The renewable energy source based generating technologies and flexible demand and storage devices exhibit significant temporal and spatial uncertainties in generating and loading profiles and introduce additional level of uncertainty in network operation. The dynamic behaviours of such a network can be affected and the stable operation may be compromised. This paper proposes a probabilistic analysis approach for the evaluation of the effect of uncertain parameters on power system voltage and angular stability. Load margin, the damping of critical eigenvalues and the transient stability index (TSI) have been chosen as the relevant stability indices for voltage stability, small-disturbance stability and transient stability analysis, respectively. The Morris screening sensitivity analysis method coupled with a multivariate Gaussian copula to account for parameter correlations is used for the priority ranking of uncertain parameters. The approach is illustrated on a number of case studies using modified IEEE 68-bus NETS-NYPS test system. The results obtained in this paper reveal that the critical parameters appear as groups if the input dataset is correlated, and hence even a parameter (which may be uninfluential individually) can have a significant impact on system dynamic behaviour due to its correlation with other influential parameters.
In modern power systems, the penetration of deregulated market structures, together with the integration of renewable energy source-based generations and nonconventional loads can exhibit inherent stochastic and intermittent behaviour. Hence, bringing uncertainties to the system generation and loading profile, affects the power system's dynamic behaviour. The identification and ranking of critical system uncertain parameters is important for the efficient operation of modern power systems since they can enable better system management with less monitoring. In this paper, 6 sensitivity analysis (SA) methods have been employed for the priority ranking of uncertain parameters in a network with renewable generations from the perspective of their influence on power system voltage stability. The performances of the 6 SA methods are evaluated and their advantages and disadvantages are discussed. The modified version of the 68 bus NETS-NYPS has been used as the test system.
Abstract-This paper introduces a probabilistic method for the ranking of influential uncertain parameters for the accurate assessment of power system voltage stability. Future power systems will be highly interconnected and complex with a variety of uncertain parameters such as the injection of intermittent renewable energy resources, the adoption of flexible hierarchical control structures and the appearance of new types of loads. Identifying and ranking the uncertain parameters are important in future power system operations since they can provide referable indexes for system operators to achieve better system management with less monitoring. This paper presents the probabilistic method for the identification and ranking of critical uncertain parameters. A modified version of the 68 bus NETS-NYPS test system is used in this study for simulation studies. The effects of uncertain parameters are modelled with Monte-Carlo method in the environments of MATLAB and DIgSILENT PowerFactory. The performances of the 'nose-point area' of P-V Curves for system load buses are used as indexes when evaluating their sensibility for specific uncertainties.Index Terms--power system dynamic analysis, probability distribution, sensitivity analysis, uncertain parameters, voltage stability.
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