Infrastructure Management

Grid Control Series: How grid frequency stays stable even when power consumption fluctuates. Curious? Let me explain below! 👇 This will be the first post of the grid control series which will cover grid control methods, why they are needed and what are the challenges within the actual energy transformation. One key aspect of each power grid system is a stable frequency. But how is it ensured that the frequency remains stable even when continuous load changes occur within the grid? The frequency of the power system depends directly on differences between the generated power and the consumed power. It can be imagined as a scale that, when there is an imbalance ➡️ the frequency will decrease if consumption is bigger than generation ➡️ the frequency will increase if consumption is lower than generation ➡️ Traditional Power Systems: In traditional power systems (Large power plants) the following mechanisms stabilize the frequency of the grid: 1️⃣ Dynamic load fluctuations are absorbed to a certain extent by the inertia of rotating masses and their stored kinetic energy. This natural inertia resists rapid frequency changes. 2️⃣ Frequency deviations are further stabilized by the provision of controllable reserve power, which is traded on the reserve power market. 3️⃣ For larger frequency deviations (e.g., ±200 mHz in Germany), inherent system functions of the power controllers like P(f) come into play. These are specified in standards (e.g., VDE AR-N-4110) in Germany and must be provided by every generation unit. ➡️ Modern Grid Approaches with Renewable Energies: As renewable and inverter-based generation increases, physical inertia decreases as they typically don't provide mechanical inertia like traditional generators. However, modern grid forming inverters combined with battery storage systems are able to emulate the inertia and thus, to stabilize the grid on dynamic load changes (1️⃣) by: ✅ Virtual Synchronous Machines (VSM) ✅ Virtual Inertia Emulation ✅ Droop Control In addtion, as in traditional approaches they are also able to participate in the reserve power market (2️⃣) as well to provide frequency control mechanisms like P(f) (3️⃣). This allows modern grids to maintain frequency stability even in low-inertia conditions. What are your main challenges in designing and controlling renewable energy systems in modern grids? #ControlSystemEngineering #GridStability #ActivePowerControl #InertiaEmulation #RenewableEnergy #PowerSystems #Simulation

⚡ The official report on the Iberian blackout confirms it was mainly a voltage instability event. The system had already experienced "intense voltage fluctuations" in the days before the incident. Wide-area oscillations prompted the system operator to increase grid meshing and reduce exports to France. These measures, unfortunately, decreased line flows, which paradoxically raised voltages due to the line charging effect, causing power plants to trip on over-voltage. This triggered a cascading failure, worsened by some plants tripping improperly before voltage limits were reached. The main conclusion from the report is a "lack of voltage control resources"; either they were poorly scheduled, or those allocated failed to provide sufficient power, despite an overall adequate generating capacity.   🔦 For the voltage control to be effective, it is important to consider the difference between high R/X and low R/X ratio systems. In high-voltage grids (transmission networks), which typically have a low R/X ratio, voltage magnitude is primarily sensitive to reactive power. Here, the voltage drop can be approximated by ignoring resistance and focusing on the reactive component. This is why traditional grid operators use reactive power to regulate voltage in these systems. Conversely, in low voltage (LV) systems and distribution networks, the high R/X ratio means voltage magnitude is more sensitive to active power injection. In these systems, the effect of resistance is significant, and the voltage drop approximation includes both active and reactive components. For instance, a PV plant can regulate voltage by reducing active power injection or providing negative reactive power, as per standards like IEEE 1547-2018. If reactive power alone is insufficient, active power control, which involves elements such as heat pumps, electric vehicles (EVs), or battery storage, may be necessary.   🪫 A notable point from the Iberian blackout report is the recommendation to "allow asynchronous installations to apply power electronics solutions to manage voltage fluctuations." This indicates that the voltage control capabilities of inverter-based resources (IBRs) were not fully utilised. Although IBRs offer considerable potential, challenges persist, particularly for real-time smart inverter Volt/Var Control (VVC). These include susceptibility to control instability caused by incorrect parameter selection, as smart inverter settings are sensitive to feeder configuration and operating conditions. An inappropriate droop (slope) setting can lead to control instability or voltage oscillations. There is an inherent trade-off between maintaining control stability and achieving accurate set-point tracking, which can cause voltage violations. Additionally, the non-adaptability of droop VVC to changing conditions can hinder deployment. #blackout #renewables #gridmodernization #powerelectronics #gridforming #voltage #cleanenergy

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.

