Distribution-agnostic stochastic optimal power flow for distribution grids
Distribution-agnostic stochastic optimal power flow for distribution grids
复制标题
配电网与分布无关的随机最优潮流
DOI：
10.1109/naps.2016.7747962
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
T. Summers
中科院分区：
文献类型：
--
作者：
K. Baker;E. Dall’Anese;T. Summers
提出了一种基于数据驱动的配电网机会约束交流最优潮流算法。不确定预测的负载和光伏（PV）系统所产生的功率被认为是，与目标最小化光伏弃电，同时满足潮流和电压调节约束。一个数据驱动的方法是用来开发一个分布鲁棒的保守凸近似的机会约束，特别是，预测误差的均值和协方差矩阵在线更新，并利用执行电压调节与预定的概率通过切比雪夫为基础的界限。通过将交流潮流方程的精确线性近似与分布鲁棒机会约束重构相结合，所得到的优化问题变得凸和计算上易于处理。
This paper outlines a data-driven, distributionally robust approach to solve chance-constrained AC optimal power flow problems in distribution networks. Uncertain forecasts for loads and power generated by photovoltaic (PV) systems are considered, with the goal of minimizing PV curtailment while meeting power flow and voltage regulation constraints. A data-driven approach is utilized to develop a distributionally robust conservative convex approximation of the chance-constraints; particularly, the mean and covariance matrix of the forecast errors are updated online, and leveraged to enforce voltage regulation with predetermined probability via Chebyshev-based bounds. By combining an accurate linear approximation of the AC power flow equations with the distributionally robust chance constraint reformulation, the resulting optimization problem becomes convex and computationally tractable.