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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Fast and Accurate Transient Stability Assessment Method Based on Deep Learning: WECC Case Study

Transient stability is one of the critical aspects of power system stability assessment. The increasing integration of inverter-based resources and the retirement of conventional synchronous generators result in the decreasing system inertia and growing complexity of system operating conditions. Using a few selected typical operating conditions cannot guarantee system transient stability in all operating conditions, and the time-domain simulation of all operating conditions requires tremendous time and is often infeasible. This paper proposes a more efficient transient stability assessment method based on deep learning. The binary search method is used to determine the critical clearing time (CCT) in creating training databased by time-domain simulation. This method is fast and accurate with 1 ms resolution. The buses whose CCTs are lower than 200 ms are considered critical buses. Buses close to each other are grouped based on their mutual admittance matrix to reduce the search space of the critical buses. This paper also proposes the generator feature normalization based on the physical model. Case study on the reduced 240-bus WECC system model demonstrates that the proposed method can predict CCT accurately and efficiently.

critical clearing time↗
DOE OSTI2022

Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems

Transient stability assessment (TSA) is a cornerstone for resilient operations of todays interconnected power grids. This paper is a confluence of quantum computing, data science and machine learning to potentially address the power system TSA issue. Here, we devise a quantum TSA (QTSA) method to enable scalable and efficient data-driven transient stability prediction for bulk power systems, which is the first attempt to tackle the TSA issue with quantum computing. Our contributions are three-fold: 1) A high expressibility, low-depth (HELD) quantum circuit is designed for accurate and noise-resilient TSA; 2) A quantum natural gradient descent algorithm is developed for efficient HELD circuit training; 3) A systematical analysis on QTSAs performance under various quantum factors is per-formed. QTSA underpins a foundation of quantum-enabled and data-driven power grid stability analytics. It renders the intractable TSA straightforward and effortless in the Hilbert space, and therefore provides stability information for power system operations. Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of QTSA.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Explainable Bayesian Neural Network for Probabilistic Transient Stability Analysis Considering Wind Energy

While several data-driven models have been developed for transient stability assessment, how to consider the uncertainties from load and renewable generations and provide interpretation of data-driven assessment results are still open. This paper proposes an explainable Bayesian Neural Network (BNN) for probabilistic transient stability assessment (TSA). By extracting the uncertainties from loads and wind farms, the BNN model can make a reliable prediction and quantify the prediction uncertainties. We also develop the Gradient Shap algorithm to make the global and local explanations for the probabilistic TSA model, a significant advantage over existing black-box data-driven methods. Numerical results on the modified IEEE 39-bus system show that the proposed method outperforms the existing methods in terms of prediction accuracy and uncertainty quantification capabilities. The explainability of the proposed method allows system operators to design preventive controls for enhancing system stability.

Bayesian Neural Network↗
DOE OSTI2023

AI-Based Faster-Than-Real-Time Stability Assessment of Large Power Systems with Applications on WECC System

Achieving clean energy goals will require significant advances in regard to addressing the computational needs for next-generation renewable-dominated power grids. One critical obstacle that lies in the way of transitioning today’s power grid to a renewable-dominated power grid is the lack of a faster-than-real-time stability assessment technology for operating a fast-changing power grid. This paper proposes an artificial intelligence (AI) -based method that predicts the system’s stability margin information (e.g., the frequency nadir in the frequency stability assessment and the critical clearing time (CCT) value in the transient stability assessment) directly from the system operating conditions without performing the conventional time-consuming time-domain simulations over detailed dynamic models. Since the AI method shifts the majority of the computational burden to offline training, the online evaluation is extremely fast. This paper has tested the AI-based stability assessment method using multiple dispatch cases that are converted and tuned from actual dispatch cases of the Western Electricity Coordinating Council (WECC) system model with more than 20,000 buses. The results show that the AI-based method could accurately predict the stability margin of such a large power system in less than 0.2 milliseconds using the offline-trained AI agent. Therefore, the proposed method has great potential to achieve faster-than-real-time stability assessment for practical large power systems while preserving sufficient accuracy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Grid and Wind Transient Ride Through Assessment of Type-5 Synchronous Wind Turbine

