chore: prepare for update of genetic algorithm (#1190)

Andreas will update the genetic algorithm for 15-minutes optimization
intervals.

Copy the current GENETIC optimization algorithm to GENETIC0 to enable
to keep the algorithm with the current functionality. Also copy resources
like the load interpolator to the GENETIC0 algorithm to keep them despite
possible later changes to the interpolator.

Make the deprecated legacy /optimize endpoint use the GENETIC0 optimization
algorithm to in-fact behave the same way even if there will later be changes
to the GENETIC algorithm by Andreas. Add a new REST endpoint to provide
the unprocessed optimisation results of the GENETIC and GENETIC0 algorithm
in case one wants to use them as done with the deprecated /optimize endpoint.

Adapt the optimization configuration to have distinct configurations for the
GENETIC and the GENETIC0 algorithm.

Create a copy of the current tests for the GENETIC algorithm to be used
for the GENETIC0 algorithm. This avoids the tests for the GENETIC0
algorithm to be influenced by later changes by Andreas.

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

src/akkudoktoreos/core/ems.py

@@ -17,6 +17,11 @@ from akkudoktoreos.core.coreabc import (
 from akkudoktoreos.core.emplan import EnergyManagementPlan
 from akkudoktoreos.core.emsettings import EnergyManagementMode
 from akkudoktoreos.core.pydantic import PydanticBaseModel
+from akkudoktoreos.optimization.genetic0.genetic0 import Genetic0Optimization
+from akkudoktoreos.optimization.genetic0.genetic0params import (
+    Genetic0OptimizationParameters,
+)
+from akkudoktoreos.optimization.genetic0.genetic0solution import Genetic0Solution
 from akkudoktoreos.optimization.genetic.genetic import GeneticOptimization
 from akkudoktoreos.optimization.genetic.geneticparams import (
     GeneticOptimizationParameters,
@@ -72,6 +77,10 @@ class EnergyManagement(
     # For classic API
     _genetic_solution: ClassVar[Optional[GeneticSolution]] = None
 
+    # Solution of the genetic0 algorithm of latest energy management run with optimization
+    # For classic API
+    _genetic0_solution: ClassVar[Optional[Genetic0Solution]] = None
+
     # energy management lock (for energy management run)
     _run_lock: ClassVar[Lock] = Lock()
 
@@ -146,14 +155,26 @@ class EnergyManagement(
         """
         return cls._genetic_solution
 
+    @classmethod
+    def genetic0_solution(cls) -> Optional[Genetic0Solution]:
+        """Get the latest solution of the genetic0 algorithm.
+
+        Returns:
+            Optional[Genetic0Solution]: The latest solution of the genetic algorithm.
+        """
+        return cls._genetic0_solution
+
     async def run(
         self,
         start_datetime: Optional[DateTime] = None,
         mode: Optional[EnergyManagementMode] = None,
         algorithm: Optional[str] = None,
         genetic_parameters: Optional[GeneticOptimizationParameters] = None,
-        genetic_individuals: Optional[int] = None,
+        genetic_generations: Optional[int] = None,
         genetic_seed: Optional[int] = None,
+        genetic0_parameters: Optional[Genetic0OptimizationParameters] = None,
+        genetic0_generations: Optional[int] = None,
+        genetic0_seed: Optional[int] = None,
         force_enable: Optional[bool] = False,
         force_update: Optional[bool] = False,
     ) -> None:
@@ -174,16 +195,25 @@ class EnergyManagement(
             algorithm (str, optional):
                 The algorithm to use. Must be one of:
                 - "GENETIC": Optimization uses the `GENETIC` optimization algorithm.
+                - "GENETIC0": Optimization uses the `GENETIC0` optimization algorithm.
 
                 Defaults to the algorithm defined in the current configuration.
             genetic_parameters (GeneticOptimizationParameters, optional): The
                 parameter set for the `GENETIC` algorithm. If not provided, it will
                 be constructed based on the current configuration and predictions.
-            genetic_individuals (int, optional): The number of individuals for the
+            genetic_generations (int, optional): The number of generations for the
                 `GENETIC` algorithm. Defaults to the algorithm's internal default (400)
                 if not specified.
             genetic_seed (int, optional): The seed for the `GENETIC` algorithm. Defaults
                 to the algorithm's internal random seed if not specified.
+            genetic0_parameters (Genetic0OptimizationParameters, optional): The
+                parameter set for the `GENETIC0` algorithm. If not provided, it will
+                be constructed based on the current configuration and predictions.
+            genetic0_generations (int, optional): The number of generations for the
+                `GENETIC0` algorithm. Defaults to the algorithm's internal default (400)
+                if not specified.
+            genetic0_seed (int, optional): The seed for the `GENETIC0` algorithm. Defaults
+                to the algorithm's internal random seed if not specified.
             force_enable (bool, optional): If True, bypasses any disabled state
                 to force the update process. This is mostly applicable to
                 prediction providers.
@@ -245,53 +275,138 @@ class EnergyManagement(
             if algorithm is None:
                 algorithm = self.config.optimization.algorithm
 
