] += verluste_eauto
 
             # Save battery SOC before inverter processing = true begin-of-interval state.
@@ -663,6 +676,40 @@ class GeneticOptimization(OptimizationBase):
         except Exception:
             return False
 
+    def _battery_state_layout(self) -> BatteryStateLayout:
+        """Build optional state indices without renumbering legacy warm starts.
+
+        The pre-existing order is retained exactly: base charge/discharge ranges,
+        two optional DC states, then optional grid export.  SELF_CONSUMPTION is
+        appended last so an old export gene never changes its meaning.
+        """
+        next_state = 3 * len(self.bat_possible_charge_values)
+        dc_not_allowed_state: Optional[int] = None
+        dc_allowed_state: Optional[int] = None
+        grid_export_state: Optional[int] = None
+        self_consumption_state: Optional[int] = None
+
+        if self.optimize_dc_charge:
+            dc_not_allowed_state = next_state
+            dc_allowed_state = next_state + 1
+            next_state += 2
+
+        if self.optimize_battery_grid_export:
+            grid_export_state = next_state
+            next_state += 1
+
+        if self.optimize_dc_charge:
+            self_consumption_state = next_state
+            next_state += 1
+
+        return BatteryStateLayout(
+            total_states=next_state,
+            dc_not_allowed_state=dc_not_allowed_state,
+            dc_allowed_state=dc_allowed_state,
+            grid_export_state=grid_export_state,
+            self_consumption_state=self_consumption_state,
+        )
+
     def _appliance_horizon_end_slot(self) -> int:
         """Exclusive upper slot bound for appliance runs (end of horizon).
 
@@ -966,9 +1013,8 @@ class GeneticOptimization(OptimizationBase):
         # AC Charge:  2*len_bat .. 3*len_bat - 1  (maps to bat_possible_charge_values)
         # DC optional: 3*len_bat (not allowed), 3*len_bat + 1 (allowed)
         # Grid export: next state, if direct marketing/export optimization is enabled
-
+        # Self-consumption: final state, with DC charging and local discharge enabled
-        # Idle states
+        state_layout = self._battery_state_layout()
-        idle_mask = (discharge_hours_bin_np >= 0) & (discharge_hours_bin_np < len_bat)
 
         # Discharge states
         discharge_mask = (discharge_hours_bin_np >= len_bat) & (
@@ -980,24 +1026,28 @@ class GeneticOptimization(OptimizationBase):
         ac_indices = (discharge_hours_bin_np[ac_mask] - 2 * len_bat).astype(int)
 
         # DC states (if enabled)
-        if self.optimize_dc_charge:
+        if state_layout.dc_allowed_state is not None:
-            dc_not_allowed_state = 3 * len_bat
+            dc_mask = discharge_hours_bin_np == state_layout.dc_allowed_state
-            dc_allowed_state = 3 * len_bat + 1
+            if state_layout.self_consumption_state is not None:
-            dc_charge = np.where(discharge_hours_bin_np == dc_allowed_state, 1, 0)
+                dc_mask |= discharge_hours_bin_np == state_layout.self_consumption_state
+            dc_charge = np.where(dc_mask, 1, 0)
         else:
             dc_charge = np.ones_like(discharge_hours_bin_np, dtype=float)
 
         # Generate the result arrays
         discharge = np.zeros_like(discharge_hours_bin_np, dtype=int)
         discharge[discharge_mask] = 1  # Set Discharge states to 1
+        if state_layout.self_consumption_state is not None:
+            discharge[discharge_hours_bin_np == state_layout.self_consumption_state] = 1
 
         ac_charge = np.zeros_like(discharge_hours_bin_np, dtype=float)
         ac_charge[ac_mask] = [self.bat_possible_charge_values[i] for i in ac_indices]
 
         battery_grid_export = np.zeros_like(discharge_hours_bin_np, dtype=int)
-        if self.optimize_battery_grid_export:
+        if state_layout.grid_export_state is not None:
-            grid_export_state = 3 * len_bat + (2 if self.optimize_dc_charge else 0)
+            battery_grid_export = np.where(
-            battery_grid_export = np.where(discharge_hours_bin_np == grid_export_state, 1, 0)
+                discharge_hours_bin_np == state_layout.grid_export_state, 1, 0
+            )
 
