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ChristinandAndreas 3098605b0f feat(optimization): support a 15-minute optimization interval

The genetic optimizer was hard-wired to an hourly grid and forced
optimization.interval to 3600 s. Generalize it to a configurable slot grid
of length prediction.hours * (3600 / interval), accepting 900 (15 min) in
addition to the default 3600 (1 hour) so the optimizer can schedule on a
quarter-hour grid for 15-minute dynamic electricity tariffs.

- genetic.py: slot_duration_h / slots_per_hour / total_slots helpers; all GA
  vectors sized by total_slots; simulate()/evaluate() indexed by start slot.
- geneticparams.py: allow {900, 3600}; scale the load power series to per-slot
  energy, mirroring the PV series.
- battery.py / inverter.py: scale power caps to per-slot energy caps via
  slot_duration_h; homeappliance.py carries the hook.
- geneticsolution.py: serialize solution and plan on the slot grid (interval
  freq, start-slot offset, second-based instruction instants).

The default 3600 s interval keeps the previous hourly behaviour; the genetic
regression suite is unchanged. Adds tests for the 15-minute slot grid.

2026-07-12 09:08:39 +02:00

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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
genetic	`EOS_OPTIMIZATION__GENETIC`	`GeneticCommonSettings`	`rw`	`required`	Genetic optimization algorithm configuration.
horizon		`int`	`ro`	`N/A`	Number of optimization steps.
horizon_hours	`EOS_OPTIMIZATION__HORIZON_HOURS`	`int`	`rw`	`24`	The general time window within which the energy optimization goal shall be achieved [h]. Defaults to 24 hours.
interval	`EOS_OPTIMIZATION__INTERVAL`	`int`	`rw`	`3600`	The optimization interval (slot length) [sec]. The genetic optimizer supports 3600 (1 hour) and 900 (15 min); other values fall back to 3600. Defaults to 3600 seconds (1 hour).
keys		`list[str]`	`ro`	`N/A`	The keys of the solution.
:::

Example Input

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

Example Output

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

General Genetic Optimization Algorithm Configuration

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

Name	Type	Read-Only	Default	Description
generations	`Optional[int]`	`rw`	`400`	Number of generations to evolve [>= 10]. Defaults to 400.
individuals	`Optional[int]`	`rw`	`300`	Number of individuals (solutions) in the population [>= 10]. Defaults to 300.
penalties	`dict[str, Union[float, int, str]]`	`rw`	`required`	Penalty parameters used in fitness evaluation.
seed	`Optional[int]`	`rw`	`None`	Random seed for reproducibility. None = random.
:::

Example Input/Output

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

3.4 KiB Raw Blame History

General Optimization Configuration

General Genetic Optimization Algorithm Configuration

3.4 KiB

Raw Blame History