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.

For decades, energy progress meant one thing: build more. Today, we’re reaching the limits of that logic. More alone is no longer enough. We're facing a massive decoupling. Energy demand - driven by the force of AI, electrification, and industrial reshoring - is moving at a speed that physical infrastructure and permitting cycles simply cannot match.   This creates a new mandate for leadership. The primary constraint is no longer just generation capacity; it is the intellectual efficiency of the systems we already have.   We see this efficiency coming to life as electrification expands where we use energy, automation drives precision into our operations, and digitalization captures data at every layer. Together, these forces are reshaping energy systems into something far more dynamic than traditional approaches can keep up with.   The result is a shift from static infrastructure to living networks. Buildings, data centers, and industrial sites are no longer passive consumers at the end of a line; they are active participants that use energy technology to generate, store, and intelligently manage the power they need.   To navigate this, we need Energy and Industrial Intelligence.   Energy and Industrial Intelligence works when it’s part of the system. It is about linking trusted data from the physical edge - the actual motors, breakers, and servers - to the strategic layer. When you connect the physical to the digital, you stop guessing where energy is wasted and start orchestrating how it is used.   I shared this perspective recently at Innovation Summit India. My message was clear: We have entered the Era of Intelligence. The next phase of advancing energy technology won’t be defined by how fast we build, but by how intelligently we design, operate, and scale what already exists.   I’ve expanded on how we bridge this gap in my latest article. Link in the comments.   #EnergyTechnology #AdvancingEnergyTech #EnergyIntelligence

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

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

"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?

AI has gone nuclear. President Trump's latest Executive Order (EO) marks a decisive shift in how the US approaches the intersection of AI and national security. The order requires the deployment of advanced nuclear reactors to power both military installations and AI data centers, treating uninterrupted AI computing power as a matter of national defense. Significantly, for commercial AI development, the Department of Energy must designate AI data centers at DOE facilities as critical defense facilities, with their nuclear power infrastructure classified as defense critical electric infrastructure. This is potentially the start of attempts to make AI a restricted technology. The scale of the challenge is immense. According to the IEA, global electricity consumption by data centers is set to more than double by 2030 to around 945 TwH annually. This figure equals Japan's entire annual electricity consumption and represents enough power to supply 85 million American homes for a year, or California for nearly four years. The urgency driving this policy becomes clear when examining the Stargate Project, the $500 billion AI infrastructure venture announced in January. The first Stargate site in Abilene, Texas will have 1.2 gigawatts of capacity when completed by mid-2026, enough to power roughly 750k small homes. These numbers underscore why conventional energy sources cannot meet AI's demands at the required scale and speed. The EO explicitly frames AI as a national security imperative. It states that advanced computing infrastructure for AI at military and national security installations demands reliable, high-density power sources that cannot be disrupted by external threats or grid failures. Military applications of AI, from surveillance and intelligence processing to autonomous systems, depend on massive computing infrastructure that traditional power sources cannot reliably support. Congress has reinforced this federal approach to AI governance. The House just passed the "One Big Beautiful Bill Act" which includes a 10-year moratorium on State enforcement of any law regulating artificial intelligence models, systems, or automated decision-making processes. The administration's intolerance for impediments to AI progress became evident with the firing of the head of the US Copyright Office. Her dismissal came one day after the Copyright Office released a report stating that technology companies' use of copyrighted works to train AI may not always be protected under U.S. law - something which may hinder AI development in the USA. These developments signal that the US has entered a new strategic phase where AI is no longer merely a technological or economic concern but an instrument of geopolitical power. The US is treating the AI race as an arms race, with nuclear energy as its fuel, centralised federal control as its governance model, and zero tolerance for resistance whether from states, regulators, or rights holders.

This isn’t just clean energy. This is how we power a digital future—without burning the planet to do it. The rise of AI, streaming, and cloud computing is fueling an energy crisis. By 2025, data centers alone will consume 20% of global electricity. That’s more power than many countries use—combined. But two countries are showing us a smarter way forward. France didn’t build new land. It built solar stations on parking lots. Overhead canopies that generate energy, provide shade, and repurpose space we already have. Switzerland didn’t build new grids. It built solar into its railways. A startup named Sun-Ways is turning train tracks into power plants: -48 panels per 100 meters -No disruption to train operations -No additional land needed And this is just the beginning. Sun-Ways aims to scale across 5,000 km of track. That’s 2.5 million panels. Enough to supply 2% of Switzerland’s energy. But the real breakthrough isn’t just solar tech. It’s a shift in mindset: → From endless expansion to smart reinvention → From grid strain to grid intelligence → From energy extraction to energy integration The spaces we pass every day—commutes, car parks, rail lines—are becoming part of the solution. Not tomorrow. Today. Because sustainability isn’t just about reducing emissions. It’s about rethinking how we build, move, and power our lives. This is clean energy. This is infrastructure with intention. This is how we keep the lights on—in every sense. When innovation meets possibilities, life changes. This is technology for humanity and our planet. Follow me, Dr. Martha Boeckenfeld , for more of tech that matters. ♻️ Share this post to trigger smarter conversations about our energy future. #CleanEnergy #TechForGood #Innovation