By Pamoda Wijetunge, Regional Manager APAC at Ampacimon
As Australia grapples with the shift to net zero, limited transmission capacity is proving to be a major bottleneck but new grid enhancing technologies are emerging as the frontrunner in releasing the hidden capacity of the electricity network.
Although Australia still relies heavily on fossil fuels for electricity generation—fossil fuels contributed 61% of total electricity in 2025, according to green thinktank Ember—it has ambitious plans for transition to net zero. Power sector emissions have fallen by 23% from their 2009 peak and Australia aims to cut emissions by up to 70% by 2035 and reach net zero by 2050.
Certainly, the Australian Energy Market Operator’s (AEMO) latest quarterly report on the National Electricity Market (NEM) for the quarter ending in December 2025 reveals continued growth in renewables. Total generation averaged 25,064MW, with renewables (including storage) delivering 51% of the overall supply for the first time, up from 46% from the equivalent quarter a year earlier. Average renewable increased by 1,256MW over the year driven by a 932MW or nearly 3% uplift in wind generation and a 324MW or 15% increase in grid-scale solar. While total generation capacity grew by more than 3%, demand rose by a modest 177MW or less than 1% over the same period.
However, not only is the changing generation profile placing far greater demands on the transmission and distribution grid, but transmission bottlenecks have also been identified as a limiting factor in bringing new generation projects online in a timely and efficient manner. In its report AEMO notes that network curtailment and economic offloading increased year-on-year, reducing potential growth in grid-scale solar output by 312MW. Average curtailment of grid-scale solar generation caused by network constraints increased from 176MW to an all-time high of 213MW for the same quarter between 2024 and 2025, an increase of 21%. This not only leads to increases in wholesale energy prices, but also puts project investment at risk, ultimately impeding Australia’s progress towards its emissions reduction targets.
Perhaps more significant is Australia’s burgeoning grid queue. According to AEMO, at the end of 2025, more than 63GW of new capacity was progressing through the grid connection process. This is 30% more than a year earlier when less than 50GW was in process.
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Australia’s grid development challenge
Alongside the changing generation profile there are a number of structural issues that also create problems when it comes to increasing grid capacity. Australia’s unique geography also creates challenges for the development of new transmission assets, with vast distances between load centres and renewable generation resources. Furthermore, where the transmission grid crosses state boundaries, infrastructure projects can face regulatory hurdles given the complex web of federal and state regulations in place, as well as fierce opposition that is raised by affected communities.
Nonetheless, the Australian government has committed to a programme to modernise and upgrade the grid. The Rewiring the Nation programme provides finance via the Clean Energy Finance Corporation (CEFC) to lower the cost to consumers, but major investment is still required for Australia to respond to climate change and transition to a renewables-led energy sector.
It’s no small feat. AEMO is developing the 2026 Integrated System Plan (ISP) but the current ISP from 2024 forecasts that an estimated 4,581km of new transmission lines are needed to meet 2030 targets. This comes at a substantial cost—Rewiring the Nation investment for New South Wales alone comes with a price tag of up to A$4.7 billion and this cost will ultimately be borne by consumers.
Beyond grid investments, Australia’s NEM is also burdened with an increase in negative prices. Negative prices, which reportedly occur about a quarter of the time in Victoria and NSW for example, arise when renewable generation capacity exceeds demand. Project owners are essentially paying the market to take their energy away. Again, this winds up as a cost to consumers but could be at least partly addressed with additional export capacity. The alternative is that renewable energy generation is curtailed. This impacts their profitability and the overall efficiency of the system. AEMO forecasts curtailments rates of between 35-65% by 2027 due to critical delays in energy transition infrastructure projects.
Grid Enhancement Technologies (GETs)
One readily proven solution to many of these issues comes from grid-enhancing technologies which can immediately increase capacity without necessarily building new lines. Perhaps the principal problem stems from how network capacity is calculated, given it is based on thermal ratings for overhead lines. Higher thermal ratings result in conductors excessively sagging, which increases the risk of an arc to objects such as vegetation or even touching grounded objects. To avoid any potential issues most Transmission Network Service Providers (TNSPs) base capacity on conservative seasonally adjusted ratings or ambient adjusted ratings.
As the names suggest, seasonally adjusted ratings are typically set for summer and winter and are broadly based on average temperatures. Ambient Adjusted Ratings (AAR) are a more sophisticated approach that adjusts line ratings based on real or forecast environmental conditions such as ambient temperature from weather stations and solar irradiance derived from IEEE/CIGRE algorithms. While this approach allows utilities to set conductor current carrying capacity in a dynamic way that is based on actual weather conditions rather than relying on conservative seasonal assumptions, this still does not account for the actual conditions along the transmission line. These AAR models do not, for example, take into account other significant influences on conductor conditions and therefore capacity, such as the cooling effect of the wind. Furthermore, even where AAR models do consider wind conditions, hyper local influences within critical spans, such as the location of a tree, can become a significant influence on conductor conditions but are extremely hard to calculate, particularly during normal operating conditions with wind speeds less than 2m/s.
