计及风电相关性的二阶锥动态随机最优潮流

可再生能源的大规模接入增加了系统运行调度中的不确定性。同时,实际运行中风电功率具有较强相关性,忽略这些因素将会带来较大的计算误差。目前二阶锥规划多应用于单个时间断面下的最优潮流,但只考虑单时段的最优潮流无法有效计及风电功率的不确定性以及相关性。而现有动态随机最优潮流缺乏对多维风电功率的准确建模,求解计算效率偏低,无法保证收敛性。针对以上问题,文中提出了计及风电相关性的二阶锥动态随机最优潮流模型。基于Pair Copula函数对多维风电功率进行建模,并通过凸松弛将非线性动态随机最优潮流模型转化为二阶锥规划模型,利用商业软件Gurobi结合改进三点估计法对模型进行求解。通过与传统模型以及不计及风电相关性的方案进行对比,验证该模型的有效性和实用性。

Dynamic Stochastic Optimal Power Flow Based on Second-order Cone Programming Considering Wind Power Correlation

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.