Artificial intelligence (AI) is playing a crucial role in enhancing utility infrastructure, boosting renewable energy research, and assisting in the approval of clean energy projects. However, it also poses a significant challenge by increasing electricity demand on the grid.
A recent report by Bain & Company predicts that the boom in AI-driven data centers could contribute to 44% of the electricity load growth in the United States between 2023 and 2028. To meet this rising demand, utilities may need to increase their annual generation by as much as 26% by the year 2028.
Neil Chatterjee, a former chairman of the Federal Energy Regulatory Commission and now an advisor to AiDash, a company that uses AI for remote monitoring of utility infrastructure, expressed concern. He noted that after nearly two decades of stable electricity demand, a surge might be on the way, and many stakeholders might not be ready to handle it.
Chatterjee lauded AiDash for its innovative approach to using AI to support the clean energy transition, particularly in vegetation management, an area that costs utilities billions annually. He emphasized the need to advocate for the role of AI in combating climate change.
Abhishek Singh, CEO of AiDash, highlighted the extensive aging infrastructure in the U.S., including 7 million miles of power lines and over 200 million utility poles. With the evolving climate, the risks to these assets have increased, and a shortage of workforce makes monitoring them more difficult.
The recent advancements in generative AI have made operations more effective for AiDash. Singh stated that prior to 2019, such applications of AI were not feasible.
However, the upswing in electricity demand triggered by AI leads to complex challenges. For example, the Federal Energy Regulatory Commission recently denied a proposal to allow a data center to operate using power directly from a nuclear plant. Chatterjee warned that such arrangements could lead to higher costs for consumers and may set a precedent for more costly agreements.
Chatterjee believes that it’s essential to establish a clear strategy for adapting to the growing role of AI in the energy sector. While recognizing the complexities, he remains hopeful that solutions are achievable by balancing the increase in electricity demand while adhering to decarbonization goals.
On another front, AI has been instrumental in optimizing processes for clean energy projects. James McWalter, co-founder and CEO of Paces, shared that their team initially struggled to develop a permitting insights tool due to high costs and outdated information. However, with the rise of AI, they have relaunched their efforts to assist clean energy developers in streamlining their permitting, siting, and risk assessments.
Paces aims to automate much of the desktop analysis involved in project development, which will allow developers to focus on community engagement and relationship-building.
McWalter recently participated in a White House roundtable discussing AI infrastructure and its implications for meeting the energy needs necessary to support a cleaner economy.
Finally, in the field of nuclear fusion, AI is helping advance research efforts. According to a report from the Clean Air Task Force, it streamlines complex data analysis required to maintain plasma stability—critical for fusion reactions. As research advances, AI promises to transform the future of energy generation, making it more efficient and potentially more affordable.
With ongoing developments, Chatterjee and Gonzales de Vicente reflect optimism for integrating AI across the energy landscape, paving the way toward innovative solutions that balance demand, reliability, and environmental goals.