Grid Management Systems

Study: Generators May Provide a Faster Path to Power A new study by energy researchers suggests that data centers could get faster access to power by adopting load flexibility, agreeing to briefly curtail utility usage and shift to generator power. In an in-depth analysis of the U.S. power grid, researchers at Duke University estimate that this approach could tap existing headroom in the system to more quickly integrate at least 76 gigawatts of new loads, arguing that even a small reduction in peak demand could reduce the need for new investments in transmission and generation capacity - as well as the need to pass on those investments to ratepayers. Data centers are all about uptime, and thus have been resistant to innovations that create additional risk around reliability. But current power constraints in key markets, along with growing demand for AI training workloads (which may be more interruptible than cloud or colocation) has prompted the industry to explore load flexibility options. Last year the Electric Power Research Institute (EPRI) launched the DCFlex project to work with utilities and a number of data center operators - including Compass Datacenters, QTS Data Centers, Google and Meta - on pilot projects for load flexibility. The Duke study, titled "Rethinking Load Growth," puts some interesting numbers on the upside potential. Their findings: - 76 gigawatts of new load could be enabled by a annual load curtailment rate of 0.25% of maximum uptime, equivalent to 1.7 hours per year operating on backup generators. - An annual curtailment rate of 0.5% (2.1 hours annually) could enable 98 GWs of new load, while a rate of 1.0% (2.5 hours) could boost that to 126 GWs. - A 0.5% curtailment could enable 18GWs in the PJM and 10 GWs in ERCOT, the research finds. At least one hyperscaler seems open to the idea. “This is a promising tool for managing large new energy loads without adding new generating capacity and should be part of every conversation about load growth,” said Michael Terrell, Senior Director of Clean Energy and Carbon Reduction at Google, in a LinkedIn post. With the acceleration of the AI arms race, speed-to-market is now a top priority, along with a competitive opportunity cost for companies that are unable to deploy new capacity. There are tradeoffs to consider (including more emissions), but the Duke paper will likely advance the conversation. Duke study: https://lnkd.in/eS3s_pvk Background on DCFlex: https://lnkd.in/euK746Zy

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. 

For most of the last century, generators stabilised the grid as a by-product of producing energy. Today, we are building assets that stabilise the grid without producing energy at all. That shift identifies the binding constraint. Electricity system transition is no longer constrained by renewable resource availability. It is constrained by deliverability and operability. In inverter-dominated systems under rapid load growth, the binding constraints are: - transmission and major substation capacity - system strength, fault levels, frequency and voltage control - connection and commissioning throughput - secure operation under worst-day conditions - execution pace across networks and system services Generation capacity remains necessary. On its own, it no longer delivers firm supply or supports large new loads. Historically, synchronous generators supplied energy and stability together. Inertia, fault current, voltage support, and controllability were implicit. As synchronous plant retires, these services must be provided explicitly. Stability shifts from physics-led to control-led. System behaviour becomes more sensitive to modelling accuracy, protection coordination, control settings, and real-time visibility. Curtailment is not excess energy. It is a deliverability or security constraint. When transmission and substations lag generation, congestion and curtailment rise. Independent analysis shows that delay increases prices and emissions by extending reliance on higher-cost thermal generation. Distribution networks are no longer passive. They now host distributed generation, storage, EV charging, and large loads at the edge of transmission. Voltage control, protection coordination, hosting capacity, and connection throughput now constrain both decarbonisation and industrial growth. Firming is a hard requirement. Batteries provide fast frequency response and contingency arrest. They do not provide multi-day energy and do not replace networks or system strength in weak grids. Demand response reduces peaks. It cannot be relied upon for system-wide security under stress. Execution speed is critical. Slow delivery increases congestion duration, curtailment exposure, reserve requirements, and reliance on ageing plant. These effects flow directly into costs, emissions, and reliability. This is why electricity bills can rise even when average wholesale prices fall. Costs are driven by peak demand, contingencies, and security, not average energy. Large digital and industrial loads are transmission-scale, continuous, and failure-intolerant. They increase contingency size and correlation risk. At that scale, loads do not connect to the grid, they shape it. Supporting growth requires time-to-power, transmission and substation capacity in load corridors, explicit system strength and fault levels, operable firming under worst-day conditions, scalable connection and commissioning, and early procurement of long lead time HV equipment. #energy

