3c862543a1
* feat: adapt configuration for multi optimization algorithms
Decouple configuration from optimization algorithm parameters. Add to_[algorithm]_param() methods
to the configuration that derive optimization algorithm specific parameters from the configuration.
Add x-scope tags to the configuration options that describe for which specific algorithms the
configuration option is for.
The whole device settings are restructured. There are now general settings for the device classes
with the afore mentioned to_[algorithm]_param() methods. The general device settings got their own
directory `devices/settings`. By this the parameter class also does not have to be a pydantic model
which can be used for future optimization/ simulations speed up.
Also the parameter class for a device is now part of the device module. This better decouples and
also is the natural place for parameters of a device.
Besides this feature there are also fixes and improvements:
* feat: extend home appliance time window settings and simulation
Home appliance can now be configured for multiple runs with per-cycle allowed time windows. The
number of remaining cycles to plan is determined at runtime by reading the
``cycles_completed_measurement_key`` from the measurement store.
* feat: specialiced CycleTimeWindowSequence for time window sequences
Sequence of time windows associated to cycles.
This model specializes ``ValueTimeWindowSequence`` so that the ``value``
field of each ``ValueTimeWindow`` encodes the **cycle index** (0-based
integer) the window belongs to.
Typical use: an appliance that must run ``n`` times per day, each run
constrained to a distinct time window. Assign ``value=0`` to windows
for the first cycle, ``value=1`` for the second, and so on. Multiple
windows may share the same cycle index (their allowed regions are unioned).
Windows with ``value=None`` are silently ignored by all cycle-aware methods.
* fix: Make test_configmigrate also regard the _ANY_SENTENIEL in key values
* chore: Make devices configurations a map instead of a list
This makes config paths stable regardless of declaration order and lets each device settings
class build its own config path from ``self.device_id`` without needing an external index.
Tests are adapted likewise.
Devices configurations are automatically migrated from lists to maps.
* chore: rename levelized_cost_of_storage_kwh to levelized_cost_of_storage_amt kwh
This better fits in the naming scheme and also makes clear the costs are money.
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
* fix: runtime config update ignored by config file
Runtime settings were handed back to pydantic-settings as init settings,
which rank below the config file and the environment. Any key already
present in EOS.config.json or in the environment silently discarded the
update, so a bulk PUT /v1/config returned 200 without applying anything,
while the granular PUT /v1/config/{path} endpoint kept working.
Add a dedicated runtime settings source ranked directly below the command
line arguments and record granular updates there as well, so both
endpoints share one store that survives re-evaluation of the settings
sources. Environment variables keep precedence over the config file for
all keys that were not set at runtime.
Also repairs revert_settings() and update(), which passed their data
through the same init settings.
Closes #1303
* fix: env vars ignored on first config build
ConfigEOS.__init__ passed self as first positional argument to _setup,
which forwards it to pydantic_settings.BaseSettings.__init__. Its first
positional parameter is _case_sensitive, so the environment source
matched the upper case variable names against the lower case field names
and returned nothing. Environment settings only took effect after the
next configuration setup.
* docs: changelog for config priority fixes
* fix(config): preserve device identities and storage costs during migration
* fix(devices): preserve charge-rate typing and public import compatibility
* ruff format fix
* feat(devices): port slot-aware battery export and direct-use physics
Port scoped device changes from d2e2d58237. Keep PR #1256 parameter conversion structure and separate GENETIC0 devices. Validate physical flows and reprice changed simulation results.
Co-authored-by: Andreas <drbacke@gmx.de>
Co-authored-by: Christin <info@bikinibottom.capital>
* fix(cache): distinguish callables in the shared EMS cache
Include the function object in cache keys so methods of one interpolator cannot reuse a probability as a power value. Cover both call orders, keyword arguments, cache hits and separate closures with identical qualified names.
* fix(devices): constrain the physics port and validate export levels
Defer inactive EV deadline fields to the optimizer port, reject nonfinite export rates, and document the hourly Optimize boundary. Verify converter IDs, rates and LCOS, separate GENETIC0 interpolation, physical boundary flows and independent GENETIC repricing.
* docs(devices): regenerate slot-physics configuration and OpenAPI schemas
* fix(config): satisfy typed device conversion and migration contracts
* docs(config): refresh validated configuration prerequisite schemas
* test(devices): align physics regressions with strict type checking
* docs(devices): refresh API version after prerequisite merge
* docs(interpolator): use portable reStructuredText markup
* docs(devices): refresh API version after docstring compatibility fix
---------
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
Co-authored-by: Bobby Noelte <b0661n0e17e@gmail.com>
Co-authored-by: r0b2g1t <r0b2g1t@users.noreply.github.com>
Co-authored-by: Christin <info@bikinibottom.capital>
Build optimized energy management plans for your home automation
AkkudoktorEOS is a comprehensive solution for simulating and optimizing energy systems based on renewable sources. Optimize your photovoltaic systems, battery storage, load management, and electric vehicles while considering real-time electricity pricing.
