Grid Management Systems

Generative AI isn’t just rewriting code. It’s rewriting the power also. By 2030, it could consume 945 TWh of electricity every year. That’s more than the entire energy use of some G20 nations. And it’s just getting started. In the U.S. alone, we’ll need 38 GW of new generation capacity by 2028. That’s like adding 25 new power plants... just for AI. So if you’re building LLMs, training inference clusters, or scaling multi-region AI workloads… Here’s how to power the future without blacking it out. 1️⃣ Model your load curves. Don’t guess. Forecast. Map kWh per inference. Track compute-to-cooling ratios. Simulate power draw by workload and zone. 2️⃣ Run smarter models. Use sparsity, quantization, and retrieval-augmented generation. Schedule training during off-peak hours. Or route to cooler regions with cleaner grids. 3️⃣ Build your own headroom. Pair your data centre with on-site solar, wind, or battery energy. Use PPAs and microgrids to stay stable even during grid congestion. 4️⃣ Design for resilience, not just redundancy. Use AI to predict peak loads and thermal stress. Throttle or reroute dynamically. Plan for multi-day surges like a utility because now, you are one. AI is no longer a software problem. It’s a physics problem. And infrastructure teams from Abu Dhabi to Mumbai ...will decide how far we go. How are you balancing AI growth with grid reality? Let’s blueprint it together.

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. 

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

Lots of people have been watching ERCOT the past couple of weeks. During an epic heatwave, so far no load shedding or system emergency conditions although the market hit record demand. Why? Thankfully over the past couple of years many investors, operators and developers who saw this coming years in advance built 10+GW of new solar in the past 24 months. Without those additional resources the daily costs for consumers would have been about $500 million higher. Daily. For weeks. And without that added resource, at current levels of demand there would be rolling blackouts midday. Does adding solar mean figuring out how to manage the resource transition in the evening as the sun sets? Yes. And investors are already doing that. Companies are building and bringing online thousands of MW of gas-fired peakers and battery storage. But having added the solar is also is saving billions of dollars for consumers already. There is another point that must be made. Load growth in ERCOT is unprecedented and is off the charts. Literally. Load in the other major power markets has been basically flat to declining for various reasons for the past 20 years, leading to erroneous conclusions around the effectiveness of market design constructs. Load in ERCOT grew 9.5% in 2022. IMM report excerpt is below. Current market constructs simply aren’t designed for compound growth that increases system needs by 30% or more in a 5-year period, but that is what the market is facing. Capacity-focused markets and classic RA-type approaches are inappropriate to both incentivize huge resource growth in flexibility and resilience while simultaneously keeping old generation around for deep reserve events. ERCOT and the PUCT are in unprecedented territory and rather than engaging in actions that limit resource participation during this critical period, need to instead correctly define the actual problems to solve while embracing that all resources are necessary to meet load growth and to deliver electricity while keeping costs reasonable. #ercot #orderlytransition #energystorage #renewables

Texas energy use is up 25% in 4 years. Load growth is not coming from people moving to Texas. It’s being driven by data centers, the electrification of industry, including oil and gas operations, and cryptocurrency mining. Oncor's filings show it clearly: 🏠 Residential load is growing very slowly, ~1% per year, even with significant population growth. 🏭 Industrial load is exploding. The Far West zone has tripled in the last 8 years. That's not a projection; it’s history through 2024. Looking forward, the interconnection queue is eye-popping: ⚡ The amount of big projects (data centers, Bitcoin mines, factories) wanting to connect to ERCOT is now 188 GW, more than double Texas’ all-time peak of ~85 GW. ⚡ So far, ERCOT has tracked ~7 GW of large loads operating and has another ~13 GW in advanced stages of study and/or interconnection. So yes, growth is dramatic, but much of what is in the queue won't get built. The takeaway: Texas’ load growth is being driven by large industrials and new digital infrastructure, not households. If large loads can be flexible and scale back, switch to batteries, and/or produce power during a small number of scarcity periods, there's lots of available power 98-99% of the hours of the year. We can have a lot more terawatt-hours of consumption while increasing grid reliability and lowering per unit costs – if we get the right regulatory and market structures in place. 🎧 Much more here: https://lnkd.in/gTTgYfaB #TexasEnergy #txenergy #ERCOT #LoadGrowth #DataCenters #GridReliability #EnergyTransition