The integration of the inverter-based renewable energy resources is increasing and replacing conventional synchronous generators in the evolving bulk power system (BPS), impacting the system inertia along the way. Also, the limited over-current capability of power electronic converters continues to deteriorate the grid strength of the evolving power systems. Type-5 wind turbines have the potential to mitigate these problems because this configuration interfaces with the grid through a synchronous generator; hence, its operation and consequent grid impacts might mimic the conventional synchronous generator. The drivetrain of this type of turbines, however, is different from the more common Type-3 and Type-4 turbines. A hydraulic torque converter and gearbox with torque limiting feature are integral parts of a Type-5 wind turbine drivetrain, to isolate the wind speed variations at the wind turbine rotor from the grid connected generator side. This supports the synchronous operation of the generator. However, high fidelity models of Type-5 wind turbine drivetrain including these core components are not openly and widely available for grid integration and transient stability studies. Moreover, Type-5 turbines are not commonly deployed and hence its power generation and frequency stability characteristics are not widely publicized. This hinders appropriate assessment of Type-5 wind power plant's contribution to bulk grid resilience. This work provides transient stability assessment of a standalone Type-5 wind turbine. Normal operation and grid transient studies are performed to see the step load change and wind speed disturbance impacts on the Type-5 drivetrain and generation dispatch. Preliminary results show that Type-5 wind turbines can support load changes of between 0.6 p.u to 1.15 p.u. Beyond this point, the synchronization of the generator starts to deviate from its set point.

17 - WIND ENERGY↗
DOE OSTI2023

Deep Sigma Point Processes-Assisted Chance-Constrained Power System Transient Stability Preventive Control

Here this paper proposes a deep sigma point processes (DSPP)-assisted chance-constrained power system transient stability preventive control method to deal with uncertain renewable energy and loads-induced stability risk. The traditional transient stability-constrained preventive control is reformulated as a chance-constrained optimization problem. To deal with the computational bottleneck of the time-domain simulation-based probabilistic transient stability assessment, the DSPP is developed. DSPP is a parametric Bayesian approach that allows us to predict system transient stability with high computational efficiency while accurately quantifying the confidence intervals of the predictions that can be used to inform system instability risk. To this end, with a given preset confidence probability, we embed DSPP into the primal dual interior point method to help solve the chance-constrained preventive control problem, where the corresponding Jacobian and Hessian matrices are derived. Comparison results with other existing methods show that the proposed method can significantly speed up preventive control while maintaining high accuracy and convergence

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1977

Experimental evaluation of a breadboard heat and product-water removal system for a space-power fuel cell designed with static water removal and evaporative cooling

A test program was conducted to evaluate the design of a heat and product-water removal system to be used with fuel cell having static water removal and evaporative cooling. The program, which was conducted on a breadboard version of the system, provided a general assessment of the design in terms of operational integrity and transient stability. This assessment showed that, on the whole, the concept appears to be inherently sound but that in refining this design, several facets will require additional study. These involve interactions between pressure regulators in the pumping loop that occur when they are not correctly matched and the question of whether an ejector is necessary in the system.

Hagedorn, N. H.↗
DOE OSTI2022

Physics-Informed Sparse Gaussian Process for Probabilistic Stability Analysis of Large-Scale Power System with Dynamic PVs and Loads

This work proposes a physics-informed sparse Gaussian process (SGP) for probabilistic stability assessment of large-scale power systems in the presence of uncertain dynamic PVs and loads. The differential and algebraic equations considering uncertainties from dynamic PVs and loads are reformulated to a nonlinear mapping relationship that allows the application of SGP. Thanks to the nonparametric characteristic of Gaussian process, the proposed framework does not require distributions of uncertain inputs and this distinguishes it from existing approaches. As the original Gaussian process is not scalable to large-scale systems with high dimensional uncertain inputs, this paper develops the SGP with a stochastic variational inference technique. It leads to approximately two orders of complex reduction. A data pre-processing step is also introduced to tackle the coexistence of stable and unstable cases by sample clustering and constructing separate SGPs. The probabilistic transient stability index is analyzed to assess system stability under different uncertain dynamics loads and PVs. Comparisons are performed with the sampling-based, the polynomial chaos expansion-based, and traditional Gaussian process-based methods on the modified IEEE 118-bus and Texas 2000-bus systems under various scenarios, including different levels of uncertainties and the existence of nonlinear correlations among dynamic PVs. The impacts of data quality and quantity issues are also investigated. It is shown that the proposed SGP achieves significantly improved computational efficiency while maintaining high accuracy with a limited number of data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Open Source High Fidelity Modeling of a Type-5 Wind Turbine Drivetrain

The increasing integration of renewable energy resources in evolving bulk power system (BPS) is impacting the system inertia. Type-5 wind turbine generation has the potential to behave like a traditional synchronous generator and can help mitigate the impact on system inertia. A hydraulic torque converter (TC) and gearbox with torque limiting feature are integral parts of a Type-5 wind turbine unit. A high fidelity model of Type-5 wind turbine drivetrain is not openly and widely available for grid integration and transient stability studies. This hinders appropriate assessment of Type-5 wind power plant’s contribution to bulk grid resilience. This work develops and validates a TC model based on those generally used in automobile’s transmission system. Moreover, the concept of torsional coupling is leveraged to integrate the TC and gearbox system dynamics. The entire integrated model will be open sourced and publicly available for grid integration studies.

