Robust Risk-Constrained Unit Commitment With Large-Scale Wind Generation: An Adjustable Uncertainty Set Approach

This paper addresses two vital issues which are barely discussed in the literature on robust unit commitment (RUC): 1) how much the potential operational loss could be if the realization of uncertainty is beyond the prescribed uncertainty set; 2) how large the prescribed uncertainty set should be wh...

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Bibliographic Details
Published in:IEEE transactions on power systems 2017-01, Vol.32 (1), p.723-733
Main Authors: Wang, Cheng, Liu, Feng, Wang, Jianhui, Qiu, Feng, Wei, Wei, Mei, Shengwei, Lei, Shunbo
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Computing time
Data buses
Decision making
Formulations
Generation dispatch
Generators
Indexes
Optimization
Power systems
Risk
risk assessment
Robustness
Uncertainty
Unit commitment
Wind farms
Wind forecasting
wind generation uncertainty
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Summary:This paper addresses two vital issues which are barely discussed in the literature on robust unit commitment (RUC): 1) how much the potential operational loss could be if the realization of uncertainty is beyond the prescribed uncertainty set; 2) how large the prescribed uncertainty set should be when it is used for RUC decision making. In this regard, a robust risk-constrained unit commitment (RRUC) formulation is proposed to cope with large-scale volatile and uncertain wind generation. Differing from existing RUC formulations, the wind generation uncertainty set in RRUC is adjustable via choosing diverse levels of operational risk. By optimizing the uncertainty set, RRUC can allocate operational flexibility of power systems over spatial and temporal domains optimally, reducing operational cost in a risk-constrained manner. Moreover, since impact of wind generation realization out of the prescribed uncertainty set on operational risk is taken into account, RRUC outperforms RUC in the case of rare events. The traditional column and constraint generation (C&CG) and two algorithms based on C&CG are adopted to solve the RRUC. As the proposed algorithms are quite general, they can also apply to other RUC models to improve their computational efficiency. Simulations on a modified IEEE 118-bus system demonstrate the effectiveness and efficiency of the proposed methodology.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2016.2564422