With large-scale wind power integration into power grid

the indeterminacy of power system operation is increased. In the actual operation

the wind power outputs are strongly correlated. If the factors above are neglected it will bring larger computational error. The second-order cone programming(SOCP)is mostly used in the single-period optimal power flow calculation which cannot include the indeterminacy and correlation of wind power. Current researches of dynamic stochastic optimal power flow are inadequate to model multiple dimensions of wind power outputs. The computational efficiency is low and the convergence cannot be assured. To solve these problems

a dynamic stochastic optimal power flow model considering wind power correlation based on SOCP is proposed. The multiple dimensions of power outputs are modeled based on the Pair Copula function. Then

the nonlinear dynamic stochastic optimal power flow models are transformed into SOCP models by the convex relaxation. The improved three-point estimation method and the business software of Gurobi are used to solve this model. Compared with the traditional method and other wind power outputs simulation without considering the wind power correlation

the effectiveness and practicability of the proposed method are verified.