24/7 Carbon-Free Energy: the race for the perfect hour
Meera Krishnan | October 6, 2025
The clean-energy transition has a new benchmark. It is no longer enough for a company to claim it buys “100 percent renewable energy.” The new aspiration is 24/7 carbon-free energy, or CFE—matching every hour of electricity consumption with carbon-free generation in the same place and time. It is the difference between paying for sunshine somewhere and actually running your factory on it when the clouds roll in.
Tech giants like Google, Microsoft, and Iron Mountain have made 24/7 pledges; utility providers like Duke Energy are experimenting with pilot products. Governments are taking notice: the United States Department of Energy launched a 24/7 CFE initiative in 2023, and the United Nations has framed it as a key metric for real decarbonization. Yet behind the lofty goal lies a harder truth—the energy system was not designed to deliver perfect cleanliness, every hour, everywhere.
Why 24/7 Is Different
Most corporate “100 percent renewable” claims rely on annual accounting. A company may consume power at night from a gas-fired grid but buy enough renewable energy certificates (RECs) during the year to offset that consumption. On paper, the books balance; in reality, the electrons don’t.
24/7 CFE changes that arithmetic. It insists that every megawatt-hour (MWh) of electricity consumed be matched by a megawatt-hour of carbon-free generation—solar, wind, nuclear, geothermal, hydro, or battery-dispatched renewables—occurring in the same hour and grid region. The idea is not to compensate for emissions after the fact, but to eliminate them in real time.
That hourly precision matters. A grid that meets the CFE standard at every moment would effectively operate without fossil fuels. It would no longer depend on “averaging out” intermittency across seasons or distant regions. The concept thus turns renewable energy from a procurement exercise into a systems challenge—one that demands granular data, flexible infrastructure, and a fundamental rethink of how power markets function.
Why It’s Hard
The first problem is mismatch. Solar panels flood the grid with cheap power at noon and fall silent by dusk. Wind farms often peak at night or in winter, not during the hot summer afternoons when demand spikes. Matching supply and demand perfectly, every hour, requires more than just clean generation—it demands clean firm capacity that can fill gaps predictably.
Today, that firm capacity is mostly fossil-based. Gas turbines, coal plants, and diesel generators step in when renewables falter. Replacing them means developing and deploying alternatives that can store, shift, or generate carbon-free power on command. The technologies exist in prototype—long-duration batteries, green hydrogen turbines, advanced geothermal, small modular nuclear reactors, and demand-response software—but few are yet affordable or widespread.
The second obstacle is geography. Hourly matching must be localized. Buying wind credits from Texas does little to clean a data center in Virginia when the grid there is coal-heavy. That means companies pursuing 24/7 must build or contract carbon-free projects within the same balancing authority as their load. In the fragmented American grid, that can mean dozens of markets and rules, each with its own regulators, transmission limits, and interconnection queues.
The third challenge is data. Hourly CFE accounting requires time-stamped generation and consumption data, verified by grid operators or trusted intermediaries. That sounds simple until you realize that most corporate energy data today arrives quarterly on utility bills. Building a reliable hourly ledger of carbon-free supply calls for sophisticated monitoring, blockchain-style tracking, and standardized emission factors for every plant on the grid.
The Economics of Precision
The appeal of 24/7 CFE is moral clarity; its challenge is cost. Achieving 90 percent carbon-free supply is relatively cheap—solar, wind, and short-term storage can get you there. The final 10 percent, however, is punishingly expensive. It requires overbuilding capacity, investing in nascent technologies, and paying for redundancy that only matters a few times a year.
That doesn’t mean it’s futile. By demanding around-the-clock coverage, early adopters can drive innovation down the cost curve—much as corporate renewable procurements did for wind and solar a decade ago. Google’s pilots in Utah and Nevada already pair solar with geothermal; Microsoft is experimenting with hydrogen fuel cells; utilities like NRG are exploring hourly CFE products that may one day replace traditional renewable credits.
The U.S. Department of Energy estimates that reaching 90–95 percent CFE on the grid would slash emissions while maintaining reliability; the final push to 100 percent will depend on scaling these emerging firm-clean technologies. It is, in essence, a moonshot—one requiring physics, finance, and policy to align.
