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Bobby NoelteandGitHub e23bb7b497
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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>
2026-07-29 12:56:08 +02:00

6.8 KiB

General Optimization Configuration

:::{table} optimization :widths: 10 20 10 5 5 30 :align: left

Name Environment Variable Type Read-Only Default Description
algorithm EOS_OPTIMIZATION__ALGORITHM str rw GENETIC The optimization algorithm. Defaults to GENETIC
algorithms list[str] ro N/A Available optimization algorithms.
genetic EOS_OPTIMIZATION__GENETIC GeneticCommonSettings rw required GENETIC optimization algorithm configuration.
genetic0 EOS_OPTIMIZATION__GENETIC0 Genetic0CommonSettings rw required GENETIC0 optimization algorithm configuration.
keys list[str] ro N/A The keys of the solution.
:::

Example Input

   {
       "optimization": {
           "algorithm": "GENETIC",
           "genetic": {
               "interval_sec": 3600,
               "horizon_hours": 24,
               "individuals": 400,
               "generations": 400,
               "seed": null,
               "penalties": {
                   "ev_soc_miss": 10
               }
           },
           "genetic0": {
               "horizon_hours": 24,
               "individuals": 400,
               "generations": 400,
               "seed": null,
               "penalties": {
                   "ev_soc_miss": 10
               }
           }
       }
   }

Example Output

   {
       "optimization": {
           "algorithm": "GENETIC",
           "genetic": {
               "interval_sec": 3600,
               "horizon_hours": 24,
               "individuals": 400,
               "generations": 400,
               "seed": null,
               "penalties": {
                   "ev_soc_miss": 10
               },
               "horizon": 24
           },
           "genetic0": {
               "horizon_hours": 24,
               "individuals": 400,
               "generations": 400,
               "seed": null,
               "penalties": {
                   "ev_soc_miss": 10
               },
               "interval_sec": 3600,
               "horizon": 24
           },
           "algorithms": [
               "GENETIC",
               "GENETIC0"
           ],
           "keys": []
       }
   }

GENETIC0 Optimization Algorithm Configuration

:::{table} optimization::genetic0 :widths: 10 10 5 5 30 :align: left

Name Type Read-Only Default Description
generations `int None` rw 400
horizon int ro N/A Number of optimization steps.
horizon_hours int rw 24 The general time window within which the energy optimization goal shall be achieved [h]. Defaults to 24 hours.
individuals `int None` rw 300
interval_sec int ro N/A The optimization interval [sec]. Fixed to 1 hour (3600 seconds).
penalties `dict[str, float int str]` rw
seed `int None` rw None
:::

Example Input

   {
       "optimization": {
           "genetic0": {
               "horizon_hours": 24,
               "individuals": 300,
               "generations": 400,
               "seed": null,
               "penalties": {
                   "ev_soc_miss": 10
               }
           }
       }
   }

Example Output

   {
       "optimization": {
           "genetic0": {
               "horizon_hours": 24,
               "individuals": 300,
               "generations": 400,
               "seed": null,
               "penalties": {
                   "ev_soc_miss": 10
               },
               "interval_sec": 3600,
               "horizon": 24
           }
       }
   }

GENETIC Optimization Algorithm Configuration

:::{table} optimization::genetic :widths: 10 10 5 5 30 :align: left

Name Type Read-Only Default Description
generations `int None` rw 400
horizon int ro N/A Number of optimization steps.
horizon_hours int rw 24 The general time window within which the energy optimization goal shall be achieved [h]. Defaults to 24 hours.
individuals `int None` rw 300
interval_sec int rw 3600 The optimization interval [sec]. Defaults to 3600 seconds (1 hour)
penalties `dict[str, float int str]` rw
seed `int None` rw None
:::

Example Input

   {
       "optimization": {
           "genetic": {
               "interval_sec": 3600,
               "horizon_hours": 24,
               "individuals": 300,
               "generations": 400,
               "seed": null,
               "penalties": {
                   "ev_soc_miss": 10
               }
           }
       }
   }

Example Output

   {
       "optimization": {
           "genetic": {
               "interval_sec": 3600,
               "horizon_hours": 24,
               "individuals": 300,
               "generations": 400,
               "seed": null,
               "penalties": {
                   "ev_soc_miss": 10
               },
               "horizon": 24
           }
       }
   }