%0 Conference Paper
%B 48th Annual Hawaii International Conference on System Sciences (HICSS)
%D 2015
%T A Risk-Averse Optimization Model for Unit Commitment Problems
%A Gabriela Martinez
%A C. Lindsay Anderson
%K CERTS
%K reliability and markets
%K RM13-001
%K stochastic optimization
%X In this paper, we consider the unit commitment problem of a power system with high penetration of renewable energy. The optimal day-ahead scheduling of the system is formulated as a risk-averse stochastic optimization model in which the load balance of the system is satisfied with a high prescribed probability level. In order to handle the ambiguous joint probability distribution of the renewable generation, the feasible set of the optimization problem is approximated by an quantile-based uncertainty set. Results highlight the importance of large sample size in providing reliable solutions to the SCUC problems. The method is flexible in allowing a range of risk into the problem from higher-risk to robust solutions. The results of these comparisons show that the higher cost of robust methods may not be necessary or efficient. Numerical results on a test network show that the approach provides significant scalability for the stochastic problem, allowing the use of very large sample sets to represent uncertainty in a comprehensive way. This provides significant promise for scaling to larger networks because the separation between the stochastic and the mixed-integer problem avoids multiplicative scaling of the dimension that is prevalent in traditional two-stage stochastic programming methods.
%B 48th Annual Hawaii International Conference on System Sciences (HICSS)
%I IEEE
%C Kauai, HI
%8 01/2015
%U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7070125&refinements%3D4259130454%26filter%3DAND%28p_IS_Number%3A7069647%29
%R 10.1109/HICSS.2015.310
%0 Conference Paper
%B 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton)
%D 2014
%T Toward a scalable chance-constrained formulation for unit commitment to manage high penetration of variable generation
%A Gabriela Martinez
%A C. Lindsay Anderson
%K CERTS
%K reliability and markets
%K RM13-001
%K variable generation
%X In this work, a risk-averse optimization model is applied to the security constrained unit commitment problem. The optimal day-ahead scheduling of the system generators is formulated as a chance-constrained optimization model in which the load balance constraint is satisfied with a user-defined probability level. The assumption of a specific underlying distribution is avoided and a flexible data-driven uncertainty set is used to obtain a feasible risk-averse scheduling of the system. Results on a test-scale system show the flexible and effective nature of this approach and indicate significant potential for application to large scale instances.
%B 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton)
%I IEEE
%C Monticello, IL, USA
%P 723 - 730
%8 09/2014
%R 10.1109/ALLERTON.2014.7028526