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The per-run fitness cache of the GENETIC optimizer is unbounded. It grows with individuals x generations (~110k keys at 300 x 400, about 1.1 kB each, 1.6 kB with an EV) and adds 120-170 MB to the peak memory of a run, while only 6-15 % of the lookups hit. On 512 MB boards this is the largest part of the optimizer's memory. Add optimization.genetic.fitness_cache_max_entries: - None (default): unbounded, the previous behaviour. - 0: no cache; every genome is evaluated. - N > 0: at most N keys. The cache is then an OrderedDict and evicts the oldest key first (FIFO, O(1)); LRU would cost bookkeeping on every hit for a cache that rarely hits. A cache hit returns exactly what the deterministic evaluation would return, so the plan for a fixed seed is identical with any setting. Co-authored-by: Christin <christin@BigBrother.bikini.bottom.zone> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com> Co-authored-by: Andreas <drbacke@gmx.de>
252 lines
9.5 KiB
Markdown
252 lines
9.5 KiB
Markdown
## General Optimization Configuration
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<!-- pyml disable line-length -->
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:::{table} optimization
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:widths: 10 20 10 5 5 30
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:align: left
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| Name | Environment Variable | Type | Read-Only | Default | Description |
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| ---- | -------------------- | ---- | --------- | ------- | ----------- |
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| algorithm | `EOS_OPTIMIZATION__ALGORITHM` | `<enum 'OptimizationAlgorithm'>` | `rw` | `required` | Optimization algorithm [GENETIC | GENETIC0]. Defaults to GENETIC. |
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| algorithms | | `list[str]` | `ro` | `N/A` | Available optimization algorithms. |
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| genetic | `EOS_OPTIMIZATION__GENETIC` | `GeneticCommonSettings` | `rw` | `required` | GENETIC optimization algorithm configuration. |
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| genetic0 | `EOS_OPTIMIZATION__GENETIC0` | `Genetic0CommonSettings` | `rw` | `required` | GENETIC0 optimization algorithm configuration. |
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| keys | | `list[str]` | `ro` | `N/A` | The keys of the solution. |
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:::
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**Example Input**
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```json
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{
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"optimization": {
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"algorithm": "GENETIC",
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"genetic": {
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"interval_sec": 3600,
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"horizon_hours": 24,
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"individuals": 400,
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"generations": 400,
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"seed": null,
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"fitness_cache_max_entries": null,
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"measurement_max_age_seconds": 300,
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"tail_horizon_hours": 48,
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"terminal_value_mode": "AUTO",
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"terminal_value_euro_per_kwh": 0.0,
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"terminal_value_window_hours": 24,
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"penalties": {
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"ev_soc_miss": 10
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}
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},
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"genetic0": {
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"horizon_hours": 24,
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"individuals": 400,
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"generations": 400,
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"seed": null,
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"penalties": {
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"ev_soc_miss": 10
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}
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}
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}
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}
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```
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**Example Output**
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```json
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{
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"optimization": {
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"algorithm": "GENETIC",
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"genetic": {
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"interval_sec": 3600,
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"horizon_hours": 24,
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"individuals": 400,
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"generations": 400,
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"seed": null,
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"fitness_cache_max_entries": null,
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"measurement_max_age_seconds": 300,
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"tail_horizon_hours": 48,
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"terminal_value_mode": "AUTO",
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"terminal_value_euro_per_kwh": 0.0,
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"terminal_value_window_hours": 24,
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"penalties": {
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"ev_soc_miss": 10
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},
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"horizon": 24
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},
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"genetic0": {
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"horizon_hours": 24,
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"individuals": 400,
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"generations": 400,
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"seed": null,
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"penalties": {
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"ev_soc_miss": 10
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},
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"interval_sec": 3600,
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"horizon": 24
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},
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"algorithms": [
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"GENETIC",
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"GENETIC0"
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],
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"keys": []
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}
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}
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```
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### GENETIC0 Optimization Algorithm Configuration
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:::{table} optimization::genetic0
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:widths: 10 10 5 5 30
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:align: left
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| Name | Type | Read-Only | Default | Description |
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| ---- | ---- | --------- | ------- | ----------- |
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| generations | `Optional[int]` | `rw` | `400` | Number of generations to evolve [>= 10]. Defaults to 400. |
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| horizon | `int` | `ro` | `N/A` | Number of optimization steps. |
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| horizon_hours | `int` | `rw` | `24` | The general time window within which the energy optimization goal shall be achieved [h]. Defaults to 24 hours. |
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| individuals | `Optional[int]` | `rw` | `300` | Number of individuals (solutions) in the population [>= 10]. Defaults to 300. |
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| interval_sec | `int` | `ro` | `N/A` | The optimization interval [sec]. Fixed to 1 hour (3600 seconds). |
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| penalties | `dict[str, Union[float, int, str]]` | `rw` | `required` | Penalty parameters used in fitness evaluation. |