Why use AkkudoktorEOS?
AkkudoktorEOS can be used to build energy management plans that are optimized for your specific setup of PV system, battery, electric vehicle, household load and electricity pricing. It can be integrated into home automation systems such as NodeRED, Home Assistant, EVCC.
🏘️ Community
We are an open-source community-driven project and we love to hear from you. Here are some ways to get involved:
-
GitHub Issue Tracker: discuss ideas and features, and report bugs.
-
Akkudoktor Forum: get direct suppport from the community.
What do people build with AkkudoktorEOS
The community uses AkkudoktorEOS to minimize grid energy consumption and to maximize the revenue from grid energy feed in with their home automation system.
- Andreas Schmitz, the Akkudoktor, uses EOS integrated in his NodeRED home automation system for OpenSource Energieoptimierung.
- Jörg, meintechblog, uses EOS for day-ahead optimization for time-variable energy prices. See: So installiere ich EOS von Andreas Schmitz
Why not use AkkudoktorEOS?
AkkudoktorEOS does not control your home automation assets. It must be integrated into a home automation system. If you do not use a home automation system or you feel uncomfortable with the configuration effort needed for the integration you should better use other solutions.
Quick Start
Run EOS with Docker (access dashboard at http://localhost:8504):
docker run -d \
--name akkudoktoreos \
-p 8503:8503 \
-p 8504:8504 \
-e OPENBLAS_NUM_THREADS=1 \
-e OMP_NUM_THREADS=1 \
-e MKL_NUM_THREADS=1 \
-e EOS_SERVER__HOST=0.0.0.0 \
-e EOS_SERVER__EOSDASH_HOST=0.0.0.0 \
-e EOS_SERVER__EOSDASH_PORT=8504 \
--ulimit nproc=65535:65535 \
--ulimit nofile=65535:65535 \
--security-opt seccomp=unconfined \
akkudoktor/eos:latest
System Requirements
- Python: 3.11 or higher
- Architecture: amd64, aarch64 (armv8)
- OS: Linux, Windows, macOS
Note
: Other architectures (armv6, armv7) require manual compilation of dependencies with Rust and GCC.
Installation
Home Assistant add-on
To install the Akkudoktor-EOS add-on in Home Assistant:
-
Add the repository URL:
In Home Assistant, go to:
Settings → Add-ons → Add-on Store → ⋮ (top-right menu) → Repositoriesand enter the URL of this Git repository:
https://github.com/Akkudoktor-EOS/EOS -
Install the add-on:
After adding the repository, the add-on will appear in the Add-on Store. Click Install.
-
Start the add-on:
Once installed, click Start in the add-on panel.
-
Access the dashboard:
Click Open Web UI in the add-on panel.
-
Configure EOS (optional): In the dashboard, go to:
Config
Docker (Recommended)
docker pull akkudoktor/eos:latest
docker compose up -d
Access the API at http://localhost:8503 (docs at http://localhost:8503/docs)
From Source
git clone https://github.com/Akkudoktor-EOS/EOS.git
cd EOS
Linux:
python -m venv .venv
.venv/bin/pip install -r requirements.txt
.venv/bin/pip install -e .
.venv/bin/python -m akkudoktoreos.server.eos
Windows:
python -m venv .venv
.venv\Scripts\pip install -r requirements.txt
.venv\Scripts\pip install -e .
.venv\Scripts\python -m akkudoktoreos.server.eos
Configuration
EOS uses EOS.config.json for configuration. If the file doesn't exist, a default configuration is
created automatically.
Custom Configuration Directory
export EOS_DIR=/path/to/your/config
Configuration Methods
- EOSdash (Recommended) - Web interface at
http://localhost:8504 - Manual - Edit
EOS.config.jsondirectly - API - Use the Server API
See the documentation for all configuration options.
Port Configuration
Default ports: 8503 (API), 8504 (Dashboard)
If running on shared systems (e.g., Synology NAS), these ports may conflict with system services. Reconfigure port mappings as needed:
docker run -p 8505:8503 -p 8506:8504 ...
API Documentation
Interactive API docs available at:
- Swagger UI:
http://localhost:8503/docs - OpenAPI Spec: View Online
Resources
Contributing
We welcome contributions! See CONTRIBUTING for guidelines.
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.