Taking inspiration from a report by the International Energy Agency (IEA), this article explores how Artificial Intelligence (AI) is rapidly becoming a transformative force across industries, with the energy sector being no exception.
The energy sector is under increasing pressure to innovate in response to growing energy demand driven by the economies of developing countries, the challenges of transitioning to sustainable energy systems, and the urgent need for decarbonization.
How will AI transform the energy sector?
In this article we will explore how AI could enhance innovation, efficiency, and sustainability within the sector. We will delve into key areas, including:
- The role of AI in the energy sector
- Applications of AI for energy innovation
- Ai for Energy Efficiency and Decarbonization
- Advanced Tools for Energy: Self-Driving Labs and Digital Twins
- Intelligent management of energy grids
- Energy system reliability and Sustainability
- Challenges and obstacles to AI adoption in energy
1. The role of AI in the energy sector
Artificial Intelligence (AI) is emerging as a general-purpose technology with the potential to revolutionize industries just as the steam engine and electricity did in the past. Like these past groundbreaking technologies, AI is driving innovation in the energy sector in manifold areas by improving renewable energy production, grid management, and material research. AI will be crucial in achieving critical goals such as global energy security and emissions reductions. Here are the various areas where AI is making a significant impact:
- New ideas: novel energy technologies need to reach the market, and AI is enhancing the creativity and capacity of scientists to generate and test new ideas. AI could accelerate innovation in energy technologies, helping develop more efficient and affordable solutions like advanced batteries for electric vehicles (EVs) or catalysts for hydrogen production.
- Ongoing improvement: Existing clean energy technologies require continuous improvement. AI is optimizing current energy processes, enhancing the efficiency of both production and distribution.
- Data-Driven insights: AI could help to optimise how extremely complex and data-rich systems like electricity networks are built and managed.
- New material research: AI is accelerating the discovery and development of innovative materials and designs, optimizing manufacturing processes, and reducing the time and cost associated with bringing new materials to market. This includes the co-design of materials and components, considering system-level requirements for grid integration and lifecycle performance.
- Smart Grid management: AI-powered smart grids are revolutionizing energy distribution. These systems intelligently manage energy flow based on real-time data, ensuring optimal distribution and reducing energy losses. Through automation and intelligent monitoring, AI also helps minimize waste, contributing to more efficient and sustainable energy systems. As a result, we see more resilient power supply systems capable of adapting to the dynamic energy demands of modern societies.
- Energy storage: In the field of energy storage, AI is enhancing the optimization of charging and discharging cycles, extending the lifespan of storage units, and enabling better integration of renewable energy sources. This ensures a stable and reliable energy supply, even when renewables face intermittency challenges.
2. AI applications for energy innovation
2.1 Discovery and design of new materials
AI is playing a critical role in accelerating the search for advanced materials that can enhance energy technologies. Traditionally, discovering a higher-performing material or one that avoids undesirable ingredients required human ingenuity and deep knowledge of chemical behaviour. However, the vast number of possible material combinations makes the process time-consuming and complex. This is where AI steps in, optimizing the search by analysing massive datasets and exploring billions of potential chemical combinations.
A prime example of AI’s impact can be seen in the development of solid-state electrolytes for lithium-based batteries. In July 2024, researchers from a US government laboratory and Microsoft used AI to evaluate 32.5 million potential new electrolytes, discovering 23 with the right characteristics. Similarly, scientists in Sweden employed AI to screen 45 million possible battery cathode molecules and identified nearly 4,600 promising candidates. These breakthroughs demonstrate how AI can significantly accelerate material discovery in energy storage. The success of these AI-driven projects is attracting significant investment, such as the partnership between AI start-up Anionics and Porsche’s battery manufacturing arm, and Mitra Chem’s USD 80 million funding to streamline the lab-to-production timeline by over 90%.
AI’s applications extend beyond material research for batteries. Researchers have also used AI to design enzymes for biofuel production, predict high-yield biofuel feedstocks, and develop catalysts for hydrogen production. Additionally, AI is aiding in the creation of materials for carbon dioxide (CO2) capture. As AI continues to evolve, it is increasingly integrated into energy research, driving innovation across multiple energy sectors and benefiting from advancements in robotics and automation. Studies show that the use of AI tools in industrial research can boost patenting by up to 39% in just under two years, with AI-assisted researchers discovering 44% more materials, leading to a 17% increase in downstream product innovation.