One of the least understood aspects of the AI-data-center boom is not the size of the load … it’s the volatility of the load. Utilities have historically treated data centers as large but relatively flat demand sources — closer to steady industrial load than to highly dynamic systems. AI changes that. Why? Because frontier AI clusters may involve tens of thousands of GPUs operating in synchronized computational cycles. Instead of millions of independent computing tasks smoothing each other out, you increasingly get giant clusters behaving almost like a single machine. That means power demand can ramp sharply — and quickly. And the volatility doesn’t stop with the chips. When GPU utilization spikes, heat spikes, cooling systems ramp, pumps and chillers respond, and power electronics react. At very large scale, those coupled swings can become significant grid events. A 1 GW AI campus experiencing a rapid 10% load swing means a 100 MW change in demand. That is utility-scale generation territory. And unlike traditional utility planning assumptions, these changes may occur in seconds — or subseconds — rather than over hours. This matters because the grid was largely designed around gradual load ramps, predictable industrial demand and hourly planning models. AI infrastructure may require a different architecture: 1) onsite batteries for power smoothing; 2) advanced inverter systems; 3) sophisticated reactive power management; 4) grid-aware workload scheduling and 5) new interconnection standards. Ironically, the future AI campus may look less like a passive customer and more like a miniature grid operator. The next era of grid planning may not just be about adding more power. It may be about managing a fundamentally different kind of load.

Power markets weren’t designed for the hyperscale era – but in a rare example of regulatory speed and innovation, Southwest Power Pool (SPP) just unveiled a new set of tools that could significantly change how large loads are integrated into the power system. Facing urgent demand and long lead times, SPP is introducing a new non-firm transmission service with a rapid 90-day connection study for large flexible loads unable to wait for completion of system upgrades, which will be curtailable under reliability conditions and designed as a bridge to firm service. It’s called CHILL – short for Conditional High Impact Large Load. SPP is also launching a companion study process, HILLGA (High Impact Large Load Generation Assessment), to evaluate paired generation that can help serve new large loads without triggering years-long delays in the generator interconnection queue. I especially love SPP’s first guiding principle for the initiative: "Inspire mindsets and employ innovative, art-of-the-possible thinking" – exactly the mindset we all need as we navigate the intersection of rapid load growth and grid transformation. More here: https://lnkd.in/gzprwnr2

I'm seeing industrial operators and data centers commission feasibility studies that don't answer the right questions. And with NERC's 2025 Long-Term Reliability Assessment flagging 13 of 23 regions at resource adequacy risk through 2030, the stakes just got higher. MISO, PJM, Texas ERCOT, WECC-Northwest, WECC-Basin, SERC-Central. High-risk regions. The same regions where data center and industrial load growth is heaviest. That's not a coincidence. The grid reliability problem isn't just about capacity. It's about the type of capacity. Coal retirements are accelerating. Solar and batteries are coming online fast. But when you model dispatch during tight hours (winter peaks, extreme weather), the reliability attributes aren't the same as the baseload capacity they're replacing. Layer surging peak demand from data centers and electrification on top of that, and the gap widens between what the grid can reliably deliver and what industrial operators need to run 24/7. Which brings us to behind-the-meter generation and microgrids. Legal since the 1970s. What's changed: the economics now justify it as a competitiveness strategy, not just a resiliency backup. Most industrial teams commission a feasibility study. It comes back with a topline number: "Yes, on-site generation is possible. Here's the estimated cost." That's not enough. You need to know: • What's the optimal configuration for the best price per megawatt? • How does on-site generation compare to utility rates over 10+ years, including rate escalation? • Which combination of assets (gas, solar, battery, hybrid) delivers the best economics under high growth, low growth, and base case scenarios? • How does this hold up if fuel costs spike or equipment costs come in higher? Most feasibility studies don't model that. They give you a snapshot, not a stress test. In the microgrid space, we do feasibility analysis, but it's a techno-economical study. We model your load. Simulate multiple generation configurations. Run sensitivity analysis across different futures. Compare on-site vs. utility economics even if you already have grid access. The result: you know the optimal price per megawatt configuration and whether the economics hold up when the assumptions change. That's the difference between making an informed decision and hoping the utility can keep up. —— Evaluating behind-the-meter generation or microgrid solutions for your data center or industrial facility? Let's talk. I'll walk you through what a proper techno-economical study covers and what the numbers look like for your site. Grab time on my calendar or give me a call. 🗓️ https://t2m.io/mMoKxRy | 📱 1-888-218-6001 Image Source: NERC LTRA 2025

