feat(devices): add bounded slot physics for GENETIC (#1327)

* feat: adapt configuration for multi optimization algorithms

Decouple configuration from optimization algorithm parameters. Add to_[algorithm]_param() methods
to the configuration that derive optimization algorithm specific parameters from the configuration.
Add x-scope tags to the configuration options that describe for which specific algorithms the
configuration option is for.

The whole device settings are restructured. There are now general settings for the device classes
with the afore mentioned to_[algorithm]_param() methods. The general device settings got their own
directory `devices/settings`. By this the parameter class also does not have to be a pydantic model
which can be used for future optimization/ simulations speed up.

Also the parameter class for a device is now part of the device module. This better decouples and
also is the natural place for parameters of a device.

Besides this feature there are also fixes and improvements:

* feat: extend home appliance time window settings and simulation

  Home appliance can now be configured for multiple runs with per-cycle allowed time windows. The
  number of remaining cycles to plan is determined at runtime by reading the
  ``cycles_completed_measurement_key`` from the measurement store.

* feat: specialiced CycleTimeWindowSequence for time window sequences

  Sequence of time windows associated to cycles.

  This model specializes ``ValueTimeWindowSequence`` so that the ``value``
  field of each ``ValueTimeWindow`` encodes the **cycle index** (0-based
  integer) the window belongs to.

  Typical use: an appliance that must run ``n`` times per day, each run
  constrained to a distinct time window.  Assign ``value=0`` to windows
  for the first cycle, ``value=1`` for the second, and so on.  Multiple
  windows may share the same cycle index (their allowed regions are unioned).
  Windows with ``value=None`` are silently ignored by all cycle-aware methods.

* fix: Make test_configmigrate also regard the _ANY_SENTENIEL in key values

* chore: Make devices configurations a map instead of a list

  This makes config paths stable regardless of declaration order and lets each device settings
  class build its own config path from ``self.device_id`` without needing an external index.
  Tests are adapted likewise.

  Devices configurations are automatically migrated from lists to maps.

* chore: rename levelized_cost_of_storage_kwh to levelized_cost_of_storage_amt kwh

  This better fits in the naming scheme and also makes clear the costs are money.

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>

* fix: runtime config update ignored by config file

Runtime settings were handed back to pydantic-settings as init settings,
which rank below the config file and the environment. Any key already
present in EOS.config.json or in the environment silently discarded the
update, so a bulk PUT /v1/config returned 200 without applying anything,
while the granular PUT /v1/config/{path} endpoint kept working.

Add a dedicated runtime settings source ranked directly below the command
line arguments and record granular updates there as well, so both
endpoints share one store that survives re-evaluation of the settings
sources. Environment variables keep precedence over the config file for
all keys that were not set at runtime.

Also repairs revert_settings() and update(), which passed their data
through the same init settings.

Closes #1303

* fix: env vars ignored on first config build

ConfigEOS.__init__ passed self as first positional argument to _setup,
which forwards it to pydantic_settings.BaseSettings.__init__. Its first
positional parameter is _case_sensitive, so the environment source
matched the upper case variable names against the lower case field names
and returned nothing. Environment settings only took effect after the
next configuration setup.

* docs: changelog for config priority fixes

* fix(config): preserve device identities and storage costs during migration

* fix(devices): preserve charge-rate typing and public import compatibility

* ruff format fix

* feat(devices): port slot-aware battery export and direct-use physics

Port scoped device changes from d2e2d58237. Keep PR #1256 parameter conversion structure and separate GENETIC0 devices. Validate physical flows and reprice changed simulation results.

Co-authored-by: Andreas <drbacke@gmx.de>

Co-authored-by: Christin <info@bikinibottom.capital>

* fix(cache): distinguish callables in the shared EMS cache

Include the function object in cache keys so methods of one interpolator cannot reuse a probability as a power value. Cover both call orders, keyword arguments, cache hits and separate closures with identical qualified names.

* 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.

* docs(devices): regenerate slot-physics configuration and OpenAPI schemas

* fix(config): satisfy typed device conversion and migration contracts

* docs(config): refresh validated configuration prerequisite schemas

* test(devices): align physics regressions with strict type checking

* docs(devices): refresh API version after prerequisite merge

* docs(interpolator): use portable reStructuredText markup

* docs(devices): refresh API version after docstring compatibility fix

---------

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
Co-authored-by: Bobby Noelte <b0661n0e17e@gmail.com>
Co-authored-by: r0b2g1t <r0b2g1t@users.noreply.github.com>
Co-authored-by: Christin <info@bikinibottom.capital>

