As the impacts of climate change become more visible worldwide, governments and industry face the urgent challenge of transitioning to a low-carbon global energy system.
Digital technologies – particularly AI – are key enablers for this transition and have the potential to deliver the energy sector’s climate goals more rapidly and at lower cost.
Written in collaboration with BloombergNEF and Deutsche Energie-Agentur (dena) – the German Energy Agency, Harnessing Artificial Intelligence to Accelerate the Energy Transition reviews the state of play of AI adoption in the energy sector, identifies high-priority applications of AI in the energy transition, and offers a road map and practical recommendations for the energy and AI industries to maximize AI’s benefits.
The report finds that AI has the potential to create substantial value for the global energy transition. Based on BNEF’s net-zero scenario modelling, every 1 percent of additional efficiency in demand creates $1.3 trillion in value between 2020 and 2050 due to reduced investment needs. AI could achieve this by enabling greater energy efficiency and flexing demand.
“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 for how AI can accelerate the transition to low-carbon energy future include:
(1) Identifying patterns and insights in data to increase efficiency and savings: According to BNEF’s net-zero scenarios, fully decarbonising the global energy system will require between $92 trillion and $173 trillion of investments in energy infrastructure between 2020 and 2050. Even single-digit percentage gains in flexibility, efficiency, or capacity in clean energy and low-carbon infrastructure systems can therefore lead to trillions of dollars in value and savings.
(2) Coordinating power systems with growing shares of renewable energy: As electricity supplies more sectors and applications, the power sector is becoming the core pillar of the global energy supply. Ramping up renewable energy deployment to decarbonize the globally expanding power sector will mean more power is supplied by intermittent sources (such as solar and wind), creating a need for better forecasting, greater coordination, and more flexible consumption to ensure that power grids can be operated safely and reliably.
(3) Managing complex, decentralized energy systems at scale: The transition to low-carbon energy systems is driving the rapid growth of distributed power generation, distributed storage, and advanced demand response capabilities, which will need to be orchestrated and integrated into much more networked, transactional power grids.
Navigating these opportunities presents huge strategic and operational challenges for energy-intensive sectors and energy systems themselves, just as they are undergoing once-in-a-lifetime digital transformations. AI can act as an intelligent layer across many applications and has the ability to identify patterns and insights in data, “learn” lessons accurately and improve system performance over time, and predict and model possible outcomes for complex, multivariate situations.