The World Economic Forum has released a new study on how artificial intelligence (AI) can be used to accelerate a fairer energy transition and build trust in the technology across the industry. As the effects of climate change become more visible around the world, governments and industries face the urgent challenge of moving to a low-carbon global energy system. Digital technologies, especially artificial intelligence, are key factors for this change and have the potential to achieve the climate goals of the energy sector faster and more cheaply.
Written in collaboration with BloombergNEF and the Deutsche EnergieAgentur (dena), the German Energy Agency, Harnessing Artificial Intelligence to Accelerate the Energy Transition examines the status of AI implementation in the energy sector, identifies high-priority applications of AI in the energy transition and offers a roadmap and practical recommendations for the energy and artificial intelligence industries to maximize the benefits of artificial intelligence. The report finds that artificial intelligence has the potential to create significant value for the global energy transition. Based on BNEF’s net-zero scenario modelling, every 1% of additional efficiency in demand creates at $ 1.3 trillion between 2020 and 2050 due to reduced investment needs. AI could achieve this by enabling more energy efficiency and making demand more flexible.
“AI is already making its mark on many parts of society and the economy. In energy, we are only seeing the beginning of what AI can do to speed up the transition to the low-emissions, ultra-efficient and interconnected energy systems we need tomorrow. This report shows the potential and what it will take to unlock it – guided by principles that span how to govern, design and enable responsible use of AI in energy. Governments and companies can collectively create a real tipping point in using AI for a faster energy transition,” said Roberto Bocca, Head of Energy, World Economic Forum.
“As dena, we have been focusing on digital technologies for years. Especially with our ‘Future Energy Lab’ we are boosting Artificial intelligence projects AI is an essential technology for the energy transition since it will provide the glue to connect the different sectors (power, heat, mobility and industry) and serve as digital technology to effectively monitor systems and processes. To efficiently control the energy system of the future, which will be very volatile due to renewable energies, such agent-based control will play an overarching role,” said Andreas Kuhlmann, Chief Executive Officer, dena.
High priority applications of how AI can accelerate the transition to the future of low carbon energy include:
(1) Identifying patterns and insights in data to increase efficiency and savings: According to BNEF’s Net zero scenarios, the full decarbonization of the global energy system between 2020 and 2050 will require investments in energy infrastructure of $ 92 trillion to $ 173 trillion. Even single-digit percentage gains in flexibility, efficiency, or capacity in clean energy and low carbon infrastructure systems can generate trillions of dollars in value and savings.
(2) Coordinating power systems with growing shares of renewable energy: As electricity powers more sectors and applications, the power sector becomes the core pillar of global energy supply, and the increased use of renewable energies to decarbonize the expanding global energy sector will result in more energy from intermittent sources (such as solar and wind), which requires better forecasts, greater coordination and more flexible consumption in order to guarantee the safe and reliable operation of the power grids.
(3) Managing complex, decentralized energy systems at scale: The transition to low-carbon power systems is driving the rapid growth of decentralized power generation, distributed storage, and advanced demand-response capabilities that need to be orchestrated and integrated into much more connected, transactional power grids.
Navigating these opportunities present enormous strategic and operational challenges for energy-intensive sectors and the energy systems themselves as they experience digital transformations once in a lifetime. AI can act as an intelligent layer in many applications and has the ability to identify patterns and insights in the data, “learn” lessons accurately and improve system performance over time, and predict and model possible outcomes for situations, complex and multidimensional . Recent efforts to implement AI in the energy sector have shown promise, but innovation and adoption remain limited. AI holds far greater potential to accelerate the global energy transition, however, it will only take place when there is a larger AI innovation, adoption and collaboration. across the industry. To address this, the white paper establishes a number of principles to help govern the industry and scale AI technology in a fast, safe and secure manner fair way.
“One of the key findings from our workshops was that whilst we could identify many tangible opportunities for AI in the energy transition, there was a real need for a set of common guiding principles to make these opportunities scalable. These principles should ideally create a framework that enables multiple stakeholder groups to work together effectively, on a pre-defined set of activities from governance, to design, to enabling infrastructure. They will enable us to move past the many ‘proofs of concept’ projects towards successful large-scale implementation of solutions,” said Jon Moore, Chief Executive Officer, BloombergNEF.
The nine principles mentioned in the report aim to build industry’s confidence in AI technologies so that they can play a greater role in the energy transition. As AI tools are increasingly adopted across energy and energy-intensive sectors, companies and policy makers must play an active role in controlling and designing the use of AI in the energy sector, define best practices for the responsible design of AI systems and create a conducive environment to exploit the full potential of AI technologies.
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