SEI: 3 Ways AI Is Improving Grid Resilience and Paving the Way for a Cleaner Future
Jack Curtis from Neara points out that outdated grid architecture is a significant obstacle as the world strives to meet ambitious renewable energy goals. The International Energy Agency’s global study indicates the need to add or replace 80 million kilometers of grid infrastructure by 2040 to support climate targets and energy security. This is equivalent to doubling the current electricity infrastructure footprint worldwide. Natural disasters expose grid vulnerabilities, necessitating costly rebuilds and posing risks to lives. As deadlines for decarbonization approach, maintaining grid resilience becomes crucial for integrating renewable energy.
Artificial intelligence (AI) can play a pivotal role in addressing challenges faced by the electric utility industry. Three ways AI contributes to grid resilience and accelerates the clean energy transition are highlighted:
AI redefines digital modeling by enabling hyper-accurate real-life network representation. Digital twins, integral to utilities’ monitoring and optimization processes, must accurately mirror real-life networks. Combining various data sources like LiDAR, satellite imagery, and GIS into a unified digital representation enhances insights, enabling utilities to make critical decisions.
AI enhances field surveys by providing unprecedented context and consistency. Even with continuous field surveys, AI offers a new standard of thoroughness and speed in network monitoring. It ensures a comprehensive understanding of complex interactions between network assets and the environment, facilitating quicker decision-making for grid resilience.
AI’s core pattern-matching strengths contribute to the clean energy transition by identifying risks like overloaded poles and clearance violations. AI’s ability to highlight discrepancies at scale is particularly valuable as utilities leverage existing assets to bring more renewable energy online. AI-based digital line rating technology allows utilities to assess true network capacity efficiently, unlocking latent capacity in the existing network for clean energy generation.
In summary, AI emerges as a key enabler for overcoming grid challenges, offering accurate modeling, enhanced field survey capabilities, and pattern-matching strengths crucial for the clean energy transition.
Reach out. We’re very friendly.
Contact us to learn more, schedule a demo, inquire about a pilot project, or discuss other needs.