         # Idle is just 0, already default.
 
@@ -1005,14 +1055,7 @@ class GeneticOptimization(OptimizationBase):
 
     def mutate(self, individual: list[int]) -> tuple[list[int]]:
         """Custom mutation function for the individual."""
-        # Calculate the number of states using battery charge levels
+        total_states = self._battery_state_layout().total_states
-        len_bat = len(self.bat_possible_charge_values)
-        if self.optimize_dc_charge:
-            total_states = 3 * len_bat + 2
-        else:
-            total_states = 3 * len_bat
-        if self.optimize_battery_grid_export:
-            total_states += 1
 
         # 1. Mutating the charge_discharge part
         charge_discharge_part = individual[: self.total_slots]
@@ -1146,14 +1189,15 @@ class GeneticOptimization(OptimizationBase):
         must re-simulate after a change so the individual's genome, simulation
         state and assigned fitness always describe the same schedule.
         """
-        if not self.optimize_ev or not self.ev_possible_charge_values:
+        ev_possible_charge_values = getattr(self, "ev_possible_charge_values", None)
+        if not self.optimize_ev or not ev_possible_charge_values:
             return False
 
         zero_charge_index = min(
-            range(len(self.ev_possible_charge_values)),
+            range(len(ev_possible_charge_values)),
-            key=lambda index: abs(self.ev_possible_charge_values[index]),
+            key=lambda index: abs(ev_possible_charge_values[index]),
         )
-        if abs(self.ev_possible_charge_values[zero_charge_index]) > 1e-12:
+        if abs(ev_possible_charge_values[zero_charge_index]) > 1e-12:
             return False
 
         _, ev_charge_indices, _ = self.split_individual(individual)
@@ -1172,7 +1216,7 @@ class GeneticOptimization(OptimizationBase):
             charge_index = int(ev_charge_indices[slot])
             if (
                 ev_soc[offset] >= 100.0 - 1e-9
-                and self.ev_possible_charge_values[charge_index] > 0.0
+                and ev_possible_charge_values[charge_index] > 0.0
             ):
                 ev_charge_indices[slot] = zero_charge_index
                 changed = True
@@ -1279,11 +1323,13 @@ class GeneticOptimization(OptimizationBase):
         slots = self.total_slots
         start_slot = self._start_day_slot()
         len_bat = len(self.bat_possible_charge_values)
+        state_layout = self._battery_state_layout()
         idle_state = 0
         discharge_state = len_bat
         ac_charge_state = 3 * len_bat - 1
-        dc_allowed_state = 3 * len_bat + 1
+        dc_allowed_state = state_layout.dc_allowed_state
-        export_state = 3 * len_bat + (2 if self.optimize_dc_charge else 0)
+        export_state = state_layout.grid_export_state
+        self_consumption_state = state_layout.self_consumption_state
 
