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Title: Duality-Free Decomposition Based Data-Driven Stochastic Security-Constrained Unit Commitment

Abstract

To incorporate the superiority of both stochastic and robust approaches, a data-driven stochastic optimization is employed to solve the security-constrained unit commitment model. This approach makes the most use of the historical data to generate a set of possible probability distributions for wind power outputs and then it optimizes the unit commitment under the worst-case probability distribution. However, this model suffers from huge computational burden, as a large number of scenarios are considered. To tackle this issue, a duality-free decomposition method is proposed in this paper. This approach does not require doing duality, which can save a large set of dual variables and constraints, and therefore reduces the computational burden. In addition, the inner max-min problem has a special mathematical structure, where the scenarios have the similar constraint. Thus, the max-min problem can be decomposed into independent sub-problems to be solved in parallel, which further improves the computational efficiency. A numerical study on an IEEE 118-bus system with practical data of a wind power system has demonstrated the effectiveness of the proposal.

Authors:
ORCiD logo [1];  [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3];  [4]; ORCiD logo [3]
  1. Xi'an Jiaotong Univ., Xi'an (China)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  3. Aalborg Univ. (Denmark)
  4. Shaanxi Electric Power Corp., Xi'an (China)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1526180
Report Number(s):
[LLNL-JRNL-749680]
[Journal ID: ISSN 1949-3029; 934688]
Grant/Contract Number:  
[AC52-07NA27344]
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Sustainable Energy
Additional Journal Information:
[ Journal Volume: 10; Journal Issue: 1]; Journal ID: ISSN 1949-3029
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING

Citation Formats

Ding, Tao, Yang, Qingrun, Liu, Xiyuan, Huang, Can, Yang, Yongheng, Wang, Min, and Blaabjerg, Frede. Duality-Free Decomposition Based Data-Driven Stochastic Security-Constrained Unit Commitment. United States: N. p., 2018. Web. doi:10.1109/TSTE.2018.2825361.
Ding, Tao, Yang, Qingrun, Liu, Xiyuan, Huang, Can, Yang, Yongheng, Wang, Min, & Blaabjerg, Frede. Duality-Free Decomposition Based Data-Driven Stochastic Security-Constrained Unit Commitment. United States. doi:10.1109/TSTE.2018.2825361.
Ding, Tao, Yang, Qingrun, Liu, Xiyuan, Huang, Can, Yang, Yongheng, Wang, Min, and Blaabjerg, Frede. Tue . "Duality-Free Decomposition Based Data-Driven Stochastic Security-Constrained Unit Commitment". United States. doi:10.1109/TSTE.2018.2825361. https://www.osti.gov/servlets/purl/1526180.
@article{osti_1526180,
title = {Duality-Free Decomposition Based Data-Driven Stochastic Security-Constrained Unit Commitment},
author = {Ding, Tao and Yang, Qingrun and Liu, Xiyuan and Huang, Can and Yang, Yongheng and Wang, Min and Blaabjerg, Frede},
abstractNote = {To incorporate the superiority of both stochastic and robust approaches, a data-driven stochastic optimization is employed to solve the security-constrained unit commitment model. This approach makes the most use of the historical data to generate a set of possible probability distributions for wind power outputs and then it optimizes the unit commitment under the worst-case probability distribution. However, this model suffers from huge computational burden, as a large number of scenarios are considered. To tackle this issue, a duality-free decomposition method is proposed in this paper. This approach does not require doing duality, which can save a large set of dual variables and constraints, and therefore reduces the computational burden. In addition, the inner max-min problem has a special mathematical structure, where the scenarios have the similar constraint. Thus, the max-min problem can be decomposed into independent sub-problems to be solved in parallel, which further improves the computational efficiency. A numerical study on an IEEE 118-bus system with practical data of a wind power system has demonstrated the effectiveness of the proposal.},
doi = {10.1109/TSTE.2018.2825361},
journal = {IEEE Transactions on Sustainable Energy},
number = [1],
volume = [10],
place = {United States},
year = {2018},
month = {4}
}

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Figures / Tables:

Fig. 1 Fig. 1: Wind speed distribution in the wind farm located in Northwest China for: (a) one day and (b) one month.

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