AI in Electrical Grid Management

As renewables scale and the grid grows more complex, one thing is clear: We can’t manage this transition manually. AI is becoming the foundational layer of a more resilient, flexible grid. Working closely with our digital grid teams, I’ve seen firsthand how AI is changing everything, from how we plan, to how we respond in real time. This POWER magazine article highlights what we’re seeing globally: 🔍 AI delivers real-time grid visibility we couldn’t access before  ⚡ It enables predictive forecasting, fault detection, and faster DER integration  🌐 It turns scattered assets into flexible, intelligent systems But here’s the real shift: AI is helping us move beyond deterministic grid models into resilient, self-optimizing systems, where data, sensing, and decision-making are embedded from the start. That’s exactly the thinking behind Schneider Electric’s new One Digital Grid platform, a unified, AI-powered architecture built to help utilities modernize faster, integrate renewables more easily, and operate more efficiently. AI allows us to move from managing parts to orchestrating the whole, exactly what the grid needs next. https://lnkd.in/d5SjT3Zd

AI has an insatiable appetite for energy. But, can AI help energy companies cook up a buffet? GE Vernova just acquired Alteia, the energy sectors first major acquisition to aimed at simultaneously powering the AI revolution and using AI to manage the resulting grid complexity. The acquisition will enable GE Vernova to, rather than building generic AI capabilities, develop visual intelligence specifically for energy infrastructure – enabling utilities to "see" their grids through AI-powered damage assessment, vegetation management, and asset inspection. Their GridOS® platform represents an AI-native approach to grid management, designed from the ground up for renewable energy integration rather than simply adding AI features to existing systems. GE Vernova's $9B commitment through 2028 represents one of the most aggressive AI investment strategies in the energy sector, far exceeding most competitors' disclosed AI-specific spending. This signals that leading energy companies view AI as fundamental infrastructure for future competitiveness, not just a technology add-on. Meanwhile, competitors across energy’s competitive landscape are taking their own approaches to AI. Siemens Energy leads with the most comprehensive strategy among traditional competitors, launching an industrial foundation model with Microsoft and pursuing workforce transformation (AI-powered learning for 250k+ employees), autonomous manufacturing (targeting 30% productivity gains), and AI-driven sales optimization. Schneider Electric, ABB, and Honeywell focus on partnerships and smaller acquisitions for IoT integration, predictive maintenance, and building automation. Notably, while some competitors have broader industrial AI portfolios, none match GE Vernova's strengthend, specific focus on AI for grid asset management; a critical differentiator as AI and visual data analysis become increasingly important for grid reliability. Every major energy company has embraced cloud partnerships (Microsoft Azure, AWS, NVIDIA) to support AI ambitions, but GE Vernova's sector-specific partnerships like its Chevron joint venture for AI data center power infrastructure demonstrate how companies are creating entirely new revenue streams. Traditional energy companies appear to be lagging in AI adoption, creating market share opportunities for AI-forward competitors. GE Vernova's is looking to win with a strategy of building proprietary AI capabilities through strategic acquisitions, rather than relying solely on partnerships. The companies that successfully integrate AI into their core operations – rather than treating it as an add-on – will likely capture disproportionate value as the energy sector digitizes.

The power sector is changing fast, and AI is at the center of this transformation. From predicting outages before they happen to improving energy distribution, AI is making electricity more reliable, efficient, and sustainable. But how exactly is AI reshaping the industry? 1. Predicting failures before they happen. Power outages can be costly and disruptive. AI-powered predictive maintenance helps utilities identify potential failures in transformers, power lines, and substations before they occur. By analyzing data from sensors and historical trends, AI reduces downtime and ensures a more stable power supply. 2. Smarter energy distribution. Electricity demand fluctuates throughout the day. AI helps balance supply and demand in real time, ensuring power is distributed where it’s needed most. This minimizes waste, lowers costs, and improves overall grid efficiency. 3. Optimizing renewable energy. Renewable energy sources like solar and wind are unpredictable. AI helps by analyzing weather patterns and adjusting energy production accordingly. This means more stable integration of renewables into the grid. While AI is transforming the power sector, technology alone isn’t enough. The biggest challenge is adoption. Getting companies, governments, and individuals to embrace these changes. For digital transformation to succeed, the industry needs: → Skilled talent → Better infrastructure → And a willingness to rethink traditional ways of managing power AI is here to stay, and its impact on energy is growing. The question is: Are we ready to maximize its potential?

The energy grid is under immense strain from extreme weather, wildfires, and rising electricity demand. As these pressures increase, so does the need for smarter, more resilient and reliable energy grids.   Utilidata, a company that is part of Microsoft's Climate Innovation Fund portfolio, is redefining energy delivery through its AI platform, Karman. This technology empowers utilities to optimize energy delivery and make better decisions about how to manage the grid by, for example, storing electricity in batteries during off-peak hours and distributing it when it's needed. As a result, electric vehicles and solar panels become flexible, valuable assets that help meet grid demand.   Embedding AI directly into the grid infrastructure helps utility decision-makers make more informed decisions and better serve customers. This innovation highlights the power of AI to modernize critical infrastructure and transform the energy sector.