Can India’s Power Grid Handle Peak Demand in 2025-26? Here’s What the Latest Report Says! With demand soaring and renewables reshaping the energy mix, grid reliability is more critical than ever. The Short-Term National Resource Adequacy Plan (ST-NRAP) 2025-26, prepared by the National Load Despatch Centre (NLDC), provides a reality check on India’s preparedness for peak power demand. 𝗪𝗵𝗮𝘁’𝘀 𝘁𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲? India’s peak power demand is expected to hit 273 GW in June 2025, with possible shortages during: ➡ May–June 2025 (summer peak) ➡ Early mornings & evenings in winter (renewable intermittency) 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 𝗥𝗲𝗽𝗼𝗿𝘁 𝗥𝗲𝘃𝗲𝗮𝗹𝘀: → Energy Shortages Expected – Best-case scenario: LOLP at 5.8%; Median scenario: 12.1%, peaking between April–October 2025. → Storage & Flexibility Are Key – Battery Energy Storage (BESS) & Pumped Storage (PSPs) are crucial to balancing renewables. → Gas-Based Generation – A Tough Choice – Can help bridge supply gaps, but high costs remain a challenge. → Thermal Power Needs Smart Scheduling – Maintenance should shift to low-demand months (Nov 2025 – Jan 2026). 𝗪𝗵𝗮𝘁 𝗡𝗲𝗲𝗱𝘀 𝘁𝗼 𝗕𝗲 𝗗𝗼𝗻𝗲? ➡ Accelerate Storage Deployment – Fast-track BESS & PSP projects. ➡ Optimize Gas-Based Power – Ensure availability during peak periods. ➡ Strengthen Spinning Reserves – Maintain 3% of all-India demand as reserves. ➡ Continuous Monitoring & Planning – Adapt strategies with evolving technologies & demand patterns. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗥𝗲𝗽𝗼𝗿𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀? → For Grid Operators & DISCOMs – Helps prevent power outages during high-demand periods. → For Power Producers – Visibility into peak load periods aids in planning. → For Policymakers & Regulators – Data-driven insights to refine energy policies. → For Investors & RE Developers – Highlights opportunities in storage & flexible generation. What do you think – will India’s grid handle rising demand this summer? Let me know your thoughts in the comments!

The most dangerous moment for the grid is not when a data centre connects. It’s when it suddenly disappears. That is one of the most important signals in NERC’s latest reliability guideline on emerging large loads. The report shows a scenario where multiple generators lose synchronism following a large load trip and rapid recovery. That should fundamentally change how we study large loads. We’ve spent years modelling large loads as demand. But under certain conditions, sudden disconnection does not just create imbalance. It triggers system-wide response. ➤ It changes power flows. ➤ It changes voltage conditions. ➤ It changes what nearby generators have to survive. The system is not most stressed when the load is connected. It can become more stressed when it suddenly disappears. That is the hidden risk. In practice, this is not always a single-site event. It can be the near-simultaneous disconnection of multiple load clusters during relatively minor disturbances, followed by uncoordinated recovery. A sudden data centre trip can leave nearby generation trying to export power through a network that may not have enough transfer capability, system strength, or damping at that moment. This is not a load problem. It is a stability problem. It can drive: • large angle swings • unstable power flows • loss of synchronism • protection operation • cascading risk And this is not a modelling detail. This is not just a stability detail. It can decide whether a project connects, waits, or triggers new system limits. It affects: • contingency definitions • transfer limits • connection assessments • system strength requirements Because a “load” can now behave like a destabilising disturbance. The connection question is no longer: “How many MW?” It is: “How does it behave during and after a disturbance?” We are no longer dealing with passive demand. We are dealing with dynamic, power-electronic, system-interacting assets. My view: We did not design the grid for loads that behave like contingencies. But that is exactly what we are now connecting. Large loads are now part of the stability problem. If designed, modelled, and coordinated properly, they can also become part of the solution. The real question is: Are we testing the load connection, or the system that has to survive it? Are others seeing this scenario appear in large load connection studies? #DataCenters #GridStability #PowerSystems #LargeLoads #NERC #SystemStrength #TransmissionPlanning #IBR #AngularStability

