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https://github.com/Akkudoktor-EOS/EOS.git
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Andreas
1c10ab83ad
The energy still stored when the horizon ends keeps its worth: it replaces grid imports that are paid for afterwards. Crediting that with a single price per kWh cannot describe it, because the value is not linear in the amount stored. The first kWh replaces the most expensive hour that PV cannot cover, the next one the second most expensive, and once every such hour is served, further energy replaces nothing. A scalar has to pick one slope for all of it. High enough for the first kWh means hoarding a full battery; low enough for the last kWh means running it empty by the end of the horizon - which is exactly what the previous default of 0 EUR/kWh did. terminal_value_mode = AUTO (the new default) builds the curve instead. There is no forecast beyond the horizon, so its trailing window stands in for the day that follows: residual load max(load - PV, 0) per slot, priced at its import price, sorted and accumulated. LCOS is subtracted from every marginal value so stored energy is not credited twice, and the tail beyond the residual load is only credited when direct marketing allows an export. The curve is built once per run; the search only interpolates on it. The solution reports what a run used as terminal_value, curve included, so the shape can be inspected instead of guessed. FIXED restores the previous scalar behaviour. In a 48 h scenario with two cheap slots at the end, AUTO keeps the battery at 50 % and credits 3.85 EUR where FIXED with 0 EUR/kWh drains it to empty. The stored optimization results move accordingly - the objective changed.
117 lines
4.9 KiB
Markdown
117 lines
4.9 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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| terminal_value_euro_per_kwh | `EOS_OPTIMIZATION__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 | `EOS_OPTIMIZATION__TERMINAL_VALUE_MODE` | `<enum 'TerminalValueMode'>` | `rw` | `AUTO` | How to value the energy left in the battery at the end of the optimization horizon. AUTO derives a concave value curve from the trailing horizon window and needs no configuration; FIXED uses 'terminal_value_euro_per_kwh'. Defaults to AUTO. |
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| terminal_value_window_hours | `EOS_OPTIMIZATION__TERMINAL_VALUE_WINDOW_HOURS` | `int` | `rw` | `24` | Length of the trailing horizon window the AUTO terminal value curve is derived from [h]. One day covers a full load and PV cycle. Defaults to 24 hours. |
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| visualize_pdf | `EOS_OPTIMIZATION__VISUALIZE_PDF` | `bool` | `rw` | `True` | Generate the PDF visualization after each optimization run. Disable for headless setups (e.g. Node-RED integration) to save several seconds per run. Defaults to True. |
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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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"visualize_pdf": true,
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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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"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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"visualize_pdf": true,
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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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"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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