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A modern power system is characterized by an increasing penetration of wind power, which results in large uncertainties in its states. These uncertainties must be quantified properly; otherwise, the system security may be threatened. Facing this challenge, we propose a cost-effective, data-driven approach for the probabilistic load-margin assessment problem. Using actual wind data, a kernel-density-estimator is applied to infer the nonparametric wind speed distributions, which are further merged into the framework of a vine copula. The latter enables us to simulate complex multivariate and highly dependent model inputs with a variety of bivariate copulae that precisely represent the tail dependence in the correlated samples. Furthermore, to reduce the prohibitive computational time of the traditional Monte-Carlo simulations processing a large amount of samples, we propose to use a nonparametric, Gaussian-process-emulator-based reduced-order model to replace the original complicated continuation power-flow model through a Bayesian learning framework. To accelerate the convergence rate of this Bayesian algorithm, a truncated polynomial chaos surrogate is developed, which serves as a highly efficient, parametric Bayesian prior. This emulator allows us to execute the time-consuming continuation power-flow solver at the sampled values with a negligible computational cost. Simulation results reveal the impressive performances of the proposed method. 

This paper proposes a simple yet effective method for power system probabilistic transient stability assessment considering the wind farm uncertainties and correlations. Specifically, the inverse Nataf-transformation-based three-point estimation method and the Cornish-Fisher expansion have been integrated together to deal with the uncertainties and the correlations among different wind farms. Then, by resorting to the extended dynamic security region approach, the transient stability criterion is derived as a linear combination of nodal injection vector under a given fault condition. New indices for the identification of critical lines have also been developed. Extensive simulation results carried out on four different systems, including the practical GZ power system in China show that the computational efficiency of the proposed method is much higher than the Monte-Carlo-based method and other methods almost without the loss of accuracy. The effectiveness of the proposed method under different penetrations of wind power with different degree of correlations is also validated. It is shown that correlation among wind farms has a larger impact on the transient stability results with a higher penetration level of renewable energy. 

This paper proposes a novel resilience assessment approach for power system. Two resilience indices are developed from the perspectives of the system and individual component levels, respectively. The former one quantifies the resilience of a power system in a system-wide manner, while the latter is intended to assess the individual component through the pre-disruption and post-disruption indices. Specifically, the pre-disruption index is used to determine the weak points of the system before the occurrence of disruptions, while the post-disruption index is for designing the optimal restoration strategies. We advocate the use of impact-increment-based state enumeration method to calculate the presented indices in an efficient way without loss of accuracy. Numerical results carried out on the IEEE RTS-79 test system and the IEEE 118-bus system validate the effectiveness of the proposed approach and indices. 

Risk assessment of power system failures induced by low-frequency, high-impact rare events is of paramount importance to power system planners and operators. In this paper, we develop a cost-effective multi-surrogate method based on multifidelity model for assessing risks in probabilistic power-flow analysis under rare events. Specifically, multiple polynomial-chaos-expansion-based surrogate models are constructed to reproduce power system responses to the stochastic changes of the load and the random occurrence of component outages. These surrogates then propagate a large number of samples at negligible computation cost and thus efficiently screen out the samples associated with high-risk rare events. The results generated by the surrogates, however, may be biased for the samples located in the low-probability tail regions that are critical to power system risk assessment. To resolve this issue, the original high-fidelity power system model is adopted to fine-tune the estimation results of low-fidelity surrogates by reevaluating only a small portion of the samples. This multifidelity model approach greatly improves the computational efficiency of the traditional Monte Carlo method used in computing the risk-event probabilities under rare events without sacrificing computational accuracy.