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This paper proposes a distributionally robust optimal power flow (OPF) modelfor transmission grids with wind power generation. The model uses theconditional value-at-risk (CVaR) constraints to control the reserve and branchflow limit violations caused by wind power forecast errors. Meanwhile, theGaussian mixture model (GMM) is integrated into the CVaR constraints to guardagainst the non-Gaussian forecast error distributions. Unlike the previousstudies considering the GMM with fixed parameters, this paper allows the GMMparameters to be variable within some credible regions and develops adata-driven GMM-based ambiguity set to achieve the distributional robustness.Also, rather than using the traditional sample-based approximation of CVaR withhigh computational burden, this paper designs a scalable cutting-planealgorithm to handle the distributionally robust CVaR constraints. Case studieson the IEEE 2736-bus system show the effectiveness and scalability of theproposed OPF model.

Gaussian Mixture Model Based Distributionally Robust Optimal Power Flow With CVaR Constraints