Your inbox warm-up is training providers to distrust you. (I'm talking about warming up new sending domains / inboxes for cold or outbound email — not newsletters.) Agency owners tell me this weekly: → "We warmed it up for 3 weeks" → "Open rates still tanked" → "Outlook keeps flagging us" Their warm-up did exactly what it was designed to do. The problem? It was designed without real deliverability infrastructure. This is where tools like Warmy.io - Email channel. Reliable. come in — not as a growth hack, but as the control layer between your domains and inbox providers. Reality #1: Volume ramp ≠ reputation engineering → Day 1: Send 10 → Day 7: Send 25 → Day 14: Send 50 → Day 21: Still flagged That's not warm-up. That's guessing with your domain. Reality #2: Generic warm-up creates generic signals Most inbox warm-up fails because it produces: → Shallow engagement patterns providers learn to discount → Repetitive behavior that looks automated at scale → No provider-specific logic (Gmail ≠ Outlook ≠ Yahoo) → No monitoring. No alerts. No guardrails. Inbox providers don't reward activity. They reward believable, consistent behavior over time. Reality #3: Authentication ≠ inbox placement I've audited sending domains with: → SPF / DKIM / DMARC valid ✓ → Domain health marked "high" ✓ → Inbox placement above 90% ✓ Still landing in spam. The difference between inboxes that recover and inboxes that burn? Controls. Monitoring. Observability. Not copy. Not timing. Not subject lines. What real inbox warm-up infrastructure looks like (how I use Warmy): → Provider-weighted logic (Gmail tolerance ≠ Outlook tolerance) → Continuous domain + inbox reputation monitoring (catches drift before damage) → Inbox placement testing by provider (not averages) → Dynamic warm-up control (auto slow-down when signals dip) → Real-time alerts (before domains get burned) → Seed lists designed for realistic engagement Cold email doesn't fail at send time. It fails weeks earlier — during warm-up. The fix isn't "write better emails." The fix is treating deliverability like infrastructure. 🔗 Try it yourself 👉 Explore Warmy here: https://lnkd.in/gGZzMhv6 Free 7-day trial — see inbox placement by provider before you scale outbound.

What is voltage regulation? Why does it matter? And how do people improve it? Everyone has probably seen the lights at a restaurant dim a little when the heaters in a fryer turn on. The lights dim due to the additional load drawing more current and causing the voltage to drop. As the current flows through the step-down transformer and the wires’ impedance, there are a series of voltage drops as described by V=I*Z. Most loads are either largely resistive, or also have a reactive component that exists to support a magnetic field, like motors. Because of this, most loads operate at or below unity power factor and tend to pull voltage down as load increases. Loads that can occasionally cause a voltage rise are power electronic loads and unloaded or overexcited synchronous machines, as they can operate capacitively. Voltage regulation is the ability of the system to maintain relatively constant voltage with changing load. If load is high, voltage tends to dip. The opposite can also be true. When load is low, voltage can drift up. The definition of voltage regulation is: Voltage Regulation = (|V_no_load| - |V_full_load|) / |V_full_load| How important is it to have good voltage regulation? Grid voltage under non-contingency conditions usually stays within about ±5 percent. That is not necessarily what arrives at your outlet. The NEC guidance is typically around 5 percent voltage drop to the farthest outlet. Stacked together, utilization voltage can approach around -10 percent, or about 108 V on a 120 V base. On the high side, +5 percent would be about 126 V. Most equipment tolerates a wider range, but poor regulation still shows up as dimming lights, reduced motor torque, overheating, and nuisance trips. How is voltage regulation managed? If regulation is unacceptable, the grid uses shunt capacitor banks and reactors that can be switched in to raise or lower voltage. Shunt reactors are less common, as low voltage is usually the problem. They are used in situations like low system loading where natural capacitance raises voltage or there are excess capacitance in comparison to the load like with underground cables. On the customer side, voltage is usually only actively managed by large industrial consumers. They place capacitor banks inside their facilities to help manage voltage, especially where large motors dominate, and may use on-load tap changers as load shifts. One thing that may be interesting with very large data centers, on the order of 1 GW, is that they may start to see voltage drifting up due to the capacitive nature of their power electronics. Most customer facilities are designed such that voltage stays roughly within -10 percent to +5 percent under normal conditions, largely by limiting conductor impedance. Utilities and large customers will switch capacitors and reactors and adjust taps as needed. #utilites #renewables #datacenters #electricalengineering

