Using Simulation to Address Grid Complexity

Dynamic Motor Starting Simulation | (132/11 kV) I recently conducted a dynamic motor starting analysis for high-power motors using ETAP for 7 × 7300 kW DOL motors at 11 kV voltage level in a critical seawater desalination project. Following key findings were observed from the motors dynamic simulation: ▪️ Analyze the transient impact of motor acceleration on the grid ▪️ Verified peak inrush current, under-voltage dips, and power factor behavior ▪️ Modeled transformer loading and reactive swing during start-up ▪️ Analyzed bus voltage recovery and adjusted motor start sequencing ▪️ Assessed 11 kV switchgear & protection logic under worst-case scenarios ▪️ Checked interaction with 12 MVAR capacitor banks & overall grid interface ▪️ Validated 50/63 MVA transformer (14% Z, YNd11) withstand during DOL start ▪️ Optimized breaker sizing & protection settings to avoid nuisance trips What this simulation taught me technically: ▪️ Motor starting isn’t just about ratings — it’s about system interaction, recovery time, and how the weakest link (often voltage dip) impacts reliability. ▪️ Transient response data helps you tune soft starters, define switching sequences, and verify thermal limits of transformers in real time. ▪️ ETAP Dynamic Simulation gave precise visibility into moment by moment behavior that we didn’t get from calculatios example behaviour at 0.1 se /0.2 sec. ▪️ Realized how capacitor banks and motor loads can conflict without phase accurate simulation. Why ETAP/PSCAD simulation Important for Engineering. Sometimes, the most important engineering comes not from drawing a perfect SLD but from simulating how real equipment behaves when things get tough. Design is not just about specs. It’s about modeling behavior, anticipating failure, and protecting infrastructure before failure happens. What began as a simulation turned into a real world engineering decision where grid stability, equipment performance, and project cost were directly influenced by our choices. Dynamic simulation is where theory meets reality and that’s where your engineering truly grows. #ETAP #MotorStarting #DynamicSimulation #PowerSystemDesign #TransientAnalysis #GridStability #132kV #11kV #ProtectionCoordination #ElectricalEngineering #SubstationDesign

🔌 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

A critical challenge in modern grid stability is that inverter-based resources (IBRs) are often “black boxes” to utilities and system operators. Inverter manufacturers and plant developers understandably hesitate to disclose proprietary control strategies, leaving operators with limited visibility into internal dynamics. The problem is further compounded by the fact that IBRs can switch among multiple control modes, which are typically unknown to operators yet can exhibit dramatically different dynamic behaviors. In the final days of 2025, we were excited to learn that our paper on black-box IBR modeling was accepted by IEEE Transactions on Smart Grid. In this work, we develop a comprehensive data-driven framework that uses only terminal measurements to discover unknown control modes and learn continuous-time models that accurately capture IBR dynamics under each mode. By leveraging physics-inspired deep learning, the proposed approach addresses four major challenges in a unified way: 🚀 High-Order Nonlinear Representation Using only terminal measurements, the framework provides a general learning approach for characterizing arbitrary high-order nonlinear dynamics of IBRs. It is not tied to any specific control paradigm and can cover anything from power/voltage/current control loops to virtual synchronous machines (VSMs) and phase-locked loops (PLLs). 🚀 Continuous-Time Modeling Unlike most data-driven methods built on discrete-time models (e.g., RNNs, LSTMs, Transformers), our approach learns continuous-time state-space models (differential-algebraic equations). This enables seamless integration of the learned IBR models into standard power-system time-domain simulations with arbitrary numerical integration schemes and step sizes. 🚀 Discovery of Unknown Control Modes A physics-inspired deep unsupervised learning mechanism automatically identifies distinct control modes from historical disturbance data and learns separate state-space models that represent the dynamics associated with each mode. 🚀 Robustness to Noise and Uncertainty Inspired by Kalman filtering, the learning architecture explicitly accounts for system uncertainties and measurement noise, both of which are ubiquitous in real-world grid systems and data. It ensures the method’s robust performance in practical settings. The examples in the paper demonstrate how the proposed framework can learn accurate time-domain models of fully black-box IBRs and deliver highly accurate long-horizon predictions of their responses to grid disturbances, e.g., subsynchronous oscillations caused by PLL interactions in weak grids. See details here: https://lnkd.in/eFd5CU4e #PowerSystem #SmartGrid #InverterBasedResources #RenewableEnergy #PowerElectronics #Control #PowerSystemStability #PowerSystemModeling #PowerSystemSimulation #SystemIdentification #DataDriven #MachineLearning #DeepLearning #ArtificialIntelligence #PhysicsInformed #IEEETransactionsOnSmartGrid