These days it can be easy to forget that load growth might come from anything other than data centers. But electrification is still a cornerstone of many decarbonization plans and actually could be a larger driver of load growth in the long run. Unlike data centers, electrification is a distribution system issue. On top of replacing aging infrastructure, we’ll need to significantly expand the distribution grid to accommodate new electricity demand from buildings and vehicles. How much will that cost, and what can we do about it? We have an answer – for the District of Columbia, at least. Our new study for Pepco analyzed hosting capacity and load growth across all of the District’s 827 feeders and 45 substations to understand what it would take to achieve nearly full electrification by 2040. Brief highlights: ◾ The rate of load growth would increase by roughly half (from 1.5%/yr to 2.3%/yr) ◾ Serving the new electrification load would more than double the otherwise required distribution system investment over that period (from $700 million to $1.6 billion) ◾ By improving system utilization, grid flexibility (DERs, energy efficiency, VPPs) could avoid up to half of the incremental investment. But… ◾ Grid flexibility HAS to reach sufficient scale when and where it’s needed. This is an all-or-nothing deal. And… ◾ That level of grid flexibility has costs and barriers which need to be analyzed further, and then addressed. There’s a lot more in the report, including a comparison to other jurisdictions, scenario analysis, and beautiful, nerdy charts. Find it below and here: https://lnkd.in/gG4iH9xK Big congrats to my The Brattle Group colleague Akhilesh Ramakrishnan, who masterfully led our growing team of phenomenal distribution system modelers, including Adam Bigelow, Oliver Grocott, and Christina Zhang, with expert input from Michael Hagerty and Sanem Sergici. Lindsay North, Jacob Burlin, Brendan Timmons, Lingo Haile, and their colleagues at Pepco provided invaluable leadership throughout.

⚡ Mastering the Grid: Why Power System Studies & Load Flow Analysis Are Non-Negotiable 💡 Our electrical grids face immense transformation—surging demands, distributed generation, and massive renewable energy integration. Ensuring 24/7 power for millions amid these changes is the challenge. That's why Power System Studies, with Load Flow Analysis at their core, are critical for designing, planning, and maintaining a safe, stable, and efficient power supply. Without these vital insights, grids risk voltage instability, costly overloads (leading to equipment damage), inefficient energy distribution, and even widespread blackouts. 📊 What Load Flow Analysis Unlocks: Grid Planning's Diagnostic Heart This isn't just theory; it's the diagnostic tool delivering critical, actionable insights to prevent costly failures and optimize performance: * ✅ Bus Voltages: Precision on magnitude and phase angles across every network point, ensuring safe operational limits. * ⚡ Power Flows: Tracks real (kW) and reactive (kVAR) power movement in every line and transformer, revealing bottlenecks. * 📉 System Losses: Pinpoints energy dissipation (I^2R losses) throughout the grid, identifying efficiency improvement areas. These analyses rely on accurate real-world data from generators, loads, transformers, and transmission lines. They answer key questions for smarter grid decisions: * Are voltages within safe limits, avoiding dangerous sags or swells? * Are transmission lines and transformers adequately sized to prevent hazardous overloads? * Can the grid seamlessly handle new industrial loads, EV charging, or large-scale renewables without faltering? 🛠️ Top Methods for Precision & Speed: *🔹 Gauss-Seidel: Simple, ideal for smaller systems, though slower for larger networks. * 🔹 Newton-Raphson: Industry standard—renowned for rapid convergence and high accuracy, perfect for complex, large grids. * 🔹 Fast Decoupled: Optimized Newton-Raphson variation, designed for massive power systems, balancing speed with accuracy. 🌱 Why Load Flow Matters More Than Ever in the Energy Transition With renewable energy, smart grids, and EV infrastructure booming, Load Flow Analysis is paramount. It enables: * 🌍 Strategic grid expansion for growth and new energy sources. * 🌞 Seamless integration of solar, wind, battery storage, and other Distributed Energy Resources (DERs). * 💡 Smart Grid functionalities and real-time optimization for resilience and efficiency. * 🔧 Prevention of voltage instability, costly overloads, and cascading failures. * ⚡ Reliable, efficient energy delivery—powering our sustainable future. 💡 Mastering Power System Studies equips you to tackle tomorrow’s grid challenges head-on, shaping a more sustainable and reliable energy future. 💬 Let’s Spark a Conversation! let's keep learning as the world's power systems evolve. 🚀 #PowerSystemAnalysis #LoadFlow #ElectricalEngineering #SmartGrid #RenewableEnergy #GridStability #PowerProjects

How AI and LLMs Are Revolutionizing Power Grid Optimization Operating a Power Grid is not an easy task: unpredictable demands, shifting constraints, and the need for precision push systems, and people, to their limits. That is where the disruptive new framework of SafePowerGraph-LLM comes in, bringing forth newer ways in which grids operate with the help of AI and Large Language Models. 🔹 Research Focus The authors' paper (https://lnkd.in/dcVr4UmY) introduces the SafePowerGraph-LLM framework, embedding graph and tabular representations toward solving Optimal Power Flow (OPF) challenges in power grids. This approach inspires velocity, scalability, and accuracy in grid management. Herein, the researchers showed some ways one might handle real-world grid complexities with AI-in-context learning and fine-tuning-for instance, without giant datasets. 🔹 Power Grid Embedding First, each of the components in the grid, buses, generators, and loads, are converted into structured data formats. These "schematics" will make LLMs even better at processing and predicting solutions involving the power grid-accurately, using less time and resources. 🔹 LLM Inference and Fine-Tuning With models such as GPT and Llama, SafePowerGraph-LLM shows how smaller models, after fine-tuning, can still reliably achieve OPF solutions. Fine-tuning with Low-Rank Adaptation (LoRA) further amplifies performance gains while reducing errors and computational costs. 🔹 Key Insights - Model Size Matters: Large models, such as Llama-70b, perform better when dealing with more complex data. - Graph vs Tabular Formats: Graph representations are better than tabular data in their performance after fine-tuning, demonstrating adaptability. - Cost-Effective Scalability: Even models as small as Llama-8b can be as good as proprietary systems if fine-tuned correctly. 📌 Takeaways SafePowerGraph-LLM simplifies complex power grid optimization tasks by offering: - Scalability: Scales to real-world complexities. - Efficiency: Speeds up decision-making while reducing costs. - Flexibility: Operates on varied grid settings through graph and tabular formats. To the power grid operators and companies, this becomes a sustainable route for reliability with the use of AI. 👉 Do you think LLMs can match the accuracy of traditional Grid optimization tools? How do you see AI improving Power Grid Management in your region? 👈 #ArtificialIntelligence #AI #GenerativeAI #EnergyManagement #PowerGrid #Energy #EnergyEfficiency #FutureOfWork Subscribe to my Newletter: https://lnkd.in/dQzKZJ79

