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/server/eos.py

@@ -48,9 +48,10 @@ from akkudoktoreos.core.pydantic import (
 )
 from akkudoktoreos.core.version import __version__
 from akkudoktoreos.devices.devices import ResourceKey
-from akkudoktoreos.optimization.genetic.geneticparams import (
-    GeneticOptimizationParameters,
+from akkudoktoreos.optimization.genetic0.genetic0params import (
+    Genetic0OptimizationParameters,
 )
+from akkudoktoreos.optimization.genetic0.genetic0solution import Genetic0Solution
 from akkudoktoreos.optimization.genetic.geneticsolution import GeneticSolution
 from akkudoktoreos.optimization.optimization import OptimizationSolution
 from akkudoktoreos.prediction.elecprice import ElecPriceCommonSettings
@@ -1161,6 +1162,38 @@ def fastapi_energy_management_optimization_solution_get() -> OptimizationSolutio
     return solution
 
 
+@app.get("/v1/energy-management/optimization/solution/{algorithm}", tags=["energy-management"])
+async def fastapi_energy_management_optimization_solution_algorithm_get(
+    algorithm: str,
+) -> Union[GeneticSolution, Genetic0Solution]:
+    """Get the latest algorithm specific solution of the optimization.
+
+    Args:
+        algorithm: Optimization algorithm
+    """
+    algorithm = algorithm.upper()
+    if algorithm not in get_config().optimization.algorithms:
+        raise HTTPException(
+            status_code=404, detail=f"Optimization algorithm '{algorithm}' unknown."
+        )
+    if algorithm == "GENETIC":
+        genetic_solution = get_ems().genetic_solution()
+        if genetic_solution is None:
+            raise HTTPException(
+                status_code=404, detail=f"'{algorithm}' optimization solution not available."
+            )
+        return genetic_solution
+    if algorithm == "GENETIC0":
+        genetic0_solution = get_ems().genetic0_solution()
+        if genetic0_solution is None:
+            raise HTTPException(
+                status_code=404, detail=f"'{algorithm}' optimization solution not available."
+            )
+        return genetic0_solution
+    # Should never happen
+    raise HTTPException(status_code=500, detail=f"'{algorithm}' validated but not handled.")
+
+
 @app.get("/v1/energy-management/plan", tags=["energy-management"])
 def fastapi_energy_management_plan_get() -> EnergyManagementPlan:
     """Get the latest energy management plan."""
@@ -1458,20 +1491,23 @@ async def fastapi_pvforecast() -> ForecastResponse:
 
 @app.post("/optimize", tags=["optimize"])
 async def fastapi_optimize(
-    parameters: GeneticOptimizationParameters,
+    parameters: Genetic0OptimizationParameters,
     start_hour: Annotated[
         Optional[int], Query(description="Defaults to current hour of the day.")
     ] = None,
     ngen: Annotated[
         Optional[int], Query(description="Number of indivuals to generate for genetic algorithm.")
     ] = None,
-) -> GeneticSolution:
+) -> Genetic0Solution:
     """Deprecated: Optimize.
 
     Endpoint to handle optimization.
 
+    Uses the `classic` GENETIC0 optimisation algorithm (__NO__ 15-minutes slots).
+
     Note:
         Use automatic optimization instead.
+        "v1/energy-management/optimization/solution/GENETIC0"
     """
     if start_hour is None:
         start_datetime = None
@@ -1483,13 +1519,14 @@ async def fastapi_optimize(
         await get_ems().run(
             start_datetime=start_datetime,
             mode=EnergyManagementMode.OPTIMIZATION,
-            genetic_parameters=parameters,
-            genetic_individuals=ngen,
+            algorithm="GENETIC0",
+            genetic0_parameters=parameters,
+            genetic0_generations=ngen,
         )
     except Exception as e:
         raise HTTPException(status_code=400, detail=f"Optimize error: {e}.")
 
-    solution = get_ems().genetic_solution()
+    solution = get_ems().genetic0_solution()
     if solution is None:
         raise HTTPException(status_code=400, detail="Optimize error: no solution stored by run.")