In Part One of our series ‘Quantum Meets Energy: Revolutionising Australia’s Energy Landscape’, we introduced quantum technologies (QTs) and their benefits, challenges to adoption, and recent government support for QT in the New Energy transition.
In Part Two, we delve into potential applications of QTs across the renewable energy sector, the immense opportunity for Australian governments and organisations, and consider the future of New Energy trends in a QT world.
Applications for Quantum Technologies
Grid Optimisation
Integrating Distributed Energy Resources
QTs may be used to analyse the vast data generated by distributed energy resources (DERs).1 In our recent bulletin, we discussed how proposed rule changes by the Australian Energy Market Commission incentivise customers to integrate DER (including rooftop solar, battery storage and electric vehicles) into the National Electricity Market (NEM). The increasing abundance of DERs presents an opportunity to leverage vast quantities of data produced by DERs that, when analysed by quantum computers, could inform decisions on how to optimise the NEM. The information and insights gathered from the data analysis could enhance energy distribution and enable smarter decision making.2 Importantly, quantum computers have the ability to run complex algorithms over DER data to quickly and accurately simulate efficient energy distribution.3 In combination with quantum sensors, energy consumption can be tracked precisely, enabling demand to be met more productively.4 As DERs become more widespread, analysing data via quantum computers will be prioritised over traditional computers, as it is unlikely existing technology will be capable of performing equivalent analyses.
Grid Infrastructure
Quantum computer simulations can also determine optimal locations of power generation facilities by analysing numerous variables relevant to decisions on where to place an energy resource.5 Further, traditional computers lack the capability to efficiently consider those variables. Supply chain considerations, supply-demand models, and emissions reduction models may all feed into decisions on the location of an energy resource.6 The volume of data and complexity of interrelated considerations when determining resource placement requires incredibly sophisticated and advanced computing abilities.7 Quantum computers are expected to have these capabilities. Additionally, once an infrastructure asset is developed, quantum sensors may provide advanced real-time monitoring and fault diagnosis of that infrastructure, helping to maximise the infrastructure’s uptime.8
These developments have important implications for energy resource proponents and local, state and federal planning authorities. For example, QTs may be able to identify ideal locations for wind farms that cannot be readily identified using existing modelling techniques. Further, once they are developed, projects may have longer life spans because developers can remediate and mitigate faults on an ongoing basis through quantum sensor monitoring.
Materials and Yield Enhancements
Batteries
QTs may also be applied in researching and modelling future battery compositions. Energy density improvements in lithium-ion (Li-ion) batteries have slowed in the past ten years.9 Li-ion batteries are forecast to see density improvements of 17% between 2020 and 2025, down from 50% between 2011 and 2016.10 To address this, quantum computers may simulate improved chemical compositions and material enhancements to Li-ion batteries that cannot be identified with existing technologies.11 Such improvements could lead to Li-ion batteries with 50% higher energy density,12 improved thermal efficiencies and extended lifespans.13 These developments could enable variable renewable energy sources such as wind and solar to be integrated into the NEM at lower costs.14 They could also reduce the capital expenditure requirements for industries likely to rely on advanced Li-ion batteries such as transport and short-haul aviation.15
Solar Cells
It is broadly accepted that existing solar photovoltaic (PV) cell technology (crystalline silicon) is not as energy efficient as it could be. Quantum computers could address this issue by accurately simulating higher efficiency solar PV cells than contemporary crystalline silicon solar PV.16 Additionally, researchers may use quantum sensors to understand the magnetic behaviours of new materials to help identify which materials should be manufactured.17 One investigation has estimated that if simulated theoretical efficiencies of an alternative solar PV cell material known as perovskite crystal can be achieved in practice, the levelised costs of electricity from solar PV could decrease by 50%.18 The chemical developments achievable through quantum computing and quantum sensor modelling may lead to greater uptake of solar PV technology by consumers as purchasing costs are reduced. Similarly, they could reduce the environmental impact of grid-scale solar PV projects by reducing the total land required. This is because the efficiency gains of individual solar PV panels may lead to smaller areas of land being required to reach energy production targets.
