# 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:

- Xi'an Jiaotong Univ., Xi'an (China)
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Aalborg Univ. (Denmark)
- 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:

*Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.*