emhass 0.3.8

An Energy Management System for Home Assistant

Energy Management for Home Assistant

EHMASS is a Python module designed to optimize your home energy interfacing with Home Assistant.

Context

This module was conceived as an energy management optimization tool for residential electric power consumption and production systems. The goal is to optimize the energy use in order to maximize autoconsumption. The main study case is a household where we have solar panels, a grid connection and one or more controllable (deferrable) electrical loads. Including an energy storage system using batteries is also possible in the code. The package is highly configurable with an object oriented modular approach and a main configuration file defined by the user. EMHASS was designed to be integrated with Home Assistant, hence it's name. Installation instructions and example Home Assistant automation configurations are given below.

The main dependencies of this project are PVLib to model power from a PV residential installation and the PuLP Python package to perform the actual optimizations using the Linear Programming approach.

The complete documentation for this package is available here.

Installation

It is recommended to install on a virtual environment. For this you will need virtualenv, install it using:

sudo apt install python3-virtualenv

Then create and activate the virtual environment:

virtualenv -p /usr/bin/python3 emhassenv
cd emhassenv
source bin/activate

Install using the distribution files:

python3 -m pip install emhass

Clone this repository to obtain the example configuration files. We will suppose that this repository is cloned to:

/home/user/emhass

This will be the root path containing the yaml configuration files (config_emhass.yaml and secrets_emhass.yaml) and the different needed folders (a data folder to store the optimizations results and a scripts folder containing the bash scripts described further below).

To upgrade the installation in the future just use:

python3 -m pip install --upgrade emhass

Using Docker

To install using docker you will need to build your image locally. For this clone this repository, setup your config_emhass.yaml file and use the provided make file with this command:

make -f deploy_docker.mk clean_deploy

Then load the image in the .tar file:

docker load -i <TarFileName>.tar

Finally launch the docker itself:

docker run -it --restart always -p 5000:5000 -e "LOCAL_COSTFUN=profit" -v $(pwd)/config_emhass.yaml:/app/config_emhass.yaml -v $(pwd)/secrets_emhass.yaml:/app/secrets_emhass.yaml --name DockerEMHASS <REPOSITORY:TAG>

The EMHASS add-on

For Home Assistant OS and HA Supervised users, I've developed an add-on that will help you use EMHASS. The add-on is more user friendly as the configuration can be modified directly in the add-on options pane and also it exposes a web ui that can be used to inspect the optimization results and manually trigger a new optimization.

You can find the add-on with the installation instructions here: https://github.com/davidusb-geek/emhass-add-on

The add-on usage instructions can be found on the documentation pane of the add-on once installed or directly here: EMHASS Add-on documentation

These architectures are supported: amd64, armv7 and aarch64.

Usage

To run a command simply use the emhass command followed by the needed arguments. The available arguments are:

• --action: That is used to set the desired action, options are: perfect-optim, dayahead-optim, naive-mpc-optim and publish-data
• --config: Define path to the config.yaml file (including the yaml file itself)
• --costfun: Define the type of cost function, this is optional and the options are: profit (default), cost, self-consumption
• --log2file: Define if we should log to a file or not, this is optional and the options are: True or False (default)
• --params: Configuration as JSON.
• --runtimeparams: Data passed at runtime. This can be used to pass you own forecast data to EMHASS.
• --version: Show the current version of EMHASS.

For example, the following line command can be used to perform a day-ahead optimization task:

emhass --action 'dayahead-optim' --config '/home/user/emhass/config_emhass.yaml' --costfun 'profit'

Before running any valuable command you need to modify the config_emhass.yaml and secrets_emhass.yaml files. These files should contain the information adapted to your own system. To do this take a look at the special section for this in the documentation.

If using the add-on or the standalone docker installation, it exposes a simple webserver on port 5000. You can access it directly using your brower, ex: http://localhost:5000.

With this web server you can perform RESTful POST commands on one ENDPOINT called action with two main options:

• A POST call to action/perfect-optim to perform a perfect optimization task on the historical data.
• A POST call to action/dayahead-optim to perform a day-ahead optimization task of your home energy.
• A POST call to action/naive-mpc-optim to perform a naive Model Predictive Controller optimization task. If using this option you will need to define the correct runtimeparams (see further below).
• A POST call to action/publish-data to publish the optimization results data for the current timestamp.

