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Title: Fast Dynamic Simulation-Based Small Signal Stability Assessment and Control

Abstract

Power grid planning and operation decisions are made based on simulation of the dynamic behavior of the system. Enabling substantial energy savings while increasing the reliability of the aging North American power grid through improved utilization of existing transmission assets hinges on the adoption of wide-area measurement systems (WAMS) for power system stabilization. However, adoption of WAMS alone will not suffice if the power system is to reach its full entitlement in stability and reliability. It is necessary to enhance predictability with "faster than real-time" dynamic simulations that will enable the dynamic stability margins, proactive real-time control, and improve grid resiliency to fast time-scale phenomena such as cascading network failures. Present-day dynamic simulations are performed only during offline planning studies, considering only worst case conditions such as summer peak, winter peak days, etc. With widespread deployment of renewable generation, controllable loads, energy storage devices and plug-in hybrid electric vehicles expected in the near future and greater integration of cyber infrastructure (communications, computation and control), monitoring and controlling the dynamic performance of the grid in real-time would become increasingly important. The state-of-the-art dynamic simulation tools have limited computational speed and are not suitable for real-time applications, given the large set ofmore » contingency conditions to be evaluated. These tools are optimized for best performance of single-processor computers, but the simulation is still several times slower than real-time due to its computational complexity. With recent significant advances in numerical methods and computational hardware, the expectations have been rising towards more efficient and faster techniques to be implemented in power system simulators. This is a natural expectation, given that the core solution algorithms of most commercial simulators were developed decades ago, when High Performance Computing (HPC) resources were not commonly available.« less

Authors:
 [1];  [1];  [1];  [1];  [1];  [1];  [1];  [2];  [2];  [2];  [2];  [2];  [2];  [2]
  1. General Electric Company, Fairfield, CT (United States)
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
General Electric Company, Fairfield, CT (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1185197
DOE Contract Number:  
OE0000626
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Acharya, Naresh, Baone, Chaitanya, Veda, Santosh, Dai, Jing, Chaudhuri, Nilanjan, Leonardi, Bruno, Sanches-Gasca, Juan, Diao, Ruisheng, Wu, Di, Huang, Zhenyu, Zhang, Yu, Jin, Shuangshuang, Zheng, Bin, and Chen, Yousu. Fast Dynamic Simulation-Based Small Signal Stability Assessment and Control. United States: N. p., 2014. Web. doi:10.2172/1185197.
Acharya, Naresh, Baone, Chaitanya, Veda, Santosh, Dai, Jing, Chaudhuri, Nilanjan, Leonardi, Bruno, Sanches-Gasca, Juan, Diao, Ruisheng, Wu, Di, Huang, Zhenyu, Zhang, Yu, Jin, Shuangshuang, Zheng, Bin, & Chen, Yousu. Fast Dynamic Simulation-Based Small Signal Stability Assessment and Control. United States. doi:10.2172/1185197.
Acharya, Naresh, Baone, Chaitanya, Veda, Santosh, Dai, Jing, Chaudhuri, Nilanjan, Leonardi, Bruno, Sanches-Gasca, Juan, Diao, Ruisheng, Wu, Di, Huang, Zhenyu, Zhang, Yu, Jin, Shuangshuang, Zheng, Bin, and Chen, Yousu. Wed . "Fast Dynamic Simulation-Based Small Signal Stability Assessment and Control". United States. doi:10.2172/1185197. https://www.osti.gov/servlets/purl/1185197.
@article{osti_1185197,
title = {Fast Dynamic Simulation-Based Small Signal Stability Assessment and Control},
author = {Acharya, Naresh and Baone, Chaitanya and Veda, Santosh and Dai, Jing and Chaudhuri, Nilanjan and Leonardi, Bruno and Sanches-Gasca, Juan and Diao, Ruisheng and Wu, Di and Huang, Zhenyu and Zhang, Yu and Jin, Shuangshuang and Zheng, Bin and Chen, Yousu},
abstractNote = {Power grid planning and operation decisions are made based on simulation of the dynamic behavior of the system. Enabling substantial energy savings while increasing the reliability of the aging North American power grid through improved utilization of existing transmission assets hinges on the adoption of wide-area measurement systems (WAMS) for power system stabilization. However, adoption of WAMS alone will not suffice if the power system is to reach its full entitlement in stability and reliability. It is necessary to enhance predictability with "faster than real-time" dynamic simulations that will enable the dynamic stability margins, proactive real-time control, and improve grid resiliency to fast time-scale phenomena such as cascading network failures. Present-day dynamic simulations are performed only during offline planning studies, considering only worst case conditions such as summer peak, winter peak days, etc. With widespread deployment of renewable generation, controllable loads, energy storage devices and plug-in hybrid electric vehicles expected in the near future and greater integration of cyber infrastructure (communications, computation and control), monitoring and controlling the dynamic performance of the grid in real-time would become increasingly important. The state-of-the-art dynamic simulation tools have limited computational speed and are not suitable for real-time applications, given the large set of contingency conditions to be evaluated. These tools are optimized for best performance of single-processor computers, but the simulation is still several times slower than real-time due to its computational complexity. With recent significant advances in numerical methods and computational hardware, the expectations have been rising towards more efficient and faster techniques to be implemented in power system simulators. This is a natural expectation, given that the core solution algorithms of most commercial simulators were developed decades ago, when High Performance Computing (HPC) resources were not commonly available.},
doi = {10.2172/1185197},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Dec 31 00:00:00 EST 2014},
month = {Wed Dec 31 00:00:00 EST 2014}
}

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