Power Systems Protection

It has been truly busy time, diving deep into the causes and dynamics of the Iberian blackout last week. After all, I wanted to take a step back and compile the most frequent technical questions I’ve received, along with my personal answers based on experience and system technology perspective. I think this recent grid event raised some important lessons for power system stability in high-renewable grids. Here’s a simplified closer look, question by question: Q1: Did renewables cause the blackout? Cannot say directly. But with ~60% solar and ~10% wind generation at the time, the grid had low inertia due to inverter-based sources. This lack of synchronous inertia left the system vulnerable actually. That means as a disturbance occurred, the frequency deviation was sharper and faster, overwhelming protection systems before corrective action could stabilize the grid. Q2: Why is inertia so critical? Inertia from synchronous generators acts instantly with the frequency deviation, slowing down frequency changes by releasing kinetic energy. Without inertia, frequency falls faster and deeper, reducing reaction time for controls and risking cascading trips. Q3: Would more thermal or hydro have prevented it? Very likely yess, because synchronous thermal and hydro plants don’t just supply inertia; they provide short-circuit strength crucial for fault clearing and relay operation. Their presence also improves voltage stability and mitigates frequency oscillations. Without these stabilizers, a high-inverter grid faces higher risk during disturbances. Q4: Can batteries (BESS) or fast frequency response (FFR) replace inertia? Unfortunately not fully (or very very less than imagined / expected). Because BESS and FFR react after(!) a frequency deviation occurs; inertia works with(!) the deviation, inherently delaying the drop. While grid-forming inverters and synthetic inertia are promising technologies, they cannot (yet) replicate the instantaneous stabilizing effect of physical rotating mass at system scale. Q5: What’s the way forward for high-renewable grids? I think a robust future grid actually should have a balance. In that scenario, renewables deliver clean energy; synchronous thermal, hydro, and pumped storage provide inertia and grid strength; grid-forming inverters enhance stability but cannot entirely replace synchronous inertia. After all as a short summary, I can clearly state that decarbonization doesn’t mean eliminating inertia; it means integrating renewables with inertia-providing resources to ensure frequency stability, fault tolerance and protection system performance. The Iberian event echoes lessons from Europe’s Jan 8, 2021 grid split. Let’s never forget, inertia remains the backbone of a stable 50 Hz synchronous grid☘️

For the last part of my Energy Resilience series, we have to talk about the worst-case scenario – when the lights actually go out. Earlier this year we saw that happen in Spain and Portugal. A major blackout left millions without power. Trains stopped, shops couldn’t take card payments, hospitals and factories switched to backup. A wake-up call that modern life depends on electricity in ways we often forget until it is gone.   This is what happens when grids are pushed to the edge by fast-moving disturbances or extreme conditions. A couple of years ago, South Australia experienced a state-wide blackout after severe weather took out multiple transmission lines. Investigations showed the system lacked enough inertia to stay stable through the shock. Part of the solution was to install synchronous condensers – giant flywheels that give the grid “weight” and stability. Siemens Energy delivered two of them as part of the response. Not the only measure of course – adapting regulation is also essential – but it showed something important: without resilience in the system, recovery is slow and uncertain. So what do we actually need if we want a fast ramp-up after a major incident? From my perspective, it comes down to three things. 1️⃣ Standardize before the crisis: When parts fail, every minute spent interpreting drawings or debating specifications is a minute the lights stay out. Standard equipment and uniform processes mean teams can move quickly because they are working with tools they already know. Recovery begins long before the fault happens. 2️⃣ Design power plants with failure in mind: A fast restart depends on assets built to recover quickly, not just run efficiently. That means black-start capability, smart redundancy where it matters and systems that can restart without waiting for the wider grid. In the U.S. for example we supported a power plant with a battery system that enables multiple restart attempts within one hour – resilience designed into the plant itself. 3️⃣ No improvisation in the dark: A blackout is the worst moment to negotiate who does what. Good restoration plans spell out which assets come back first, how to stabilize small sections of the grid and when to reconnect them safely. Regular drills with operators, authorities and major customers turn these plans into routine rather than theory. These steps matter because in any major incident skilled people are often the scarcest resource – grid operators, field crews and technical specialists. That is why preparation matters so much. Clear roles, common standards and trusted partnerships mean limited teams can do more in less time. Because when the worst happens what people remember is how long it stayed dark. I hope you have found this mini-series useful. I know social media is often about speed and short takes but sometimes – especially on important topics like this – I find it worthwhile digging into the detail together.✍️ I’d be interested to hear if you agree.

