Integrating AI: The superpower in India’s energy transition toolkit

At CoP28, global leaders agreed on the goal of scaling clean energy rapidly in the near term. Specifically, the CoP28 declaration called for actions towards achieving a tripling of renewable energy capacity and doubling energy efficiency improvements by 2030. The UNFCCC also noted that the “CoP28 Agreement signalled the beginning of the end of the fossil fuel era”. These are worthy goals, but, in reality, the transition is proving hard to achieve. Data for 2023 from the EI Statistical Review of World Energy 2024 clearly demonstrates that despite unprecedented growth in renewables in the past decade, the share of fossil fuels has hardly been dented.

Asia is home to more than half the world’s population. It is now responsible for over half of the global primary energy consumption. The energy growth curve has been steep. Asia’s share in primary energy consumption grew from 28.8 per cent in 1991 to 53.5 per cent by 2021. The region is now the leading contributor to annual greenhouse gas emissions, with a 51 per cent share in global carbon emissions. This trend is likely to continue. Within Asia, India is a major hotspot of economic growth. Unfortunately, this growth hitherto has been heavily reliant on fossil fuels. As the country seeks to scale up its economy in the direction of being a developed nation, it must also modernise and decarbonise its mammoth energy system that will only grow rapidly in line with economic growth metrics.

It is in this context that AI is of vital importance. Modern-day AI is transformational and influences much of modern scientific activity. It touches every aspect, from materials, equipment design, production, supply chain, project design, development and construction, to operations and more. Be it for the creation of new/essential drug molecules that are significantly cheaper than what is currently available, crop sciences, or advanced weather forecasting mechanisms that help protect people and their livelihoods, AI applications are pervasive.

AI’s contribution to the energy transition thus far has been restricted to specific and niche use cases, some of which are highly specialised. With the advent of Generative AI (GenAI), new possibilities have started to emerge. On the supply side, the renewables sector is increasingly integrating AI/GenAI for enhanced efficiency, optimisation and sustainability for far-reaching effects. The complexities of renewable energy production make the sector ideal for AI and GenAI applications. The machine learning and data processing capabilities are key to enhancing the efficiency and reliability of renewable energy systems. From predicting optimal times for energy production to optimising grid distribution and managing energy demand, AI/GenAI tools provide significant advantages over traditional management methods.

The overall expansion trajectory of AI in clean energy production can be characterised in four broad stages:

This stage had applications such as predictive maintenance for wind turbines and solar panels, enhanced efficiency and reduced downtime. AI algorithms enhanced efficiency by dynamically adjusting aspects such as turbine blade angles and solar panel orientations. The seamless integration of AI into renewable energy systems highlights capabilities for cost reduction and performance improvement.

Growth in renewable energy adoption has led to AI’s escalated role in optimising energy production via predictive analytics for demand, supply and pricing forecasts. AI algorithms’ application in managing the grid, balancing supply with demand, leveraging decentralised grids and optimising energy storage, enhances grid resilience and stability. AI-driven advancements also led to the discovery of efficient materials for solar panels, batteries and wind turbine components.

The emergence of GenAI has marked a significant shift. Compared to traditional AI, GenAI contributes novel solutions, generating designs, systems and code, marking an impactful progression. In renewables, it enables advanced wind turbine design, supply chain management, optimal solar panel arrangement, and innovative energy storage solutions. GenAI has also accelerated innovation in renewable energy research and development by exploring a number of design areas. The effects of GenAI can be felt across the value chain, creating new possibilities.

The combination of GenAI, quantum computing (QC) and high-performance computing (HPC) can help the world solve very complex problems for the energy sector that have hitherto eluded solution, or for which only approximate and imperfect solutions have been possible, being of very little practical use. HPC lends scale, AI provides speed and QC brings in accuracy (even as QC capabilities are evolving). Together, AI, QC and HPC are paving the way for a more efficient, sustainable and resilient energy future.

Some of the HPC use cases are already in play. For example, a major wind turbine harnessed the power of AI through reinforcement learning using HPC. The platform enabled complex simulations at large scales to train models (controllers) that can autonomously react to wind conditions and adjust turbine orientations to minimise power loss from wake-effect turbulence. Such use cases will be performed on a much larger scale and much more effectively with the advent of QC, and a combination of HPC and QC with Gen AI.

The above four stages are overlapping and not necessarily sequential. Even as subsequent stages of AI development and adoption take place, the previous stages are also witnessing continuous refinement. Together, these application stages have set in motion a massive transformative journey. The world is only at the start of this journey. However, it is a fast-evolving scenario and by the end of this decade, large-scale enablement of the energy transition journey, and indeed its transformation, can be anticipated. At a time when the world is beset with challenges and doubts on the energy transition, AI is creating new hopes.

Modern AI, in conjunction with other technologies, has opened up a world of new possibilities.  This world is, however, not without challenges. Data security, privacy and reliability are immediate concerns. In supply chains, it increases cybersecurity risks, including system breaches and attacks on broader energy infrastructure, while also raising privacy issues and data biases that threaten operational efficiency and infrastructure stability due to algorithmic inaccuracies and coding errors. AI systems are prone to adversarial attacks such as evasion and data extraction. These kinds of attacks take advantage of the fact that it relies heavily on data and can disrupt the reliability of energy systems. The rapid growth in demand for AI specialists creates a recruitment bottleneck, with a significant shortage of skilled professionals in the energy sector. AI’s higher energy consumption compared to traditional computing poses challenges for its integration in energy efficient systems. Finally, concerns about accountability emerge when decisions based on AI models lack full understanding or control, risking misallocation of resources or suboptimal outcomes due to reliance on opaque AI-driven recommendations.

Regulatory frameworks for responsible AI deployment are essential and slowly falling in place, but are being constantly challenged. Technological progress often outpaces the development of regulations and policies. It becomes crucial for governments and regulatory agencies to evolve and establish precise directives for deploying AI applications; however, it is a tough task. Inevitably there will be a need for stronger guardrails to be established as AI applications become pervasive. However, the possibilities of AI and other related modern technologies are immense, especially when it comes to containing or dealing with the effects of climate change. Enabling the energy transition will indeed be a worthwhile theatre of play for AI, and it gives us practical hope of achieving the goals set out in CoP 28 for renewable energy and energy efficiency.

As the foremost theatre for containing and mitigating the effects of climate change, India has to remain invested in trusted AI deployment for its energy sector, be it for renewable energy, energy efficiency in end-use applications, demand-side management and anything that comes in between. AI and QC will be demanding frontiers of technology. However, being the fastest growing energy market with needs of deep decarbonisation, India can undoubtedly be a pioneer and leader in these modern technologies. The country’s recent track record on digital innovation is impressive, as is our talent base for cutting edge applications. We have a unique opportunity with a large market, a large-scale carbon problem and a vast competent talent pool that must be brought together for leading the way for the world.

 A version of this article was published in Poweronline.net on 18 October 2024. The same can be read here