Flexibility Solutions for Grid Management

As grid operators and planners deal with a wave of new large loads on a resource-constrained grid, we need fresh approaches beyond just expecting reduced electricity use under stress (e.g. via recent PJM flexible load forecast or via Texas SB 6). While strategic curtailment has become a popular talking point for connecting large loads more quickly and at lower cost, this overlooks a more flexible, grid-supportive strategy for large load operators. Especially for loads that cannot tolerate any load curtailment risk (like certain #datacenters), co-locating #battery #energy storage systems (BESS) in front of the load merits serious consideration. This shifts the paradigm from “reduce load at utility’s command” to “self-manage flexibility.” It’s BYOB – Bring Your Own Battery and put it in front of the load. Studies have shown that if a large load agrees to occasional grid-triggered curtailment, this unlocks more interconnection capacity within our current grid infrastructure. But a BYOB approach can unlock value without the compromise of curtailment, essentially allowing a load to meet grid flexibility obligations while staying online. Why do this? For data centers (DC’s), it’s about speed to market and enhanced reliability. The avoidance of network upgrade delays and costs, along with the value of reliability, in many cases will justify the BESS expense. The BYOB approach decouples flexibility from curtailment risk with #energystorage. Other benefits of BYOB include: -Increasing the feasible number of interconnection locations. -Controlling coincident peak costs, demand charges, and real-time price spikes. -Turning new large loads into #grid assets by improving load shape and adding the ability to provide ancillary services. No solution is perfect. Some of the challenges with the BYOB approach include: -The load developer bears the additional capital and operational cost of the BESS. -Added complexity: Integrating a BESS with the grid on one side and a microgrid on the other is more complex than simply operating a FTM or BTM BESS. -Increased need for load coordination with grid operators to maintain grid reliability. The last point – large loads needing to coordinate with grid operators - is coming regardless. A recent NERC white paper shows how fast-growing, high intensity loads (like #AI, crypto, etc.) bring new #electricty reliability risks when there is no coordination. The changing load of a real DC shown in the figure below is a good example. With more DC loads coming online, operators would be severely challenged by multiple >400 MW loads ramping up or down with no advanced notice. BYOB’s can manage this issue while also dealing with the high frequency load variations seen in the second figure. References in comments. 

"One of the key ways to make energy systems more reliable is by maximizing flexibility — improving how well the system can adapt in real time to changes in supply and demand. The more flexible the system, the better it can handle sudden demand spikes in the event of extreme weather, such as cold snaps or heat waves, or respond to supply disruptions such as plant outages. Improving flexibility includes upgrading aging infrastructure. Much of the U.S. grid was built decades ago under different demand patterns. Modernizing the grid — by updating substations and transmission equipment, deploying advanced sensors and incorporating advanced transmission technologies (ATTs), for example — can reduce failure rates during extreme heat and cold. These technologies help operators detect problems quicker, reroute power if equipment is damaged and restore service fast. Modernization not only improves reliability but also reduces expensive emergency interventions and lowers long-term maintenance costs. Increasing grid capacity, both through deployment of ATTs and building regional and interregional transmission lines, can reduce the risk of a local weather event turning into a widespread outage. Creating a more interconnected grid allows regions to share power during shortages. Having this greater transmission capacity also help keep prices down by allowing lower-cost electricity to reach areas facing higher demand. Demand-side management options can help ease pressure on the system during extreme weather events. These include encouraging customers and large users to reduce or shift electricity use during peak periods in exchange for lower bills or leveraging distributed energy resources to help prevent shortages. Systems that rely too much on a single fuel are more vulnerable to disruption. Diversification across energy sources and technologies helps reduce the risk of issues related to fuel shortages, infrastructure failures and localized weather impacts. Finally, policy is also critical. It’s vital that incentives are properly aligned with modern needs for flexibility and preparedness. This can help utilities make system investments that really work in extreme weather and minimize costs to consumers in both the short and the long run." Kelly Lefler World Resources Institute https://lnkd.in/e5syqXQp

