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

Journal Article · 09 April 2018 · IEEE Transactions on Sustainable Energy

DOI: https://doi.org/10.1109/TSTE.2018.2825361 · OSTI ID:1526180

Ding, Tao  ^[1]; Yang, Qingrun ^[1]; Liu, Xiyuan  ^[1]; Huang, Can  ^[2]; Yang, Yongheng  ^[3]; Wang, Min ^[4]; Blaabjerg, Frede  ^[3]

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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)

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.

• Research Organization: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC52-07NA27344
• OSTI ID: 1526180
• Report Number(s): LLNL-JRNL--749680; 934688
• Journal Information: IEEE Transactions on Sustainable Energy, Journal Name: IEEE Transactions on Sustainable Energy Journal Issue: 1 Vol. 10; ISSN 1949-3029
• Publisher: IEEECopyright Statement
• Country of Publication: United States
• Language: English