01945nas a2200169 4500008003900000245006600039210006300105260002900168520131600197653001001513653002801523653001301551653002801564100002301592700002201615856013801637 2015 d00aA Risk-Averse Optimization Model for Unit Commitment Problems0 aRiskAverse Optimization Model for Unit Commitment Problems aKauai, HIbIEEEc01/20153 aIn 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.10aCERTS10areliability and markets10aRM13-00110astochastic optimization1 aMartinez, Gabriela1 aAnderson, Lindsay uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7070125&refinements%3D4259130454%26filter%3DAND%28p_IS_Number%3A7069647%29