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Distributionally Robust Joint Chance Constrained Problem under Moment Uncertainty

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  • Ke-wei Ding

Abstract

We discuss and develop the convex approximation for robust joint chance constraints under uncertainty of first- and second-order moments. Robust chance constraints are approximated by Worst-Case CVaR constraints which can be reformulated by a semidefinite programming. Then the chance constrained problem can be presented as semidefinite programming. We also find that the approximation for robust joint chance constraints has an equivalent individual quadratic approximation form.

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  • Ke-wei Ding, 2014. "Distributionally Robust Joint Chance Constrained Problem under Moment Uncertainty," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-8, June.
    • Handle: RePEc:hin:jnljam:487178
    DOI: 10.1155/2014/487178
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    Cited by:

    1. Aigner, Kevin-Martin & Clarner, Jan-Patrick & Liers, Frauke & Martin, Alexander, 2022. "Robust approximation of chance constrained DC optimal power flow under decision-dependent uncertainty," European Journal of Operational Research, Elsevier, vol. 301(1), pages 318-333.

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