AI and Energy Transformation

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.

NEW RESEARCH - WHY THE ENERGY TRANSITION IS DISRUPTIVE & COULD BE MUCH FASTER THAN WE THINK: The clean energy transition isn’t just about swapping out old tech for new—it’s a complex, non-linear process full of feedback loops, tipping points, and unexpected consequences. Our new “Systems Archetypes of the Energy Transition” brief is a must-read for anyone shaping policy, investing, or innovating in this space. Key takeaways: 1) Feedback loops drive change: Reinforcing loops (like learning-by-doing and economies of scale) have made solar, wind, and batteries cheaper and more widespread, often outpacing even the boldest forecasts. 2) Path dependence is real: Early advantages for a technology (think BEVs vs. hydrogen cars) can snowball into market dominance, making policy choices and timing critical. 3) Limits and synergies: As renewables grow, market dynamics like “cannibalisation” can dampen investment—unless we design markets and storage solutions to keep the momentum going. 4) Policy design is everything: Well-intentioned fixes (like price caps or broad subsidies) can backfire, while smart, targeted interventions can unlock positive feedbacks across sectors. 5) Tipping points and decline: The decline of fossil fuels isn’t just a mirror image of clean tech growth—it comes with its own feedbacks, risks, and opportunities for a just transition. The brief also offers practical guidance on using causal loop diagrams and participatory systems mapping—powerful tools for understanding and managing the complexity of the transition. If you’re working on energy, climate, or innovation policy, I highly recommend giving this a read. Let’s move beyond linear thinking and embrace the systems view—because the future will be shaped by those who understand the dynamics beneath the surface. This briefing was led by Simon Sharpe at S-Curve Economics CIC, Max Collett 柯墨, Pete Barbrook-Johnson, me at Environmental Change Institute (ECI), University of Oxford & Oriel College, Oxford & the Regulatory Assistance Project (RAP) and Michael Grubb at UCL Institute for Sustainable Resources.

We’ve called efficiency the unsung hero of the energy transition in the past. While the energy transition will happen first through the transition of energy usages, like the shift with transport, from internal combustion engines to electric vehicles, or from fuel or gas boilers to heat pumps, we cannot ignore the utmost priority of the energy transition: efficiency. Efficiency is the greatest path to reduce our energy use, our impact on the world’s climate through CO2 emission reduction, and very importantly, the best way to make solid and practical savings. In its most historical form, energy efficiency is about better insulation, to reduce heating (or cooling) loss in buildings like family homes, warehouses, office high rises, and shopping malls. This is useful, but expensive and tedious to realize on existing installations. Digitizing home, buildings, industries and infrastructure brings similar benefits at a much lower cost and a much higher economic return. The combination of IoT, big data, software and AI can significantly reduce energy use and waste by detecting leaky valves, or automatically adjusting heating, lighting, processes and other systems to the number of people present at any given time, using real-time data analysis. It also allows owners to measure precisely progress, report automatically on their energy and sustainability parameters, and benefit from new services through smart grid interaction. And this is just the energy benefit. Automation and digital tools also optimize the processes, safety, reliability, and uptime leading to greater productivity and performance.

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

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

The shift from "smart" to "autonomous" infrastructure isn't optional – it's essential for the electrification of everything. When electricity grids started accepting renewable power from volatile sources in the 1990s, smart systems with dashboards and sensors were the answer. They’ve been a great success, enabling energy savings and managing decentralized power. But today’s challenges demand more than human decision-making supported by data – they require systems that act autonomously in milliseconds. The distinction is like GPS versus an autopilot. GPS tells you where to go; the autopilot flies the plane. As fluctuations in supply and demand bring existing grids to their limits, depending on dashboards is like flying through turbulence by hand. Autonomous buildings juggle multiple power sources minute-by-minute. Autonomous grids detect faults and reroute power in milliseconds using digital twins. The business case is compelling: smart buildings command higher valuations and higher rent, while saving on energy costs. Autonomous buildings can bring even more benefits. For grid operators, digitalized networks can double existing asset capacity and cut transformer upgrade costs significantly. The technology exists – AI, digital twins, and advanced semiconductors. What we need now is scale. Without autonomy, electrification risks stalling. With it, we get resilience, profitability and accelerated clean energy transition. #AutonomousInfrastructure #SmartGrids #DigitalTransformation #AI #Electrification

Energy still shapes global power—but technology now decides who leads. What do you think? Countries with the largest oil reserves (Saudi Arabia, Venezuela, Iran, Iraq, Russia, Canada) have long influenced geopolitics. Oil still supplies ~30% of global primary energy, and ~95% of global transportation depends on it. But the real power shift is happening elsewhere. 📊 Why tech, AI, and semiconductors now matter more than reserves AI data centers already consume ~2–3% of global electricity—expected to double by 2030 Advanced semiconductor fabs require: Massive, uninterrupted power Ultra-stable grids Water + energy co-optimization A single leading-edge fab can consume >100 MW—equivalent to a mid-size city 🔌 Energy → Compute → Power Oil-rich countries may control supply, but: Compute-rich countries control productivity Chip-rich countries control AI, defense, and economic scaling Grid-resilient countries control uptime and competitiveness That’s why we see: Oil exporters investing in AI infrastructure, hyperscale DCs, and chip manufacturing Import-dependent nations doubling down on energy efficiency, nuclear, and advanced semiconductors Governments linking energy policy directly to AI and semiconductor sovereignty 🧠 The new power equation ➡️ Energy abundance ➡️ Grid intelligence ➡️ Semiconductor capacity ➡️ AI at scale Oil remains a strategic asset—but AI, semiconductors, and energy-efficient compute are becoming the real levers of global influence. The future belongs to countries that convert energy into intelligence. #Energy #AI #Semiconductors #DataCenters #Geopolitics #EnergyTransition #Compute #TechnologyLeadership #FutureOfEnergy

⚡ Power Demand from AI Data Quadruples in 10 years. When I read that AI data centers could consume 1,600 terawatt-hours of power by 2035 — 4.4% of global electricity — it stopped me. That’s not just a data point. That’s an inflection point. I’ve always believed AI would accelerate human progress. But now, I find myself asking: can progress keep running when the grid can’t? Here’s what the data reveals: → AI’s power demand will quadruple over the next decade. → If data centers were a nation, they’d rank 4th in the world for electricity use — just behind China, the US, and India. → In the US, they’re already outpacing EVs, hydrogen, and every other clean tech in energy growth. The irony? The tech meant to optimize the world might soon strain the very system that powers it. In my opinion, the next race in AI won’t be about who builds the smartest model — but who builds the cleanest, most energy-efficient intelligence. ✅ Smarter chips, cooler systems. ✅ Renewable-powered data centers. ✅ AI that learns to optimize its own footprint. Because intelligence without sustainability isn’t progress — it’s a short circuit waiting to happen. So here’s the real debate: Will the next frontier of AI be defined by compute power — or by energy power? ⚡ #AI #EnergyCrisis #DataCenters #FutureofAI #Sustainability #TechDebate

"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

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?