Grid Automation and Control Systems

🔌 𝗙𝗿𝗼𝗺 𝗣𝗼𝘄𝗲𝗿 𝗣𝗹𝗮𝗻𝘁 𝘁𝗼 𝗬𝗼𝘂𝗿 𝗛𝗼𝗺𝗲 — 𝗧𝗵𝗲 𝗢𝗧 & 𝗣𝗼𝘄𝗲𝗿 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 Ever wondered how electricity actually reaches your home? It’s not just wires and poles — there’s an entire Operational Technology (OT) and ICS ecosystem at work to keep the lights on safely and reliably. 1️⃣ Generation — Power Plant ⚡ Electricity is produced in thermal, hydro, nuclear, or renewable plants. 🎛 Controlled by DCS/PLC systems using Modbus TCP, PROFINET. 2️⃣ Transmission Substation ⚡ Voltage is stepped up for long-distance travel (110–765 kV). 🛡 Managed by IEDs with IEC 61850 MMS, GOOSE, Sampled Values. 3️⃣ Transmission Lines ⚡ Power flows hundreds of km via towers and cables. 📡 RTUs monitor health via IEC-104 over Ethernet/MPLS. 4️⃣ Distribution Substation ⚡ Voltage stepped down to 11–33 kV for local supply. 🛡 Controlled via DNP3 (TCP) with automated protection relays. 5️⃣ Feeder & Distribution Automation ⚡ Sectionalizers, reclosers, and capacitor controllers ensure reliability. 📡 Communicate via DNP3 (TCP), IEC 61850 locally. 6️⃣ Smart Metering ⚡ AMI meters measure and report usage/outages. 📡 Use DLMS/COSEM over RF Mesh, PLC, or 4G/5G. 7️⃣ Home & Loads ⚡ Power reaches your panel, feeding lights, HVAC, EV chargers. 📊 Optional home energy management for efficiency and demand response. 💡 Control Centers oversee the entire journey via ICCP/TASE.2, with security layers like IEC 62351. They send commands, receive data, and keep the grid stable — in real-time. Why this matters: Understanding both power flow and data/control flow is critical for OT security, grid resilience, and energy reliability. ♻️ Reshare to Help Others Learn. 🔔 Follow and press bell to get notified of my posts. 🤝 Subscribe OT Security Digest Newsletter Subscribe on LinkedIn https://lnkd.in/gWSn-TzS #OTSecurity #ICSSecurity #SCADA #IEC61850 #IEC104 #DNP3 #DLMS #ICCP #Energy #GridCybersecurity #PowerSystems #SmartGrid #CyberSecurity

We are installing BESS faster than we are learning how to operate them. That’s dangerous. Everyone talks about MW and MWh. Almost no one talks about how the BESS actually behaves when the grid is stressed. That’s the real problem. A Battery Energy Storage System is not a big power bank. It is a grid-active machine. And the wrong control philosophy can quietly turn a “grid support asset” into a grid destabilizer. Following up on my previous post about the coming BESS protection crisis, control modes are the next blind spot no one wants to admit. PQ Mode — The Comfortable Default • Fixed active and reactive power setpoints • Pure grid-following behavior • Zero inertia contribution Great for: – Energy shifting – Peak shaving – Load smoothing But let’s be honest: PQ mode assumes the grid is strong, stiff, and forgiving. In real disturbances? PQ doesn’t help. It waits. The grid leads. The BESS follows. VSG Mode — The Uncomfortable Reality • Emulates inertia and damping • Actively stabilizes frequency and voltage • Can operate in weak or islanded systems • Enables grid-forming and black start This is not “advanced control.” This is what replacing synchronous machines actually requires. The BESS leads. The grid follows. Why this is becoming critical Renewables didn’t just change generation. They changed grid physics. • Mechanical inertia is disappearing • Frequency events are faster than protection can react • Weak grids are no longer edge cases—they are becoming standard And yet… We keep deploying BESS in PQ mode by default because it’s cheaper, familiar, and easier to interconnect. That’s how fragile grids are born. PQ vs VSG is NOT a preference It is a design decision with system-wide consequences. • Strong grids → PQ may survive • Weak grids → PQ can amplify instability • Future grids → hybrid or grid-forming control is unavoidable This is not about control philosophy. It is about whether the grid has a leader during a disturbance. Hard truth Treating BESS as plug-and-play storage is one of the fastest ways to create: • Protection miscoordination • Frequency collapse scenarios • “Mysterious” trips no one predicted Control mode selection belongs at the same table as: protection studies, SCR assessment, fault ride-through, and system stability. Not as an afterthought. Not as a checkbox. BESS is not about energy. It is about control, stability, and responsibility. Real question for the industry: On your projects— Are control modes being selected based on system strength and stability studies… Or are we still optimizing for minimum compliance and lowest CAPEX, hoping the grid will figure out the rest? Engineers, operators, planners—what are you actually seeing in the field? Hanane Oudli🌍

