Assessment of Integrated MV-LV OE Calculations to Orchestrate DERs - OE Algorithm 3: Asset Capacity & Critical Voltage

This repository is part of the project Accelerating the Implementation of Operating Envelopes Across Australia funded by CSIRO. This project provided key metrics, recommendations, and guidance for distribution companies (known as Distribution Network Service Providers [DNSPs] in Australia) and AEMO (the Australian system operator) to assist them in improving and, hence, accelerating the use of Operating Envelopes (OEs) across Australia.

In this repository, we use interactive code via Jupyter Notebook and Python as well as a realistic integrated medium voltage and low voltage (MV-LV) network to demonstrate the process and necessary calculations for the Asset Capacity & Critical Voltage OE Algorithm with Integrated MV-LV Calculation produced by The University of Melbourne. This demonstration is useful for different stakeholders (e.g., DNSPs, AEMO, CSIRO, regulators, consultancy companies, technology providers) as it can help them familiarise with the corresponding algorithm and the required inputs as well as the pros and cons.

Asset Capacity & Critical Voltage OE with Integrated MV-LV Calculation

The Asset Capacity & Critical Voltage OE with Integrated MV-LV Calculations is an intermediate approach - it is more advanced than the Asset Capacity OE as both voltage and thermal aspects are considered, but less advanced than the Ideal OE - that is still relatively simple to be implemented since it does not need network models and only an extra monitoring (e.g., smart meter, temporary network meter) at the critical customer when compared to the Asset Capacity OE. In this OE approach, thermal issues are solved by estimating the spare capacity of MV and LV networks, while voltages issues are solved by estimating the voltage (using simple P-V sensitivity curves) at the critical customer of each LV network.

• Monitoring: At MV and LV head of feeders (P, Q, and V, all per phase), and at the critical customer of each LV network (net demand P, and voltage magnitude V).
• Electrical models: None.

Run the Code

Choose one of the options below to run the code.

A. Cloud Option ☁️: Google Colab

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

B. Local Option 💻: Jupyter Notebook

Make sure you have installed all the pre-requisites (Anaconda, dss_python, requirements). Otherwise, you will not be able to go through the repository.

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 instructions will be in the ipynb file.

Credits

Arthur Gonçalves Givisiez ([email protected])
Andres Avila Rojas ([email protected])
Nando Ochoa ([email protected] ; https://sites.google.com/view/luisfochoa)

Acknowledgements

We acknowledge AusNet Services for providing the data listed below, which was essential to create this repository.

• Anonymised historical active power demand of some customers (smart meter data)
• The data (i.e., topology, impedances, distribution transformers) of one of their MV feeders (indirectly used by this repository)

More details of this data can be found in the Team Nando GitHub repository for Australian MV-LV Networks, files named "Network_4_Urban_CRE21" and "Profiles".

We also acknowledge the Australian Bureau of Meteorology for providing the solar radiation data.

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).