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Add Markdown linter (pymarkdown) to pre-commit.
Adapt current markdown files to fulfill linter rules.

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
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Bobby Noelte 2025-02-12 14:24:17 +01:00
parent c87bf2e4fc
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9
.pre-commit-config.yaml
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@ -33,3 +33,12 @@ repos:
- "pandas-stubs==2.2.3.241009"
- "numpy==2.1.3"
pass_filenames: false
- repo: https://github.com/jackdewinter/pymarkdown
rev: main
hooks:
- id: pymarkdown
files: ^docs/
exclude: ^docs/_generated
args:
- --config=docs/pymarkdown.json
- scan

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docs/akkudoktoreos/architecture.md
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@ -20,17 +20,22 @@ EOS Architecture
### Configuration
The configuration controls all aspects of EOS: optimization, prediction, measurement, and energy management.
The configuration controls all aspects of EOS: optimization, prediction, measurement, and energy
management.
### Energy Management
Energy management is the overall process to provide planning data for scheduling the different devices in your system in an optimal way. Energy management cares for the update of predictions and the optimization of the planning based on the simulated behavior of the devices. The planning is on the hour. Sub-hour energy management is left
Energy management is the overall process to provide planning data for scheduling the different
devices in your system in an optimal way. Energy management cares for the update of predictions and
the optimization of the planning based on the simulated behavior of the devices. The planning is on
the hour. Sub-hour energy management is left
### Optimization
### Device Simulations
Device simulations simulate devices' behavior based on internal logic and predicted data. They provide the data needed for optimization.
Device simulations simulate devices' behavior based on internal logic and predicted data. They
provide the data needed for optimization.
### Predictions
@ -38,7 +43,8 @@ Predictions provide predicted future data to be used by the optimization.
### Measurements
Measurements are utilized to refine predictions using real data from your system, thereby enhancing accuracy.
Measurements are utilized to refine predictions using real data from your system, thereby enhancing
accuracy.
### EOS Server

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docs/akkudoktoreos/configuration.md
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@ -31,10 +31,10 @@ Use endpoint `POST /v1/config/update` to update the configuration from the `EOS
The configuration sources and their priorities are as follows:
1. **Settings**: Provided during runtime by the REST interface
2. **Environment Variables**: Defined at startup of the REST server and during runtime
3. **EOS Configuration File**: Read at startup of the REST server and on request
4. **Default Values**
1. `Settings`: Provided during runtime by the REST interface
2. `Environment Variables`: Defined at startup of the REST server and during runtime
3. `EOS Configuration File`: Read at startup of the REST server and on request
4. `Default Values`
### Settings

11
docs/akkudoktoreos/integration.md
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@ -17,18 +17,17 @@ APIs, and online services in creative and practical ways.
Andreas Schmitz uses [Node-RED](https://nodered.org/) as part of his home automation setup.
### Resources
### Node-Red Resources
- [Installation Guide (German)](https://meintechblog.de/2024/09/05/andreas-schmitz-joerg-installiert-mein-energieoptimierungssystem/) — A detailed guide on integrating an early version of EOS with
`Node-RED`.
- [Installation Guide (German)](https://meintechblog.de/2024/09/05/andreas-schmitz-joerg-installiert-mein-energieoptimierungssystem/)
\— A detailed guide on integrating an early version of EOS with `Node-RED`.
## Home Assistant
[Home Assistant](https://www.home-assistant.io/) is an open-source home automation platform that
emphasizes local control and user privacy.
### Resources
### Home Assistant Resources
- Duetting's [EOS Home Assistant Addon](https://github.com/Duetting/ha_eos_addon) — Additional
details can be found in this
[discussion thread](https://github.com/Akkudoktor-EOS/EOS/discussions/294).
details can be found in this [discussion thread](https://github.com/Akkudoktor-EOS/EOS/discussions/294).

6
docs/akkudoktoreos/measurement.md
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@ -5,9 +5,9 @@
Measurements are utilized to refine predictions using real data from your system, thereby enhancing
accuracy.
- **Household Load Measurement**
- **Grid Export Measurement**
- **Grid Import Measurement**
- Household Load Measurement
- Grid Export Measurement
- Grid Import Measurement
## Storing Measurements

36
docs/akkudoktoreos/optimization.md
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@ -4,11 +4,13 @@
## Introduction
The `POST /optimize` API endpoint optimizes your energy management system based on various inputs including electricity prices, battery storage capacity, PV forecast, and temperature data.
