AI is missing link in Australia’s renewable energy race

Right now, Australia cannot connect the required renewable generation to meet clean energy targets without risking energy reliability and uninterrupted access. The industry needs to guarantee energy reliability for the next six years and beyond, with solar, wind and transmission projects falling behind schedule and leaving a significant  shortfall in renewable energy available whilst overall demand continues to rise.

Australia’s energy sector must look beyond the current industry buzz topics of extensively delayed new build renewable infrastructure, coal plant life extensions, and nuclear debates. Across the globe, artificial intelligence (AI) is being heralded for its unparalleled ability to address critical societal problems, revolutionising industries like healthcare, manufacturing, and agriculture.

Despite AI’s rapid advancement and adoption, Australia’s energy industry has not fully embraced AI, discussing only superficial applications instead of the technology’s real potential. The capabilities of AI require the collective industry to act with intent. If harnessed to its full potential, AI could solve the energy transition’s core energy reliability challenge via the underlying pressing issues that are causing it – like improving network capacity, enabling new generation connection, optimising the existing grid and energy distribution, efficiently planning new builds, prioritising grid maintenance, and more.

AI–enabled grid optimisation and renewable integration

Utilities must meet fluctuating energy demand requirements 24/7 to ensure reliability across residential, commercial and industrial customers. Yet Australia is currently predicted to fall over 1.5 GW short of demand by 2030 based on current load increase predictions versus generation project development timelines.

The Australian Energy Market Operator (AEMO) found an additional 6 GW of energy must be introduced into the grid every year over the next five years to address this shortfall. But based on the current trajectory, the network will see 4 GW introduced per year at most.

The development, optimisation and integration of clean energy sources into existing power infrastructure has presented a unique set of challenges specific to reliability.

The first auction of the government’s Capacity Investment Scheme (CIS), which seeks to underwrite at least 32 GW of new wind and solar capacity by 2030, was incredibly popular and oversubscribed with 40 GW of projects registering their interest in the 6 GW auction.

These projects can increase the flow of renewable energy into our grid, allowing us to meet targets. However, growing challenges in this space including growing generation project delays, unclear approval requirements, network connection site determination, and lack of supporting infrastructure are adding pressure to the grid and reducing investor interest. These challenges are exacerbated by the increasing strain on existing infrastructure, which will only continue without optimisation and hardening of the existing network.

Leveraging the current grid to its full potential

For Australia’s end–of–life coal plants to shut down on time, the equivalent amount of energy to service existing demand must be available in the grid from alternatively sourced energy. This will ensure that there are no service disruptions when the switch is flipped. But to be successful requires the network to hold double the capacity of total demand in the lead–up. This is not currently possible due to the network not having capacity for this much energy. Increasing demand, delays in infrastructure build or upgrades, and the manual processes in place to source and access latent network capacity are all further compounding this issue.

AI–based solutions can supply utilities with precise analysis and recommendations based on digital asset models of their assets – like connection sites to generation, transmission lines, and distribution networks. These AI–generated models provide a detailed view of the current grid and a holistic view of its conditions, including potentially underutilised infrastructure.

The power of AI to unlock new capacity in the existing grid has already been identified, with AI–powered digital modelling analysis finding 5–10 GW of latent capacity sitting within the existing grid of one New South Wales utility alone. Unlocking additional capacity on the existing distribution network with targeted minor upgrades will remove constraints on integrating renewable energy projects and reaching that double–total–demand requirement.

Collaboration between utilities and regulators is critical to accessing the large volume of untapped capacity sitting across the broader NEM. Industry collaboration and data sharing between utilities and government bodies have been largely overlooked when concerning the clean energy transition. Coordination amongst industry leaders must start with a shift in energy data usage which remains largely underutilised and data retention, which is often thrown away despite housing a trove of critical insights.

By improving data transparency and AI analysis across generators, utilities, and government bodies, the industry will gain unprecedented insights into shared challenges impacting network reliability— energy market trends, distribution patterns, consumption demand, and the impact of extreme weather events. Industry transparency, collaboration and the uptake of AI will only occur through legislated industry standards and mechanisms encouraging the sector to assess where AI can have the greatest impact to meet the energy transition’s goals.

Missing piece of the clean energy transition puzzle

AI can turn the clean energy transition on its head – but requires the industry to actively engage with the technology to improve understanding and reap long–term rewards. It cannot afford to place AI in the ‘too hard’ basket. Our nation is already behind schedule and we need revolutionary technologies to overcome energy access and availability issues whilst addressing our greatest energy challenge – reliability.

The existing network is rapidly ageing and requires urgent intervention to manage increasing load demands, the increasing frequency of extreme weather conditions, and the growing energy demands from new technologies – including AI itself. By utilising industry data and AI on the network and planned infrastructure, Australia can unlock a simplified, streamlined pathway to the energy transition and keep the 2030 and 2050 targets within reach.