论文标题
最佳功率流与分销网络循环中的状态估计
Optimal Power Flow with State Estimation In the Loop for Distribution Networks
论文作者
论文摘要
我们提出了一个将最佳功率流量（OPF）与循环中的状态估计（SE）集成的框架。我们的方法将基于基于双重梯度的OPF求解器与SE反馈循环结合在一起，基于有限的系统监视传感器集，而不是假设所有状态的确切知识。估计算法基于一些适当的在线状态测量和嘈杂的“伪测量”，降低了未衡量的网格状态的不确定性。我们分析了提出的算法的收敛性，并根据加权最小二乘（WLS）估计量量化统计估计误差。 4521节点网络上的数值结果表明，这种方法可以扩展到极大的网络，并为大型伪测量变异性和固有的传感器测量噪声提供鲁棒性。
We propose a framework for integrating optimal power flow (OPF) with state estimation (SE) in the loop for distribution networks. Our approach combines a primal-dual gradient-based OPF solver with a SE feedback loop based on a limited set of sensors for system monitoring, instead of assuming exact knowledge of all states. The estimation algorithm reduces uncertainty on unmeasured grid states based on a few appropriate online state measurements and noisy "pseudo-measurements". We analyze the convergence of the proposed algorithm and quantify the statistical estimation errors based on a weighted least squares (WLS) estimator. The numerical results on a 4521-node network demonstrate that this approach can scale to extremely large networks and provide robustness to both large pseudo measurement variability and inherent sensor measurement noise.
