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feat: add EnergyCharts feed-in tariff provider (#1165)
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The `FeedInTariffEnergyCharts` provider uses the raw Energy-Charts day-ahead market price as the
feed-in tariff. It stores prices in `feed_in_tariff_wh` without adding electricity import charges
or VAT. The data is loaded from the Energy-Charts `/price` endpoint for the configured bidding
zone. The native Energy-Charts resolution, including quarter-hour data, is retained.

Energy-Charts usually supplies prices only for the published day-ahead period. If that data does
not cover the complete configured prediction horizon, the provider extends it as follows:

- With more than 800 hours of history, an ETS (Holt-Winters exponential smoothing) forecast with
  weekly seasonality is used.
- With more than 168 hours of history, an ETS forecast with daily seasonality is used.
- With less history, the median of the available values is used as a constant fallback.

The seasonal periods are adjusted to the source resolution. For example, quarter-hour data uses
four values per hour. Values already supplied by Energy-Charts are kept unchanged; only missing
future slots after the last published price are forecast. Consequently, a 15-minute optimization
uses four forecast values per hour without converting them to hourly averages.

Signed-off-by: Andreas Schmitz <akkudoktor.net>
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
This commit is contained in:
Bobby Noelte
2026-07-23 15:48:29 +02:00
committed by GitHub
parent 6093d8d348
commit ed61918fe0
15 changed files with 697 additions and 24 deletions
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# Akkudoktor-EOS
**Version**: `v0.3.0.dev2607231185985809`
**Version**: `v0.3.0.dev2607231331447302`
<!-- pyml disable line-length -->
**Description**: This project provides a comprehensive solution for simulating and optimizing an energy system based on renewable energy sources. With a focus on photovoltaic (PV) systems, battery storage (batteries), load management (consumer requirements), heat pumps, electric vehicles, and consideration of electricity price data, this system enables forecasting and optimization of energy flow and costs over a specified period.