The Promise of the Perfect Match
The logic of 24/7 CFE is both simple and radical: to make clean energy not just abundant, but synchronous with human activity. It challenges the comfortable fiction that buying enough renewable certificates makes a company carbon-neutral. Instead, it forces us to confront the messy physics of time and space in electricity.
If the 20th century grid was built around fuel on demand, the 21st must be built around clean certainty—power that is carbon-free whenever and wherever it’s needed. That future won’t arrive overnight, and it won’t be cheap. But hour by hour, the race for the perfect kilowatt is redefining what it means to be truly green.
This article was published with the help of AI.
Powering up: how artificial intelligence is quietly transforming renewable energy
Meera Krishnan | June 6, 2025
In the sprawling control rooms of California’s energy grid, software is beginning to think for itself. Algorithms monitor the weather, anticipate spikes in demand, and decide in milliseconds whether to draw power from a solar array, a battery, or a wind farm. The shift is subtle but seismic. As wind and solar power scale up, artificial intelligence (AI) is emerging as the quiet partner in keeping the lights on.
AI’s encroachment into the energy sector is less about sleek futurism and more about pragmatic engineering. Renewable energy, for all its ecological promise, remains unpredictable. The sun does not always shine, and the wind is a fickle business partner. Traditional energy systems, built around controllable fossil-fuel plants, are ill-suited to this variability. AI, by contrast, thrives in complexity.
Forecasting the Inconstant
Start with forecasting. Grid operators have long relied on meteorological models to estimate how much power solar panels or wind turbines might generate. Now, AI tools trained on vast troves of historical data can do the same job with far greater finesse. DeepMind, an AI firm owned by Google’s parent company, claims its machine-learning models improved the predictability of wind output by nearly a fifth. For energy markets that rely on forward contracts and real-time balancing, such improvements are worth millions.
Smarter Grids, Not Just Bigger Ones
Once renewable energy enters the grid, the challenge becomes one of orchestration. AI is proving adept here, too. Modern grids increasingly resemble vast digital marketplaces, where supply and demand fluctuate by the second. AI systems can process streams of data from weather satellites, household batteries, industrial plants, and vehicle chargers—then decide how best to balance them.
This is particularly valuable in jurisdictions with high levels of decentralised generation. In Germany, for example, rooftop solar now accounts for a significant share of total capacity. Without AI to coordinate flows between thousands of micro-generators and consumers, grid stability would be unmanageable.
Storage Plays a Role
Batteries, meanwhile, are evolving from static assets to strategic instruments. AI manages when to charge or discharge, taking into account not only grid conditions but also electricity prices and weather forecasts. Such predictive control improves efficiency and prolongs battery life—a crucial consideration given the capital costs involved.
Firms like Tesla, Fluence, and CATL are already embedding AI into their energy storage platforms. In time, AI may help unlock the full value of seasonal storage—allowing excess solar in summer to be shifted, in part, to winter.
Prevention Over Cure
AI is also proving valuable in maintenance. Rather than waiting for turbines to fail or solar panels to degrade, predictive systems analyse vibrations, heat signatures, or visual anomalies to detect faults before they become serious. Drones equipped with AI-based image recognition can identify micro-cracks on wind blades or dirt accumulation on panels, improving performance and reducing downtime.
For offshore wind farms, where maintenance is costly and sometimes dangerous, such tools are more than convenient—they are economically essential.
From Black Boxes to Public Goods?
Yet the embrace of AI in energy is not without risk. Many models are opaque by design, offering recommendations without clear rationales. In a system as critical as the power grid, the lack of explainability could be troubling. Worse, reliance on AI trained with biased or incomplete data may exacerbate existing inequities—raising electricity prices in poorer neighbourhoods or misallocating grid resources.
Regulators and energy planners will need to keep pace. That may mean mandating greater transparency in AI models, developing common standards, or investing in public data infrastructure to reduce dependence on proprietary systems.
A Quiet Revolution
The integration of AI into renewable energy is unlikely to generate headlines. There are no gleaming new turbines or glistening solar fields to photograph. But beneath the surface, the energy transition is being reshaped not just by technology that captures sunlight or wind—but by systems that can make sense of it.
The revolution may be quiet. But it is very much underway.
This article was published with the help of AI.