         prices = np.asarray(self.simulation.elect_price_hourly, dtype=float)
         feed_in = np.asarray(self.simulation.elect_revenue_per_hour_arr, dtype=float)
@@ -1333,15 +1379,20 @@ class GeneticOptimization(OptimizationBase):
             for slot in range(start_slot, slots):
                 high_feed_in = (
                     export_quantile is not None
-                    and self.optimize_battery_grid_export
+                    and export_state is not None
                     and feed_spread > 1e-12
                     and feed_in[slot] > 0.0
                     and feed_in[slot] >= export_threshold
                 )
                 pv_surplus = pv[slot] > load[slot] * pv_surplus_ratio
-                if high_feed_in:
+                if high_feed_in and export_state is not None:
                     battery_genes[slot] = export_state
-                elif self.optimize_dc_charge and pv_surplus:
+                elif self_consumption_state is not None and pv[slot] > 0.0 and load[slot] > 0.0:
+                    # The probabilistic inverter model can see a residual load
+                    # and a PV surplus within the same coarse slot. Normal
+                    # self-consumption must therefore allow both directions.
+                    battery_genes[slot] = self_consumption_state
+                elif dc_allowed_state is not None and pv_surplus:
                     battery_genes[slot] = dc_allowed_state
                 elif allow_ac_arbitrage and prices[slot] <= low_price_threshold:
                     battery_genes[slot] = ac_charge_state
@@ -1415,7 +1466,9 @@ class GeneticOptimization(OptimizationBase):
             for slot in random.sample(future_slots, min(perturbations, len(future_slots))):  # noqa: S311
                 if randomized[slot] != idle_state:
                     randomized[slot] = idle_state
-                elif self.optimize_dc_charge and pv[slot] > load[slot]:
+                elif self_consumption_state is not None and pv[slot] > 0.0 and load[slot] > 0.0:
+                    randomized[slot] = self_consumption_state
+                elif dc_allowed_state is not None and pv[slot] > load[slot]:
                     randomized[slot] = dc_allowed_state
                 elif load[slot] > pv[slot] and prices[slot] >= high_import_price:
                     randomized[slot] = discharge_state
@@ -1480,12 +1533,8 @@ class GeneticOptimization(OptimizationBase):
         # AC-Charge: len_bat states  (maps to bat_possible_charge_values)
         # With DC: + 2 additional states
         # With battery grid export: + 1 additional state
-        if self.optimize_dc_charge:
+        # With DC: + 1 final SELF_CONSUMPTION state
-            total_states = 3 * len_bat + 2
+        total_states = self._battery_state_layout().total_states
-        else:
-            total_states = 3 * len_bat
-        if self.optimize_battery_grid_export:
-            total_states += 1
 
         # State space: 0 .. (total_states - 1)
         self.toolbox.register("attr_discharge_state", random.randint, 0, total_states - 1)
@@ -1579,7 +1628,10 @@ class GeneticOptimization(OptimizationBase):
         worst_case: bool,
     ) -> tuple[float]:
         """Evaluate an individual, using run-local canonical memoization when active."""
-        if not self._fitness_cache_enabled:
+        # Some lightweight callers construct the optimizer without __init__
+        # (for example isolated penalty evaluations). Memoization is opt-in, so
+        # a missing flag must behave exactly like a disabled cache.
+        if not getattr(self, "_fitness_cache_enabled", False):
             return self._evaluate_uncached(individual, parameters, start_hour, worst_case)
 
         original_key = tuple(int(value) for value in individual)

tests/test_genetic_seeding.py

@@ -187,9 +187,11 @@ def test_educated_guesses_encode_high_price_direct_marketing(config_eos: ConfigE
 
     dc_allowed_state = 4
     export_state = 5
+    self_consumption_state = 6
     assert len(guesses) == opt.EDUCATED_GUESS_TARGET
     assert all(len(guess) == slots for guess in guesses)
-    assert any(guess[0] == dc_allowed_state for guess in guesses)
+    assert any(dc_allowed_state in guess or self_consumption_state in guess for guess in guesses)
+    assert any(self_consumption_state in guess for guess in guesses)
     assert any(guess[-1] == export_state for guess in guesses)
 
 

tests/test_geneticsimulation.py

@@ -338,7 +338,7 @@ def test_simulation(genetic_simulation):
 
     # Verify the total balance
     assert (
-        abs(result["Gesamtbilanz_Euro"] - 7.025236588371921) < 1e-5
+        abs(result["Gesamtbilanz_Euro"] - 7.224316588371922) < 1e-5
     ), "Total balance should reflect the shared per-slot battery power limit."
 
     # Check total revenue and total costs
@@ -346,7 +346,7 @@ def test_simulation(genetic_simulation):
         abs(result["Gesamteinnahmen_Euro"] - 2.3247787887715) < 1e-5
     ), "Total revenue should respect the shared per-slot battery power limit."
     assert (
-        abs(result["Gesamtkosten_Euro"] - 9.350015377143421) < 1e-5
+        abs(result["Gesamtkosten_Euro"] - 9.549095377143422) < 1e-5
     ), "Total costs should respect the shared per-slot battery power limit."
 
     # Check the losses
@@ -379,6 +379,47 @@ def test_simulation(genetic_simulation):
     print("All tests passed successfully.")
 
