https://github.com/sibyjackgrove/gym-solarpvder-environment

Solar Photovoltaic Distributed Energy Resource environment for Open AI Gym
https://github.com/sibyjackgrove/gym-solarpvder-environment

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Solar Photovoltaic Distributed Energy Resource environment for Open AI Gym

• Host: GitHub
• URL: https://github.com/sibyjackgrove/gym-solarpvder-environment
• Owner: sibyjackgrove
• License: mit
• Created: 2019-03-18T16:05:35.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2020-10-15T22:49:05.000Z (about 4 years ago)
• Last Synced: 2024-06-11T20:17:29.634Z (7 months ago)
• Language: Python
• Size: 477 KB
• Stars: 14
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
**Status:** Expect regular updates and bug fixes.

# Gym environment for PV-DER

[![CodeFactor](https://www.codefactor.io/repository/github/sibyjackgrove/gym-solarpvder-environment/badge/master)](https://www.codefactor.io/repository/github/sibyjackgrove/gym-solarpvder-environment/overview/master)

Solar photovoltaic distributed energy resources (PV-DER) are power electronic inverter based generation (IBG) connected to the electric power distribution system (eg. roof top solar PV systems). This environment consists of a single DER connected to a stiff voltage source as shown in the following schematic:

![schematic of PV-DER](PVDER_schematic.png)

## Basics

The dynamics of the DER are modelled using dynamic phasors. One step in the environment is equivalent to **n** simulation time steps and each simulation time step is one half-cycle (1/120 s).

### Events in the environment

There are two types of perturbations in the environment: a) Change in grid voltage magnitude b) Change in solar insolation.

## Installation

First install the [Solar PVDER simulation utility.](https://github.com/sibyjackgrove/SolarPV-DER-simulation-utility) Then you can install the PVDER environment using following commands:

```

git clone https://github.com/sibyjackgrove/gym-SolarPVDER-environment.git

cd gym-SolarPVDER-environment

pip install -e .

```

Other dependencies: OpenAI Gym, Numpy, SciPy

## Using the environment

The environment can be instantiated just like any other OpenAI Gym environment as show below:

```

import gym

import gym_PVDER

env = gym.make('PVDER-v0')

```

### Available user options

* DISCRETE_REWARD: If this is set to *True* discrete rewards (i.e. +1, -1, -5 etc) will be returned every time step. Otherwise reward will be the error between goals and actual values.

 

### Try it out in Google Colab:

Create environment and make a random agent interact with it:

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sibyjackgrove/gym-SolarPVDER-environment/blob/master/examples/gym_PVDER_environment_import_test.ipynb)

The environment can also be used with TensorFlow agents. Try out the demo below which trains a DQN agent using the environment:

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sibyjackgrove/gym-SolarPVDER-environment/blob/master/examples/gym_PVDER_environment_tf_agents_DQN_demo.ipynb)

## Citation

If you use this code please cite it as:

```

@misc{gym-PVDER,

  title = {{gym-SolarPVDER-environment}: A environment for solar photovoltaic distributed energy resources},

  author = "{Siby Jose Plathottam}",

  howpublished = {\url{https://github.com/sibyjackgrove/gym-SolarPVDER-environment}},

  url = "https://github.com/sibyjackgrove/gym-SolarPVDER-environment",

  year = 2019,

  note = "[Online; accessed 18-March-2019]"

}

```