Unfortunately most of the work Grids carries out for clients is not public. But every so often it is! Below is a list of some of them.

The 2023 DER in Energy Markets Report

The DER in Energy Markets Report is an annual review of how distributed energy resources are interacting with markets in Australia’s National Electricity Market.

Rule Changes to Improve FCAS Procurement

Two rule changes Grids submitted to improve FCAS cost allocations and ensure central dispatch better manages these costs. These changes are intended to lower the costs in the energy system.

A Review of Australian Microgrid Technologies

This report describes different technology solutions and functions that microgrid projects require, as well as a review of vendor offerings in relation to these functions. The information contained in this report serves as a guide and examples of what groups can consider when assessing and procuring microgrid technologies.

DEIP DER Market Integration Trials Summary Report

An ARENA funded report that presents a summary of the approaches to DER market integration being tested by AEMO’s Project EDGE, Western Power’s Project Symphony, Ausgrid’s Project Edith and Evoenergy’s Project Converge.

AFR: Everyone wants a piece of $1.9trn renewable energy opportunity

A piece outlining the transition to electrification and the breadth of established players innovating and new companies entering the industry to seize the opportunity.

DER 2.0: Customer Focused Design for DER Participation

An ARENA funded project with UPowr where consumer-centric and commercially viable virtual power plant (VPP) programs were designed that would appeal to customers.

Energy Watch: Mobile App for Wholesale Market Prices

Energy market apps are often quite complicated. This simple app displays NEM wholesale market prices in an easy to consume way, similar to a weather report. Used by hundreds of energy professionals.

Machine Learning Training Sets for Wholesale Price Prediction

Large amounts of market and weather data that can be used to train machine learning electricity price predictors in the NEM. This also includes instructions on how to easily train and operationalise a price predictor.