The `POST /optimize` API endpoint optimizes your energy management system based on various inputs
including electricity prices, battery storage capacity, PV forecast, and temperature data.
## Input Payload
### Sample Request
```json
{
"ems": {
@ -49,31 +51,39 @@ The `POST /optimize` API endpoint optimizes your energy management system based
### Energy Management System (EMS)
#### Battery Cost (`preis_euro_pro_wh_akku`)
- Unit: €/Wh
- Purpose: Represents the residual value of energy stored in the battery
- Impact: Lower values encourage battery depletion, higher values preserve charge at the end of the simulation.
#### Feed-in Tariff (`einspeiseverguetung_euro_pro_wh`)
- Unit: €/Wh
- Purpose: Compensation received for feeding excess energy back to the grid
#### Total Load Forecast (`gesamtlast`)
- Unit: W
- Time Range: 48 hours (00:00 today to 23:00 tomorrow)
- Format: Array of hourly values
- Note: Exclude optimizable loads (EV charging, battery charging, etc.)
##### Data Sources:
1. Standard Load Profile: `GET /v1/prediction/list?key=load_mean` for a standard load profile based on your yearly consumption.
2. Adjusted Load Profile: `GET /v1/prediction/list?key=load_mean_adjusted` for a combination of a standard load profile based on your yearly consumption incl. data from last 48h.
##### Data Sources
1. Standard Load Profile: `GET /v1/prediction/list?key=load_mean` for a standard load profile based
on your yearly consumption.
2. Adjusted Load Profile: `GET /v1/prediction/list?key=load_mean_adjusted` for a combination of a
standard load profile based on your yearly consumption incl. data from last 48h.
#### PV Generation Forecast (`pv_prognose_wh`)
- Unit: W
- Time Range: 48 hours (00:00 today to 23:00 tomorrow)
- Format: Array of hourly values
- Data Source: `GET /v1/prediction/series?key=pvforecast_ac_power`
#### Electricity Price Forecast (`strompreis_euro_pro_wh`)
- Unit: €/Wh
- Time Range: 48 hours (00:00 today to 23:00 tomorrow)
- Format: Array of hourly values
@ -84,20 +94,24 @@ Verify prices against your local tariffs.
### Battery Storage System
#### Configuration
- `capacity_wh`: Total battery capacity in Wh
- `charging_efficiency`: Charging efficiency (0-1)
- `discharging_efficiency`: Discharging efficiency (0-1)
- `max_charge_power_w`: Maximum charging power in W
#### State of Charge (SoC)
- `initial_soc_percentage`: Current battery level (%)
- `min_soc_percentage`: Minimum allowed SoC (%)
- `max_soc_percentage`: Maximum allowed SoC (%)
### Inverter
- `max_power_wh`: Maximum inverter power in Wh
### Electric Vehicle (EV)
- `capacity_wh`: Battery capacity in Wh
- `charging_efficiency`: Charging efficiency (0-1)
- `discharging_efficiency`: Discharging efficiency (0-1)
@ -107,6 +121,7 @@ Verify prices against your local tariffs.
- `max_soc_percentage`: Maximum allowed SoC (%)
### Temperature Forecast
- Unit: °C
- Time Range: 48 hours (00:00 today to 23:00 tomorrow)
- Format: Array of hourly values
@ -115,6 +130,7 @@ Verify prices against your local tariffs.
## Output Format
### Sample Response
```json
{
"ac_charge": [0.625, 0, ..., 0.75, 0],
@ -137,6 +153,7 @@ Verify prices against your local tariffs.
### Output Parameters
#### Battery Control
- `ac_charge`: Grid charging schedule (0-1)
- `dc_charge`: DC charging schedule (0-1)
- `discharge_allowed`: Discharge permission (0 or 1)
@ -147,9 +164,11 @@ Verify prices against your local tariffs.
`ac_charge` multiplied by the maximum charge power of the battery results in the planned charging power.
#### EV Charging
- `eautocharge_hours_float`: EV charging schedule (0-1)
#### Results
The `result` object contains detailed information about the optimization outcome.
The length of the array is between 25 and 48 and starts at the current hour and ends at 23:00 tomorrow.