🔍 Ever faced unexpected voltage drops in your distribution network? ⚠️ Low voltage issues can lead to inefficient power delivery, equipment failures, and customer complaints. But why does it happen? And more importantly, how can we fix it? ⚠️ Here are 6 common causes of low voltage problems in distribution lines—and the best ways to fix them! 🔹 1️⃣ Overloaded Transformers ✅ Cause: Transformers operating beyond their rated capacity fail to maintain voltage levels. ✅ Fix: Upgrade to higher-rated transformers, optimize load distribution, or add additional transformers. 🔹 2️⃣ Long Distribution Feeder Lengths ✅ Cause: The longer the feeder, the greater the voltage drop due to resistance. ✅ Fix: Use voltage regulators, install capacitors, and choose conductors with lower resistance. 🔹 3️⃣ Poor Conductor Sizing ✅ Cause: Undersized conductors create excessive resistance, causing voltage drops. ✅ Fix: Select larger cross-sectional area conductors based on load and distance. 🔹 4️⃣ Weak Voltage Regulation ✅ Cause: Faulty or inadequate voltage regulators lead to unstable supply. ✅ Fix: Install Automatic Voltage Regulators (AVRs), capacitor banks, and voltage-controlled transformers. 🔹 5️⃣ High Reactive Power Demand ✅ Cause: Poor power factor results in voltage drops across the system. ✅ Fix: Install capacitor banks or synchronous condensers to improve power factor and stabilize voltage. 🔹 6️⃣ Faulty Connections & Corroded Joints ✅ Cause: Loose or corroded connections cause resistance buildup and voltage drops. ✅ Fix: Conduct regular maintenance, use infrared thermography for fault detection, and secure all connections. 🔧 Final Thoughts ✔️ Voltage drops can be prevented with proper planning, maintenance, and the right equipment. ✔️ Regular system checks ensure long-term reliability and efficiency. Have you ever tackled a low voltage issue in a distribution network? What was your solution? Let’s discuss in the comments! 👇⚡ #ElectricalEngineering #PowerDistribution #VoltageDrop #PowerSystems

I think Red Hat’s launch of 𝗹𝗹𝗺-𝗱 could mark a turning point in 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜. While much of the recent focus has been on training LLMs, the real challenge is scaling inference, the process of delivering AI outputs quickly and reliably in production. This is where AI meets the real world, and it's where cost, latency, and complexity become serious barriers. 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗶𝘀 𝘁𝗵𝗲 𝗡𝗲𝘄 𝗙𝗿𝗼𝗻𝘁𝗶𝗲𝗿 Training models gets the headlines, but inference is where AI actually delivers value: through apps, tools, and automated workflows. According to Gartner, over 80% of AI hardware will be dedicated to inference by 2028. That’s because running these models in production is the real bottleneck. Centralized infrastructure can’t keep up. Latency gets worse. Costs rise. Enterprises need a better way. 𝗪𝗵𝗮𝘁 𝗹𝗹𝗺-𝗱 𝗦𝗼𝗹𝘃𝗲𝘀 Red Hat’s llm-d is an open source project for distributed inference. It brings together: 1. Kubernetes-native orchestration for easy deployment 2. vLLM, the top open source inference server 3. Smart memory management to reduce GPU load 4. Flexible support for all major accelerators (NVIDIA, AMD, Intel, TPUs) AI-aware request routing for lower latency All of this runs in a system that supports any model, on any cloud, using the tools enterprises already trust. 𝗢𝗽𝘁𝗶𝗼𝗻𝗮𝗹𝗶𝘁𝘆 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 The AI space is moving fast. New models, chips, and serving strategies are emerging constantly. Locking into one vendor or architecture too early is risky. llm-d gives teams the flexibility to switch tools, test new tech, and scale efficiently without rearchitecting everything. 𝗢𝗽𝗲𝗻 𝗦𝗼𝘂𝗿𝗰𝗲 𝗮𝘁 𝘁𝗵𝗲 𝗖𝗼𝗿𝗲 What makes llm-d powerful isn’t just the tech, it’s the ecosystem. Forged in collaboration with founding contributors CoreWeave, Google Cloud, IBM Research and NVIDIA and joined by industry leaders AMD, Cisco, Hugging Face, Intel, Lambda and Mistral AI and university supporters at the University of California, Berkeley, and the University of Chicago, the project aims to make production generative AI as omnipresent as Linux. 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 For enterprises investing in AI, llm-d is the missing link. It offers a path to scalable, cost-efficient, production-grade inference. It integrates with existing infrastructure. It keeps options open. And it’s backed by a strong, growing community. Training was step one. Inference is where it gets real. And llm-d is how companies can deliver AI at scale: fast, open, and ready for what’s next.