 
+def test_ev_charging_uses_raw_input_energy_for_load_and_grid(config_eos):
+    config_eos.merge_settings_from_dict(
+        {"prediction": {"hours": 1}, "optimization": {"horizon_hours": 1}}
+    )
+    ev = Battery(
+        ElectricVehicleParameters(
+            device_id="ev1",
+            capacity_wh=1000,
+            charging_efficiency=0.8,
+            max_charge_power_w=100,
+            initial_soc_percentage=0,
+            min_soc_percentage=0,
+        ),
+        prediction_hours=1,
+    )
+    inverter = Inverter(InverterParameters(device_id="inverter1", max_power_wh=1000.0))
+    simulation = GeneticSimulation()
+    simulation.prepare(
+        GeneticEnergyManagementParameters(
+            pv_prognose_wh=[0.0],
+            strompreis_euro_pro_wh=[0.001],
+            einspeiseverguetung_euro_pro_wh=[0.0],
+            preis_euro_pro_wh_akku=0.0,
+            gesamtlast=[0.0],
+        ),
+        optimization_hours=1,
+        prediction_hours=1,
+        inverter=inverter,
+        ev=ev,
+    )
+    simulation.ev_charge_hours = np.array([1.0])
+
+    result = simulation.simulate(start_hour=0)
+
+    assert result["Last_Wh_pro_Stunde"][0] == pytest.approx(100.0)
+    assert result["Netzbezug_Wh_pro_Stunde"][0] == pytest.approx(100.0)
+    assert result["Kosten_Euro_pro_Stunde"][0] == pytest.approx(0.1)
+    assert result["Verluste_Pro_Stunde"][0] == pytest.approx(20.0)
+    assert ev.current_soc_percentage() == pytest.approx(8.0)
+
+
 def test_direct_marketing_curtails_negative_feed_in(config_eos, monkeypatch):
     config_eos.merge_settings_from_dict(
         {"prediction": {"hours": 2}, "optimization": {"horizon_hours": 2}}

tests/test_geneticsolution.py

@@ -54,3 +54,23 @@ def test_decode_charge_discharge_has_separate_battery_grid_export_state():
     assert dc_charge.tolist() == [0]
     assert discharge.tolist() == [0]
     assert battery_grid_export.tolist() == [1]
+
+
+def test_decode_charge_discharge_has_self_consumption_state_after_legacy_export():
+    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()
+    ac_charge, dc_charge, discharge, battery_grid_export = (
+        optimization.decode_charge_discharge(np.array([6]))
+    )
+
+    assert layout.total_states == 7
+    assert layout.grid_export_state == 5
+    assert layout.self_consumption_state == 6
+    assert ac_charge.tolist() == [0.0]
+    assert dc_charge.tolist() == [1]
+    assert discharge.tolist() == [1]
+    assert battery_grid_export.tolist() == [0]

