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ChristinandAndreas
3098605b0f
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.
105 lines
3.4 KiB
Markdown
105 lines
3.4 KiB
Markdown
## General Optimization Configuration
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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` | `str` | `rw` | `GENETIC` | The optimization algorithm. Defaults to GENETIC |
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| genetic | `EOS_OPTIMIZATION__GENETIC` | `GeneticCommonSettings` | `rw` | `required` | Genetic optimization algorithm configuration. |
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| horizon | | `int` | `ro` | `N/A` | Number of optimization steps. |
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| 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. |
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| 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). |
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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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"horizon_hours": 24,
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"interval": 3600,
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"algorithm": "GENETIC",
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"genetic": {
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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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"horizon_hours": 24,
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"interval": 3600,
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"algorithm": "GENETIC",
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"genetic": {
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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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"keys": [],
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"horizon": 24
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}
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}
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```
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### General 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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| generations | `Optional[int]` | `rw` | `400` | Number of generations to evolve [>= 10]. Defaults to 400. |
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| individuals | `Optional[int]` | `rw` | `300` | Number of individuals (solutions) in the population [>= 10]. Defaults to 300. |
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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/Output**
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```json
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{
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"optimization": {
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"genetic": {
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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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