As power systems transition toward higher shares of Inverter-Based Resources (IBRs), traditional Root Mean Square (RMS) models are no longer sufficient to fully capture the dynamic interactions between converters and the grid. ✓ RMS models provide averaged, simplified representations that are effective for conventional synchronous machines. ✓ However, IBR control dynamics — such as phase-locked loops (PLL), fast inner control loops, and ride-through strategies — can lead to sub-synchronous oscillations, control interactions, or stability issues that RMS models simply cannot detect. This is where Electro-Magnetic Transient (EMT) models become indispensable EMT simulations operate at microsecond-level time steps (10–20 µs) and include detailed switching and control behaviours. They allow engineers to: ▪️Analyze sub-synchronous oscillations and converter-grid interactions. ▪️Validate protection schemes under unbalanced faults. ▪️Accurately assess plant performance during disturbances. ▪️Ensure interoperability between multiple IBR technologies (e.g., hybrid BESS + PV). In essence: ▪️RMS = quick overview. ▪️EMT = high-resolution “slow-motion” insight into system dynamics.

⚡ Analysing the stability criteria of a power system containing a large number of voltage source converters (VSCs) can become a complex task due to their non-linear nature and the various time scales different controllers employ. Therefore, it is no surprise that over time, many methods to model such systems arose to analyse under which conditions they became unstable.   💡 Small-signal modelling and stability analysis have a strong theoretical foundation and are widely used due to their relative simplicity and effectiveness. The best-known approach is based on state-space models and then uses either eigenvalue or root locus analysis to derive stability criteria. A similar approach is adopted by the methods, representing the systems as a source with impedance cascading with load impedance. The core advantage of the impedance modelling methods is that they can be used for systems containing several "black boxes", i.e., converters with unknown internal structures and parameters. However, impedance-based approaches face challenges when systems contain multiple VSCs with coupling over a wide frequency range. use numerical simulation with detailed models in the time domain. However, an approach based on Lyapunov’s stability criterion can be employed to prove the asymptotic stability of the nonlinear system. However, the key challenge that this approach faces in many practical applications is finding the Lyapunov function, i.e., the function that captures the dynamics of the system and can be used to prove that the system will eventually dampen the oscillations. A detailed overview of these methods can be found at https://lnkd.in/dKStX6Xs.   🎯 The grid or converter port impedance can be obtained by active injection of current or voltage and pertinent measurement. Hence, the external characteristics of converters and grids can be obtained regardless of the internal information of the system. However, the impedance measurement methods must fulfil several criteria. First, the equipment used during measurement must impose the operating point of interest, i.e., the nominal operating voltage and currents. Second, the disturbance injection must have enough voltage and power to create disturbances that can be distinguished from system noise. Third, the equipment must be able to generate disturbances across a wide frequency range to capture important dynamics. Using EGSTON amplifiers, measuring the impedance of a battery, a converter, or a grid up to 30 kHz is possible. If you would like to know more about EGSTON solutions for impedance measurement, check out https://lnkd.in/dny_ze_x. #smartgrids #stability #renewableenergy #testing #hil