+            # --- GENETIC algorithm ---
             if algorithm == "GENETIC":
                 # Prepare optimization parameters
                 # This also creates default configurations for missing values and updates the predictions
-                logger.info("Starting optimzation parameter preparation.")
+                logger.info(f"{algorithm}: Starting optimzation parameter preparation.")
                 if genetic_parameters is None:
                     genetic_parameters = await GeneticOptimizationParameters.prepare()
                     if genetic_parameters is None:
                         logger.error(
-                            "Energy management run canceled. Could not prepare optimisation parameters."
+                            f"{algorithm}: Energy management run canceled. "
+                            "Could not prepare optimisation parameters."
                         )
                         EnergyManagement._stage = EnergyManagementStage.IDLE
                         return
 
                 # Take values from config if not given
-                if genetic_individuals is None:
-                    genetic_individuals = self.config.optimization.genetic.individuals
+                if genetic_generations is None:
+                    genetic_generations = self.config.optimization.genetic.generations
                 if genetic_seed is None:
                     genetic_seed = self.config.optimization.genetic.seed
 
                 if EnergyManagement._start_datetime is None:  # Make mypy happy - already set by us
-                    raise RuntimeError("Start datetime not set.")
+                    raise RuntimeError(f"{algorithm}: Start datetime not set.")
 
                 # --- Optimization (CPU-bound → MUST offload) ---
                 try:
-                    optimization = GeneticOptimization(
+                    genetic_optimization = GeneticOptimization(
                         verbose=bool(self.config.server.verbose),
                         fixed_seed=genetic_seed,
                     )
 
                     loop = get_running_loop()
                     start_hour = EnergyManagement._start_datetime.hour
-                    solution = await loop.run_in_executor(
+                    genetic_solution = await loop.run_in_executor(
                         None,
-                        lambda: optimization.optimize_ems(
+                        lambda: genetic_optimization.optimize_ems(
                             start_hour=start_hour,
                             parameters=cast(
                                 GeneticOptimizationParameters, genetic_parameters
                             ),  # cast for mypy
-                            ngen=genetic_individuals,
+                            ngen=genetic_generations,
                         ),
                     )
 
                 except Exception:
-                    logger.exception("Energy management optimization failed.")
+                    logger.exception(f"{algorithm}: Energy management optimization failed.")
                     EnergyManagement._stage = EnergyManagementStage.IDLE
                     return
 
+                # Make genetic solution public
+                EnergyManagement._genetic_solution = genetic_solution
+
+                # Make optimization solution public
+                EnergyManagement._optimization_solution = (
+                    await genetic_solution.optimization_solution()
+                )
+
+                # Make plan public
+                EnergyManagement._plan = genetic_solution.energy_management_plan()
+
+                logger.debug(
+                    "{}: Energy management genetic solution:\n{}",
+                    algorithm,
+                    EnergyManagement._genetic_solution,
+                )
+
+            # --- GENETIC0 algorithm ---
+            elif algorithm == "GENETIC0":
+                # Prepare optimization parameters
+                # This also creates default configurations for missing values and updates the predictions
+                logger.info(f"{algorithm}: Starting optimzation parameter preparation.")
+                if genetic0_parameters is None:
+                    genetic0_parameters = await Genetic0OptimizationParameters.prepare()
+                    if genetic0_parameters is None:
+                        logger.error(
+                            f"{algorithm}: Energy management run canceled. "
+                            "Could not prepare optimisation parameters."
+                        )
+                        EnergyManagement._stage = EnergyManagementStage.IDLE
+                        return
+
+                # Take values from config if not given
+                if genetic0_generations is None:
+                    genetic0_generations = self.config.optimization.genetic0.generations
+                if genetic0_seed is None:
+                    genetic0_seed = self.config.optimization.genetic0.seed
+
+                if EnergyManagement._start_datetime is None:  # Make mypy happy - already set by us
+                    raise RuntimeError(f"{algorithm}: Start datetime not set.")
+
+                # --- Optimization (CPU-bound → MUST offload) ---
+                try:
+                    genetic0_optimization = Genetic0Optimization(
+                        verbose=bool(self.config.server.verbose),
+                        fixed_seed=genetic0_seed,
+                    )
+
+                    loop = get_running_loop()
+                    start_hour = EnergyManagement._start_datetime.hour
+                    genetic0_solution = await loop.run_in_executor(
+                        None,
+                        lambda: genetic0_optimization.optimize_ems(
+                            start_hour=start_hour,
+                            parameters=cast(
+                                Genetic0OptimizationParameters, genetic0_parameters
+                            ),  # cast for mypy
+                            ngen=genetic0_generations,
+                        ),
+                    )
+
+                except Exception:
+                    logger.exception(f"{algorithm}: Energy management optimization failed.")
+                    EnergyManagement._stage = EnergyManagementStage.IDLE
+                    return
+
+                # Make genetic0 solution public
+                EnergyManagement._genetic0_solution = genetic0_solution
+
+                # Make optimization solution public
+                EnergyManagement._optimization_solution = (
+                    await genetic0_solution.optimization_solution()
+                )
+
+                # Make plan public
+                EnergyManagement._plan = genetic0_solution.energy_management_plan()
+
+                logger.debug(
+                    "{}: Energy management genetic solution:\n{}",
+                    algorithm,
+                    EnergyManagement._genetic0_solution,
+                )
+
             else:
                 logger.error(f"Unknown optimization algorithm: '{algorithm}'. Skipping.")
                 EnergyManagement._stage = EnergyManagementStage.IDLE
@@ -299,82 +414,19 @@ class EnergyManagement(
 