@ -173,6 +192,9 @@ The length of the array is between 25 and 48 and starts at the current hour and
- `akku_soc_pro_stunde`: Array of hourly battery state of charge values (%)
## Timeframe overview
<a href="../_static/optimization_timeframes.png" target="_blank">
<img src="../_static/optimization_timeframes.png" alt="EOS Price in euro per watt hour in battery" width="300">
</a>
```{figure} ../_static/optimization_timeframes.png
:alt: Timeframe Overview
Timeframe Overview
```

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@ -5,10 +5,10 @@
Predictions, along with simulations and measurements, form the foundation upon which energy
optimization is executed. In EOS, a standard set of predictions is managed, including:
- **Household Load Prediction**
- **Electricity Price Prediction**
- **PV Power Prediction**
- **Weather Prediction**
- Household Load Prediction
- Electricity Price Prediction
- PV Power Prediction
- Weather Prediction
## Storing Predictions
@ -56,13 +56,15 @@ A dictionary with the following structure:
#### 2. DateTimeDataFrame
A JSON string created from a [pandas](https://pandas.pydata.org/docs/index.html) dataframe with a
`DatetimeIndex`. Use [pandas.DataFrame.to_json(orient="index")](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html#pandas.DataFrame.to_json).
`DatetimeIndex`. Use
[pandas.DataFrame.to_json(orient="index")](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_json.html#pandas.DataFrame.to_json).
The column name of the data must be the same as the names of the `prediction key`s.
#### 3. DateTimeSeries
A JSON string created from a [pandas](https://pandas.pydata.org/docs/index.html) series with a
`DatetimeIndex`. Use [pandas.Series.to_json(orient="index")](https://pandas.pydata.org/docs/reference/api/pandas.Series.to_json.html#pandas.Series.to_json).
`DatetimeIndex`. Use
[pandas.Series.to_json(orient="index")](https://pandas.pydata.org/docs/reference/api/pandas.Series.to_json.html#pandas.Series.to_json).
## Adjusted Predictions
@ -196,13 +198,20 @@ Configuration options:
- `latitude`: Latitude in decimal degrees, between -90 and 90, north is positive (ISO 19115) (°)"
- `longitude`: Longitude in decimal degrees, within -180 to 180 (°)
- `pvforecast<0..5>_surface_tilt`: Tilt angle from horizontal plane. Ignored for two-axis tracking.
- `pvforecast<0..5>_surface_azimuth`: Orientation (azimuth angle) of the (fixed) plane. Clockwise from north (north=0, east=90, south=180, west=270).
- `pvforecast<0..5>_surface_azimuth`: Orientation (azimuth angle) of the (fixed) plane.
Clockwise from north (north=0, east=90, south=180, west=270).
- `pvforecast<0..5>_userhorizon`: Elevation of horizon in degrees, at equally spaced azimuth clockwise from north.
- `pvforecast<0..5>_peakpower`: Nominal power of PV system in kW.
- `pvforecast<0..5>_pvtechchoice`: PV technology. One of 'crystSi', 'CIS', 'CdTe', 'Unknown'.
- `pvforecast<0..5>_mountingplace`: Type of mounting for PV system. Options are 'free' for free-standing and 'building' for building-integrated.
- `pvforecast<0..5>_mountingplace`: Type of mounting for PV system. Options are 'free' for free-standing
and 'building' for building-integrated.
- `pvforecast<0..5>_loss`: Sum of PV system losses in percent
- `pvforecast<0..5>_trackingtype`: Type of suntracking. 0=fixed, 1=single horizontal axis aligned north-south, 2=two-axis tracking, 3=vertical axis tracking, 4=single horizontal axis aligned east-west, 5=single inclined axis aligned north-south.
- `pvforecast<0..5>_trackingtype`: Type of suntracking. 0=fixed,
1=single horizontal axis aligned north-south,
2=two-axis tracking,
3=vertical axis tracking,
4=single horizontal axis aligned east-west,
5=single inclined axis aligned north-south.
- `pvforecast<0..5>_optimal_surface_tilt`: Calculate the optimum tilt angle. Ignored for two-axis tracking.
- `pvforecast<0..5>_optimalangles`: Calculate the optimum tilt and azimuth angles. Ignored for two-axis tracking.
- `pvforecast<0..5>_albedo`: Proportion of the light hitting the ground that it reflects back.
@ -216,37 +225,74 @@ Configuration options:
------
Some of the configuration options directly follow the [PVGIS](https://joint-research-centre.ec.europa.eu/photovoltaic-geographical-information-system-pvgis/getting-started-pvgis/pvgis-user-manual_en) nomenclature.
Some of the configuration options directly follow the
[PVGIS](https://joint-research-centre.ec.europa.eu/photovoltaic-geographical-information-system-pvgis/getting-started-pvgis/pvgis-user-manual_en)
nomenclature.