In a further advance though, Dynamic Line Rating (DLR) models are based on real-time data of actual conductor conditions, often coupled with highly accurate weather forecasting models. Ampacimon, however, has developed patented sensor technology fitted with accelerometers that directly measures the vibration frequency in the conductor caused by wind or thermal convection. By analysing the vibration spectrum, accurate assessment of both the sag and the perpendicular wind speed can be derived along with the temperature. The sag measurement accuracy is within 20cm, thus providing an extremely high confidence level in meeting the statutory clearance requirements as more line capacities are extracted. Crucially, this method gathers accurate data from the line itself, resulting in a fully autonomous system that performs reliably even in complex or remote environments typically found in Australia.
This data stream is coupled with weather forecasts to provide not only accurate real-time line ratings, but also predictions for line capacity many hours ahead to shape the dynamics of the NEM.
DLR delivery
TNSPs in Australia have been using AAR-based rating methodologies using weather station sensors for more than a decade. Now though, recognising the potential advantages that can be achieved by adopting grid enhancing technologies, they are primed to take the next leap with on-conductor sensor-based DLR technology. For example, the Australian Government under the auspices of the Department of Climate Change, Energy, the Environment and Water is investing $30 million through a Grid Enhancing Technologies grants programme. Running between 2025–26 through to 2028–29, a grant application round opened in July last year. The goal of the programme is to reduce delays in delivering new renewable energy projects and make the electricity system stronger by allowing existing networks to carry more electricity.
Along with the US, Europe and parts of Asia, AEMO is also using real time dynamic line rating data in its dispatch processes on selected circuits. It is pleasing to note that AEMO’s Dispatch Engine provides information on the possible market benefit associated with each additional megawatt transfer on a constrained line via Marginal Values in $/MW, so the market benefit can be quantified. As Renewable Energy Zones (REZ) are being developed across the NEM, the significance of DLR will likely become even more prominent in a market structure operating with only a spot market. These REZs will have significant renewable energy generation resources and will therefore become highly suitable for DLR given high wind speeds imply more transmission capacity just as wind power reaches a maximum. If integrated efficiently, DLR would reduce entry costs for renewable project developers and enable pathways for future dynamic congestion management.
The ability of DLR systems to forecast future line ratings hours will also undoubtedly play a role in managing wholesale electricity price volatility and curbing the potential for negative pricing and subsequent redispatch costs. For this reason, DLR is fast becoming a well-entrenched application in utility controls room operations, with integration to Energy Management Systems (EMS) and Market Systems via SCADA protocols, APIs and other real time data exchange interfaces. However, there are a number of structural issues with Australia’s electricity market that could potentially act as barriers to widespread deployment of DLR technology. For example, regulatory incentive mechanisms such as Network Capability Incentive Parameter Action Plan (NCIPAP) & Demand Management Innovation Allowance (DMIA) exist for network operators to address network constraints and improve utilisation. However, the allowance limits are extremely low when considering DLR is a fair dinkum alternative to capex deferrals or avoidance altogether. As it stands, the framework does not fully incentivise those TNSPs which choose to maximise the utilisation of existing grid infrastructure. This contrasts with much of Europe where the cost benefit is quantified by the avoidance of redispatch costs.
According to TenneT, Germany’s largest transmission system operator, in 2024 German system operators collectively had to spend approximately €2.8 billion on redispatch to stabilise the grid. However, they calculate that using DLR allowed German TSOs to save around €1 billion in redispatch costs in 2023 alone. A similar approach to policy may be needed in Australia to ensure consumers are able to reap the full rewards of DLR.
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Further, the intelligent line sensor installations no longer require lengthy outage planning processes with the market operator as they can be simply installed on live lines in a matter of minutes at voltages of up to 500kV via drones, thus ensuring personnel safety as well as resource efficiency. A video showing the installation can be seen here. The line sensors are powered through induction and are maintenance-free set-and-forget devices. Equipped with cellular, satellite or IoT M2M (Machine to Machine) communications, seamless connectivity on every transmission corridor is achievable in minutes and DLR functionality can be implemented in just a few months.
Modern DLR systems are proven to yield up to 40% extra capacity, while still maintaining safe operating margins. This capability is likely to become even more important over time as climate change impacts make it far harder to estimate safe boundaries of traditional rating approaches. The future is looking bright as AI starts to play a pivotal role in GETs such as DLR and the focus would shifts from line to grid-wide optimisation.
With the electricity transmission sector grappling with multiple challenges from changing generation and consumption patterns, climate change, community backlash to supply chain bottlenecks, DLR is emerging as the hidden gem in unlocking untapped network capacity.