Prolonged periods of negative prices and gird congestion: How should we deal with the increasing shares of solar in our power grids? Innovative actors in Germany show how energy storage can provide a solution for congestion management and energy shifting. 🌞 For context, Germany has 93 GW of solar installed today. In the summer, when the production of solar is the highest, the German load is around 75 GW. And the build out of solar is further accelerating. Solar integration creates two major challenges 💸 Negative Prices With too much solar in the system power prices go negative. Solar is no longer earning money when it is producing 🛑 Congestion Grids get congested during solar peak, especially on the lower voltage side. What is the solution? In short: Energy Storage In long: co-located storage for peak shifting and grid-based storage for congestion management ☀ 🔋 Co-located storage Integrating energy storage with renewable asset allows to store solar power during low or negative price periods and sell the power instead when prices are high, e.g. in the evenings. Statkraft is currently building the largest such plant in Germany (https://shorturl.at/jIadq) The 47 MW solar park will be complemented with a 16 MW /56 MWh battery system. Proud to say that we just announced to provide Statkraft with the battery system, which marks the 6th project in the third country between Fluence and Statkraft. 🛑 🔋 Storage for congestion management The German DSO Bayernwerke announced the tender fir a 5 MW / 20 MWh battery for congestion management in their medium voltage grid. (https://shorturl.at/gEjMS) The battery will help the DSO to manage congestion during peak production and replace the need for gris extension. This is the first time a German DSO makes use of the possibility to procure flexibility services under the German energy law (§14c EnWG). There are not a lot of details available yet, how the tender will be structured, but the DSO reserving ability to shift solar production into the battery to relieve the grid during peak solar production is most likely. On a funny sidenote, somebody from the German regulator had asked me a few weeks ago, how can we get more batteries into the distribution grid to support solar congestion management, and I told him §14c EnWG. Great to see it now actually happening. This energy peak shifting is thereby a different application than the German grid booster assets, which increase the line-rating of transmission lines by replacing the n-1 requirement in grid operations. But good to see, the second major way to use BESS for congestion management is now deployed in Germany for the first time as well. Finally, as a little blast from the past, what Bayernwerke plans to do now, UKPN executed already 9 years ago in Leighton-Buzzard.

🔥 #AI #datacenters are being treated like “just another big load.” That’s a dangerous planning assumption. Most of the power they draw isn’t flexible by default - it’s reliability-driven and must stay on to keep compute running. Backup systems aren’t demand response, they are continuity systems. And GPUs don’t pull smooth power - they fluctuate in ways grids were not designed for. But here’s where the story has potential to shift 👇 📌 Batteries and energy storage aren’t just backup anymore - they can make large power users behave like flexible grid assets. With the right controls, storage can charge when the grid is abundant and discharge when it’s stressed, helping balance supply and demand and support frequency and stability, all while keeping compute running. This is backed by recent grid research on dispatch and optimal BESS use. 📌 Growing work on grid-interactive UPS and storage systems shows that data centers can participate in ancillary markets and provide services like fast frequency response and other flexibility if designed and governed with that intent. In #Europe, this is already moving from theory to planning reality ⚡ Reports show that grid congestion and connection constraints are now influencing where data centres are built, with utilities reassessing connection rules and flexibility incentives as grid capacity becomes a decisive factor in investment decisions. So the real shift isn’t debating whether AI loads are “flexible” - it’s about engineering them to be grid-interactive assets, not inflexible liabilities. 👉 If we plan for them as firm loads PLUS intentional, contracted flexibility, we unlock new options for reliability, carbon goals, and grid stability. This isn’t future talk - credible research and emerging deployments are already pointing toward hybrid storage, smarter dispatch, and real grid value. In our work where we help design control centers of the future for utilities and system operators, this needs to be part of the discussion. https://lnkd.in/dXDvf3BE https://lnkd.in/dnMXjKzh ⚡ #GridPlanning #AIInfrastructure #DataCenters #EnergyStorage #BESS #GridFlexibility #EnergyTransition photo: Interactive map of data centre hubs alongside associated power and digital infrastructure // IEA's Energy and AI Observatory