⚡ Spain’s April 28 Blackout: It Wasn’t About Renewables or Inertia On April 28, Spain experienced a major power outage. Very quickly, some voices rushed to blame renewables: “Too much wind and solar! Not enough inertia from coal and gas!” But now, Spain’s official report is out. And it tells a very different story. 🚨 What really happened? • There was a voltage control failure on the grid — not a frequency drop. • A large thermal power plant was mistakenly taken offline. • The grid was hit by oscillations across Europe, and couldn’t absorb the shock. • The loss of voltage control caused a chain reaction of outages — even if the system had run on 100% coal, it wouldn’t have helped. In short: ➡️ The problem wasn’t lack of inertia. It was a lack of fast, flexible control systems. 🔧 So… what’s the difference? 🌀 Inertia Comes from big spinning machines (coal, gas, nuclear). Helps slow down changes in frequency. Useful — but not what failed here. ⚡ Voltage Control Keeps the power level stable so the grid doesn’t “wobble”. Needs fast-response systems to inject or absorb “reactive power”. That’s what was missing. ✅ What could have actually helped? Here’s what really supports a modern, stable grid: 1. Fast voltage stabilizers → Devices like STATCOMs, synchronous condensers, or smart inverters in wind and solar that react instantly. 2. Grid-forming technology → Batteries and advanced inverters that actively shape and support the grid. 3. Better system operations → Avoiding errors like taking a key power plant offline during sensitive periods. 4. Real-time coordination across borders → Tools that manage European grid oscillations before they snowball. 🧠 Personal Final Thought This blackout wasn’t a failure of renewables. It was a failure to invest in the tools that make any modern grid work — fast, smart, automated stability systems. The energy transition doesn’t just need clean generation — It needs a resilient, digital-ready grid to carry it. 🙋♂️ would be happy to have your views on this topic… will it happen again… more and more? Tikehau Capital International Energy Agency (IEA) David Martín Christian ROUQUEROL Rafael Pinedo Mendizabal Jose María Mateu Sánchez-Ocaña Marta Ramirez Segura Alessandro Blasi

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

Following the wide recognition of Grid-Forming (GFM) inverters as a cornerstone for grid stability, the focus of innovation is rapidly shifting from “forming” the grid to actively orchestrating it. The next frontier blends intelligence, adaptability, and cross-domain interaction — pushing power systems into what experts now call the Grid 3.0 era. Here’s where research and advanced practice are heading : ① Multi-Mode & Hybrid-Compatible Inverters (HC-GFIs) Next-gen converters can seamlessly operate in GFM or GFL modes depending on system strength — enhancing flexibility and resilience under changing conditions (Nature Scientific Reports, 2025; ArXiv Energy Systems, 2024). ② Unified AC/DC & Dual-Port Architectures Dual-port inverters are enabling hybrid microgrids, dynamically balancing AC and DC power flows to integrate solar, storage, and EV systems with unprecedented efficiency. ③ Wide-Area Damping via PMU-Driven Control Using synchronized phasor measurements and edge computing, wide-area damping control (WADC) coordinates multiple GFMs, HVDC links, and FACTS devices — achieving real-time system stabilization even in weak grids. ④ Digital, Predictive & AI-Assisted Operations AI-enabled predictive control is now being used to anticipate voltage instabilities, optimize inertia emulation, and coordinate fleets of distributed GFMs (NREL Digital Twin Grid Initiative, 2024). ⑤ Virtual Power Plants (VPPs) & Hydrogen-Linked Storage Thousands of GFMs, EVs, and hydrogen fuel systems are being aggregated into Virtual Power Plants capable of grid support, black-start, and ancillary services at national scale. ▪️In essence: we’re evolving from grid-forming to grid-intelligent systems — adaptive, self-healing, and data-driven. The future grid will not only be stable; it will be strategically aware. #GridForming #GridIntelligence #PowerSystems #BESS #HybridGrids #AIinEnergy #VPP #EnergyTransition #IEEE_PES

