Reduced line length in text

docs/akkudoktoreos/introduction.md

@@ -2,21 +2,41 @@
 
 # Introduction
 
-The Energy System Simulation and Optimization System (EOS) 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.
-
-After successfully installing a PV system with or without battery storage, most owners first priority is often to charge the electric car with surplus energy in order to use the electricity generated by the PV system cost-effectively for electromobility.
-
-After initial experiences, the desire to include battery storage and dynamic electricity prices in the solution soon arises. The market already offers various commercial and non-commercial solutions for this, such as the popular open source hardware and software solutions evcc or openWB.
-
-However, many solutions only take current values such as PV power output, charge level of the battery storage system or the current electricity price into account and/or only focus on specific consumers, such as the electric car.
-
-The Akkudoktor EOS takes the possibilities to a new level by taking into account the relevant values ​​of the upcoming hours of the generation system with battery storage as well as other energy-intensive consumers.
+The Energy System Simulation and Optimization System (EOS) 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.
+
+After successfully installing a PV system with or without battery storage, most owners
+first priority is often to charge the electric car with surplus energy in order to use
+the electricity generated by the PV system cost-effectively for electromobility.
+
+After initial experiences, the desire to include battery storage and dynamic electricity
+prices in the solution soon arises. The market already offers various commercial and
+non-commercial solutions for this, such as the popular open source hardware and software
+solutions evcc or openWB.
+
+However, many solutions only take current values such as PV power output, charge level of
+the battery storage system or the current electricity price into account and/or only
+focus on specific consumers, such as the electric car.
+
+The Akkudoktor EOS takes the possibilities to a new level by taking into account the
+relevant values ​​of the upcoming hours of the generation system with battery storage as
+well as other energy-intensive consumers.
 
 ![Introdution](../_static/introduction/introduction.png)
 
-The challenge is to charge (electric car) or start the consumers (washing machine, dryer) at the right time and to do so as cost-efficiently as possible. If PV yield forecast, battery storage and dynamic electricity price forecasts are included in the calculation, the possibilities increase, but unfortunately so does the complexity.
+The challenge is to charge (electric car) or start the consumers (washing machine, dryer)
+at the right time and to do so as cost-efficiently as possible. If PV yield forecast,
+battery storage and dynamic electricity price forecasts are included in the calculation,
+the possibilities increase, but unfortunately so does the complexity.
 
-The Akkudoktor EOS addresses this challenge by simulating energy flows in the household based on target values, forecast data and current operating data over a 48-hour observation period, running through a large number of different scenarios and finally providing a cost-optimized plan for the current day controlling the relevant consumers.
+The Akkudoktor EOS addresses this challenge by simulating energy flows in the household
+based on target values, forecast data and current operating data over a 48-hour
+observation period, running through a large number of different scenarios and finally
+providing a cost-optimized plan for the current day controlling the relevant consumers.
 
 ## Prerequisites
 
@@ -26,7 +46,8 @@ The Akkudoktor EOS addresses this challenge by simulating energy flows in the ho
 ### Technical requirements
 
 - reasonably fast computer on which EOS is installed
-- controllable energy system consisting of photovoltaic system, solar battery, energy intensive consumers that must provide the appropriate interfaces
+- controllable energy system consisting of photovoltaic system, solar battery,
+  energy intensive consumers that must provide the appropriate interfaces
 - integration component for integrating the energy system and EOS
 
 ### Input Data
@@ -54,14 +75,22 @@ Target values
 - Charge level the electric car should reach in the next few hours
 - Consumers to run in the next few hours
 
-There are various service providers available for PV forecasting that calculate forecast data for a PV system based on the various influencing factors, such as system size, orientation, location, time of year and weather conditions. EOS also offers a [simple PV forecasting service](#prediction-page) which can be used. This service uses public data in the background.
+There are various service providers available for PV forecasting that calculate forecast
+data for a PV system based on the various influencing factors, such as system size,
+orientation, location, time of year and weather conditions. EOS also offers a
+[simple PV forecasting service](#prediction-page) which can be used. This service uses
+public data in the background.
 
