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

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...

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Veröffentlicht in:IEEE transactions on sustainable energy Jg. 10; H. 1; S. 82 - 93
Hauptverfasser: Ding, Tao, Yang, Qingrun, Liu, Xiyuan, Huang, Can, Yang, Yongheng, Wang, Min, Blaabjerg, Frede
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.01.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Computational modeling
Computer applications
Computing time
Constraints
Cybersecurity
Data buses
Data-driven stochastic optimization
Decomposition
distributionally robust optimization
duality-free decomposition
Mathematical models
Optimization
Probability distribution
Probability theory
Robustness
Robustness (mathematics)
security-constrained unit commitment
Stochastic processes
Uncertainty
Unit commitment
Wind power
Wind power generation
Wind speed
ISSN:1949-3029, 1949-3037
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Zusammenfassung: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 subproblems 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.
Bibliographie:ObjectType-Article-1
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ISSN:1949-3029
1949-3037
DOI:10.1109/TSTE.2018.2825361