Grid-forming control to achieve a 100% power electronics interfaced power transmission systems by Taoufik Qoria -”Nouvelles lois de contrˆole pour former des r´eseaux de transport avec 100% d’´electronique de puissance” ´ECOLE DOCTORALE SCIENCES ET M´ETIERS DE L’ING´ENIEUR L2EP - Campus de Lille  Abstract: The rapid development of intermittent renewable generation and HVDC links yields an important increase of the penetration rate of power electronic converters in the transmission systems. Today, power converters have the main function of injecting power into the main grid, while relying on synchronous machines that guaranty all system needs. This operation mode of power converters is called "Grid-following". Grid-following converters have several limitations: their inability to operate in a standalone mode, their stability issues under weak-grids and faulty conditions and their negative side effect on the system inertia.To meet these challenges, the grid-forming control is a good solution to respond to the system needs and allow a stable and safe operation of power system with high penetration rate of power electronic converters, up to a 100%. Firstly, three grid-forming control strategies are proposed to guarantee four main features: voltage control, power control, inertia emulation and frequency support. The system dynamics and robustness based on each control have been analyzed and discussed. Then, depending on the converter topology, the connection with the AC grid may require additional filters and control loops. In this thesis, two converter topologies have been considered (2-Level VSC and VSC-MMC) and the implementation associated with each one has been discussed. Finally, the questions of the grid-forming converters protection against overcurrent and their post-fault synchronization have been investigated, and then a hybrid current limitation and resynchronization algorithms have been proposed to enhance the transient stability of the system. At the end, an experimental test bench has been developed to confirm the theoretical approach.  VIEW FULL THESIS: https://lnkd.in/dcTJU-9v

⚖️🔧⚡ Transitioning from Grid-Following (GFL) to Grid-Forming (GFM) in Solar + BESS Projects As more renewable projects move toward grid-forming capabilities, it’s critical to understand that success depends on two distinct but equally important layers: 👉 Power Electronics (device level) 👉 GPM – Grid Performance Management (plant/system level) They solve different parts of the problem — and both must evolve together. 🔌 1. Power Electronics – The Foundation Before (GFL): -Inverters follow grid voltage & frequency (PLL-based) -Require a strong grid -Limited stability support (no inertia, -weak voltage control) After (GFM): -Inverters create voltage & frequency -Act like synchronous machines (virtual inertia, droop control) -Operate in weak grids or islanded mode 🔧 Key Changes: Control shift: PLL → Droop / Virtual Synchronous Machine (VSM) Add: Frequency droop (P–f) Voltage droop (Q–V) Synthetic inertia OEM firmware & protection updates (e.g., Sungrow, Tesla, SMA) Integration of BESS for fast dynamic support Enhanced fault response & ride-through capability 🧠 2. GPM – The System-Level Brain GPM coordinates the entire plant: Inverters BESS Plant Power Controller (PPC) Interfaces with utilities (e.g., Oncor) and ISOs (e.g., ERCOT) 🔧 What Changes with GFM: ✔ PPC Upgrades Grid-forming dispatch Multi-unit coordination Voltage & frequency reference control Black start capability ✔ EMS Enhancements BESS dispatch optimization SOC management (maintain headroom for grid support) ✔ Grid Compliance Meet requirements like NOGRR272 Fast frequency response Voltage ride-through Disturbance support ✔ Protection Updates Adaptive protection schemes Revised relay coordination Anti-islanding updates ✔ Operational Modes Grid-connected ↔ Grid-forming Grid-forming ↔ Islanded Black start sequences ⚖️ Power Electronics vs GPM – Key Difference Power Electronics: Creates voltage & frequency (device-level stability) GPM: Coordinates and sustains plant-wide performance ⚡ Real Example: 40 MW Solar + 10 MW / 20 MWh BESS Without GFM: PV becomes unstable in weak grids No meaningful frequency support With GFM: BESS + inverter form the grid Stabilize voltage & frequency GPM ensures: SOC ~50–70% (bidirectional support) Dynamic dispatch Alignment with ERCOT signals 🚧 Key Risks if Not Done Right Control instability (oscillations) BESS depletion → loss of support Protection miscoordination Non-compliance (e.g., NOGRR272) Interconnection delays ✅ Bottom Line ⚡ Power Electronics = “Can we form the grid?” 🧠 GPM = “Can we control it reliably at scale?” 👉 You need both: Power electronics enables the capability GPM ensures it works in real-world grid conditions #SolarEnergy #RenewableEnergy #EnergyStorage #BESS #GridForming #GridFollowing #PowerElectronics #EnergyTransition #ERCOT #GridStability #CleanEnergy #Inverters #Engineering #PowerSystems #EnergyManagement #UtilityScale #SolarProjects #Transmission #Infrastructure

Why the grid must become data-driven Modern substations are operating beyond their original design assumptions. High inverter penetration, EV charging, and AI/data-center loads are driving bi-directional power flows, fast transients, and dynamic topology changes that fixed settings and device-centric architectures can’t manage in real time. A data-driven grid treats the substation as a real-time system—continuously streaming and correlating process, protection, and asset data to drive adaptive decisions. What vPAC changes vPAC virtualizes protection, automation, and control as software on shared, deterministic compute. Built on IEC 61850, it replaces hardwired, panel-based designs with a common logical data layer. Why it matters • Faster engineering and commissioning via software, not rewiring • Unified, time-aligned visibility for fault analysis and PQ insights • Compute-level redundancy and consistent security • Lower lifecycle cost by decoupling apps from hardware • Better support for inverter-based resources and large AI loads If substations remain dominated by dedicated relays and hardwired logic, flexibility and observability are capped. The next generation will be software-defined, standards-based, and data-driven. #DigitalSubstation #vPAC #IEC61850 #ProtectionEngineering #GridModernization #vpacalliance

