AI and Energy Transformation

Improving AI's energy efficiency is key to solving the world's biggest challenges. The first step is understanding its environmental impact. For the first time, Google is releasing a comprehensive methodology for measuring the energy and water impact of Google's AI models. Here are some of our key findings: Today, a median Gemini text prompt uses: 📺 0.24 watt-hours of energy, the equivalent of watching TV for a little less than nine seconds 💧0.26 milliliters of water, about five drops of water We’re approaching efficiency from many angles — investing in new infrastructure, engineering smarter and more resilient grids, and scaling both mature and next-generation sources of clean energy. The results are telling. Over a 12-month period, while delivering higher-quality responses: ⚡ the median energy consumption per Gemini Apps text prompt decreased by a factor of 33x 👣 the median carbon footprint per Gemini Apps text prompt decreased by a factor of 44x By sharing our methodology, we hope to contribute to collective understanding and drive industry-wide progress towards more efficient and beneficial AI for everyone — including the planet. Learn more in the video below and dive into all the details in our Keyword blog here: g.co/AI/energyefficiency #GoogleSustainability #Gemini

Something VERY cool just happened in California and… it could be the future of energy.   On July 29, just as the sun was setting, California’s electric grid was reaching peak demand.   However, instead of ramping up fossil fuel resources, the California Independent System Operator (CAISO) and local utilities decided to lean on a network of thousands of home batteries.   More than 100,000 residential battery systems (made up primarily by Sunrun and Tesla customers) delivered about 535 megawatts of power to California’s grid right as demand peaked, visibly reducing net load (as shown in the graphic).   Now, this may not seem like a lot but 535 megawatts is enough to power more than half of the city of San Francisco and that can make all the difference when a grid is under stress.   This is what’s called a Virtual Power Plant or VPP. It’s a network of distributed energy resources that grid operators can call on in an emergency to provide greater resilience to our energy systems. Homeowners are compensated for the dispatch, grid operators are given another tool for reliability, and ratepayers are saved from instability. It’s a win-win-win.   Now, this was just a test to prepare for other need-based dispatches during heat waves in August and September. But it’ historic.   As homeowners add more solar and storage resources, the impact of these dispatch events will become even more profound and even more necessary. This was the second time this summer that VPPs have been dispatched in California and I expect to see even more as this technology improves.   Shout out to Sunrun, Tesla, and all companies who participated. Keep up the great work.

Last week 100,000 home batteries operated like a mid-sized power plant. On July 29, California aggregated more than 100,000 residential batteries and discharged them for two hours during the evening peak. The result: 535 MW of coordinated output, comparable to a gas peaker plant, but distributed across rooftops instead of built on a single plot of land. These were some of the most promising outcomes: Truly additive output: The batteries weren’t just doing what they normally do. Compared to the prior day’s profile, almost all 535 MW was additional discharge triggered by the event, which is clear evidence this was coordinated grid support, not incidental customer behavior. Stable performance: Telemetry showed steady power delivery for the full two-hour window with no noticeable drop-off. That’s the level of reliability grid planners typically expect from conventional plants. Well-timed to system stress: The event aligned with CAISO’s net peak (that’s California’s grid operator, balancing demand minus wind and solar). Hitting that window matters because this is when power is most scarce and expensive, and when the “duck curve” ramps hardest. Visible grid impact: Net load dropped measurably during the dispatch, demonstrating that thousands of small batteries can move the needle at the system level. Program design matters: Nearly 90% of participants were enrolled in California’s Demand-Side Grid Support program, with others in the Emergency Load Reduction Program. Incentive structures like these are what make broad participation possible across multiple aggregators and OEMs. The takeaway is bigger than one test: virtual power plants are crossing the line from pilot to planning-grade resource. If properly integrated—through refined dispatch algorithms, better coordination with CAISO, and markets that actually value flexibility—they can defer costly peaker plants, absorb excess solar, and flatten the evening ramp without the stranded costs of centralized infrastructure. The technology is ready. The economics pencil out. The question now is whether market design will catch up. ---- Read the full report from The Brattle Group here: https://lnkd.in/gwYbFiPz

