2014 Aurecon recruitment banner

Thinking

How can digital engineering solve congestion in Australia's energy grid?

There’s congestion, not only on our roads

Clean energy will be providing 35 per cent of Australia’s total electricity needs within two years, as new data underlines the pace at which solar power is transforming the national energy market.

Australia’s electricity network is undergoing its greatest transformation since the 1950s. These changes are driven by economic, engineering and environmental factors, including the rise of renewable generation. Consumer preferences are also changing, with an increasing desire for independence and control over electricity supply and use.

Similar to road and rail networks, our electricity grid can become congested too.

Unlike other electricity networks around the world, in Australia, generators cannot currently purchase transmission capacity in the National Electricity Market (NEM). Rather, it is an open market where the lowest cost generators (within physical constraints) are dispatched into the network. For the developers of renewable generation projects, they have the right to connect, but no automatic right to generate revenue in the NEM.

As forces of change in the power sector bring more alternative and renewable sources of power, so too they bring the issue of electricity grid congestion. This occurs because Australia’s electricity grid hasn’t significantly changed since its inception in the early 1900s. Major transmission infrastructure was located to transport electricity from the country’s fossil fuel producing regions, which are in different geographical locations to our renewable energy resources.

Resulting from electricity grid congestion is the more widespread use of the term curtailment. Curtailment refers to how grid congestion, due to lack of transmission capacity, may impact a power generator’s potential to earn revenue.

Electricity grid congestion tends, obstacles, solutions

Curtailment assessments as the future becomes today

Curtailment today is more relevant to generators than it was 20 years ago due to the utility-scale deployment of wind and solar power, and the evolution of wholesale power markets. The opportunity now is to transform the electricity grid so that it can provide the capacity to bring energy from renewable energy sources to the points of demand. As this historic change takes steps into the future, there are teething issues for new renewable generators, including curtailment.

As early identifiers of the growing issue for power sector clients, Aurecon has developed a digital curtailment assessment methodology.

Our methodology has been specifically developed to assist power generation clients in determining the effect that curtailment might have on the revenue of wind and solar energy projects. It enables owners, developers and financiers to better understand the associated operational and economic risks.

Curtailment impacts may result from a lack of transmission capacity to transmit generated electricity from high renewable resource locations and are specific to location due to differences in grid capacity, time of generation, network redundancy and proximity to load.

Curtailing the curtailment

At the moment, most of Australia’s high-capacity electricity transmission infrastructure is built around fossil fuel energy sources. Similar infrastructure is not present near our wind and solar resources as they have only recently started to be developed.

As we transition to an energy supply mix with more renewables, the challenge that power generation developers and network operators face is how to facilitate the delivery of energy from renewable-rich locations to consumers.

With an electricity grid that is presently transitioning from predominantly fossil fuel generation to renewables, the ability to effectively model grid congestion is of value to financiers, utilities, governments and large-scale energy producers. This is where Aurecon’s curtailment assessments can help.

Understanding curtailment levels can be complicated by lack of visibility of where future generators will connect on the grid. Aurecon’s curtailment methodology supports data analysis with publicly available information on future generators, as well as identifying material risk scenarios of what could happen, what is likely to happen and how it would impact the generator at the centre of specific projects.

Energy curtailment infographic

Reaching into the toolkit

Aurecon’s methodology tests curtailment assumptions and provides a clear picture of whether they are consistent with a site’s generation and revenue expectations over the lifetime of the asset.

It’s part of Aurecon’s power advisory toolkit. Using this methodology and publicly available electricity market information, Aurecon can provide clients with an assessment of the amount of energy that is likely to be curtailed due to network constraints. This indicates the impact on the client’s ability to earn revenue over the life of their asset.

The curtailment assessment tool is a software program which assists in forecasting the amount of energy that a generator might have curtailed due to network capacity limitations under a range of scenarios. It is of value to generation asset owners and investors with a desire to understand financial impacts of curtailment as it quantifies the risk and identifies the operating conditions under which curtailment occurs. From this information, the investor or owner may identify a strategic response to the problem. This might include accepting the risk, changing the time at which they generate power, or installing battery energy storage systems to store energy which would otherwise be spilled. 

 

Aurecon curtailment model

Putting the future under a magnifying glass

With curtailment assessments analysing the return on investment for renewables asset developers and owners, just imagine what’s ahead of us.

Could a power generating asset be optimised through the creation of a digital twin, as a test bed to prototype new ideas in a low-risk environment? A digital twin is a virtual model of the physical world.

With advancement of Aurecon’s digital methodology, the tool could build a virtual model of an owner or developer’s power infrastructure and connection to the grid. This digital twin of an asset would allow us to contemplate ways to improve the physical infrastructure and apply those improvements in the digital version and track the responses. The opportunity would then exist to carry these lessons over to ‘real world’ applications so that assets could be made more resilient, more responsive, and less exposed to physical, financial and operational risks.

It may offer dynamic, rapid, low-risk and real-time diagnostic and problem-solving capabilities.

Big Data

What is it and how does it have relevance to curtailment? Big Data is essentially large datasets, and part of Aurecon’s curtailment methodology is collecting and analysing Big Data for renewable generation projects.

The Machine learning approach used in the modelling allows the team to run numerous scenarios of “what could” happen. They then use the results of this analysis to identify key material scenarios which could impact risk. A judgement call can then be made with the client as to whether these scenarios are credible in reality.

Machine Learning

Just imagine going another step into the future and being able to leverage machine learning techniques to help optimise the outputs associated with the forecasts that the curtailment methodology provides.

With the application of machine learning, Aurecon’s curtailment tool could possibly assess every possible curtailment scenario that could conceivably happen in the future and provide users with quantitative analysis, rather than assessing a limited number of scenarios as defined by the humans working on a project.

Machine learning infographic

It’s a huge leap from today and would require extensive and complex developments in Machine Learning to enable the speed and computational efficiency to perform the level of analysis required. However, an environment is fast emerging that might enable digital twins and machine learning to flourish, connecting the physical to the digital world in ways previously unprecedented.

Of course, there’s the question of being ready for all of this. It is crucial to ensure that the accelerating pace of technology does not move faster than our ability to understand the consequences and plan how best to use a digital twin and Machine Learning for the benefit of all stakeholders.

In today’s world though, where energy patterns and technology change rapidly, the future may be upon us quicker than we expect.


About the author

Tomas Keraitis is the Leader of Aurecon's Future Energy team spanning Sydney, Melbourne and Brisbane. He works closely with clients across multiple industry sectors to help them realise their most exciting opportunities or address their most pressing risks relating to the future of energy.

To top