As we have all been saying, the grid is no longer just an engineering challenge—it’s the primary bottleneck for the future of AI and the energy transition. The barrier: human and bureaucratic processes. Who has a solution for this? At CERAWeek 2026 this week, the atmosphere has shifted from "How do we decarbonize?" to a much more urgent "How do we plug in?" With interconnection queues stretching 5–10 years and turbine lead times hitting 2030, speed to power is the new global currency. A new wave of "Grid-Tech" companies is moving past legacy manual processes to solve the bottleneck through software, digital twins, and flexible load. Here are the innovators leading the charge to break the logjam: 1. As I wrote in my last post, NVIDIA & Emerald AI’s solution: By treating AI data centers as "virtual batteries," this software allows hyperscalers to bypass years of grid study. Instead of a fixed-load connection, they use AI to dynamically flex power consumption during grid stress. This "flexible interconnection" model could unlock up to 100 GW of capacity by optimizing the grid we already have. 2. Enverus (Pearl Street Technologies)’s solution: Interconnect™ (Study Automation) The manual process of "power flow studies" is a primary cause of queue delays. Enverus is using its SUGAR™ engine to automate these complex reliability simulations, reducing the time required for interconnection studies from months to just a few days. 3. @Tapestry (X, The Moonshot Factory)’s solution: Grid Digital Twin (Visibility) I’ve been excited about Tapestry building a high-fidelity "Google Maps for electrons." By creating a unified digital twin of the grid, they allow operators like PJM to run transient simulations in real-time, identifying exactly where new projects can fit without triggering expensive, time-consuming network upgrades. 4. Neara The Solution: 3D Infrastructure Modeling (Reconductoring) Before building new towers, we must maximize existing ones. Neara’s platform uses 3D digital twins to simulate "reconductoring"—replacing old wires with high-capacity advanced conductors. This allows developers to find "low-hanging fruit" capacity that can be brought online in a fraction of the time. 5. GridStatus The Solution: Real-Time Data Transparency You can't manage what you can't see. GridStatus has become the de facto data layer for the energy transition, providing the real-time transparency into grid congestion and pricing that developers need to site projects where the grid can actually handle them. The technology is ready. The capital is waiting. We need regulatory frameworks to keep pace with these digital solutions. #CERAWeek #CleanTech #EnergyTransition #GridModernization #AI #DataCenters #SpeedToPower

America’s grid faces a stress test: demand is surging, but supply can’t keep up. Data centers, EVs, and electrified heating are pushing U.S. electricity demand up 21.5% this decade. AI alone is creating jaw-dropping energy needs, with Microsoft and Google racing to secure 24/7 clean power for their data centers. Yet new plants and transmission take years, stuck in queues, permitting delays, and regulatory gridlock. So how do we meet demand today without waiting a decade for steel in the ground? A recent paper by Norris, Profeta, Patino-Echeverri, and Cowie-Haskell highlights one answer: load flexibility. Instead of treating demand as fixed, flexible loads (data centers, industrial plants, EV fleets) can temporarily scale back when the grid is stressed. The findings are striking: - With just 0.25% annual curtailment (~1.7 hrs/yr), the U.S. could integrate 76 GW of new load. - At 1% curtailment, that expands to 126 GW. - In PJM (the nation’s largest power market, serving 65 million people across 13 states) 18 GW of new demand could be added without building new plants. Flexibility isn’t a silver bullet, meaning it can’t replace the need to build new clean generation, transmission, and storage. But it buys time, reduces costs, and makes the system more resilient. Software, sensors, and batteries can unlock efficiency at a fraction of the price of new steel in the ground. The lesson is simple: flexibility is capacity. Execution is survival. But we need both efficiency and investment if we want a grid that keeps up with the 21st century. Here's the full paper from Nicholas Institute for Energy, Environment & Sustainability at Duke University: https://lnkd.in/gBh_3Fva

Everyone now knows data centers are huge, city-size loads and are putting new strains on the grid. How do we connect them in a timely manner and avoid driving up costs for other electricity consumers? The solution could be a two-part combination of flexible interconnection agreements & bring-your-own-capacity (BYOC) arrangements, according to a new report out today from Camus Energy, my Princeton University ZERO Lab, and encoord. We use an ensemble of realistic grid models to show how "conditional firm" transmission service (where a portion of data center demand is flexible or can be served by on-site resources) can avoid costly, poorly used grid upgrades and BYOC contracts (where data centers bilaterally procure new capacity to serve their firm demand) can avoid cost impacts to other consumers — while both can accelerate time to power by years. https://lnkd.in/epSHEFDN This was a great, collaborative effort, and I think this report has something for just about everyone following the data center and energy story (or living it!). Note: this work was sponsored by Google.