-For the forecast of household consumption EOS provides a standard load curve for an average day based on annual household consumption that you can fetch via API. This data was compiled based on data from several households and provides an initial usable basis.
-Alternatively your own collected historical data could be used to reflect your personal consumption behaviour.
+For the forecast of household consumption EOS provides a standard load curve for an
+average day based on annual household consumption that you can fetch via API. This data
+was compiled based on data from several households and provides an initial usable basis.
+Alternatively your own collected historical data could be used to reflect your personal
+consumption behaviour.
 
 ## Simulation Results
 
-Based on the input data, the EOS uses a genetic algorithm to create a cost-optimized schedule for the coming hours from numerous simulations of the overall system.
+Based on the input data, the EOS uses a genetic algorithm to create a cost-optimized
+schedule for the coming hours from numerous simulations of the overall system.
 
 The plan created contains for each of the coming hours
 
@@ -85,29 +114,48 @@ The plan created contains for each of the coming hours
   - Overall balance (revenue-costs)
   - Cost development
 
-If required, the simulation result can also be created and downloaded in graphical form as a PDF from EOS.
+If required, the simulation result can also be created and downloaded in graphical
+form as a PDF from EOS.
 
 ## Integration
 
-The Akkudoktor EOS can be integrated into a wide variety of systems with a variety of components.
+The Akkudoktor EOS can be integrated into a wide variety of systems with a variety
+of components.
 
 ![Integration](../_static/introduction/integration.png)
 
-However, the components are not integrated by the EOS itself, but must be intergrated by the owner using an integration component and currently requires some effort and technical know-how.
+However, the components are not integrated by the EOS itself, but must be intergrated by
+the owner using an integration component and currently requires some effort and technical
+know-how.
 
-Any [integration](#integration-page) solution that can act as an intermediary between the components and the REST API of EOS can be used. One possible solution that enables the integration of components and EOS is Node-RED. Another solution could be Home Assistant itself.
+Any [integration](#integration-page) solution that can act as an intermediary between the
+components and the REST API of EOS can be used. One possible solution that enables the
+integration of components and EOS is Node-RED. Another solution could be Home Assistant
+itself.
 
-Access to the data and functions of the components can be done in a variety of ways. Node-RED offers a large number of types of nodes that allow access via the protocols commonly used in this area, such as Modbus or MQTT. Access to any existing databases, such as InfluxDB or PostgreSQL, is also possible via nodes provided by Node-RED.
+Access to the data and functions of the components can be done in a variety of ways.
+Node-RED offers a large number of types of nodes that allow access via the protocols
+commonly used in this area, such as Modbus or MQTT. Access to any existing databases,
+such as InfluxDB or PostgreSQL, is also possible via nodes provided by Node-RED.
 
-It becomes easier if a smart home solution like Homa Assistant, openHAB or ioBroker or solutions such as evcc or openWB are already in use. In this case, these solutions already take over the technical integration and communication with the components at a technical level and Node-RED offers nodes for accessing these solutions, so that the corresponding sources can be easily integrated into a flow.
+It becomes easier if a smart home solution like Homa Assistant, openHAB or ioBroker or
+solutions such as evcc or openWB are already in use. In this case, these solutions
+already take over the technical integration and communication with the components at a
+technical level and Node-RED offers nodes for accessing these solutions, so that the
+corresponding sources can be easily integrated into a flow.
 
-In Home Assistant you could use an automation to prepare the input payload for EOS and then use the RESTful integration to call EOS. Based on this concept there is already a solutuion created by [Duetting](#duetting-solution).
+In Home Assistant you could use an automation to prepare the input payload for EOS and
+then use the RESTful integration to call EOS. Based on this concept there is already a
+solutuion created by [Duetting](#duetting-solution).
 
-The plan created by EOS must also be executed via this integration component, with the respective devices receiving their instructions according to the plan.
+The plan created by EOS must also be executed via this integration component, with the
+respective devices receiving their instructions according to the plan.
 
 ## Limitations
 
-The plan calculated by EOS is cost-optimized due to the genetic algorithm used, but not necessarily cost-optimal, since genetic algorithms do not always find the global optimum, but usually find good local optima very quickly in a large solution space.
+The plan calculated by EOS is cost-optimized due to the genetic algorithm used, but not
+necessarily cost-optimal, since genetic algorithms do not always find the global optimum,
+but usually find good local optima very quickly in a large solution space.
 
 ## Links