A challenge of our time: how will the United States will compete in the global AI race? 🇺🇸 The simple constraint in the US: access to power ⚡ 🇨🇳The simple constraint in China: access to compute 💻 The reality: the compute challenge is a technology challenge, and the power constraint is a human and policy challenge. Technology challenges are *always* easier to solve than people and policy challenges. The US will lose unless we find a way to access more power. 💡 Enter… load flexibility. In a Duke University study published by Tyler Norris (and others) in February, the grid was shown to have massive excess capacity if large loads (aka data centers) could self‑generate for short intervals. But that work was system‑level and therefore not easily actionable. For the first time, Congruent Ventures portfolio company Camus Energy led by Astrid Atkinson, Google (where Tyler is now an energy lead), Princeton University's Zero Lab led by Jesse Jenkins, and encoord ran an in‑depth study on a 500 MW data center load, analyzing impacts on transmission, generation, and ratepayer (consumer) cost allocations. The results? ✅ Data centers get access to power in 1–2 years vs. ~7 years ✅ Electricity rates may *decrease* due to higher utilization in the region ✅ Grid costs are borne by the data center ⚡Demand flexibility is the only way out of our near‑term power constraints without massive inflationary pressure. 📉 I love me some nuclear, but the only way out of this jam in the short term is demand flexibility, solar + batteries, geothermal, a bit of gas, and a lot of innovation! #AI #Energy #DataCenters #GridFlexibility #Policy Get the exec summary here: https://lnkd.in/gPPqhCqW Duke study here: https://lnkd.in/gHs39jbt https://lnkd.in/gHMpSjPn Shout-out to Marianne Wu, Nicholas Adeyi, Steven Brisley, and Nathan Case.

🔌 Grid operators are implementing various strategies to manage the declining inertia caused by the increased penetration of variable generation (VG) resources, such as wind and solar. These strategies fall into three main categories: maintaining inertia, providing more response time, and enhancing fast frequency response. To maintain inertia, operators can ensure that a mix of synchronous generators is online to exceed critical inertia levels. Additionally, synchronous renewable energy sources and synchronous condensers can be deployed to provide inertia. To provide more response time, operators can reduce contingency sizes and adjust underfrequency load shedding (UFLS) settings. Finally, enhancing fast frequency response involves leveraging load resources, extracting wind kinetic energy, and dispatching inverter-based resources to improve the grid's ability to respond to frequency changes. 🍃 Extracted wind kinetic energy refers to the capability of wind turbines to provide fast frequency response (FFR) by utilising the kinetic energy stored in their rotating blades. This approach can be particularly effective in addressing the challenges posed by declining inertia in power systems with high wind penetration. By extracting kinetic energy, wind turbines can respond rapidly to frequency deviations, thereby helping to stabilise the grid. This method can be used in conjunction with other resources to enhance overall system reliability and maintain frequency within acceptable limits. 💡 High deployment of variable generation (VG) resources can be effectively managed by combining extracted kinetic energy from wind turbines and increasing output from curtailed wind plants. The figure below illustrates that when these two strategies are combined, they significantly mitigate frequency decline. The simulation shows that relying solely on extracted kinetic energy results in frequency falling below UFLS (underfrequency load shedding), while using only FFR barely avoids UFLS. However, when both methods are applied together, the frequency decline is minimal, demonstrating that these approaches can serve as viable alternatives to traditional inertia and primary frequency response from conventional generators. #gridmodernization #stability #gridforming #powerelectronics #renewables #cleanenergy #solidstate