/Tutorial-DERHostingCapacity-4-MonteCarlo_MV-LV

Primary LanguageJupyter NotebookBSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause

Tutorial on DER Hosting Capacity - Part 4: Monte Carlo Assessment of PV Hosting Capacity of an Integrated MV-LV Network

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 4: Monte Carlo Assessment of PV Hosting Capacity of an Integrated MV-LV Network

The objectives of this tutorial are:

  1. To familiarise with the process by which power engineers can carry out Monte Carlo-based time-series analyses and determine the PV Hosting Capacity of a given MV-LV (22kV-0.4kV) distribution network considering uncertainties due to customer demand, customer phase connection, PV generation, and PV location.
  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.

Pre-Requisites for Part 4

  • You should have completed Part 1, Part 2 and Part 3.
  • For this part, you just need Python (Anaconda) and the dss_python module. OpenDSS was only needed in Part 0 for demonstration purposes.

Run Part 4

Make sure you have installed Anaconda, the dss_python module, etc. 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

This Repo and Adaptations to the Original Python Code

Angela Simonovska (asimonovska@student.unimelb.edu.au)
Orlando Pereira Guzman (opereiraguzm@student.unimelb.edu.au)
Yushan Hou (yushou@student.unimelb.edu.au)
Jing Zhu (jinzhu5@unimelb.edu.au)
Muhammad Zulqarnain Zeb (m.zeb@unimelb.edu.au)
Fahmi Angkasa (angkasaf@student.unimelb.edu.au)
Nando Ochoa (luis.ochoa@unimelb.edu.au ; https://sites.google.com/view/luisfochoa)

Original Python Code

Andreas Procopiou (andreasprocopiou@ieee.org)

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).