Cognit Energy Use Case Basic Demo

Running the demo

To prepare the environment please follow the steps:

• Prepare Python virtual environment:

python3 -m venv demo_venv
source demo_venv/bin/activate
pip3 install -r requirements.txt

• Inside this directory put the cognit runtime config file cognit.yml (with all the necessary authorization data).

• Remember that Cognit SLR uses IPv6, so take care of that if necessary.

In order to run the demo call:

python3 -i demo_runner.py [--live][--offload][--scenario SCENARIO]

Optional arguments:

• live – disables the schedules for PV and auto-consumption states, heating preferences and temperature outside. Enables usage of functions that change these parameters live.
• offload – enables usage of Cognit Serverless Runtime for offloading the decision algorithm
• scenario – enables providing scenario file

NOTE: One should always use exactly one of the --live and --scenario options.

Logging

The demo produces three log files:

• simulation.log – current state of the devices and the simulation environment
• user_app.log – input and output of the decision algorithm
• cognit.log – Cognit Serverless Runtime device API logs

Configuration and scenario

The configuration of the simulation, user application and devices can be found in scenario/config.py. The most important parameters are:

• SPEEDUP (default: 360) – the speedup factor of the simulation
• USER_APP_CYCLE_LENGTH (default: 3600 seconds) – time (in "virtual" seconds) between consecutive calls of the decision algorithm

These two parameters can be changed during the simulation (see below).

Files scenario/config.toml and scenario/updates.csv are SEM Simulator config files (see ../sem-simulator/README.md for more details).

Scenarios

There are 4 predefined schedules with consumption approx. 33 kWh/1 day:

• spring - temperatures during day between 6 and 18°C, production approx. 60 kWh/1 day
• summer - temperatures during day between 17 and 30°C, production approx. 75 kWh/1 day
• autumn - temperatures during day between 5 and 15°C, production approx. 50 kWh/1 day
• winter - temperatures during day between -5 and 4°C, production approx. 15 kWh/1 day

The proposed values assumes the simulation of a single-story smart home with an area of approx. 150 m2 with an array of PV panels with a power of 15 kWp and an energy storage with a capacity of 24 kWh and a nominal power of 12.8 kW, heated with heating mats with a total power of 16 kW.

For simplicity, in these scenarios all devices are plugged into phase L1.

To understand the structure of scenario files take a look at scenario/scenario_autumn.py, where additional comments have been provided.

Live tweaking of the parameters

We recommend running the demo in interactive Python, as we already suggested above. In both live and scenario modes there are the following functions one can use:

• print_help() – prints the help message

• set_speedup(speedup: int) – changes the speedup of the simulation (by default speedup is 360)

• set_cycle_length(seconds: int) – changes the frequency of running the decision algorithm

• offload() – performs an unscheduled call of decision algorithm

• set_slr_config(perc: int) – updates Serverless Runtime scheduling preferences in terms of green energy usage

• finish() – finishes the simulation, deletes Serverless Runtime if present

In live mode additionally one can use:

• set_heating_preferences(temp: float) – sets user preferences of heating

• set_pv_state(current: float) – sets PV production (use negative values for production)

• set_consumption(current: float) – sets auto-consumption (use positive values for consumption)

• set_temp_outside(temp: float) – sets temperature outside

• set_ev_driving(driving_power: float) - sets EV driving power. Setting the power to positive number means that the car is driving and its charge level will decrease according to this parameter. However, setting it to 0 is interpreted as the car arriving and connecting it to the home charger.

• set_ev_departure_time(ev_departure_time: str) - sets user-planned EV departure time in format %H:%M interpreted by decision algorithm as time when EV must be charged for driving.