Controlled Islanding Strategy Considering Uncertainty of Renewable Energy Sources Based on Chance-constrained Model

doi:10.35833/MPCE.2020.000411

Home > Archive>Volume 10, Issue 2, 2022 >471-481. DOI:10.35833/MPCE.2020.000411

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Controlled Islanding Strategy Considering Uncertainty of Renewable Energy Sources Based on Chance-constrained Model

DOI:

10.35833/MPCE.2020.000411

Author:

Shengyuan Liu ^{^1,2}
Shengyuan Liu
School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China;Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA
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Tianhan Zhang ^¹
Tianhan Zhang
School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
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Zhenzhi Lin ^{^1,3}
Zhenzhi Lin
School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China;School of Electrical Engineering, Shandong University, Jinan 250061, China
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Yilu Liu ^{^2,4}
Yilu Liu
Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA;Electrical and Electronics Systems Research Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
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Yi Ding ^¹
Yi Ding
School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
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Li Yang ^¹
Li Yang
School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
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Affiliation:

1.School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China;2.Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA;3.School of Electrical Engineering, Shandong University, Jinan 250061, China;4.Electrical and Electronics Systems Research Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA

Fund Project:

This work was supported by the National Natural Science Foundation of China (No. 51777185), National Key R&D Program of China (No. 2016YFB0900100), and Zhejiang University Academic Award for Outstanding Doctoral Candidates.

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Abstract:

Controlled islanding plays an essential role in preventing the blackout of power systems. Although there are several studies on this topic in the past, no enough attention is paid to the uncertainty brought by renewable energy sources (RESs) that may cause unpredictable unbalanced power and the observability of power systems after islanding that is essential for back-up black-start measures. Therefore, a novel controlled islanding model based on mixed-integer second-order cone and chance-constrained programming (MISOCCP) is proposed to address these issues. First, the uncertainty of RESs is characterized by their possibility distribution models with chance constraints, and the requirements, e.g., system observability, for rapid back-up black-start measures are also considered. Then, a law of large numbers (LLN) based method is employed for converting the chance constraints into deterministic ones and reformulating the non-convex model into convex one. Finally, case studies on the revised IEEE 39-bus and 118-bus power systems as well as the comparisons among different models are given to demonstrate the effectiveness of the proposed model. The results show that the proposed model can result in less unbalanced power and better observability after islanding compared with other models.

Key words:Controlled islanding ; second-order cone ; chance-constrained programming ; renewable energy source (RES) ; black-start ; observability

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Received:June 28,2020
Revised:October 25,2020
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Online: March 30,2022
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