             optimization_duration = to_datetime() - optimization_start
             logger.info(
-                "Energy management optimization ({}) completed in {:.1f} seconds.",
+                "{}: Energy management optimization completed in {:.1f} seconds.",
                 algorithm,
                 optimization_duration.total_seconds(),
             )
 
             logger.debug(
-                "Energy management optimization solution:\n{}",
+                "{}: Energy management optimization solution:\n{}",
+                algorithm,
                 EnergyManagement._optimization_solution,
             )
-            logger.debug("Energy management plan:\n{}", EnergyManagement._plan)
+            logger.debug("{}: Energy management plan:\n{}", algorithm, EnergyManagement._plan)
 
-            # --- Control dispatch by adapters ---
-            EnergyManagement._stage = EnergyManagementStage.CONTROL_DISPATCH
-
-            # Make genetic solution public
-            EnergyManagement._genetic_solution = solution
-
-            # Make optimization solution public
-            EnergyManagement._optimization_solution = await solution.optimization_solution()
-
-            # Make plan public
-            EnergyManagement._plan = solution.energy_management_plan()
-
-            logger.debug(
-                "Energy management genetic solution:\n{}", EnergyManagement._genetic_solution
-            )
-
-            if genetic_parameters is None:
-                genetic_parameters = await GeneticOptimizationParameters.prepare()
-
-            if not genetic_parameters:
-                logger.error("Energy management run canceled. Could not prepare parameters.")
-                EnergyManagement._stage = EnergyManagementStage.IDLE
-                return
-
-            EnergyManagement._stage = EnergyManagementStage.OPTIMIZATION
-
-            if genetic_individuals is None:
-                genetic_individuals = self.config.optimization.genetic.individuals
-            if genetic_seed is None:
-                genetic_seed = self.config.optimization.genetic.seed
-
-            if EnergyManagement._start_datetime is None:
-                raise RuntimeError("Start datetime not set.")
-
-            # --- Optimization (CPU-bound → MUST offload) ---
-            try:
-                optimization = GeneticOptimization(
-                    verbose=bool(self.config.server.verbose),
-                    fixed_seed=genetic_seed,
-                )
-
-                loop = get_running_loop()
-                start_hour = EnergyManagement._start_datetime.hour
-                solution = await loop.run_in_executor(
-                    None,
-                    lambda: optimization.optimize_ems(
-                        start_hour=start_hour,
-                        parameters=genetic_parameters,
-                        ngen=genetic_individuals,
-                    ),
-                )
-
-            except Exception:
-                logger.exception("Energy management optimization failed.")
-                EnergyManagement._stage = EnergyManagementStage.IDLE
-                return
-
-            EnergyManagement._genetic_solution = solution
-            EnergyManagement._optimization_solution = await solution.optimization_solution()
-            EnergyManagement._plan = solution.energy_management_plan()
-
-            logger.debug("Genetic solution:\n{}", EnergyManagement._genetic_solution)
-            logger.debug("Optimization solution:\n{}", EnergyManagement._optimization_solution)
-            logger.debug("Plan:\n{}", EnergyManagement._plan)
-            logger.info("Energy management run done (optimization updated)")
+            logger.info("{}: Energy management run done (optimization updated)", algorithm)
 
             # --- Dispatch control by adapters ---
             EnergyManagement._stage = EnergyManagementStage.CONTROL_DISPATCH