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Probabilistic small signal stability analysis of power system with wind power and photovoltaic power based on probability collocation method


Abstract

Recently, with increasing improvements in the penetration of wind power and photovoltaic power in the world, probabilistic small

signal stability analysis (PSSSA) of a power system consisting of multiple types of renewable energy has become a key problem.

To address this problem, this study proposes a probabilistic collocation method (PCM)-based PSSSA for a power system consisting

of wind farms and photovoltaic farms. Compared with the conventional Monte Carlo method, the proposed method meets the accuracy

and precision requirements and greatly reduces the computation; therefore, it is suitable for the PSSSA of this power system. Case

studies are conducted based on a 4-machine 2-area and New England systems, respectively. The simulation results show that, by

reducing synchronous generator output to improve the penetration of renewable energy, the probabilistic small signal stability (PSSS)

of the system is enhanced. Conversely, by removing part of the synchronous generators to improve the penetration of renewable

energy, the PSSS of the system may be either enhanced or deteriorated.

Keywords


Renewable energy, Probabilistic small signal stability, Probabilistic collocation method, Wind power, Photovoltaic power.


Biographies

Cai Yan

received his bachelor degree in electrical engineering from Wuhan University, China, in 2018. Currently, he is pursuing the Ph.D. degree in stability analysis and control of renewable energy generations from Huazhong University of Science and Technology (HUST).

Linli Zhou

received her bachelor and master degrees in electrical engineering from Huazhong University of Science and Technology (HUST), in 2014 and 2017, respectively. She is currently working at UHV AC/DC Transport and Inspection Center of Hubei Electric Power Co., Ltd, Hubei, China. Her research interests include stability analysis and control of renewable energy generations.


Wei Yao

received his bachelor and Ph.D. degrees in electrical engineering from Huazhong University of Science and Technology (HUST), in 2004 and 2010, respectively. He was a post-doctoral researcher in HUST, from 2010 to 2012 and a post-doctoral research associate in the University of Liverpool, from 2012 to 2014. Currently, he is an associate professor in the School of Electrical and Electronics Engineering of HUST. His research interests include power system stability analysis and control, HVDC & FACTS, and renewable energy.

Jinyu Wen

received his bachelor and Ph.D. degrees both in electrical engineering

from Huazhong University of Science and Technology (HUST), in 1992 and 1998, respectively. He was a visiting student from 1996 to 1997 and research fellow from 2002 to 2003 at the University of Liverpool, UK. He was a senior visiting researcher at the University of Texas in 2010. From 1998 to 2002, he was a director engineer in Xinjiang Electric Co. Ltd. in China. In 2003 he started to work in the HUST and now is a professor at HUST. His current research interests include renewable energy integration, energy storage application, DC grid, and power system operation and control.

Shijie Cheng

received his bachelor degree from Xi’an Jiaotong University, in 1967, master degree from Huazhong University of Science and Technology (HUST), in 1981, and Ph.D. degree from the University of Calgary, in 1986, respectively, all in electrical engineering. He has been a professor in HUST since 1991. His research interests are power system control, stability analysis, application of artificial intelligence, and energy storage. Prof. Cheng is a Fellow of the Chinese Academy of Sciences.



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Editor:Chenyang Liu

Reviewer:Yingmei Liu