Detailed definitions taken from **PVGIS**:
- `pvforecast<0..5>_pvtechchoice`
The performance of PV modules depends on the temperature and on the solar irradiance, but the exact dependence varies between different types of PV modules. At the moment we can estimate the losses due to temperature and irradiance effects for the following types of modules: crystalline silicon cells; thin film modules made from CIS or CIGS and thin film modules made from Cadmium Telluride (CdTe).
The performance of PV modules depends on the temperature and on the solar irradiance, but the exact
dependence varies between different types of PV modules. At the moment we can estimate the losses
due to temperature and irradiance effects for the following types of modules: crystalline silicon
cells; thin film modules made from CIS or CIGS and thin film modules made from Cadmium Telluride
(CdTe).
For other technologies (especially various amorphous technologies), this correction cannot be calculated here. If you choose one of the first three options here the calculation of performance will take into account the temperature dependence of the performance of the chosen technology. If you choose the other option (other/unknown), the calculation will assume a loss of 8% of power due to temperature effects (a generic value which has found to be reasonable for temperate climates).
For other technologies (especially various amorphous technologies), this correction cannot be
calculated here. If you choose one of the first three options here the calculation of performance
will take into account the temperature dependence of the performance of the chosen technology. If
you choose the other option (other/unknown), the calculation will assume a loss of 8% of power due
to temperature effects (a generic value which has found to be reasonable for temperate climates).
PV power output also depends on the spectrum of the solar radiation. PVGIS can calculate how the variations of the spectrum of sunlight affects the overall energy production from a PV system. At the moment this calculation can be done for crystalline silicon and CdTe modules. Note that this calculation is not yet available when using the NSRDB solar radiation database.
PV power output also depends on the spectrum of the solar radiation. PVGIS can calculate how the
variations of the spectrum of sunlight affects the overall energy production from a PV system. At
the moment this calculation can be done for crystalline silicon and CdTe modules. Note that this
calculation is not yet available when using the NSRDB solar radiation database.
- `pvforecast<0..5>_peakpower`
This is the power that the manufacturer declares that the PV array can produce under standard test conditions (STC), which are a constant 1000W of solar irradiation per square meter in the plane of the array, at an array temperature of 25°C. The peak power should be entered in kilowatt-peak (kWp). If you do not know the declared peak power of your modules but instead know the area of the modules and the declared conversion efficiency (in percent), you can calculate the peak power as power = area * efficiency / 100.
This is the power that the manufacturer declares that the PV array can produce under standard test
conditions (STC), which are a constant 1000W of solar irradiation per square meter in the plane of
the array, at an array temperature of 25°C. The peak power should be entered in kilowatt-peak (kWp).
If you do not know the declared peak power of your modules but instead know the area of the modules
and the declared conversion efficiency (in percent), you can calculate the peak power as
power = area * efficiency / 100.
Bifacial modules: PVGIS doesn't make specific calculations for bifacial modules at present. Users who wish to explore the possible benefits of this technology can input the power value for Bifacial Nameplate Irradiance. This can also be can also be estimated from the front side peak power P_STC value and the bifaciality factor, φ (if reported in the module data sheet) as: P_BNPI = P_STC * (1 + φ * 0.135). NB this bifacial approach is not appropriate for BAPV or BIPV installations or for modules mounting on a N-S axis i.e. facing E-W.
Bifacial modules: PVGIS doesn't make specific calculations for bifacial modules at present. Users
who wish to explore the possible benefits of this technology can input the power value for Bifacial
Nameplate Irradiance. This can also be can also be estimated from the front side peak power P_STC
value and the bifaciality factor, φ (if reported in the module data sheet) as:
P_BNPI = P_STC \* (1 + φ \* 0.135). NB this bifacial approach is not appropriate for BAPV or BIPV
installations or for modules mounting on a N-S axis i.e. facing E-W.
- `pvforecast<0..5>_loss`
The estimated system losses are all the losses in the system, which cause the power actually delivered to the electricity grid to be lower than the power produced by the PV modules. There are several causes for this loss, such as losses in cables, power inverters, dirt (sometimes snow) on the modules and so on. Over the years the modules also tend to lose a bit of their power, so the average yearly output over the lifetime of the system will be a few percent lower than the output in the first years.
