Worst-Case Robust Profit in Generation Self-Scheduling

Recent research has shown that portfolio optimization theory can be extended to generation self-scheduling in a competitive energy market. This letter considers the self-scheduling problem in the case where the mean vector and covariance matrix of the probability distribution of prices are only known within given bounds, and the probability distribution is otherwise arbitrary. Under the above assumptions, it is shown that a method for optimization over symmetric cones can be used to (1) compute the worst-case robust profit with probability level beta and (2) optimize the self-schedule for a given probability level beta of the corresponding worst-case robust profit.