Hydrogen
In a previous article, we discussed how hydrogen can be produced by electrolysis, a chemical reaction. However, electrolysis is sometimes inefficient. Quantum computers could help simulate more complimentary electrolysis chemicals and materials so that hydrogen is produced more efficiently.19 It has been estimated that electrolysis enhancements demonstrated by quantum computing modelling could produce hydrogen more efficiently and at 35% less cost.20 These improvements have important implications for the viability of hydrogen technology as a future fuel source, particularly in transportation and aviation industries where narrow profit margins influence sustainability strategies.
Biomass
QTs could also be applied in Australian biofuel production by identifying the optimal biomass conversion materials and techniques for generating increased yields of biofuel.21 In doing so, QTs could help unlock up to 2,600 petajoules of biomass capacity per annum in a manner that is more efficient and more cost-effective than is available with existing conversion techniques.22 Theoretical biomass represents more than 40% of Australia’s current primary energy supply, and so unlocking it using QT simulations may have profound influence on Australia’s energy future.23
Future Outlook
As Australia matures on its renewable energy journey, QTs offer unique opportunities for industry participants. QTs hold the potential to optimise the grid, discover new materials, increase efficiencies and uncover improvements in energy yields. These quantum leaps will empower Australia to meet and exceed its sustainability goals, setting a global benchmark in harnessing the power of nature with the precision of quantum science. The journey ahead is as exciting as it is essential, promising a cleaner, more efficient, and resilient energy landscape.
For more information, please contact Matt Baumgurtel, Adriaan van der Merwe and William Ryan.
1Ulrich Scholten and Dawn Illing, ‘How to Leverage the Opportunities and Prepare for the Threats of Quantum Computers in the Energy and Utilities Industry’, (Web Page, 27 July 2023) <https://utimaco.com/news/blog-posts/quantum-computers-in-the-energy-utilities-industry>.
2Ibid.
3Ibid.
4Scott Crawford et al, ‘Quantum Sensing for Energy Applications: Review and Perspective’ (2021) 4(8) Advanced Quantum Technologies 2100049:1-33, 3.
5Ulrich Scholten (n 1).
6Akshay Ajagekar and Fengqi You ‘Quantum computing for energy systems optimisation: challenges and opportunities’ (2019) 179 Energy 76, 76.
7Obafemi Olatunji, Paul Adedeji and Nkosinathi Madushele, ‘Quantum computing in renewable energy exploration: status, opportunities, and challenges’ in Ahmad Taher Azar and Nashwa Ahmad Kamal (eds), Design, Analysis and Applications of Renewable Energy Systems (Academic Press, 2021) 549, 559.
8Ibid, 561.
9Peter Cooper et al, ‘Quantum computing might just save the planet’, (Article, 19 May 2022) <https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/quantum-computing-just-might-save-the-planet>.
10Ibid.
11Ibid.
12Ibid.
13Deloitte, ‘Quantum Computing for Climate Action’, (Report, September 2023) 9 <https://www2.deloitte.com/content/dam/Deloitte/us/Documents/quantum-computing-climate-change-2023.pdf>.
14Peter Cooper (n 9).
15Ibid.
16Ibid.
17Erin Grant, ‘Making sense of quantum sensing’, (Web Page, 6 June 2024) <https://www.csiro.au/en/news/All/Articles/2024/June/quantum-sensing>.
18Peter Cooper (n 9).
19Ibid.
20Ibid.
21Obafemi Olatunji (n 7) 560.
22ENEA Australia Pty Ltd and Deloitte Financial Advisory Pty Ltd, ‘Report: Australia’s Bioenergy Roadmap’, Australia’s Bioenergy Roadmap Report (Report, November 2021) 23 <https://arena.gov.au/assets/2021/11/australia-bioenergy-roadmap-report.pdf>.
23Ibid.
