Probabilistic small signal stability analysis with large-scale integration of wind power considering dependence

Significance Statement

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.

small signal stability analysis large scale integration wind power renewable energy global innovations

About The Author

Jin Xu (S’16) received his B.S. degree in electrical engineering from Sichuan University, Chengdu, China, in 2013. He is currently pursuing the Ph.D. degree at Department of Electrical Engineering School of Electronic information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.

His research interests include power system dynamic modelling and stability analysis, electromagnetic modelling and simulation.

About The Author

Peter Kairu Kanyingi was born in 1986. He received his undergraduate degree in Energy Engineering from Kenyatta University in the year 2011. In the year 2016, he received his MSc degree from Shanghai Jiao Tong University with a Major in Power Systems and its Automation. From Sep 2016 to March 2017 he was with the Department of Renewable Energy at Jaramogi Oginga Odinga University as a part time lecturer. Currently he is a lecturer in the Department of Energy Technology at Kenyatta University, Nairobi Kenya.

His major research interests include power system stability, probabilistic modeling of power systems, modeling of complex dependence and uncertainty existing in renewable energy sources, analysis of the impact of wind and solar power integration into existing power systems, smart grid, HVDC and power system design, modeling and simulation.

About The Author

Keyou Wang (S’05–M’09) received the B.S. and M.S. degrees in electrical engineering from Shanghai Jiao Tong University, Shanghai, China, in 2001 and 2004, respectively, and the Ph.D. degree from the Missouri University of Science and Technology (formerly University of Missouri-Rolla), Rolla, MO, USA, in 2008.

He is currently an Associate Professor and the Deputy Department Head of Electrical Engineering with Shanghai Jiao Tong University. His research interests include power system dynamics and stability, renewable energy integration, and converter dominated power system. He serves as an Associated Editor of IET Generation Transmission & Distribution.

About The Author

Guojie Li (M’09-SM’12) received his B.E. and M.E. degrees in Electrical Engineering from Tsinghua Univ., Beijing, China in 1989 and 1993, respectively. He also received PhD degree in the School of EEE, Nanyang Technological University Singapore in 1999.

He was an associate professor in the Dept. of Electrical Engineering, Tsinghua Univ., Beijing, China. He is now a professor in the Dept. of Electrical Engineering, Shanghai Jiao Tong Univ., Shanghai, China. His current research interests include ac/dc power system analysis and control, wind and PV power control and integration, and DAB control.

About The Author

Bei Han received M.S degree in electrical engineering from Shanghai Jiao Tong University and received Ph.D degree in electrical engineering from Politecnico di Torino. Currently, she is an Assistant Professor of Shanghai Jiao Tong University. Her research interests are complex distribution system modeling with multi-microgrids and DER uncertainties.

About The Author

Xiuchen Jiang was born in Shandong, China. He received the B.E. degree in high voltage and insulation technology from Shanghai Jiao Tong University, Shanghai, China, in 1987, the M.S. degree in high voltage and insulation technology from Tsinghua University, Beijing, China, in 1992, and the Ph.D. degree in electric power system and automation from Shanghai Jiao Tong University in 2001.

Currently, he is a Professor in the Department of Electrical Engineering, Shanghai Jiao Tong University. His research interests are electrical equipment online monitoring as well as condition-based maintenance and automation.

Reference

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.

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