The estimated system losses are all the losses in the system, which cause the power actually
delivered to the electricity grid to be lower than the power produced by the PV modules. There are
several causes for this loss, such as losses in cables, power inverters, dirt (sometimes snow) on
the modules and so on. Over the years the modules also tend to lose a bit of their power, so the
average yearly output over the lifetime of the system will be a few percent lower than the output
in the first years.
We have given a default value of 14% for the overall losses. If you have a good idea that your value will be different (maybe due to a really high-efficiency inverter) you may reduce this value a little.
We have given a default value of 14% for the overall losses. If you have a good idea that your value
will be different (maybe due to a really high-efficiency inverter) you may reduce this value a little.
- `pvforecast<0..5>_mountingplace`
For fixed (non-tracking) systems, the way the modules are mounted will have an influence on the temperature of the module, which in turn affects the efficiency. Experiments have shown that if the movement of air behind the modules is restricted, the modules can get considerably hotter (up to 15°C at 1000W/m2 of sunlight).
For fixed (non-tracking) systems, the way the modules are mounted will have an influence on the
temperature of the module, which in turn affects the efficiency. Experiments have shown that if the
movement of air behind the modules is restricted, the modules can get considerably hotter
(up to 15°C at 1000W/m2 of sunlight).
In PVGIS there are two possibilities: free-standing, meaning that the modules are mounted on a rack with air flowing freely behind the modules; and building- integrated, which means that the modules are completely built into the structure of the wall or roof of a building, with no air movement behind the modules.
In PVGIS there are two possibilities: free-standing, meaning that the modules are mounted on a rack
with air flowing freely behind the modules; and building- integrated, which means that the modules
are completely built into the structure of the wall or roof of a building, with no air movement
behind the modules.
Some types of mounting are in between these two extremes, for instance if the modules are mounted on a roof with curved roof tiles, allowing air to move behind the modules. In such cases, the performance will be somewhere between the results of the two calculations that are possible here.
Some types of mounting are in between these two extremes, for instance if the modules are mounted on
a roof with curved roof tiles, allowing air to move behind the modules. In such cases, the
performance will be somewhere between the results of the two calculations that are possible here.
- `pvforecast<0..5>_userhorizon`
@ -260,7 +306,8 @@ degrees west of north.
------
Most of the configuration options are in line with the [PVLib](https://pvlib-python.readthedocs.io/en/stable/_modules/pvlib/iotools/pvgis.html) definition for PVGIS data.
Most of the configuration options are in line with the
[PVLib](https://pvlib-python.readthedocs.io/en/stable/_modules/pvlib/iotools/pvgis.html) definition for PVGIS data.
Detailed definitions from **PVLib** for PVGIS data.
@ -288,7 +335,8 @@ The following general configuration options of the PV system must be set:
For each plane `<0..5>` of the PV system the following configuration options must be set:
- `pvforecast<0..5>_surface_tilt`: Tilt angle from horizontal plane. Ignored for two-axis tracking.
- `pvforecast<0..5>_surface_azimuth`: Orientation (azimuth angle) of the (fixed) plane. Clockwise from north (north=0, east=90, south=180, west=270).
- `pvforecast<0..5>_surface_azimuth`: Orientation (azimuth angle) of the (fixed) plane.
Clockwise from north (north=0, east=90, south=180, west=270).
- `pvforecast<0..5>_userhorizon`: Elevation of horizon in degrees, at equally spaced azimuth clockwise from north.
- `pvforecast<0..5>_inverter_paco`: AC power rating of the inverter. [W]
- `pvforecast<0..5>_peakpower`: Nominal power of PV system in kW.
@ -370,8 +418,8 @@ Configuration options:
- `weather_provider`: Load provider id of provider to be used.
- `BrightSky`: Retrieves from https://api.brightsky.dev.
- `ClearOutside`: Retrieves from https://clearoutside.com/forecast.
- `BrightSky`: Retrieves from [BrightSky](https://api.brightsky.dev).
- `ClearOutside`: Retrieves from [ClearOutside](https://clearoutside.com/forecast).
- `LoadImport`: Imports from a file or JSON string.
- `weatherimport_file_path`: Path to the file to import weatherforecast data from.

42
docs/develop/getting_started.md
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@ -75,37 +75,53 @@ This project uses the `EOS.config.json` file to manage configuration settings.
### Default Configuration
A default configuration file `default.config.json` is provided. This file contains all the necessary configuration keys with their default values.
A default configuration file `default.config.json` is provided. This file contains all the necessary
configuration keys with their default values.
### Custom Configuration
Users can specify a custom configuration directory by setting the environment variable `EOS_DIR`.