tests/testdata/optimize_result_2.json

@@ -202,15 +202,15 @@
     ],
     "result": {
         "Last_Wh_pro_Stunde": [
-            10230.07,
+            10713.07,
-            7618.91,
+            7963.91,
-            7875.5599999999995,
+            8220.56,
-            7565.03,
+            7772.03,
-            12840.67,
+            13323.67,
-            9042.82,
+            9456.82,
-            9082.22,
+            9496.22,
-            5036.78,
+            5243.78,
-            2177.92,
+            2233.12,
             1178.71,
             1050.98,
             988.56,
@@ -313,7 +313,7 @@
             0.0,
             0.0,
             0.0,
-            0.05936927575872234,
+            0.053661230306193436,
             0.05435777788576338,
             0.0,
             0.0,
@@ -322,9 +322,9 @@
             0.0
         ],
         "Gesamt_Verluste": 6227.580163897914,
-        "Gesamtbilanz_Euro": 11.420868526053463,
+        "Gesamtbilanz_Euro": 12.033027623527284,
-        "Gesamteinnahmen_Euro": 0.11372705364448572,
+        "Gesamteinnahmen_Euro": 0.10801900819195681,
-        "Gesamtkosten_Euro": 11.534595579697948,
+        "Gesamtkosten_Euro": 12.14104663171924,
         "Home_appliance_wh_per_hour": [
             0.0,
             0.0,
@@ -408,15 +408,15 @@
             ]
         },
         "Kosten_Euro_pro_Stunde": [
-            2.1203521199999997,
+            2.23047612,
-            1.4545721927072854,
+            1.5308798733801507,
-            1.4167558060312964,
+            1.4889564723943407,
-            1.2032659414129376,
+            1.242149738556099,
-            1.2892826874611185,
+            1.3742255912165289,
-            0.770122611209644,
+            0.8479283355019763,
-            1.1459236583154115,
+            1.2336553365360492,
-            0.4965507655670841,
+            0.5407163922683618,
-            0.1912938683161112,
+            0.20558284318867343,
             0.1614775630079859,
             0.0,
             0.0,
@@ -448,15 +448,15 @@
             0.16484566
         ],
         "Netzbezug_Wh_pro_Stunde": [
-            9299.789999999999,
+            9782.789999999999,
-            6575.823656000386,
+            6920.795087613701,
-            6769.01961792306,
+            7113.9821901306295,
-            6403.757005923032,
+            6610.695787951565,
-            7014.595688036554,
+            7476.74423948057,
-            3842.9272016449304,
+            4231.179318872138,
-            5213.483431826258,
+            5612.626644840988,
-            2187.4483064629258,
+            2382.0105386271443,
-            638.2845122326032,
+            685.962106068313,
             505.40708296709204,
             0.0,
             0.0,
@@ -519,7 +519,7 @@
             0.0,
             0.0,
             0.0,
-            848.1325108388907,
+            766.589004374192,
             776.5396840823341,
             0.0,
             0.0,
@@ -529,14 +529,14 @@
         ],
         "Verluste_Pro_Stunde": [
             483.0,
-            345.0196387200463,
+            345.0162105136441,
-            345.0239541507672,
+            345.0194628156755,
-            207.05004071076388,
+            207.04269455418785,
-            506.1294825643864,
+            503.6273087376684,
-            452.3012641973916,
+            449.2115182646565,
-            426.13721181915116,
+            424.3543973809186,
-            227.23539677555112,
+            225.74286463525735,
-            103.61214146791241,
+            102.70945272819762,
             40.06404995605101,
             106.80230977350088,
             133.7321802766326,
@@ -559,7 +559,7 @@
             538.2984000000001,
             441.9379674303641,
             261.1952696860876,
-            75.0748095419566,
+            84.86003031772043,
             0.0,
             111.78035844493081,
             90.18403329253945,
@@ -570,36 +570,36 @@
         "akku_soc_pro_stunde": [
             80.0,
             80.0,
-            80.00054552000128,
+            80.00045029204567,
-            80.00121091307815,
+            80.00099092581443,
-            80.0026009328216,
+            80.00217688565297,
-            80.6450865596101,
+            80.57515768392155,
-            81.70901056509321,
+            81.55325541349534,
-            82.04615533784741,
+            81.8408775629653,
-            82.60824969272383,
+            82.36151269172245,
-            83.95303140016584,
+            83.68121971195016,
-            85.06592167672281,
+            84.79410998850713,
-            81.89125383667168,
+            81.619442148456,
-            77.66999557804807,
+            77.39818388983241,
-            77.66999557804807,
+            77.39818388983241,
-            77.66999557804807,
+            77.39818388983241,
-            75.44732856702878,
+            75.17551687881311,
-            71.71088745132629,
+            71.43907576311062,
-            68.72216499953565,
+            68.45035331131999,
-            66.10168462763482,
+            65.82987293941916,
-            63.7070995725384,
+            63.43528788432273,
-            61.60271768548606,
+            61.33090599727039,
-            59.42077037143647,
+            59.14895868322081,
-            55.97788013048819,
+            55.706068442272525,
-            52.48021001194556,
+            52.208398323729895,
-            53.09999027665313,
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tests/testdata/optimize_result_2_be.json

@@ -202,14 +202,14 @@
     ],
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             0.0,
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@@ -408,14 +408,14 @@
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