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Fix documentation for the loadforecast_power_w key. Fix documentation to explain the usage of import file/ JSON string to primarily initialise prediction data. Fix code scanning alert no. 6: URL redirection from remote source Enable to automatically save the configuration to the configuration file by default, which is a widespread user expectation. Make the genetic parameters non optional for better pydantic compliance. Update: - bump pytest to 9.0.3 - bump pillow to 12.2.0 - bump platformdirs to 4.9.6 - bump typespyyaml to 6.0.12.20260408 - bump tzfpy to 1.2.0 - bump pydantic to 2.13.0 - bump types-requests to 2.33.0.20260408 Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com> Co-authored-by: Normann <github@koldrack.com>
105 lines
3.3 KiB
Markdown
105 lines
3.3 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 [sec]. 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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