AI adoption is accelerating faster than the energy systems built to support it. Data centers are already among the most power-intensive assets on the grid and are seeing demand rise at rates that legacy infrastructure, static operating models, and fragmented regional grids were simply not designed to handle. The consequence is predictable: higher costs, growing emissions, and mounting pressure on utilities and operators trying to maintain reliability while integrating renewables. I’ve spent much of my career working at the intersection of technology, energy policy, and industrial systems, and this challenge is proving to be one of the defining infrastructure questions of the decade. It’s increasingly clear that the sector needs new ways to manage load, forecast demand, and coordinate resources across highly variable conditions. This week, I had the opportunity to hear from senior leaders at Hanwha Qcells about a model they are developing that aims to address these pressures. What stood out to me was the architectural shift behind the technology: using AI, interoperable language, and digital twins to unify diverse equipment, link operations to real-time grid signals, and automate many of the repetitive, checklist-style decisions that currently consume operator time. This broader concept of treating data centers as intelligent, grid-aware assets aligns with conversations happening across industry and government. The framework they described integrates clean generation, storage, and control software into a single adaptive system. The goal is straightforward but ambitious: reduce wasted energy, cut emissions, and improve resilience as AI demand grows. Their lofty projections (20–30% cost reductions, up to 35% emissions cuts, faster response times through agentic operations) reflect why approaches like this are gaining momentum. What interests me most is how these ideas fit into the larger trend: the shift toward an “Intelligent Age” where digital growth and energy management are inseparable... remember when VPPs were unheard of? Solutions that improve transparency, interoperability, and operational flexibility will be essential, and not just for data centers, but for manufacturing, transportation, and other power-intensive sectors facing similar constraints. As we look ahead, the real opportunity is in building systems that scale, adapt, and operate with far greater situational awareness. The conversation with Qcells underscored how quickly this space is evolving and why collaboration across utilities, technology developers, operators, and policymakers will be critical in the years ahead. Article link: https://bit.ly/4qggMLd #Hanwha | #HanwhaQcells | #Microsoft | #AI | #DataCenters | #EnergyManagement | #GridModernization | #CleanEnergy | #Innovation

AI is starting to change the grid in a way most people aren’t seeing yet. Utilities are quietly building “digital twins” of their systems—AI models that simulate the grid in real time. Not just power plants and wires, but rooftop solar, batteries, EVs, and demand response all interacting dynamically. And here’s what those models are showing: We don’t always need to build our way out of the problem. In many cases, flexible resources—virtual power plants, smart charging, distributed storage—can meet peak demand faster and cheaper than new generation or transmission. They can relieve congestion. They can defer upgrades. They can keep the system stable. In other words, as many of us have been saying, the grid we already have is more capable than we’ve been giving it credit for. But here’s the catch: Our regulatory system hasn’t caught up. California, for example, allows DERs and VPPs to participate in markets—but the rules that determine what counts as reliable capacity haven’t fully caught up to what these resources can actually do. But this isn’t just a California issue. From New York to PJM to the Midwest, markets allow flexibility—but still struggle to value it, to count it, as reliable capacity. So we have a mismatch: • Engineering reality is moving fast • Regulatory frameworks are not And that mismatch is expensive. It means we default to building more infrastructure than we may actually need. It means higher costs for ratepayers. And it means we’re slower to integrate the clean energy already coming online. The opportunity here is enormous. If we update the rules—so utilities can be rewarded for using flexibility, not just for building assets—we can: • Lower costs • Move faster • Make the grid more resilient Same electrons. Smarter system. That’s the next chapter of the energy transition. #EnergyTransition #DataCenters #AI #ElectricGrid

"AI data centers represent the most significant opportunity for grid economics in a generation. Today’s electric grid operates at less than 40% utilization for much of the year. When AI data centers are interconnected strategically to leverage existing capacity, they don’t strain the system— they optimize it. By spreading fixed grid costs across substantially more kilowatt-hours, these AI facilities become catalysts for lower rates and accelerated infrastructure investment." "Our analysis of a 1 GW of data center deployment in a representative mid-sized electric utility with one million customers shows: - Customer rates can decrease by nearly 5%—providing tangible relief to millions of Americans. - Over $1.35 billion in new capital investment becomes justifiable— without any rate increases. - Critical grid modernization accelerates—funded by new revenue streams rather than ratepayer burden." - GridCARE

Imagine 10,000 warehouses, factories, and campuses across India, each sitting on a battery. 🇮🇳 During the day, they store cheap solar power from the grid at Rs 2–3/kWh. At night, they run on it. No diesel. No paying Rs 12/kWh to the DISCOM. And together, those 10,000 batteries can eventually act as multiple clusters of power plants, absorbing surplus when the grid is flooded, releasing it when demand peaks. A virtual power plant, distributed across the country, owned by no one and operated by software. The unit economics work today. The regulation is moving. The solar surplus is already here. Cell prices have fallen 95% in 15 years. A battery at an industrial site today pays for itself in 3–4 years, purely on energy savings. VoltSeal is building the intelligence layer that enables this. Rainmatter by Zerodha is backing them. A short interaction with founder Mudit below 👇