2.2 Simulations and Virtual Prototypes
AI-powered simulations are accelerating the development of innovative energy technologies by enabling virtual prototyping. These simulations use advanced algorithms to model complex systems and test how new technologies might perform in real-world conditions without the need for costly and time-consuming physical experiments. One breakthrough in this area is the concept of self-driving labs, which utilize AI to autonomously conduct experiments, analyse results, and even make decisions about subsequent testing.
By using AI to perform simulations and refine prototypes virtually, researchers can identify the most promising designs and solutions far more efficiently. This not only reduces costs but also accelerates the time it takes to bring new energy technologies to market, pushing the boundaries of what is possible in areas like renewable energy, energy storage, and carbon capture.
3. Energy Efficiency and Decarbonization with AI
3.1 Optimization of energy consumption
Energy efficiency and decarbonization are deeply interconnected goals that are critical in achieving global sustainability targets. As we strive to reduce carbon emissions and transition to a cleaner energy future, optimizing energy consumption plays a pivotal role in this process. Artificial Intelligence (AI) stands at the forefront of these efforts, offering innovative solutions to streamline energy use and accelerate decarbonization. By leveraging AI’s ability to analyse real-time energy data, industries can not only enhance efficiency but also reduce their carbon footprints, helping to meet the pressing challenges of climate change.
One notable example of AI in action is the implementation of AI-based energy management systems. These systems work by analysing vast amounts of energy consumption data. Advanced algorithms and machine learning models detect patterns, trends, and anomalies, offering valuable insights that allow for more accurate energy forecasting and smarter decision-making. As a result, energy waste is minimized, and energy usage is optimized. This can be particularly critical across energy intensive industries such as glass, cement, steel, and chemicals. Artificial intelligence, combined with existing technologies like waste heat recovery and industrial heat pumps, will enable these industries to overcome the challenges of decarbonization.
There are already several success stories showcasing the power of AI in energy management. Google’s DeepMind AI system, for example, was able to reduce energy consumption for cooling their data centres by an astounding 40%.
By identifying areas of energy waste and implementing corrective measures, AI plays a crucial role in creating more sustainable practices. Through continuous monitoring and intelligent adjustments, AI enables industries to save energy, reduce waste, and contribute to a low-carbon economy, proving itself indispensable in the global shift towards environmental sustainability.
3.2 Support for renewable energy
AI also plays a key role in optimizing the management of intermittent energy sources like solar and wind, geothermal. Through accurate forecasting and real-time data analysis, AI can optimize energy production from these sources, reducing waste and improving the integration of renewables into the electrical grid. This support enhances efficiency and contributes to environmental sustainability by lowering greenhouse gas emissions and supporting global decarbonization goals.
4. Advanced energy tools: Self-Driving Labs and Digital Twins
4.1 Self-Driving Labs
Self-driving labs are an innovative leap in scientific research, enabling the rapid acceleration of discovery and testing in fields like energy. These automated laboratories use robots to conduct experiments and synthesize materials predicted by computer models. A prime example is the A-Lab at the US Department of Energy’s Lawrence Berkeley National Laboratory. Since February 2024, this self-driving lab has been able to process up to 100 times more samples per day than human-run labs, focusing on synthesizing energy storage chemicals that show major potential for performance improvements. This approach dramatically speeds up the research process and allows for faster material discovery, reducing the time between concept and experimentation.
4.2 Digital Twins
Digital twins are virtual representations of physical systems or processes, designed to optimize the design, testing, and performance of complex systems. In the energy sector, digital twins powered by AI are being used to simulate and optimize systems like nuclear fusion and CO2 capture. For instance, in sectors like nuclear fusion, digital twins help in the design and testing of equipment, significantly reducing the cost and risk of engineering projects. These virtual models can help refine processes and make improvements before physical prototypes are built, ultimately speeding up development timelines. As these technologies evolve, they hold the potential to revolutionize sectors such as industrial decarbonization, geothermal energy, and synthetic fuel processes, lowering costs for expensive first-of-a-kind projects and enabling the scaling of new energy solutions. However, challenges remain in making these tools more widely accessible to innovators at the scale-up stage, and addressing the skills gaps in a rapidly advancing field will be essential for maximizing their impact.