For years the data center industry chased bigger. Bigger campuses. Bigger power contracts. 1,000-MW mega facilities. But the AI era is exposing a flaw in that model. AI inference doesn’t want to live 1,000 miles away. When decisions must happen in milliseconds — for power grids, public safety, robotics, financial systems, or smart cities — sending data to a distant hyperscale cloud and waiting for it to come back simply doesn’t work. So the architecture is changing. Instead of one massive campus: • 1,000 smaller urban sites • Compute next to where data is created • AI inference at the edge • Capacity that can scale in weeks, not years That’s the idea behind distributed AI infrastructure. Projects like Project Qestrel are rolling out fleets of edge data centers across U.S. cities — bringing HPC and AI inference directly into metro networks. Hyperscale isn’t going away. But the future of AI won’t be one giant brain in the desert. It will be a nervous system of distributed intelligence. And the closer compute gets to the edge, the faster the world gets. #EdgeComputing #AIInfrastructure #DataCenters #AIInference

The G7 Toolkit for Artificial Intelligence in the Public Sector, prepared by the OECD.AI and UNESCO, provides a structured framework for guiding governments in the responsible use of AI and aims to balance the opportunities & risks of AI across public services. ✅ a resource for public officials seeking to leverage AI while balancing risks. It emphasizes ethical, human-centric development w/appropriate governance frameworks, transparency,& public trust. ✅ promotes collaborative/flexible strategies to ensure AI's positive societal impact. ✅will influence policy decisions as governments aim to make public sectors more efficient, responsive, & accountable through AI. Key Insights/Recommendations: 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 & 𝐍𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬: ➡️importance of national AI strategies that integrate infrastructure, data governance, & ethical guidelines. ➡️ different G7 countries adopt diverse governance structures—some opt for decentralized governance; others have a single leading institution coordinating AI efforts. 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬 & 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 ➡️ AI can enhance public services, policymaking efficiency, & transparency, but governments to address concerns around security, privacy, bias, & misuse. ➡️ AI usage in areas like healthcare, welfare, & administrative efficiency demonstrates its potential; ethical risks like discrimination or lack of transparency are a challenge. 𝐄𝐭𝐡𝐢𝐜𝐚𝐥 𝐆𝐮𝐢𝐝𝐞𝐥𝐢𝐧𝐞𝐬 & 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 ➡️ focus on human-centric AI development while ensuring fairness, transparency, & privacy. ➡️Some members have adopted additional frameworks like algorithmic transparency standards & impact assessments to govern AI's role in decision-making. 𝐏𝐮𝐛𝐥𝐢𝐜 𝐒𝐞𝐜𝐭𝐨𝐫 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 ➡️provides a phased roadmap for developing AI solutions—from framing the problem, prototyping, & piloting solutions to scaling up and monitoring their outcomes. ➡️ engagement + stakeholder input is critical throughout this journey to ensure user needs are met & trust is built. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞𝐬 𝐨𝐟 𝐀𝐈 𝐢𝐧 𝐔𝐬𝐞 ➡️Use cases include AI tools in policy drafting, public service automation, & fraud prevention. The UK’s Algorithmic Transparency Recording Standard (ATRS) and Canada's AI impact assessments serve as examples of operational frameworks. 𝐃𝐚𝐭𝐚 & 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞: ➡️G7 members to open up government datasets & ensure interoperability. ➡️Countries are investing in technical infrastructure to support digital transformation, such as shared data centers and cloud platforms. 𝐅𝐮𝐭𝐮𝐫𝐞 𝐎𝐮𝐭𝐥𝐨𝐨𝐤 & 𝐈𝐧𝐭𝐞𝐫𝐧𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧: ➡️ importance of collaboration across G7 members & international bodies like the EU and Global Partnership on Artificial Intelligence (GPAI) to advance responsible AI. ➡️Governments are encouraged to adopt incremental approaches, using pilot projects & regulatory sandboxes to mitigate risks & scale successful initiatives gradually.

Modern data center strategy has become a strategic differentiator in the AI era. Leaders can no longer rely on hybrid-by-default environments shaped by fragmented cloud, colocation, and on-premises decisions. Instead, a deliberate, hybrid-by-design approach is now essential to scale innovation, manage risk, and enhance value across cloud, on-premises, colocation, and edge.    In our latest Deloitte perspective (https://deloi.tt/4rkttVw), my colleagues Lou DiLorenzo, Jagjeet Gill, Heather Rangel, and I outline practical steps for leaders driving this shift, including:    🟢 Intentional workload placement based on latency, control, data sovereignty, economics, and resiliency needs 🟢 Strategic segmentation of AI-intensive workloads to manage compute, power, and cooling demands 🟢 Transparent economics that tie infrastructure cost to business value 🟢 Built-in governance across hybrid environments through standardized controls and automation The goal is not incremental modernization, but intentional architecture that turns complexity into advantage and enables resilient, responsible AI at scale.    Proud of our team's work in helping organizations build forward-thinking data center strategies and leading our hybrid infrastructure managed services, led by Erin Abbey, Rahul Bajpai, Micah Bible, Megan Ellis, Christian Grant, Kelly Marchese, Nicholas Merizzi, and Myke Miller. Let me know if building a hybrid-by-design strategy is top of mind for your organization in 2026; would love to connect!