Stability Analysis of Grid-Following and GridForming Converters Based on State-Space Modelling Xian Gao, Student Member, IEEE, Dao Zhou, Senior Member, IEEE, Amjad Anvari-Moghaddam, Senior Member, IEEE, and Frede Blaabjerg, Fellow, IEEE AAU Energy, Aalborg University, Aalborg, Denmark Abstract - This paper conducts a comprehensive analysis and comparison of the control loops of the grid-following and grid-forming voltage source converters connected to the power grid. Eigenvalue trajectories are studied in order to obtain an accurate stability analysis. A timedomain simulation model of a 1.5 kW grid-connected converter is developed by using Matlab/Simulink to investigate the stability of the grid-following and gridforming control under different short-circuit ratios. The stability boundaries of the grid-following control and the grid-forming control are explored and compared with theoretical analysis. The result reveals that the gridfollowing control is better suited for a stiff power grid, while the grid-forming control is more suitable for a weak power grid. Finally, an experimental prototype is established to verify the effectiveness of the theoretical analysis. VIEW ARTICLE : https://lnkd.in/dRRk9Uwq

I’m a power systems modeler and a lot of folks wonder what it is we do all day. I’d like to go over what power systems modeling is and how we do it. Now what is a grid model? It’s a mathematical representation of the grid that can be used in simulations. We take the entire power grid that you see in the field, all those power lines, substations, and generators and convert that into a form that can be used in computer simulations to predict its behavior. As a power systems modeler, we build models of the grid to be used in various simulations. For us a model is a snapshot of the grid at a specific point in time meant to simulate a specific operating state. We are ultimately in the business of collecting data that represents these grid elements, then figuring out how to manage it and store that data before validating it. Then finally we transform the data into different formats for different simulators. Power systems modelers typically have to work in an interdisciplinary manner combining both power systems engineering and software development. You need to know how devices operate at a fundamental level but also how to collect and manage large amounts of data using programming and software development skills. Knowing programming languages like SQL, Python, Java, C#, and more are very useful in this role. You may use software like PSSE, PowerWorld, TARA, PSLF, Aspen Oneliner, CAPE, PSCAD, or EMTP. You may also use the Common Information Model or CIM to convey operational modeling information. On a daily basis the work is very cross-functional. A modeler is typically gathering data from many other stakeholders both internal and external. Then they need to understand their customers needs to deliver the models they need to plan and operate the grid. To do this you’ll have to spend a lot of time communicating with stakeholders. You’ll need to work with engineers who design infrastructure, planners, operators, and more. Ultimately the models we develop can be used in both the operations and planning horizons. These models may include steady state, dynamic, short circuit, electromagnetic transient, geomagnetic disturbance, market models, operations models, and more. At the end of the day we create a model representing the grid as shown in the image below for many different softwares. It’s a constant effort to keep the models up to date as the grid changes. But ultimately our work forms the foundation of reliable power system operations and planning. Without good models every study would be wrong. #powersystems #powersystemsmodeling #engineering

"One of the key ways to make energy systems more reliable is by maximizing flexibility — improving how well the system can adapt in real time to changes in supply and demand. The more flexible the system, the better it can handle sudden demand spikes in the event of extreme weather, such as cold snaps or heat waves, or respond to supply disruptions such as plant outages. Improving flexibility includes upgrading aging infrastructure. Much of the U.S. grid was built decades ago under different demand patterns. Modernizing the grid — by updating substations and transmission equipment, deploying advanced sensors and incorporating advanced transmission technologies (ATTs), for example — can reduce failure rates during extreme heat and cold. These technologies help operators detect problems quicker, reroute power if equipment is damaged and restore service fast. Modernization not only improves reliability but also reduces expensive emergency interventions and lowers long-term maintenance costs. Increasing grid capacity, both through deployment of ATTs and building regional and interregional transmission lines, can reduce the risk of a local weather event turning into a widespread outage. Creating a more interconnected grid allows regions to share power during shortages. Having this greater transmission capacity also help keep prices down by allowing lower-cost electricity to reach areas facing higher demand. Demand-side management options can help ease pressure on the system during extreme weather events. These include encouraging customers and large users to reduce or shift electricity use during peak periods in exchange for lower bills or leveraging distributed energy resources to help prevent shortages. Systems that rely too much on a single fuel are more vulnerable to disruption. Diversification across energy sources and technologies helps reduce the risk of issues related to fuel shortages, infrastructure failures and localized weather impacts. Finally, policy is also critical. It’s vital that incentives are properly aligned with modern needs for flexibility and preparedness. This can help utilities make system investments that really work in extreme weather and minimize costs to consumers in both the short and the long run." Kelly Lefler World Resources Institute https://lnkd.in/e5syqXQp

