Abstract
We study a version of stochastic dual dynamic programming (SDDP) with a distributionally robust objective. The classical SDDP algorithm uses a finite (nominal) probability distribution for the random outcomes at each stage. We modify this by defining a distributional uncertainty set in each stage to be a Euclidean neighbourhood of the nominal probability distribution. We derive a formula for the worst-case expectation of future costs over this set that can be applied in the backward pass of SDDP. We verify the correctness of this algorithm, show its almost sure convergence under standard assumptions, and illustrate it by applying it to a model of the New Zealand hydrothermal electricity system.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