5. Intelligent management of energy grids
Smart Grids, resilience and Demand-Response
AI plays a crucial role in strengthening the resilience of smart grids by optimizing energy resource management and enabling rapid adaptation to fluctuations in energy demand. In particular, AI is transforming demand-response systems, which regulate energy consumption in real-time to prevent system overloads and avoid blackouts. By leveraging machine learning (ML) algorithms to analyse large datasets, AI can predict energy consumption patterns, improving energy distribution and reducing waste. For example, AI-based energy management systems can optimize both energy generation and consumption, leading to reduced operational costs and lower environmental impacts.
6. Energy system reliability and Sustainability
6.1 Monitoring and Predictive Maintenance
AI is transforming energy infrastructure management by enabling real-time monitoring of assets and systems. Through advanced machine learning algorithms and real-time data analysis, AI can detect anomalies and inefficiencies, ensuring that potential failures are identified before they disrupt operations. This continuous monitoring provides a detailed overview of the health of energy plants and infrastructure, facilitating proactive maintenance strategies. Predictive maintenance, powered by AI, anticipates issues based on historical and current data, allowing companies to address problems before they escalate. This approach not only reduces operational costs by preventing emergency repairs but also enhances the safety and reliability of energy networks, ensuring a steady and resilient energy supply.
6.2 Promoting the Circular Economy
AI plays a key role in promoting the circular economy by optimizing recycling and waste reduction processes. By analysing data from production and consumption cycles, AI helps improve resource efficiency and supports sustainable practices across industries. An example of circular model practise where AI could be beneficial is to assess the waste heat available in industry process and aid in optimizing energy recovery processes through available technologies as ORC and Heat Pumps.
7. Challenges and barriers for AI in the energy sector
Despite the many benefits, artificial intelligence (AI) still faces significant challenges in the energy sector. One of the main obstacles is the initial cost of implementation. Setting up AI systems often requires substantial investment, which can be a major hurdle, especially for smaller companies with fewer resources. Another critical issue is the availability and quality of data. AI requires vast amounts of high-quality, accessible information to function effectively. In many cases, this data is not readily available, limiting the performance of AI systems. Additionally, there is resistance to change within the industry. Not all professionals are prepared to adopt new technologies, which slows down AI adoption, particularly in more traditional companies. Furthermore, the use of AI and connected devices, such as the Internet of Things (IoT), brings cybersecurity risks. The energy sector is a prime target for cyberattacks, making it crucial to develop secure systems to protect data and infrastructure. Overcoming these challenges could unlock the full potential of AI, enabling it to deliver significant benefits in the energy industry. One last factor is that AI is energy hungry. The rise in AI deployment has led to a surge in investment in new data centres, raising concerns about soaring power demand. While the electricity demand from data centres is still small in the context of the global energy system, it has increased in recent years and is expected to continue growing. Overcoming these challenges could unlock the full potential of AI, enabling it to deliver significant benefits in the energy industry.
Conclusion
AI offers significant opportunities to transform the energy sector by enhancing innovation, efficiency, and sustainability. With its ability to optimize energy management, improve the integration of renewable resources, and drive smarter energy consumption, AI plays a pivotal role in accelerating the decarbonization of the energy industry. As a result, it not only helps reduce carbon footprints but also improves the competitiveness of energy companies by driving cost savings and operational efficiencies.
However, to fully harness the potential of AI, it is crucial to address the technical and political challenges that remain, including the integration of new technologies and the development of appropriate policies. Collaboration between policymakers, researchers, and industry leaders is key to eliminate existing barriers. Overcoming these hurdles will allow the industry to maximize the benefits of AI and move toward a more sustainable and resilient energy future.
Looking ahead, by integrating AI within a framework of thoughtful policy and strategic investment, it can be harnessed to drive the development and deployment of new and existing green technologies, such as ORCs systems and heat pumps. This synergy will not only accelerate the transition to more sustainable energy solutions but also create a powerful foundation for innovation.
Read the IEA’s article here.
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