- If the directory specified by `EOS_DIR` contains an existing `EOS.config.json` file, the application will use this configuration file.
- If the `EOS.config.json` file does not exist in the specified directory, the `default.config.json` file will be copied to the directory as `EOS.config.json`.
- If the directory specified by `EOS_DIR` contains an existing `EOS.config.json` file, the
application will use this configuration file.
- If the `EOS.config.json` file does not exist in the specified directory, the `default.config.json`
file will be copied to the directory as `EOS.config.json`.
### Configuration Updates
If the configuration keys in the `EOS.config.json` file are missing or different from those in `default.config.json`, they will be automatically updated to match the default settings, ensuring that all required keys are present.
If the configuration keys in the `EOS.config.json` file are missing or different from those in
`default.config.json`, they will be automatically updated to match the default settings, ensuring
that all required keys are present.
## Classes and Functionalities
This project uses various classes to simulate and optimize the components of an energy system. Each class represents a specific aspect of the system, as described below:
This project uses various classes to simulate and optimize the components of an energy system. Each
class represents a specific aspect of the system, as described below:
- `Battery`: Simulates a battery storage system, including capacity, state of charge, and now charge and discharge losses.
- `Battery`: Simulates a battery storage system, including capacity, state of charge, and now
charge and discharge losses.
- `PVForecast`: Provides forecast data for photovoltaic generation, based on weather data and historical generation data.
- `PVForecast`: Provides forecast data for photovoltaic generation, based on weather data and
historical generation data.
- `Load`: Models the load requirements of a household or business, enabling the prediction of future energy demand.
- `Load`: Models the load requirements of a household or business, enabling the prediction of future
energy demand.
- `Heatpump`: Simulates a heat pump, including its energy consumption and efficiency under various operating conditions.
- `Heatpump`: Simulates a heat pump, including its energy consumption and efficiency under various
operating conditions.
- `Strompreis`: Provides information on electricity prices, enabling optimization of energy consumption and generation based on tariff information.
- `Strompreis`: Provides information on electricity prices, enabling optimization of energy
consumption and generation based on tariff information.
- `EMS`: The Energy Management System (EMS) coordinates the interaction between the various components, performs optimization, and simulates the operation of the entire energy system.
- `EMS`: The Energy Management System (EMS) coordinates the interaction between the various
components, performs optimization, and simulates the operation of the entire energy system.
These classes work together to enable a detailed simulation and optimization of the energy system. For each class, specific parameters and settings can be adjusted to test different scenarios and strategies.
These classes work together to enable a detailed simulation and optimization of the energy system.
For each class, specific parameters and settings can be adjusted to test different scenarios and
strategies.
### Customization and Extension
Each class is designed to be easily customized and extended to integrate additional functions or improvements. For example, new methods can be added for more accurate modeling of PV system or battery behavior. Developers are invited to modify and extend the system according to their needs.
Each class is designed to be easily customized and extended to integrate additional functions or
improvements. For example, new methods can be added for more accurate modeling of PV system or
battery behavior. Developers are invited to modify and extend the system according to their needs.

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@ -24,7 +24,7 @@ akkudoktoreos/serverapi.md
akkudoktoreos/api.rst
```
# Indices and tables
## Indices and tables
- {ref}`genindex`
- {ref}`modindex`

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@ -0,0 +1,20 @@
{
"plugins": {
"md007": {
"enabled": true,
"code_block_line_length" : 160
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"md013": {
"enabled": true,
"line_length" : 120
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"md041": {
"enabled": false
}
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"extensions": {
"front-matter" : {
"enabled" : true
}
}
}

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@ -1,12 +1,12 @@
% SPDX-License-Identifier: Apache-2.0
# Welcome to the EOS documentation!
# Welcome to the EOS documentation
This documentation is continuously written. It is edited via text files in the
[Markdown/ Markedly Structured Text](https://myst-parser.readthedocs.io/en/latest/index.html)
markup language and then compiled into a static website/ offline document using the open source tool
[Sphinx](https://www.sphinx-doc.org) and will someday land on
[Read the Docs](https://akkudoktoreos.readthedocs.io/en/latest/index.html).
[Sphinx](https://www.sphinx-doc.org) and is available on
[Read the Docs](https://akkudoktor-eos.readthedocs.io/en/latest/).
You can contribute to EOS's documentation by opening
[GitHub issues](https://github.com/Akkudoktor-EOS/EOS/issues)