The AI & Energy Map (by Remarkable Ventures Climate)⚡ From energy storage to grid management, AI startups are building the intelligence layer that will accelerate the clean energy transition. Here are 4 areas shaping the future of energy: ▪️ 𝗥𝗲𝗻𝗲𝘄𝗮𝗯𝗹𝗲𝘀 𝗮𝗻𝗱 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 - battery & fuel cell analytics, new energy solutions, optimization, trading, and more! Startups: Aurora Solar, Atomic Canyon, Energsoft, Nuclearcore.ai, Arenko, Aevy, enSights ▪️ 𝗚𝗿𝗶𝗱 𝗢𝗽𝘀, 𝗧𝗿𝗮𝗻𝘀𝗺𝗶𝘀𝘀𝗶𝗼𝗻, 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 - grid analytics and management, planning, pricing, forecasting, and more! Startups: Eneryield, Looq AI, GRIDX, Climavision, Amperon, Splight, Gridraven, GridAstra ▪️ 𝗘𝗻𝗲𝗿𝗴𝘆 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 - residential, datacenter, and industrial management, tracking, reporting, and more! Startups: Climative, Companion.energy, Neuralwatt, Lunar Energy, Flexidao, WattCarbon, Arcadia, Coolgradient ▪️ 𝗖𝗼𝗻𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 - demand response, load flexibility, trading, forecasting, and more! Startups: NOX Energy, Orus Energy, Utilidata, Silurian AI, enspired, Innowatts, Voltus, WeaveGrid Explore the full map below 👇 --- 📍 For more climate-tech insights, follow me @Hugo Rauch or search for "VCo2 Podcast" on Apple, Spotify, or YouTube.

A topic as urgent as the relationship between AI and energy deserves a bigger platform. That’s why I’m so excited about the International Energy Agency (IEA)’s trailblazing Energy and AI Observatory—and even more excited Tapestry is a part of it! The IEA’s Energy and AI Observatory is the biggest deep-dive I’ve found so far on this critical subject matter, bringing together comprehensive data, case studies, and visualizations that show AI’s transformative potential to address humanity’s growing appetite for energy. And rather than speculation and hypotheticals, this important resource features real, tangible use cases of how AI technology is being applied to energy challenges all over the world. As I shared during several workshops organized by the IEA, the century-old electric grid faces unprecedented strain in order to accommodate both our surging demand and the intricacies of how we make, move, and use energy in the modern era. Estimates show that we’ll need to build out an additional 80 million kilometers of new grid in the next decade (the equivalent of what we’ve constructed in the past 100 years!), and connect thousands of new energy generation sources each year compared to the dozens we’re accustomed to. While AI is not a panacea, it’s a powerful tool for managing the scale and complexity of this challenge. AI has the potential to make the grid visible in a way that’s never before been possible: allowing for real-time analysis that human operators can’t achieve alone, optimizing planning and operations, enhancing its ability to withstand disruptions, and more. For instance, the IEA found in a recent study that using AI, we have the potential to free up 175 gigawatts of transmission capacity—enough to power roughly 150 million homes. Tapestry is developing a suite of AI-powered tools to help grid operators and planners meet this moment, and I’m pumped to see our work featured in the Energy and AI Observatory alongside Google, Siemens, Hitachi, and others. If you enjoy nerding out on the future of energy as much as I do, this incredible new resource is definitely worth your time: https://lnkd.in/gc3xwkpE

🚨 Utilidata Raises $60M to Bring Edge AI to the Grid — Right Where the Electrons Flow ⚡ AI isn’t just for GPUs and data centers. It’s hitting the grid edge. Rhode Island-based Utilidata just secured a $60 million Series D, backed by NVIDIA, NextEra, and DTE, to deploy real-time AI at the meter — pushing intelligence out of the control room and into the field. 📊 What Makes This a Game-Changer: 🔌 Smart meters with NVIDIA Jetson chips = edge inferencing for voltage, frequency, and load conditions ⚡ Enables real-time decision-making across distributed energy resources (DERs), EVs, and microgrids 🧠 Moves from “data collection” to in-situ intelligence — where AI can act before a centralized system even finishes syncing 🌍 Aligns with the need for AI-native infrastructure as utilities navigate renewables, grid instability, and load spikes 💰 Backed by big names: NVIDIA, Sands Capital, Energy Impact Partners, and two of the largest utilities in the U.S. 📌 Why It Matters: As AI reshapes compute, Utilidata is showing us what it means to reshape control. This is grid-native AI — not just smarter forecasting, but autonomous operations at the edge. It’s the intelligence layer the energy transition has been waiting for. Read More: https://lnkd.in/d8E8vR3V 💬 Are we entering the era of the “thinking grid”? Or is this just the first node of something bigger? #GridModernization #EdgeAI #EnergyInfrastructure #Utilities #AITransformation