17 WIND ENERGY↗
DOE OSTI2023

Open Source High Fidelity Modeling Of A Type-5 Wind Turbine Drivetrain

The purpose of this code is to provide a high-fidelity model of Type-5 wind turbine drivetrain for grid integration and transient stability studies. The increasing integration of renewable energy resources in evolving bulk power system (BPS) is impacting the system’s inertia. Type-5 wind turbine generation has the potential to behave like a traditional synchronous generator and can help mitigate the aforementioned impact. A hydraulic torque converter (TC) and gearbox with torque limiting feature are integral parts of a Type-5 wind turbine unit. A generic and high-fidelity model of a Type-5 wind turbine drivetrain is not openly and widely available for grid integration and transient stability studies. This hinders appropriate assessment of Type-5 wind power plant's contribution to the BPS resilience. This work develops and validates a TC model based on those generally used in automobile's transmission system.

Hussain, Tanveer↗
DOE OSTI2023

Operational resilience metrics for power systems with penetration of renewable resources

Abstract Modern power grid is evolving towards carbon neutrality by deploying increasing amount of renewable energy resources. However, the impact of renewable generation on power system planning and operation is not sufficiently investigated, especially the capability of renewable penetrated power systems to resist and recover from major disturbances, which is a critical concern for system operators. Novel metrics and evaluation methodologies are needed to depict systems’ ability in response to events caused by natural disasters, and quantitatively evaluate system performance in various time scales. In this paper, operational resilience metrics are proposed for power systems with penetration of renewable energy resources based on transient stability principles. A systematic methodology is proposed to quantitatively assess the evolution of system performance during various stages of the disaster process. Based on the proposed metrics, a resilience‐oriented disaster management strategy is designed and validated using the modified IEEE 39‐bus test system. The simulation results demonstrate the validity of the proposed metrics and strategy, and show that the system resilience is enhanced during the mitigation of fault conditions.

Gui, Jianzhong↗
DOE OSTI2024

Small-Signal Stability Impacts of Load and Network Dynamics on Grid-Forming Inverters

This paper presents several stability analyses for grid-forming inverters and synchronous generators considering the dynamics of transmission lines and different load models. Load models are usually of secondary importance compared to generation source models, but as the results show, they play a crucial role in stability studies with the introduction of inverter-based resources. Given inverter control time scales, the implications of considering or neglecting electromagnetic transients of the network are very relevant in the stability assessments. In this paper, we perform eigenvalue analyses for inverter-based resources and synchronous machines connected to a load and explore the effects of multiples models under different network representations. We explore maximum loadability of inverter-based resources and synchronous machines, while analyzing the effects of load and network dynamic models on small-signal stability. The results show that the network representation plays a fundamental role in the stability of the system of different load models. The resulting stability regions are significantly different depending on the source and load model considered.

grid forming inverters↗
DOE OSTI2025

Transitioning CO2-EOR Field to Dedicated CO2 Storage: Risk Considerations and Quantifications

Presentation slides on “Transitioning CO2-EOR Field to Dedicated CO2 Storage: Risk Considerations and Quantifications” for the CCUS 2025 conference held in Houston, Texas March 3-5, 2025. CO2 injection wells for enhanced oil recovery (EOR) are currently regulated as Class II wells under the U.S. Environmental Protection Agency’s (EPA) Underground Injection Control (UIC) program, while dedicated geological CO2 storage (GCS) wells are considered Class VI wells. The UIC classification of wells for a transitioning CO2-EOR facility has the potential to substantially impact the feasibility of operations. This paper reports a case study of risk assessment of this transition, including areas review, plume stability and pressure transient and its impacts in dedicated CO2 storage.

carbon storage↗
DOE OSTI2022

Structure-Informed Graph Learning of Networked Dependencies for Online Prediction of Power System Transient Dynamics

Online transient analysis plays an increasingly important role in dynamic power grids as the renewable generation continues growing. Traditional numerical methods for transient analysis not only are computationally intensive but also require precise contingency information as input, and therefore, are not suitable for online applications. Existing online transient assessment studies focus on the determination of post-contingency system stability or stability margin. Here, this paper develops a novel graph-learning framework, Deep-learning Neural Representation or DNR, for online prediction, of the time-series trajectories of the system states using initial system responses that can be measured by phasor measurement units (PMUs). The proposed DNR framework consists of two sequential modules: a Network Constructor that captures network dependencies among generators, and a Dynamics Predictor that predicts the system trajectories. The key to improved prediction performance is the introduction of the spatio-temporal message-passing operations into graph neural networks with structural knowledge. Its effectiveness and scalability are validated through comparative studies, demonstrating the prediction performance under different contingency scenarios for systems of different sizes. This framework provides a solution to online predicting post-fault system dynamics based on real-time PMU measurements. Additionally, it can also be applied to facilitate the offline transient simulation without simulating the entire trajectories.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Multi-Timescale Integrated Dynamics and Scheduling for Solar (MIDAS-Solar) (Final Technical Report)

