Tutorial on DER Hosting Capacity - Part 1: Advanced Tools for the Analysis of Three-Phase Unbalanced LV Networks

December 5, 2024 · View on GitHub

Tutorial on DER Hosting Capacity

This multi-part Tutorial on Distributed Energy Resource (DER) Hosting Capacity will guide you, using interactive code via Jupyter Notebook and Python, through the different steps to run advanced, detailed time-series simulations to properly assess the technical impacts of DERs (such as solar photovoltaics ☀️🏡) on realistic three-phase unbalanced distribution networks.

This Tutorial is designed for power engineering students (undergraduate and postgraduate), power engineers, researchers, consultants, etc. It requires some knowledge of coding (of course! 🤓) but not too advanced. If you are a decent coder, you will manage 😉.

Part 1: Advanced Tools for the Analysis of Three-Phase Unbalanced LV Networks

The objectives of this tutorial are:

  1. To familiarise with the process by which power engineers can run a power flow for a given three-phase unbalanced low voltage (LV) distribution network. To achieve this, you will learn the basics of how to model an LV distribution network and investigate certain aspects such as voltage regulation and energy losses.

  2. To continue familiarising with advanced tools useful to run distribution network studies involving DERs. You will continue using OpenDSS via the DSS-Python module. And, to guide you, all will be done using a notebook on Jupyter Notebook.

Run Part 1

To make the most of Part 1, you should have completed Part 0 and, of course, be familiar with OpenDSS for Beginners and the modelling of distribution networks and DERs.

Choose one of the options below to run Part 1.

Cloud Option ☁️: Google Colab

Just click on the badge . You don't need to install anything 🤓💪.

Local Option 💻: Jupyter Notebook

Make sure you have installed Anaconda and the DSS-Python module as specified in Part 0. Otherwise, you will not be able to go through the tutorial. To guarantee that you have all the necessary packages you can also run the requirements.txt file using pip install -r requirements.txt on the Anaconda prompt.

  1. Download all the files using the green <> Code button at the top right.
    • You will get a ZIP file with a folder that contains all the files.
    • Unzip the file and place the folder somewhere on your computer/laptop.
  2. To open the Jupyter Notebook file (extension ipynb) you need to:
    • Open Anaconda Navigator
    • Click on Launch Jupyter Notebook (it will open in your browser)
    • Upload the unzipped folder (with all the corresponding files) to Jupyter Notebook (the location is up to you)
    • Go inside the folder and open the ipynb file

All the tutorial instructions will be in the ipynb file.

Enjoy! 🤓

Credits

Angela Simonovska (asimonovska@student.unimelb.edu.au)
Eshan Karunarathne (akarunarathn@student.unimelb.edu.au)
Fahmi Angkasa (angkasaf@student.unimelb.edu.au)
Andres Avila Rojas (aavilarojas@student.unimelb.edu.au)
Nando Ochoa (luis.ochoa@unimelb.edu.au ; https://sites.google.com/view/luisfochoa)

Acknowledgement

The content of this repository has been produced with direct and/or indirect inputs from multiple members (past and present) of Prof Nando Ochoa’s Research Team. So, special thanks to all of them (many of whom are now in different corners of the world).

Licenses

Since this repository uses DSS-Python which is based on OpenDSS, both licenses have been included. This repository uses the BSD 3-Clause "New" or "Revised" license. Check all corresponding files (LICENSE-OpenDSS, LICENSE-DSS-Python, LICENSE).