building-energy-storage-simulation 0.5.2

A simulation of a building to optimize energy storage utilization.

Building Energy Storage Simulation

The Building Energy Storage Simulation serves as open Source OpenAI gym (now gymnasium) environment for Reinforcement Learning. The environment represents a building with an energy storage (in form of a battery) and a solar energy system. The aim is to control the energy storage in such a way that the energy of the solar system can be used optimally.

The inspiration of this project and the data profiles come from the CityLearn environment. Anyhow, this project focuses on the ease of usage and the simplicity of its implementation.

Documentation

The documentation is available at https://building-energy-storage-simulation.readthedocs.io/

Installation

By using pip just:

pip install building-energy-storage-simulation

or if you want to continue developing the package:

git clone https://github.com/tobirohrer/building-energy-storage-simulation.git && cd building-energy-storage-simulation
pip install -e .[docs,tests]

Cotribute & Contact

As i just started with this project, I am very happy for any kind of contribution! In case you want to contribute, or if you have any questions, contact me via discord.

Usage

from building_energy_storage_simulation import Environment
env = Environment()
env.reset()
env.step(42)
...

Important note: This environment is implemented by using gymnasium (the proceeder of OpenAI gym). Meaning, if you are using a reinforcement learning library like Stable Baselines3 make sure it supports gymnasium environments.

Task Description

The simulation contains a building with an energy consumption profile attached to it. The resulting electricity demand is always automatically covered by

• primarily using electricity generated by the solar energy system,
• and secondary by using electricity provided by the grid to cover the full demand.

The simulated building has a battery which can be used to store energy. You can think of using the battery as shifting the electricity demand. By storing energy, you are increasing the energy demand of the building and by discharging/obtaining energy from the battery you are decreasing the energy demand of the building. The task is to choose when to charge and when to discharge the battery. Hereby, the goal is to utilize the battery so energy usage from the solar energy system is maximized and usage of the energy grid is minimized. Note, that excess energy from the solar energy system which is not used by the electricity load or used to charge the battery is considered lost. So better use the solar energy to charge the battery in this case ;-)

Action Space

Action	Min	Max
Charge	-1	1

The actions lie in the interval of [-1;1]. The action represents a fraction of the maximum energy which can be retrieved from the battery (or used to charge the battery) per time step.

• 1 means maximum charging the battery. The maximum charge per time step is defined by the parameter max_battery_charge_per_timestep.
• -1 means maximum discharging the battery, meaning "gaining" electricity out of the battery
• 0 means don't charge or discharge

Observation Space

Index	Observation	Min	Max
0	State of Charge (in kWh)	0	Max Battery Capacity
[1; n]	Forecast Electric Load (in kWh)	0	Max Load in Profile
[n+1; 2*n]	Forecast Solar Generation (in kWh)	0	Max Solar Generation in Profile

The length of the observation depends on the length of the forecast used. By default, the simulation uses a forecast length of 4. This means 4 time steps of an electric load forecast and 4 time steps of a solar generation forecast are included in the observation. In addition to that, the information about the current state of charge of the battery is contained in the observation.

The length of the forecast can be defined by setting the parameter num_forecasting_steps of the Environment().

Reward

As our goal is to use as less energy as possible, the reward is defined by the energy consumed at every time step (times -1):

$$ r_t = -1 * electricity_consumed_t $$

It is important to note, that the term electricity_consumed cannot be negative. This means, excess energy from the solar energy system which is not consumed by the electricity load or by charing the battery is considered lost (electricity_consumed is 0 in this case).

Episode Ends

The episode ends if the max_timesteps of the Environment() are reached.