Solar photovoltaic (PV) installations have experienced unprecedented growth in the United States. PV will become not only an energy producer but also a necessary provider of ancillary services at multiple timescales. Conventional methods to simulate power systems operations - such as long-term production simulation (which typically considers schedules from hours to minutes by using an optimization framework) and short-term transient studies (which simulate dynamics from seconds to sub-seconds using state variables and differential equations) - are not sufficient for studying the multiple-timescale variation of solar generation and its impact on system reliability. Long-term system economics and short-term system dynamics are highly coupled, particularly when the penetration level of renewable generation is extremely high, because the uncertainty and variability of solar generation will impact both power system steady-state and dynamic performance. This project helps meet and exceed the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office goal of systems integration by directly addressing this stability and reliability challenge for power grid planning and operation. We have developed a temporally comprehensive, closed-loop simulation model, named Multi-timescale Integrated Dynamics and Scheduling (MIDAS), that seamlessly simulates power system operations from economic scheduling (day-ahead to hours) to dynamic response analysis (seconds to sub-seconds). For schedules with very high levels of inverter-based resources (IBRs), up to and including 100%, the stability of grid controls has been evaluated through electromagnetic transient (EMT) simulations and power-hardware-in-the-loop (PHIL) simulations of key transient events at key schedule points. Specifically, MIDAS provides: 1) a closed-loop simulation framework for simulating timescales from economic scheduling to dynamic stability analysis; 2) machine learning-based stability assessment; 3) EMT modeling and analysis for large-scale power systems; 4) MIDAS PHIL test bed. We worked with Hawaii Electric Companies to apply the MIDAS study framework to a Maui grid study. The entire island's transmission system was modeled in detail - from a yearly scheduling model, to a second-level frequency dynamic model, down to a sub-second-scale EMT model to address critical stability issues. The project demonstrated how MIDAS can help system planners and operators assess system reliability and stability while the power grid is marching toward a high-renewable, high-IBR future. In this Maui grid study, we found that 100% instantaneous IBR operation is achievable in EMT simulation and PHIL testing, and grid planners and operators might need new analysis/simulation tools to assess grid reliability and stability in the scheduling stage. MIDAS will bring Maui and other systems closer to 100% clean and stable energy futures. (In this study, we examined transient stability. Other topics necessary for 100% IBR operation, such as protection and resource adequacy, were not examined.)

100% Renewables↗
DOE OSTI2025

Critical Clearing Time for Droop-Controlled Grid-Forming Inverters With Circular Current Limiting and Virtual Impedance Current Limiting

The critical clearing time is a crucial parameter for assessing the large-signal stability of grid-forming (GFM) inverters. Variations in current limiters can significantly impact an inverter's transient behavior during large disturbances. Here, this letter introduces a method based on the power-angle relationship to evaluate the fault recovery of a droop-controlled GFM inverter under a three-phase bolted fault scenario, considering both the circular current limiter and the virtual impedance method. High-fidelity simulations validate the accuracy of the proposed formulas in estimating the critical clearing time of the GFM inverter.

42 ENGINEERING↗
DOE OSTI2023

Fuel Flow Modeling under Thermal Fluidics Considerations within Operational Domains of Molten Salt Reactors

Molten Salt Reactors (MSRs) offer significant versatility supported by their design flexibility and safety advantages due to high temperatures, design customization capabilities, strong negative reactivity feedback effects, and salt chemistry characteristics. Different control schedules and different mass flow rates in the MSR primary systems impact neutronics and thermal hydraulics coupling. The result is two-fold. First, it leads to adaptability in various applications when the customization of a MSR unit is desired. Second, it calls for the quantification of dynamics and safety characteristics to assess the operational domain and make sure reactor stability and inherent safety characteristics are maintained. This paper provides an in-depth transient analysis of the operational implications of MSRs having heat-generating fuel and heat-transporting salt mixed forming liquid fuel salts contained within primary systems. A coupling model was developed to simulate fuel flow in molten salt reactors. A zero-dimensional reactor kinetics model was used with a one-dimensional heat transfer model in the reactor core. The temperature reactivity feedbacks of the fuel salt resulting from the thermal-hydraulics model were used in the reactor kinetics model to complete the coupled code. The model was applied to simulate the Molten Salt Breeder Reactor (MSBR) response at steady state and transients. The developed model was shown to be a suitable tool for the dynamic analysis of molten salt reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