EOS/docs/_generated/configoptimization.md

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	<enum 'OptimizationAlgorithm'>	rw	required	Optimization algorithm [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.
self_consumption_interpolator	EOS_OPTIMIZATION__SELF_CONSUMPTION_INTERPOLATOR	<enum 'GridInterpolatorBackend'>	rw	scipy	How the inverter's self-consumption probability table is evaluated [scipy
:::

Example Input

   {
       "optimization": {
           "algorithm": "GENETIC",
           "genetic": {
               "interval_sec": 3600,
               "horizon_hours": 24,
               "individuals": 400,
               "generations": 400,
               "seed": null,
               "fitness_cache_max_entries": null,
               "workers": null,
               "pin_workers": true,
               "measurement_max_age_seconds": 300,
               "tail_horizon_hours": 48,
               "terminal_value_mode": "AUTO",
               "terminal_value_euro_per_kwh": 0.0,
               "terminal_value_window_hours": 24,
               "penalties": {
                   "ev_soc_miss": 10
               }
           },
           "genetic0": {
               "horizon_hours": 24,
               "individuals": 400,
               "generations": 400,
               "seed": null,
               "penalties": {
                   "ev_soc_miss": 10
               }
           },
           "self_consumption_interpolator": "scipy"
       }
   }

Example Output

   {
       "optimization": {
           "algorithm": "GENETIC",
           "genetic": {
               "interval_sec": 3600,
               "horizon_hours": 24,
               "individuals": 400,
               "generations": 400,
               "seed": null,
               "fitness_cache_max_entries": null,
               "workers": null,
               "pin_workers": true,
               "measurement_max_age_seconds": 300,
               "tail_horizon_hours": 48,
               "terminal_value_mode": "AUTO",
               "terminal_value_euro_per_kwh": 0.0,
               "terminal_value_window_hours": 24,
               "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
           },
           "self_consumption_interpolator": "scipy",
           "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	Optional[int]	rw	400	Number of generations to evolve [>= 10]. Defaults to 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	Optional[int]	rw	300	Number of individuals (solutions) in the population [>= 10]. Defaults to 300.
interval_sec	int	ro	N/A	The optimization interval [sec]. Fixed to 1 hour (3600 seconds).
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

   {
       "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
fitness_cache_max_entries	Optional[int]	rw	None	Maximum number of keys in the per-run fitness cache. None = unbounded (default), 0 = cache disabled, N > 0 = keep at most N keys and evict the oldest first. Every cached evaluation takes one or two keys. The cache only saves repeated evaluations; the result for a fixed seed is the same with any value. Limit it to cap memory on small devices.
generations	Optional[int]	rw	400	Number of generations to evolve [>= 10]. Defaults to 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	Optional[int]	rw	300	Number of individuals (solutions) in the population [>= 10]. Defaults to 300.
interval_sec	Literal[900, 3600]	rw	3600	The optimization interval [sec]. Defaults to 3600 seconds (1 hour)
measurement_max_age_seconds	int	rw	300	Maximum age of SoC measurements for configuration-based optimization [s].
penalties	dict[str, Union[float, int, str]]	rw	required	Penalty parameters used in fitness evaluation.
pin_workers	bool	rw	True	Pin the evaluation workers to the last 'workers' CPU cores. The server process stays unpinned and keeps a core that is not busy with the optimization, so it answers requests during a run, and the run never uses more than 'workers' cores. Needs at least one core more than workers; Linux only.
seed	Optional[int]	rw	None	Random seed for reproducibility. None = random.
tail_horizon_hours	int	rw	48	Forecast lookahead after the control horizon [h]. No tail commands are issued. Set 0 to disable.
terminal_value_euro_per_kwh	float	rw	0.0	Value assigned to usable battery energy remaining at the end of the optimization horizon [EUR/kWh]. This terminal value is independent of the battery LCOS. Only used with terminal_value_mode = FIXED. Defaults to 0 EUR/kWh.
terminal_value_mode	<enum 'TerminalValueMode'>	rw	AUTO	How to value the energy left in the battery at the end of the control horizon. AUTO solves the forecast tail with an AUTO continuation proxy at its end (or only the proxy if tail is zero); FIXED uses 'terminal_value_euro_per_kwh'. Defaults to AUTO.
terminal_value_window_hours	int	rw	24	Length of the trailing window at the effective tail end the AUTO continuation curve is derived from [h]. One day covers a full load and PV cycle. Defaults to 24 hours.
workers	Optional[int]	rw	None	Processes that evaluate candidate solutions in parallel. None = automatic: one CPU core always stays free and at most two are used (1-2 cores -> 1, 3 or more cores -> 2), capped by a container CPU limit. 1 = evaluate in the server process only. The result for a fixed seed is the same with any value.
:::

Example Input

   {
       "optimization": {
           "genetic": {
               "interval_sec": 3600,
               "horizon_hours": 24,
               "individuals": 300,
               "generations": 400,
               "seed": null,
               "fitness_cache_max_entries": null,
               "workers": null,
               "pin_workers": true,
               "measurement_max_age_seconds": 300,
               "tail_horizon_hours": 48,
               "terminal_value_mode": "AUTO",
               "terminal_value_euro_per_kwh": 0.0,
               "terminal_value_window_hours": 24,
               "penalties": {
                   "ev_soc_miss": 10
               }
           }
       }
   }

Example Output

   {
       "optimization": {
           "genetic": {
               "interval_sec": 3600,
               "horizon_hours": 24,
               "individuals": 300,
               "generations": 400,
               "seed": null,
               "fitness_cache_max_entries": null,
               "workers": null,
               "pin_workers": true,
               "measurement_max_age_seconds": 300,
               "tail_horizon_hours": 48,
               "terminal_value_mode": "AUTO",
               "terminal_value_euro_per_kwh": 0.0,
               "terminal_value_window_hours": 24,
               "penalties": {
                   "ev_soc_miss": 10
               },
               "horizon": 24
           }
       }
   }