About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
Energy
Paper
5.2 Decomposition and importance sampling for stochastic linear models
Abstract
Linear models that have uncertain parameters with known probability distributions are called stochastic linear models. This paper focuses on the difficulties introduced by these stochastic parameters and reviews different approaches to handle them. The following solution method uses decomposition techniques and importance sampling, and its illustration is based upon a case study of a power system with random fluctuations in demand and equipment availabilities. Numerical results are presented. © 1990.