4.2.2 Tripping (anti-islanding and limits for sustained operation) curtailment

Microsoft [42]. The grant credits have been used for the storage and computational expenses of the CANVAS technical stream.

A schematic for the data access and analysis platform is presented in Figure 8 below. The shared dataset was migrated to a local New South Wales (NSW) Microsoft data centre via Azure Data Share and stored in a newly created Azure Storage account. The stored data was then transferred to Azure’s data analysis platform, Azure Data Explorer (ADX). The data exploration and preliminary analysis was done in the ADX platform through using its native query language, Kusto Query Language (KQL). More detailed curtailment analysis was carried out in Jupyter Lab/Python through the ADX - Jupyter Lab plug-in. The results were visualized by Python’s visualization package Matplotlib and Microsoft Power BI. The dataset provided by Solar Analytics consisted of ‘csv’ files, and as a result data could be directly analysed within Jupyter Lab/Python.

Figure 8 A schematic for data access and analysis structure for AGL’s VPP dataset

DER inverters can trip under two different conditions as specified in AS/NZS 4777.2 (year depends on inverters installation date):

• Anti-islanding: When the voltages are outside the lower and upper bounds of the anti-islanding settings for a short period • Limits for sustained operation: When voltages are sustained above an upper bound for 10 minutes

The studied datasets capture a snapshot of the voltage every interval (e.g. a snapshot at the end of each 60s period in the case of the Solar Analytics dataset) and therefore the dataset does not offer a complete picture of voltage conditions experienced by the inverter. Further, a 10min average calculated using the