Reference to an increase in the wind power generation, power system stability and reliability has been affected by the wind farms. The attributes of winds farms have been observed to be different from the normal power plants, such as thermal or nuclear and hydraulic. Intermittency characterizes wind power and consequently introduces many uncertainties in the electric power systems. Therefore, for optimal operation of the power systems integrated with more sources of uncertainties, it is important to incorporate probabilistic models in the management systems.
Probabilistic methodologies proposed in most studies are perfect for the analyses of uncertainties, in order to account for the uncertainty arising from the generation of wind power. The methodologies are also important in modelling the uncertainty from the loads. More research works have focused on how conventional power systems made of synchronous generators respond to wind power integration, and how their electromechanical modes of oscillation are affected.
The results of the effect of various levels of wind power integration to a system’s small signal stability reveal that the small signal stability is affected negatively when the wind power penetration is increased. On the other hand, similar analyses reveal that wind power integration has both negative and positive effects on the system’s small signal stability.
Keyou Wang and his colleague from Shanghai Jiao Tong University presented a critical and timely review of the methodologies used in the probabilistic small signal stability analysis and dependence modelling. They also presented in their work, a comparative analysis of these methodologies and preferences of the methodologies under various wind power integration scenarios. The report is now published in Renewable and Sustainable Energy Reviews.
Wind turbines do not participate directly in the electromechanical oscillations in power systems; however, they affect the small signal stability by altering the power dispatch to synchronous generators as well as transmission networks. An evaluation of how sources of uncertainties affect a system’s small signal stability is fundamental for optimal operation.
A variety of methodologies has been proposed to account for the uncertainties, and can be classified into three categories: numerical, analytical, and approximate methods, which are represented by the Monto Carlo simulation, cumulant-based method, and point of estimation method, respectively.
Point of estimation and cumulant-based methods have been identified to be better due many advantages. However, the cumulant-based method demands less deterministic simulations as compared to point of estimation method.
The authors realized that accounting for the dependence between the various uncertainty sources was necessary in a bid to determine dependency structures that bear the actual state of the system, and consequently, after carrying out the small signal stability analysis, the outcomes would bear the actual response of the system to small perturbations taking into account dependence and uncertainty.
Out of all the dependence modeling methodologies considered, the pair-copula was identified as the most accurate but time consuming. It was suitable for power systems with small-scale wind power sources integration, and linear correlation coefficients as well as normal copula models were less accurate but were more efficient in large-scale power plants with large-scale wind power sources integration. Therefore, the choice of dependence modeling methods would largely depend on the scale of wind power integration, efficiency and accuracy needed.
Jin Xu, Peter Kairu Kanyingi, Keyou Wang, Guojie Li, Bei Han, and Xiuchen Jiang. Probabilistic small signal stability analysis with large-scale integration of wind power considering dependence. Renewable and Sustainable Energy Reviews, volume 69 (2017), pages 1258–1270.Go To Renewable and Sustainable Energy Reviews