A curl command can then be used to launch an optimization task like this: curl -i -H "Content-Type: application/json" -X POST -d '{}' http://localhost:5000/action/dayahead-optim.

Home Assistant integration

To integrate with home assistant we will need to define some shell commands in the configuration.yaml file and some basic automations in the automations.yaml file.

In configuration.yaml:

shell_command:
  dayahead_optim: /home/user/emhass/scripts/dayahead_optim.sh
  publish_data: /home/user/emhass/scripts/publish_data.sh

If using the add-on you can use this instead on the configuration.yaml file:

shell_command:
  dayahead_optim: curl -i -H "Content-Type: application/json" -X POST -d '{}' http://localhost:5000/action/dayahead-optim
  publish_data: curl -i -H "Content-Type: application/json" -X POST -d '{}' http://localhost:5000/action/publish-data 

And in automations.yaml:

- alias: EMHASS day-ahead optimization
  trigger:
    platform: time
    at: '05:30:00'
  action:
  - service: shell_command.dayahead_optim
- alias: EMHASS publish data
  trigger:
  - minutes: /5
    platform: time_pattern
  action:
  - service: shell_command.publish_data

In these automations the day-ahead optimization is performed everyday at 5:30am and the data is published every 5 minutes.

Create the file dayahead_optim.sh with the following content:

#!/bin/bash
. /home/user/emhassenv/bin/activate
emhass --action 'dayahead-optim' --config '/home/user/emhass/config_emhass.yaml'

And the file publish_data.sh with the following content:

#!/bin/bash
. /home/user/emhassenv/bin/activate
emhass --action 'publish-data' --config '/home/user/emhass/config_emhass.yaml'

Then specify user rights and make the files executables:

sudo chmod -R 755 /home/user/emhass/scripts/dayahead_optim.sh
sudo chmod -R 755 /home/user/emhass/scripts/publish_data.sh
sudo chmod +x /home/user/emhass/scripts/dayahead_optim.sh
sudo chmod +x /home/user/emhass/scripts/publish_data.sh

The final action will be to link a sensor value in Home Assistant to control the switch of a desired controllable load. For example imagine that I want to control my water heater and that the publish-data action is publishing the optimized value of a deferrable load that I have linked to my water heater desired behavior. In this case we could use an automation like this one below to control the desired real switch:

automation:
  trigger:
    - platform: numeric_state
      entity_id:
        - sensor.p_deferrable1
      above: 0.1
  action:
    - service: homeassistant.turn_on
      entity_id: switch.water_heater

A second automation should be used to turn off the switch:

automation:
  trigger:
    - platform: numeric_state
      entity_id:
        - sensor.p_deferrable1
      below: 0.1
  action:
    - service: homeassistant.turn_off
      entity_id: switch.water_heater

The publish-data command will push to Home Assistant the optimization results for each deferrable load defined in the configuration. For example if you have defined two deferrable loads, then the command will publish sensor.p_deferrable1 and sensor.p_deferrable2 to Home Assistant. When the dayahead-optim is launched, after the optimization, a csv file will be saved on disk. The publish-data command will load the latest csv file and look for the closest timestamp that match the current time using the datetime.now() method in Python. This means that if EMHASS is configured for 30min time step optimizations, the csv will be saved with timestamps 00:00, 00:30, 01:00, 01:30, ... and so on. If the current time is 00:05, then the closest timestamp of the optimization results that will be published is 00:00. If the current time is 00:25, then the closest timestamp of the optimization results that will be published is 00:30.

Passing your own data

In EMHASS we have basically 4 forecasts to deal with:

• PV power production forecast (internally based on the weather forecast and the characteristics of your PV plant). This is given in Watts.

• Load power forecast: how much power your house will demand on the next 24h. This is given in Watts.

• Load cost forecast: the price of the energy from the grid on the next 24h. This is given in EUR/kWh.

• PV production selling price forecast: at what price are you selling your excess PV production on the next 24h. This is given in EUR/kWh.

Maybe the hardest part is the load data: parameter var_load in the configuration file. As we want to optimize the household energies, when need to forecast the load power conumption. The default method for this is a naive approach using 1-day persistence, this mean that the load data variable should not contain the data from the deferrable loads themselves. For example, lets say that you set your deferrable load to be the washing machine. The variable that you should enter in EMHASS will be: var_load: 'sensor.power_load_no_var_loads' and sensor_power_load_no_var_loads = sensor_power_load - sensor_power_washing_machine. This is supposing that the overall load of your house is contained in variable: sensor_power_load. The sensor sensor_power_load_no_var_loads can be easily created with a new template sensor in Home Assistant.