The FERC ANOPR Is Asking the Right Questions. Flexible Loads Help Answer Them. This week, Verrus submitted comments explaining how a modern, performance-based framework can turn large-load growth from a liability into a reliability and affordability opportunity instead. The Federal Energy Regulatory Commission’s new ANOPR on large-load interconnection is one of the most important regulatory efforts underway. It recognizes a basic truth: the scale and speed of new large loads—data centers, industrial electrification, AI compute—are redefining what the grid must be able to handle. At Verrus, we’re building the world’s first fleet of purpose-built, grid-supportive flexible data centers designed to deliver those benefits for decades. The ANOPR is a chance to embed that value into national policy—and ensure that the next wave of large-load growth strengthens the grid instead of straining it. But here’s the part of the policy conversation that’s still missing:  Large loads are not inherently a threat. When sufficiently flexible, asset-backed large loads are part of the solution. Here are the high points from the comments we submitted today: 1️⃣ Define flexible load nationally as a physical, asset-backed capability with a required verification protocol—so operators can trust its performance, not just its intent. 2️⃣ Recognize flexible loads as reliability assets and model them in planning, just as we do new supply resources. 3️⃣ Create a fast-lane interconnection pathway for flexible loads that can avoid or defer upgrades rather than drive them. 4️⃣ Require transparent and nondiscriminatory interconnection rules, so access isn’t governed by negotiations or discretionary utility actions. 5️⃣ Modernize credit assurance, so collateral reflects actual stranded-cost risk—not blunt requirements that favor only the largest incumbents. The U.S. is on track to add the largest electric loads in its history—166 GW in just five years, according to Grid Strategies LLC . If we apply processes built for smaller, inflexible loads, we’ll end up with more delays, higher costs, and infrastructure we didn’t need. But when we distinguish between loads that increase peak and those that don’t, we unlock flexible, asset-backed large loads that can actually moderate the peak, improve grid balance, and reduce total system costs. That’s the real opportunity: flexible loads are not only reliability assets—they’re the fastest path to scaling compute and keeping the U.S. at the forefront of global AI leadership. The ANOPR is our moment to embed that strategy into national policy.

Often, the link between data centers, rising power demand, and retail electricity prices is oversimplified. This excellent PBS News Hour segment and recent analyses from Lawrence Berkeley National Lab (LBNL) and The Brattle Group demonstrates that the relationship is far more nuanced and, in some instances, optimistic. Data centers and industrial load growth can actually help lower electricity prices by spreading fixed grid costs such as transmission, distribution, and modernization, across a larger user base. When utilities already have unused capacity, adding large, steady loads improves asset utilization and can reduce per-unit costs for everyone. On the other hand, the real culprits behind rising rates are aging grid assets, escalating equipment costs, extreme weather events, and the massive investments required for reliability and resilience. States with strong data center growth have often seen stable or even declining electricity prices, while states with stagnant or shrinking demand have experienced the opposite where higher rates are driven by declining utilization of fixed infrastructure. As for renewables, when paired with flexible data center demand, renewable generation can prosper. Large, predictable loads can anchor new solar and wind projects, improve financing, and accelerate decarbonization. With smart grid design (workload shifting, on-site storage, and dynamic pricing) data centers can act as balancing agents instead of just consumers. However, when demand is inflexible or poorly located, it can strain constrained transmission zones and drive localized price spikes. The next decade of energy policy and infrastructure strategy will be heavily focused on: ➡️ How we align digital growth with grid modernization ➡️ How flexible load becomes a resource ➡️ What new models of utility partnership, tariff design, and market structure will unlock shared value PBS News: How Data Center Power Demand Could Help Lower Electricity Prices https://lnkd.in/gMQzyB4h #GridModernization #DataCenters #Energy #Decarbonization #LoadGrowth #RenewableEnergy #PowerMarkets #EnergyPolicy #Power #Infrastructure