Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem

• Published in: IEEE transactions on power systems 2013-02, Vol.28 (1), p.52-63
• Main Authors: Bertsimas, D., Litvinov, E., Sun, Xu Andy, Jinye Zhao, Tongxin Zheng
• Format: Article
• Language: English
• Subjects: Adaptation models; Algorithms; Approximation algorithms; Bilevel mixed-integer optimization; Computational modeling; Constraints; Economics; Electric power generation; Mathematical models; Operations research; Optimization; power system control and reliability; robust and adaptive optimization; Robustness; Security; security constrained unit commitment; Studies; Uncertainty; Unit commitment
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2012.2205021

Unit commitment, one of the most critical tasks in electric power system operations, faces new challenges as the supply and demand uncertainty increases dramatically due to the integration of variable generation resources such as wind power and price responsive demand. To meet these challenges, we propose a two-stage adaptive robust unit commitment model for the security constrained unit commitment problem in the presence of nodal net injection uncertainty. Compared to the conventional stochastic programming approach, the proposed model is more practical in that it only requires a deterministic uncertainty set, rather than a hard-to-obtain probability distribution on the uncertain data. The unit commitment solutions of the proposed model are robust against all possible realizations of the modeled uncertainty. We develop a practical solution methodology based on a combination of Benders decomposition type algorithm and the outer approximation technique. We present an extensive numerical study on the real-world large scale power system operated by the ISO New England. Computational results demonstrate the economic and operational advantages of our model over the traditional reserve adjustment approach.