Research has highlighted the environmental impact of generative AI, particularly as it relates to the energy demands of data centers. A recent Morgan Stanley report predicts that AI-related industries could emit up to 2.5 billion tons of greenhouse gases by 2030, largely due to the growing need for data centers to support AI workloads. The Green Software Foundation(GSF) Software Carbon Intensity (SCI) Specification provides a practical framework for addressing these concerns. While SCI is applicable to all software, its core principles are particularly impactful in reducing the carbon footprint of AI systems, with the goal being to reduce emissions actively, not just offset them: 1️⃣ Energy Efficiency: Optimizing AI models to use less energy is critical. Techniques like model pruning and distillation help make AI models more efficient by reducing the number of parameters and complexity without sacrificing performance, thus cutting down the energy required for training and deployment. 2️⃣ Hardware Efficiency: Using energy-efficient chipsets and maximizing hardware utilization can help reduce emissions from AI workloads. This involves developing hardware that can handle AI computations more efficiently and extending the lifecycle of existing hardware to reduce the need for frequent replacements, which contribute to emissions during production and disposal. 3️⃣ Carbon Awareness: AI systems can be made carbon-aware, meaning workloads are scheduled to run when energy grids are powered by cleaner, renewable energy. This minimizes the reliance on carbon-intensive power sources and reduces the overall environmental impact. For meaningful progress, policymakers must implement robust regulatory frameworks that support these efforts. Regulations that enforce carbon reporting for AI systems, incentivize the use of renewable energy, and establish standards for emissions will be key to aligning the AI industry with global sustainability goals. By integrating SCI principles with strong policy support, the AI industry can make substantial strides in reducing emissions while continuing to innovate responsibly. (Link - https://lnkd.in/drMQhDEY) #greenai #sustainability #genai

Energy is no longer just delivered; it's produced everywhere. Millions of homes, businesses, and microgrids now generate their own power. The old grid, built for one-way flow, can't coordinate what the energy system has become. AI agents are stepping in. They predict supply fluctuations using weather and satellite data before they happen. They autonomously balance energy flows across distributed networks. Digital twins simulate storms and equipment failures, so operators can prepare rather than react. With these advances, no human team can manage that volume of decisions at that speed. The coordination gap is what makes AI necessary here, not optional. This need for AI-driven coordination applies well beyond energy. Any business running distributed operations across regions, assets, or suppliers faces the same math. The complexity grows faster than headcount ever will. The companies embedding AI into coordination, not just reporting, will handle that growth.   #EnergyTransition #EnterpriseAI #SmartGrid #RenewableEnergy #DistributedSystems #AIAdoption #OperationalExcellence #DigitalTwin #Sustainability #AILeadership #BusinessStrategy

As cooling costs increase, "buildings as power plants" is a more appealing concept than ever because you reduce your energy bill and simultaneously own your energy independence. This is also good news for utilities, because if they can aggregate a bunch of distributed energy resources like solar, batteries, smart thermostats, they can make a more stable grid and reduce reliance on peaker plants (and rising natural gas costs). The grid is strained in the face of increasing electrification and energy demand, at the FERC’s five-year load forecast for the U.S. grid has nearly doubled from 2.6% to 4.7% At the same time adding capacity to the grid is increasingly complex, as interconnection queues are on the rise: projects built in 2000–2007 had a less than two year wait, while projects built in 2023 have a median wait of 5 years. Electricity costs are rising, while rooftop solar and battery costs are significantly declining and will continue to do so. If you put solar on your building you still may need to interconnect depending how you do it, but it will go much faster – usually a year or less. Solar + storage allows buildings to generate on-site electricity and sell that back to utilities, unlocking both new revenue and energy savings amid rising electricity prices. A network of buildings with distributed energy resources (like solar and storage) becomes a virtual power plant. According to the DOE, tripling virtual power plants by 2030 would ease stress on the grid and use of peaker plants, ultimately saving $10 billion. As buildings become power plants of their own, it will fundamentally change the relationship between the real estate industry and utilities from customer to partner. It will also allow the real estate industry to subsume the gas station industry, as passive EV charging becomes more common than trips to the gas station. This is going to take time and significant investment. For now, stay cool...