If you are implementing a MPC controller, then you should also need to provide some data at the optimization runtime using the key runtimeparams.

The valid values to pass for both forecast data and MPC related data are explained below.

Forecast data

It is possible to provide EMHASS with your own forecast data. For this just add the data as list of values to a data dictionnary during the call to emhass using the runtimeparams option.

For example:

emhass --action 'dayahead-optim' --config '/home/user/emhass/config_emhass.yaml' --runtimeparams '{"pv_power_forecast":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 70, 141.22, 246.18, 513.5, 753.27, 1049.89, 1797.93, 1697.3, 3078.93, 1164.33, 1046.68, 1559.1, 2091.26, 1556.76, 1166.73, 1516.63, 1391.13, 1720.13, 820.75, 804.41, 251.63, 79.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}'

If using the add-on or the standalone docker installation you can pass this data as list of values to the data dictionnary during the curl POST:

curl -i -H "Content-Type: application/json" -X POST -d '{"pv_power_forecast":[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 70, 141.22, 246.18, 513.5, 753.27, 1049.89, 1797.93, 1697.3, 3078.93, 1164.33, 1046.68, 1559.1, 2091.26, 1556.76, 1166.73, 1516.63, 1391.13, 1720.13, 820.75, 804.41, 251.63, 79.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}' http://localhost:5000/action/dayahead-optim

The possible dictionnary keys to pass data are:

• pv_power_forecast for the PV power production forecast.

• load_power_forecast for the Load power forecast.

• load_cost_forecast for the Load cost forecast.

• prod_price_forecast for the PV production selling price forecast.

A naive Model Predictive Controller

A MPC controller was introduced in v0.3.0. This an informal/naive representation of a MPC controller.

A MPC controller performs the following actions:

• Set the prediction horizon and receding horizon parameters.
• Perform an optimization on the prediction horizon.
• Apply the first element of the obtained optimized control variables.
• Repeat at a relatively high frequency, ex: 5 min.

This is the receding horizon principle.

When applying this controller, the following runtimeparams should be defined:

• prediction_horizon for the MPC prediction horizon. Fix this at at least 5 times the optimization time step.

• soc_init for the initial value of the battery SOC for the current iteration of the MPC.

• soc_final for the final value of the battery SOC for the current iteration of the MPC.

• def_total_hours for the list of deferrable loads functioning hours. These values can decrease as the day advances to take into account receding horizon daily energy objectives for each deferrable load.

A correct call for a MPC optimization should look like:

curl -i -H "Content-Type: application/json" -X POST -d '{"pv_power_forecast":[0, 70, 141.22, 246.18, 513.5, 753.27, 1049.89, 1797.93, 1697.3, 3078.93], "prediction_horizon":10, "soc_init":0.5,"soc_final":0.6,"def_total_hours":[1,3]}' http://localhost:5000/action/naive-mpc-optim

Development

Create a developer environment:

virtualenv -p /usr/bin/python3 emhass-dev

To develop using Anaconda use (pick the correct Python and Pip versions):

conda create --name emhass-dev python=3.8 pip=21.0.1

Then activate environment and install the required packages using:

pip install -r requirements.txt

Add emhass to the Python path using the path to src, for example:

/home/user/emhass/src

If working on linux we can add these lines to the ~/.bashrc file:

# Python modules
export PYTHONPATH="${PYTHONPATH}:/home/user/emhass/src"

Don't foget to source the ~/.bashrc file:

source ~/.bashrc

Update the build package:

python3 -m pip install --upgrade build

And generate distribution archives with:

python3 -m build

Or with:

python3 setup.py build bdist_wheel

Create a new tag version:

git tag vX.X.X
git push origin --tags

Upload to pypi:

twine upload dist/*

Troubleshooting

Some problems may arise from solver related issues in the Pulp package. It was found that for arm64 architectures (ie. Raspberry Pi4, 64 bits) the default solver is not avaliable. A workaround is to install a new solver. The glpk solver is an option and can be installed with:

sudo apt-get install glpk-utils

After this it should be available for use and EMHASS can use it as a fallback option.

License

MIT License

Copyright (c) 2021-2022 David HERNANDEZ

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.