The 20th century grid is dead. While utilities scramble to restart coal plants and chase nuclear dreams, data centers just proved they can cut power consumption by 25% in three hours. Salt River Project and Nvidia quietly demonstrated what Australia figured out years ago: flexibility beats capacity every time. The numbers are embarrassing. Massachusetts spent $7 million on demand response programs. Return? 2.39x benefit-to-cost ratio. Puget Sound Energy's virtual power plant delivered 86.9 MW of peak reduction. Cost? Fraction of a new gas turbine that won't arrive until 2030. Meanwhile, we're doubling down on the wrong solution: • Five-year wait for new gas turbines • Untested SMR technology promises • Keeping coal plants on life support • $47 billion in unnecessary infrastructure Here's where it gets interesting. Duke University found data centers can eliminate 10% of national peak demand by flexing just 0.25% of their uptime. That's 15 minutes per year. Former FERC Chair Wellinghoff dropped the uncomfortable truth: "Millions of small loads coordinated by AI can act like large power plants." The solution is embarrassingly simple: use software to flex demand instead of concrete to add supply. But utilities face a $47 billion question: why invest in smart systems when dumb infrastructure guarantees returns? Southern California Edison gets it. They're using AI to orchestrate "complex inputs and outputs shifting constantly throughout the day." Translation: the grid becomes a network, not a one-way pipe. Australia proved it. Puerto Rico's virtual power plant confirmed it. Now Nvidia's showing data centers can be the solution, not the problem. So here's my controversial take: Every new gas plant announced is an admission of failure to innovate. Are we building the 21st century grid, or just supersizing the 20th? What wins: flexibility or more steel in the ground? #EnergyTransition #GridModernization #DataCenters #CleanEnergy #VirtualPowerPlants

Can India's grid stay steady in 2032, when most of its power comes from the sun and the wind? By 2031-32, India's National Electricity Plan envisions ~900 GW of capacity — with solar and wind alone making up nearly 486 GW. On paper, there's more than enough electricity. But the real question is different: at every hour of the day and night, can the grid keep supply and demand in balance as solar and wind swing up and down? I built an 8,760-hour (full-year) simulation, calibrated to CEA's NEP demand and capacity figures, to answer it. A few findings stood out: 🦆 The "duck curve" becomes the defining challenge. Solar floods the grid at midday — leftover demand drops to almost nothing — and then the grid must climb by up to 186 GW in just three hours every evening as the sun sets and households switch on. 🔋 Storing midday sun for the evening is essential — about 924 GWh shifted on a typical day, well within India's planned battery + pumped-storage fleet, if it's used well. ⚡ The plan works — but on one condition. With coal genuinely flexibilised (lower minimum load, faster ramping), the grid is short of flexibility only ~2 hours a year and wastes just 1.5% of clean energy. Without that reform, the same fleet can't keep up with the evening swing. 💰 Flexibility pays. The overall package returns ₹2.54 for every ₹1 spent — coal flexibility and demand-side resources carry the economics; batteries earn their keep through reliability. The study also proposes a new planning metric — a National Flexibility Margin (NFM) — as the flexibility counterpart to the familiar Planning Reserve Margin, so that "is the system flexible enough?" gets checked as rigorously as "is there enough capacity?"

Data centers have always been the grid’s biggest load. Now they’re being asked to help manage it. Instead of just limiting how many data centers can be built, regulators and utilities are starting to require them to be flexible, adjusting when they draw power, not just how much. That’s demand response. Large electricity users reduce or shift power use during grid stress in exchange for incentives, better rates, or faster interconnection. For data centers, this doesn’t mean shutting down. It means adjusting when certain workloads run to reduce power use during peak periods. Google has already signed agreements with utilities to provide about 1 GW of demand response across its U.S. data centers. That’s the equivalent of a large power plant’s output. This means Google can reduce or shift up to 1 GW of its power use when the grid is constrained, turning part of its electricity usage into a flexible resource. This is done by shifting non-time-critical workloads, such as some AI training processes, to times when the grid is under less pressure. The goal is to reduce peak demand, not disrupt real-time services like search or streaming. Google is working with utilities including Indiana Michigan Power, Tennessee Valley Authority, ENTERGY ARKANSAS INC., Minnesota Power, and DTE Energy. It is also working with regulators and the Electric Power Research Institute (EPRI) to develop frameworks that treat this flexibility as a grid resource. But there are limits. Not every data center can provide the same level of flexibility. It depends on design, workload type, and location. There are also limits on how much demand can be reduced without affecting performance. This does not replace building new generation and storage. It helps manage the gap while demand grows faster than supply. And that gap is getting bigger. As much as 50 GW of new data center load is expected to come online by 2030. At the same time, demand response agreements are still relatively limited. States are starting to formalize this approach: • Texas passed legislation that includes demand-response requirements for large loads • California is considering similar rules for data center contracts • Maryland and Colorado have proposed related policies • Pennsylvania introduced tariffs that reward large customers for reducing usage during peak demand Data centers have long been treated as firm demand, something the grid just has to carry. Now they’re being asked to actively support the grid. If data centers cut power during peak periods, how much can they safely reduce without hurting performance? And just because they can, will they? Should the grid pay them to be flexible, or should that flexibility be required as part of their grid-access conditions? When the grid is under stress, who reduces demand first? Large data centers with flexibility agreements, or smaller businesses and residential customers? It will be interesting to see how far this can actually go.