The roles of PSCAD and PSSE in achieving grid compliance. Both are powerful tools, but they excel in different areas, making them complementary rather than one being universally "best." Here are a few options, ranging from general to more detailed, to help you choose the best fit for your system. 1. PSSE, primarily an RMS-based program, excels in large-scale power system analysis, including power flow, short-circuit, and transient stability studies, providing a comprehensive view of steady-state and longer-term dynamic performance vital for grid code adherence. 2. Conversely, PSCAD, as an Electromagnetic Transients (EMT) simulation tool, offers unparalleled detail for high-frequency phenomena, intricate control system validation, and precise modeling of power electronics, which is crucial for assessing harmonics, sub-synchronous interactions, and fast fault ride-through of inverter-based resources. 3. PSSE stands out for its capabilities in bulk power system analysis, providing the foundation for steady-state power flow, extensive short-circuit calculations, and RMS-based transient stability assessments across vast interconnected networks. This makes it the preferred tool for evaluating system-wide voltage and frequency response, contingency performance, and general fault ride-through characteristics as stipulated by many grid codes. 4. In contrast, PSCAD's strength lies in its meticulous Electromagnetic Transients (EMT) simulations, which are critical for detailed harmonic analysis, sub-synchronous resonance (SSR) studies, and the precise validation of highly dynamic control systems intrinsic to IBRs, HVDC, and FACTS devices. Its ability to capture high-frequency transients and intricate switching behavior makes it indispensable for verifying compliance for specific, rapid phenomena where traditional RMS models are insufficient, ensuring power quality and system stability under stressed conditions. So, both PSCAD and PSSE are indispensable simulation tools; they serve distinct yet complementary roles.

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

Optimizing Energy Networks for a Sustainable Future My recent advancement in energy systems modeling—a high-performance Energy Network Optimization Model, built in #Julia using #JuMP and #HiGHS. This model integrates fossil generation, renewable sources, and battery storage to provide cost-effective, environmentally compliant, and highly reliable energy dispatch strategies. Key Highlights: High-Performance Optimization with Julia & JuMP: - Implemented using JuMP, a powerful algebraic modeling language for optimization. - Solved using HiGHS, an industry-leading solver known for its speed and efficiency in handling large-scale linear programming problems. - Julia’s computational speed and efficient memory handling make this model scalable for real-time market applications. Cost Minimization & Operational Efficiency: - The objective function minimizes total operational costs, balancing generation, start-up, and battery operation expenses for optimal market performance. Renewable Energy Integration & Curtailment Management: - The model maximizes clean energy penetration while effectively managing renewable curtailment to mitigate intermittency. Advanced Battery Storage Dynamics: - Explicit constraints model charging, discharging, and storage efficiency losses, enhancing grid flexibility. Emission Compliance: - Enforces emission cap constraints, ensuring regulatory compliance and supporting sustainability targets. Reliability Through Operational Constraints: - Incorporates demand balance, unit commitment, ramp rate limits, and spinning reserve requirements to maintain grid stability and resilience against unexpected demand fluctuations. Market Advantages: The model leverages mixed integer programming (MIP) for global optimality, ensuring transparent, scalable, and real-time deployable decision-making. Julia + JuMP dramatically improves computational efficiency, making it ideal for real-world energy markets, utility operators, and policymakers seeking cost savings and carbon reductions. Full project access, including source code, CI/CD pipelines, and detailed documentation, is available on my GitHub upon request: https://lnkd.in/eDC7VVHS Looking forward to engaging with industry experts on how this model can be adapted, extended, and applied in real-world energy systems. Let’s push the boundaries of smart, sustainable energy optimization! #EnergyOptimization #JuliaLang #JuMP #CleanEnergy #Sustainability #LinearProgramming #EnergyMarkets #SmartGrid #Innovation