𝗚𝗲𝗻𝗲𝗿𝗮𝗹 𝗚𝗿𝗶𝗱 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝗻 𝘁𝗵𝗲 𝗘𝗿𝗮 𝗼𝗳 𝗥𝗲𝗻𝗲𝘄𝗮𝗯𝗹𝗲 𝗗𝗼𝗺𝗶𝗻𝗮𝗻𝗰𝗲 As renewables like solar and wind surge, our grids face unprecedented challenges: 𝗹𝗼𝘄 𝗶𝗻𝗲𝗿𝘁𝗶𝗮, 𝘃𝗼𝗹𝘁𝗮𝗴𝗲 𝘀𝘄𝗶𝗻𝗴𝘀, and 𝘄𝗲𝗮𝗸 𝗴𝗿𝗶𝗱 𝗰𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝘀 that extend beyond rural areas into urban networks during low-load times. Inverter-based resources (IBRs) amplify these issues, risking instability, poor fault recovery, and control interactions. The key to resilience? 𝗥𝗶𝗴𝗼𝗿𝗼𝘂𝘀 𝗱𝘆𝗻𝗮𝗺𝗶𝗰 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀 to model behaviors under stress, ensuring compliance with standards like 𝗘𝗡𝗧𝗦𝗢-𝗘 and 𝗜𝗘𝗘𝗘 𝟐𝟖𝟎𝟎. Tools such as 𝗣𝗦𝗖𝗔𝗗™, 𝗣𝗦𝗦®𝗘, and 𝗗𝗜𝗴𝗦𝗜𝗟𝗘𝗡𝗧 𝗣𝗼𝘄𝗲𝗿𝗙𝗮𝗰𝘁𝗼𝗿𝘆 enable precise testing of fault ride-through, frequency support, and power control—reducing risks and accelerating project approvals. Attached: An infographic breaking down weak grid risks vs. simulation benefits—check it out! #GridStability #Renewables #IBR #EnergyTransition #PSCAD #PSSE #PowerFactory

Grid Bottleneck Crisis: The AI era faces a power problem From Industrial X in NYC: Microsoft is spending $1.5 billion per week on data centers. That’s $80 billion annually, up from $20 billion just three years ago. But here’s the shock: connection request wait times have jumped from 2-3 years to 5 years, with 2.6 terawatts queued for grid connection in the US alone. Three critical insights from Microsoft’s Darryl Willis, Siemens’ Dr. Sabine Erlinghagen, and former Shell CIO, Jay Crotts: 1. The scale is staggering: AI factories will demand power equivalent to Japan’s entire economy (125M people). We’re looking at 50% electricity demand increase by 2030, requiring $1.4 trillion in US grid infrastructure investment alone. 2. The approach must shift: Microsoft threw out their “selective energy” playbook. It’s now “all hands on deck” - nuclear, gas, solar, wind, geothermal. Even Three Mile Island is back in play. As Willis put it: “It’s going to take every type of energy we can put our hands on.” 3. The hidden optimization: When one Canadian utility combined Siemens grid simulation with IFS Copperleaf financial optimization, they discovered neither their technical best (scenario 1) nor financial best (scenario 2) was optimal. The co-simulation revealed scenario 3—avoiding billions in misallocated capital while maintaining grid stability. The math is brutal: US grid outages cost $150 billion last year—up 175%. Getting even 1% of that $1.4 trillion investment wrong means tens of billions wasted. What’s Your Edge? If you’re managing utility infrastructure or energy-intensive operations: Stop optimizing technical and financial decisions in silos. Deploy integrated simulation that co-optimizes grid stability, capital deployment, and asset resilience simultaneously. The difference between good and optimal is measured in billions - and your competitors are already closing the gap. The Enterprise Edge Mark Moffat Oliver Pilgerstorfer Andy Oliver Adam Gillbe Christian Pedersen #industrialAI #energy #power #infrastructure #utilities