Australia’s energy sector faces a delicate balancing act in achieving 82 per cent renewable energy by 2030 and net-zero emissions by 2050, while maintaining safe, reliable and affordable service.
The race to stabilise a decentralised grid increasingly powered by renewable energy is no small feat in itself. At the same time, energy providers must also keep up with the surging power requirements of energy-hungry data centres amid the artificial intelligence (AI) revolution.
The International Energy Agency (IEA) forecasts that by 2026, data centres worldwide will require as much energy as Japan needs to serve its population of 125 million people. In addition, the growing adoption of distributed energy resources (DERs) like electric vehicles, rooftop solar and residential battery storage is driving a fundamental shift in electricity management – what once was a one-way flow of electricity has evolved into a dynamic two-way exchange.
The Australian Energy Market Operator (AEMO) forecasts that Australia’s energy demand will double by 2050, compared to 2024 levels. These unprecedented forecasts are challenging traditional approaches of building capacity to meet this demand. Utilities can benefit from a new generation of technologies that will help them create efficiencies and meet these intensifying demands. Both a challenge and a solution, they are turning to AI and machine learning (ML) to maximise grid efficiency. This may well be the answer in helping transform energy operations toward a greener, more sustainable future.
Managing rising demand with DERs and AI
As utilities urgently expand capacity to meet rising energy demand, it’s unrealistic to expect them to phase out all fossil fuels in the immediate future. Australia will continue to rely on a combination of fossil fuels and renewable energy, especially as DERs gain traction and reshape the grid.
While it will help to meet the growing demand for energy, AI is also accelerating that demand. Generative AI queries – which leverage the technology to create new content – use 33 times more energy than a task-specific model. Conversely, AI can also be a part of the solution – utilities have the opportunity to leverage AI to drive smarter, more efficient infrastructure and ensure AI’s energy consumption doesn’t outweigh its benefits.
Even in managing renewables, AI can play a decisive role in bridging the growing gap between potential generation and actual usage. For example, it can optimise grid performance, forecast load fluctuations and support flexible pricing mechanisms such as location-based tariffs in order to match supply and demand more efficiently.
In Victoria, Jemena, in collaboration with Itron, is utilising machine learning algorithms to monitor grid health and manage load fluctuations across its electricity distribution network. It’s allowing for early detection of stress points and supports more proactive grid management as DER volumes continue to grow.
AI’s role in solving the supply-demand mismatch toward net-zero
The IEA suggests that, when harnessed effectively, the widespread adoption of AI could lead to emission reductions that far exceed the emissions generated by data centres themselves. As AI technologies shift closer to the edge of the grid, they strengthen the case for grid-edge intelligence – for example, the ability to improve fault detection and isolation, which are capabilities that Itron has incorporated into its Grid Edge Intelligence solution.
With electric vehicle (EV) adoption rising fast, vehicle-to-grid (V2G) technology intends to shift EV’s away from being just passive loads to the use of AI to help orchestrate when and how EVs import or export power. This turns hundreds of thousands of these vehicles into a valuable peer-to-peer energy resource that truly reduces curtailment and keeps the grid balanced.
Additionally, AI’s ability to manage battery storage and distribution in real time means that surplus energy –such as from solar peaks – can be stored in distributed batteries and deployed strategically when needed. In the wider context of battery management, AI algorithms can predict when to charge or discharge batteries based on real-time and forecasted energy prices, household energy consumption patterns, weather forecasts and grid conditions, such as congestion or peak demand.
AI can also empower customers through analysis of their data, providing personalised tips on energy usage and recommend participation in relevant programs, a capability ranked amongst the top five AI advantages in Itron’s Resourcefulness Insight report.
While 51 per cent of Australian utilities have made substantial investments in mature AI projects, greater sector-wide action is needed to capitalise on AI’s full potential and drive scalable innovation.
A smarter way forward for clean energy
The integration of AI into Australia’s energy landscape marks a significant step in modernising grid operations and accelerating the clean energy transition. While AI is adding to energy demand today, it’s also becoming a powerful ally. Itron said it has seen firsthand how it’s helping utilities operate more efficiently, manage resources more intelligently, predict demand, reduce waste and discover breakthroughs in renewable technologies.
The real test ahead, Itron said, is to integrate AI such that the complex tasks we cannot do now can be orchestrated by AI in a simple manner to criteria we can enunciate. Equally, the company said we need to embrace AI rather than fear it, lest we fall into the trap Marie Curie cautioned: “There is nothing in life to be feared, it is only to be understood. Now is the time to understand more, so that we may fear less.”
Only through that engagement can we ensure AI works in unison with renewables, storage and DERs to build Australia’s resilience and meet our future needs.
To learn more, visit aunz.itron.com