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| seed | `Optional[int]` | `rw` | `None` | Random seed for reproducibility. None = random. |
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:::
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**Example Input**
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```json
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{
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"optimization": {
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"genetic0": {
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"horizon_hours": 24,
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"individuals": 300,
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"generations": 400,
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"seed": null,
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"penalties": {
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"ev_soc_miss": 10
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}
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}
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}
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}
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```
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**Example Output**
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<!-- pyml disable line-length -->
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```json
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{
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"optimization": {
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"genetic0": {
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"horizon_hours": 24,
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"individuals": 300,
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"generations": 400,
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"seed": null,
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"penalties": {
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"ev_soc_miss": 10
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},
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"interval_sec": 3600,
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"horizon": 24
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}
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}
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}
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```
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### GENETIC Optimization Algorithm Configuration
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:::{table} optimization::genetic
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:widths: 10 10 5 5 30
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:align: left
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| Name | Type | Read-Only | Default | Description |
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| ---- | ---- | --------- | ------- | ----------- |
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| 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. |
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| generations | `Optional[int]` | `rw` | `400` | Number of generations to evolve [>= 10]. Defaults to 400. |
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| horizon | `int` | `ro` | `N/A` | Number of optimization steps. |
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| horizon_hours | `int` | `rw` | `24` | The general time window within which the energy optimization goal shall be achieved [h]. Defaults to 24 hours. |
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| individuals | `Optional[int]` | `rw` | `300` | Number of individuals (solutions) in the population [>= 10]. Defaults to 300. |
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| interval_sec | `Literal[900, 3600]` | `rw` | `3600` | The optimization interval [sec]. Defaults to 3600 seconds (1 hour) |
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| measurement_max_age_seconds | `int` | `rw` | `300` | Maximum age of SoC measurements for configuration-based optimization [s]. |
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| penalties | `dict[str, Union[float, int, str]]` | `rw` | `required` | Penalty parameters used in fitness evaluation. |
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| seed | `Optional[int]` | `rw` | `None` | Random seed for reproducibility. None = random. |
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| tail_horizon_hours | `int` | `rw` | `48` | Forecast lookahead after the control horizon [h]. No tail commands are issued. Set 0 to disable. |
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| 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. |
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| 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. |
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| 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. |
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:::
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<!-- pyml enable line-length -->
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<!-- pyml disable no-emphasis-as-heading -->
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**Example Input**
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<!-- pyml enable no-emphasis-as-heading -->
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<!-- pyml disable line-length -->
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```json
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{
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"optimization": {
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"genetic": {
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"interval_sec": 3600,
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"horizon_hours": 24,
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"individuals": 300,
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"generations": 400,
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"seed": null,
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"fitness_cache_max_entries": null,
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"measurement_max_age_seconds": 300,
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"tail_horizon_hours": 48,
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"terminal_value_mode": "AUTO",
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"terminal_value_euro_per_kwh": 0.0,
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"terminal_value_window_hours": 24,
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"penalties": {
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"ev_soc_miss": 10
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}
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}
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}
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}
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```
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<!-- pyml enable line-length -->
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<!-- pyml disable no-emphasis-as-heading -->
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**Example Output**
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<!-- pyml enable no-emphasis-as-heading -->
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<!-- pyml disable line-length -->
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```json
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{
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"optimization": {
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"genetic": {
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"interval_sec": 3600,
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"horizon_hours": 24,
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"individuals": 300,
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"generations": 400,
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"seed": null,
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"fitness_cache_max_entries": null,
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"measurement_max_age_seconds": 300,
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"tail_horizon_hours": 48,
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"terminal_value_mode": "AUTO",
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"terminal_value_euro_per_kwh": 0.0,
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"terminal_value_window_hours": 24,
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"penalties": {
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"ev_soc_miss": 10
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},
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"horizon": 24
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}
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}
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}
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```
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