Akkudoktor Energy Management / Optimization System: Optimized Control of Home Energy Systems
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cross-posted from: https://feddit.org/post/20437303
This is Akkudoktor’s (Andreas Schmitz) home energy management and optimization system. Some people will know Andreas by his YouTube channel @akkudoktor in which he discusses DYI home energy systems. That channel was born out of a frustration how technical hurdles were obstructing the energy transition in Germany. Among other things, Andreas showed that one can build a home battery with a fraction of the cost of commercial systems - provided that one has engineering knowledge - and he is a control engineer.
So, because just before, I did post a link to the evcc project, I should explain what are the differences between evcc and Akkudoktor’s EOS:
evcc is mainly concerned with charging electrical vehicles (EVs) from home solar or dynamically priced power from the grid.
- it is set up to be easy to use with phone interfaces etc.
- it already supports a wide range of hardware
- it is competatively mature
- it is limited in optimization capabilities
- it is written in Go language
Akkudoktor EOS has the top priority of high level optimization - getting the most bang out of each buck
- it is a rather new project in alpha stage. So, it might be more interesting for people looking to contribute - or scratch their own itch.
- it tries to optimize home photovoltaics, home batteries, heat pumps, grey water heat pumps, other heating and manageable devices, and the remaining household demand
- electrical vehicles are supported (and currently, they are an important economical use case because batteries are still expensive and the only other large type of consumers are heat pumps).
- Such an optimization is complex because it requires predicting renewable generation (both in the home and as wind power from the grid), electric power price prediction (if dynamic or day/night prices are used), and also the individual consumption (which could depend on the forecasted weather, time of the day, day of the week, or time of the year). Things like the insulation of the house modify the impact of the weather. Also, usage pattern of components such as heat pump or battery csn have influence in their life time. So that’s a complex optimization problem.
- And a good optimization also requires sufficient input data. This also needs to observe data privacy aspects (I guess you don’t want to give a burglar info on when nobody is at home)
- The targeted integration is via Home Assistant, or HA.
Oh, last not least, there is also a (mostly German-language) forum on home energy systems which is also used to discuss the software:
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