Bendong Tan

The uncertainties and intermittency associated with renewable generation sources, such as solar and wind, can pose significant overloading risks to power systems under N − k contingencies, potentially leading to cascading outages. Accurately quantifying these risks in independent system operator scale power systems, which may include tens of thousands of buses, remains a grand challenge. This paper proposes a computationally efficient, tail-distribution-aware approach for accurate overloading risk quantification in large-scale power systems. Specifically, a deep-kernel sparse vector-valued Gaussian process is developed and serves as a surrogate model. This model incorporates generation dispatch, predefined contingencies, and uncertain inputs, such as photovoltaic power and load demand, to predict their impacts on branch power flows, which are treated as the model outputs. To improve the fidelity of overloading risk assessment, we introduce an adaptive resampling mechanism based on power flow solver, which corrects biases in surrogate model predictions near the overloading threshold. Extensive results obtained on the realistic 21k+ bus New England power system demonstrate that the proposed method accelerates the risk assessment process by 22 times compared to the benchmark Monte Carlo sampling method, while maintaining high accuracy. Additionally, we validate the robustness of the approach across a wide range of distribution types and correlation scenarios between renewable generation and load demands.

With the increasing penetration of intermittent inverter-based resources (IBRs) and flexible loads, identifying critical variables that affect the monitoring and control of stochastic power systems is essential for ensuring secure operation. This paper proposes a hierarchically spatio-temporal global sensitivity analysis framework for probabilistic transient stability assessment (PTSA), capable of quantifying the temporal and spatial sensitivity to correlated uncertain sources, such as IBRs and loads, for correlated generator rotor angle dynamics. To accelerate the computation of sensitivity indices, a computationally efficient surrogate model, i.e., a multi-output Gaussian process emulator (MOGPE), is further developed to replace the time-consuming time-domain simulator used in PTSA. Numerical results on the modified IEEE 9-bus system and the NPCC 140-bus system demonstrate the accuracy, efficiency, and scalability of the proposed method.

Calibration of the composite load model with distributed generation (CMPLDWG) can be challenging due to its nonlinear and high-dimensional characteristics. To address this issue, this paper proposes a Bayesian load calibration approach for the CMPLDWG model considering realistic measurement errors. The critical parameters that have large sensitivities to the load model are selected through sparse polynomial chaos expansion (SPCE)-based global sensitivity analysis. Subsequently, a deep neural network (DNN)-kernelized vector-valued Gaussian process (VVGP) is developed to learn the relationship between these critical parameters and the load response. Consequently, the DNN-VVGP is utilized to substitute the CMPLDWG model simulator, enabling efficient gradient-based posterior distribution sampling of load parameters via Hamiltonian Monte Carlo (HMC). Numerical results on realistic event measurements verify that the proposed method is more robust to measurement error and achieves higher estimation accuracy than existing state-of-the-art methods.

Predicting post-fault dynamic trajectories is important for corrective control to ensure stable and secure power system operation. This article proposes a new Bayesian post-fault power system dynamic trajectory prediction method. Specifically, a novel Bayesian multi-output prediction model termed as stochastic variational deep kernel regressor (SVDKR), is proposed for predicting the trajectories of all generators using several time steps of measurements. An out-of-step generator detector is developed to identify in-synchronization and out-of-step generators. Subsequently, an ensemble prediction mechanism is proposed to utilize two SVDKRs for separately predicting stable and unstable scenarios. This allows correcting the predictions for in-synchronization generators within unstable scenarios, thereby achieving accurate trajectory predictions for generators. Numerical results on the IEEE 39-bus and the Illinois 200-bus power systems demonstrate the effectiveness and scalability of the proposed method.

When the current limiter activates during large disturbances, the grid-forming inverter (GFM) switches from a voltage source to a current source. This oversight may undermine the accuracy of dynamic state estimation (DSE). To address this issue, this paper models the current limiter as a time-varying impedance, thereby presenting a unified state-space representation for DSE. This allows capturing the dynamics of GFMs in the presence and absence of current limitations. Numerical results on GFMs connected to a modified IEEE 39- bus system demonstrate the effectiveness of the proposed method.

Risk assessment of rare events has become increasingly important in power system planning and operation with the increasing integration of renewable energy and the presence of system uncertainties. However, quantifying the risk posed by rare events via the traditional method, i.e., Monte Carlo sampling (MCS), incurs substantial computational expense stemming from the vast ensemble of power flow simulations. To accelerate the assessment, this paper proposes a Deep Neural Network (DNN)-kernelized vector-valued Gaussian Process (VVGP) approach with excellent computational efficiency while maintaining high accuracy. Consequently, serving as a surrogate model for the power flow solver, the DNN-kernelized VVGP enables significantly faster but accurate risk assessment compared to the power flow solver. The developed surrogate model evaluates low-order $N-k$ events that contain more than 90% instances by adeptly capturing the topological features while the high-order $N-k$ events are assessed via a power flow solver, thereby striking a balance between computational efficiency and uncertainty quantification accuracy. Moreover, the model incorporates a Support Vector Machine (SVM) classifier to resample concerning low-probability tail events to counteract the biases potentially introduced during the DNN-kernelized VVGP evaluations. Simulations conducted on the modified IEEE 24-bus, 118-bus, and European 1354-bus systems demonstrate that the proposed method maintains the accuracy benchmark set by MCS while significantly reducing computational demands in large-scale power systems as compared to other state-of-the-art methods.

Grid-forming inverters, such as the virtual synchronous generator (VSG), can emulate constant or time-varying inertia to mitigate frequency stability issues. This paper proposes a data-driven variational Bayesian adaptive unscented Kalman filter (VBAUKF) to estimate the VSG-based inverter inertia and damping factor using its terminal measurements. By adopting the Thevenin equivalent idea, the virtual frequency of VSG is estimated first. Utilizing the estimated virtual frequency and considering the effects of the inverter current limiter, the time-varying inertia and damping factor estimation problem is reformulated into the state-space model-based dynamic state estimation framework. The measurements include the obtained virtual frequency, inverter terminal real, and reactive power while the unknowns are inverter inertia, damping factor, internal virtual rotor speed, and angle. To this end, an innovative VBAUKF is proposed with the advantages of dealing with unknown and time-varying models and measurement uncertainties. Numerical results on the modified IEEE 39-bus system and IEEE 118-bus power system demonstrate that the proposed estimator significantly outperforms other state-of-the-art approaches under various scenarios.

With the high penetration level of renewable power generation, their impacts on the power system frequency dynamics need to be considered, especially the doubly-fed induction generator (DFIG) based wind turbines. By analogy with the synchronous generator, this article proposes the simplified transient model of DFIG wind turbines, which consists of the equivalent circuit model and the equivalent rotor motion model. It provides a viable and tractable way to analytically quantify the contribution of DFIG wind turbines to the post-disturbance frequency, both in the center of inertia (COI) frequency and frequency spatial variation cases. The unique frequency-supporting features of the DFIG wind turbine are discussed. The model implementation at the wind farm level is provided. The correctness of the proposed model is verified by simulation results of a weak power system. The comparison with the existing models on frequency dynamics analysis is discussed.