src/akkudoktoreos/prediction/interpolator.py

@@ -15,31 +15,113 @@ class SelfConsumptionProbabilityInterpolator:
         # Load the RegularGridInterpolator
         with open(self.filepath, "rb") as file:
             self.interpolator: RegularGridInterpolator = pickle.load(file)  # noqa: S301
+        self.load_power_min_w = float(self.interpolator.grid[0][0])
+        self.load_power_max_w = float(self.interpolator.grid[0][-1])
+        self.minute_load_levels_w = np.asarray(self.interpolator.grid[1], dtype=float)
+        self.minute_load_max_w = float(self.interpolator.grid[1][-1])
+
+    def _load_distribution(self, mean_load_power_w: float) -> tuple[np.ndarray, np.ndarray]:
+        """Return the conditional minute-load distribution for a mean load.
+
+        The table stores one probability mass for each 50 W minute-load bin.
+        Linear interpolation between its mean-load rows can introduce very small
+        numerical deviations, so negative masses are removed and the result is
+        normalized explicitly.
+        """
+        bounded_mean_load_w = float(
+            np.clip(mean_load_power_w, self.load_power_min_w, self.load_power_max_w)
+        )
+        points = np.column_stack(
+            (
+                np.full(self.minute_load_levels_w.shape, bounded_mean_load_w),
+                self.minute_load_levels_w,
+            )
+        )
+        probabilities = np.maximum(np.asarray(self.interpolator(points), dtype=float), 0.0)
+        probability_sum = float(probabilities.sum())
+        if probability_sum <= 0.0:
+            return self.minute_load_levels_w, probabilities
+        return self.minute_load_levels_w, probabilities / probability_sum
 
     def _generate_points(
-        self, load_1h_power: float, pv_power: float
+        self, mean_load_power_w: float, pv_power_w: float
     ) -> tuple[np.ndarray, np.ndarray]:
-        """Generate the grid points for interpolation."""
-        partial_loads = np.arange(0, pv_power + 50, 50)
-        points = np.array([np.full_like(partial_loads, load_1h_power), partial_loads]).T
+        """Generate in-bounds grid points for interpolation.
+
+        The bundled probability table was calibrated from a one-hour mean load
+        and one-minute samples. Sub-hourly optimization still passes *power* in
+        watts here; a native 15-minute mean is therefore a documented
+        approximation until a separately calibrated table is available.
+        """
+        bounded_mean_load_w = float(
+            np.clip(mean_load_power_w, self.load_power_min_w, self.load_power_max_w)
+        )
+        bounded_pv_power_w = float(np.clip(pv_power_w, 0.0, self.minute_load_max_w))
+        partial_loads = np.arange(0.0, bounded_pv_power_w + 1.0, 50.0)
+        points = np.column_stack((np.full(partial_loads.shape, bounded_mean_load_w), partial_loads))
         return points, partial_loads
 
     @cache_energy_management
-    def calculate_self_consumption(self, load_1h_power: float, pv_power: float) -> float:
-        """Calculate the PV self-consumption rate using RegularGridInterpolator.
+    def calculate_self_consumption(self, mean_load_power_w: float, pv_power_w: float) -> float:
+        """Return the legacy cumulative minute-load probability.
+
+        This method is retained for API compatibility. Its result is the
+        probability that the minute load is no greater than ``pv_power_w``;
+        it is not an energy self-consumption ratio. New energy-flow code must
+        use ``calculate_expected_direct_consumption``.
 
         The results are cached until the start of the next energy management run/ optimization.
 
         Args:
-         - last_1h_power: 1h power levels (W).
-         - pv_power: Current PV power output (W).
+         - mean_load_power_w: Mean load power for the current forecast interval (W).
+         - pv_power_w: Current PV power output (W).
 
         Returns:
          - Self-consumption rate as a float.
         """
-        points, partial_loads = self._generate_points(load_1h_power, pv_power)
+        points, _ = self._generate_points(mean_load_power_w, pv_power_w)
         probabilities = self.interpolator(points)
-        return probabilities.sum()
+        return float(np.clip(probabilities.sum(), 0.0, 1.0))
+
+    @cache_energy_management
+    def calculate_expected_direct_consumption(
+        self, mean_load_power_w: float, pv_power_w: float
+    ) -> float:
+        """Calculate expected direct PV-to-load power in watts.
+
+        For conditional minute-load probabilities ``p_i`` and load-bin powers
+        ``L_i``, the expected direct consumption is
+
+        ``sum(p_i * min(L_i, pv_power_w))``.
+
+        The tabulated load-bin powers are rescaled to preserve the supplied
+        forecast mean exactly. This compensates for discretization and the
+        finite upper table boundary while retaining the distribution shape.
+
+        Args:
+            mean_load_power_w: Mean load power of the forecast interval [W].
+            pv_power_w: Mean PV power of the forecast interval [W].
+
+        Returns:
+            Expected direct PV-to-load power [W].
+        """
+        mean_load_power_w = max(float(mean_load_power_w), 0.0)
+        pv_power_w = max(float(pv_power_w), 0.0)
+        if mean_load_power_w == 0.0 or pv_power_w == 0.0:
+            return 0.0
+
+        load_levels_w, probabilities = self._load_distribution(mean_load_power_w)
+        modeled_mean_load_w = float(np.dot(probabilities, load_levels_w))
+        if modeled_mean_load_w <= 0.0:
+            return 0.0
+
+        # Preserve the requested mean load while keeping the conditional shape
+        # from the probability table.
+        normalized_load_levels_w = load_levels_w * (mean_load_power_w / modeled_mean_load_w)
+        expected_direct_power_w = float(
+            np.dot(probabilities, np.minimum(normalized_load_levels_w, pv_power_w))
+        )
+        return float(np.clip(expected_direct_power_w, 0.0, min(mean_load_power_w, pv_power_w)))
 
     # def calculate_self_consumption(self, load_1h_power: float, pv_power: float) -> float:
     #     """Calculate the PV self-consumption rate using RegularGridInterpolator.