The AI Stack for Power Systems: How Intelligence Maps to the Grid As utilities embrace AI transformation, a critical question emerges: Which AI goes where? Not all AI is created equal — and not every grid layer needs the same intelligence. Here's how the AI stack layers map to power system architecture, from edge to core: Layer 1: Customers/Edge → Agentic AI (Emerging) Autonomous coordination at the grid edge: DER orchestration and optimization Intelligent EV charging management Dynamic microgrid islanding Peer-to-peer energy trading Real-time demand response Status: Emerging technology, limited deployment, regulatory frameworks evolving Layer 2: Distribution Systems → Generative AI Scenario generation and strategy synthesis: DER hosting capacity optimization Voltage regulation scenario generation Coordinated switching sequences Non-technical loss detection Grid edge intelligence Use Case: GenAI creates optimized distribution strategies that human planners refine Layer 3: Substations → Deep Learning Complex pattern recognition in asset health: Digital twins of substations Predictive asset diagnostics Transformer thermal modeling Real-time equipment anomaly detection Partial discharge pattern analysis Value: Predict failures weeks/months in advance, optimize maintenance Layer 4: Transmission Network → Neural Networks High-frequency signal processing and pattern detection: Fault pattern recognition Power system oscillation detection Power quality and harmonics analysis Traveling wave-based fault location Wildfire and corridor risk modeling Impact: Sub-second fault detection, proactive grid protection Layer 5: Generation Fleet → Machine Learning Time-series forecasting and optimization: Load forecasting Renewable generation prediction Optimal unit commitment Predictive maintenance for generators Fuel and emissions optimization Foundation: Traditional ML still dominates here — and works well Layer 6: Grid Foundation → Classical AI Rule-based, deterministic systems across all layers: Protection schemes and relay logic SCADA alarm processing Compliance rule enforcement State estimation and contingency analysis Reality: The grid's safety still depends on proven, deterministic logic. Key Insight: The future grid isn't "AI vs. traditional systems" — it's a hybrid intelligence stack where: Classical AI ensures safety and reliability Machine Learning optimizes operations Deep Learning predicts failures Generative AI creates strategies Agentic AI (someday) acts autonomously The winning utilities will master integration across layers, not just deployment of individual tools. The real question isn't "Should we use AI?" It's "Which AI, where, and how do we orchestrate the stack?" #AI #PowerSystems #GridModernization #UtilityAI #SmartGrid #EnergyTransformation #MachineLearning #DeepLearning #GenerativeAI #L&T #LTTS #GridAscent

Grid stability and security are becoming data + control problems. Utilities and large energy operators are already using Artificial Intelligence (AI) to move from reactive alarms to predictive, resilient, and cyber-aware operations—especially as renewables increase volatility. Here’s where Machine Learning (ML) and Deep Learning (DL) deliver real impact: ✅ Anomaly Detection: clustering + autoencoders to flag abnormal grid states and potential cyber events ✅ Fault Detection & Classification: Decision Trees, Random Forests, Support Vector Machine (SVM) models using voltage/current/frequency features ✅ Predictive Maintenance: Remaining Useful Life (RUL) forecasting to reduce unplanned outages (breakers, transformers, lines) ✅ Voltage Stability: Recurrent Neural Network (RNN) + Long Short-Term Memory (LSTM) models to anticipate instability and corrective actions ✅ Cybersecurity: Intrusion Detection System (IDS) + Anomaly Detection System (ADS) using supervised and unsupervised Machine Learning (ML) ✅ Optimal Power Flow (OPF): faster optimization with Machine Learning (ML) surrogates + Linear Programming (LP), Quadratic Programming (QP), Interior Point Method (IPM) constraint handling ✅ Forecasting: Autoregressive Integrated Moving Average (ARIMA) + Seasonal Autoregressive Integrated Moving Average (SARIMA) for load and generation inputs ✅ Uncertainty: Monte Carlo simulation + stochastic programming for renewables and market variability ✅ Autonomous control (next wave): Reinforcement Learning (RL) + Multi-Agent Reinforcement Learning (MARL), plus Federated Learning for privacy-preserving training What’s your biggest grid pain right now: false alarms, asset failures, voltage events, congestion, or cybersecurity? #ArtificialIntelligence #MachineLearning #DeepLearning #PowerSystems #GridReliability #Cybersecurity #PredictiveMaintenance #EnergyTransition