Robust Data-driven Sparse Estimation of Power Flow Sensitivities for Smart Grid Monitoring and Operation

<p>Distribution factors (DFs) are sensitivities of active power line flows with respect to system control variables, which play a crucial role in smart grid monitoring and secure operation. This paper proposes a novel data-driven framework for robust sparse DF estimation that can handle the collinearity of high-dimensional synchrophasor measurements in large-scale smart grids. It does not require power flow models, thereby facilitating power flow sensitivity analysis with adaptiveness to operating-point changes and data uncertainties from renewable generations. An Elastic-net (Enet) estimator is used to sparsify DFs, and a new LARS-Enet algorithm is designed to achieve the data-driven estimation. Owing to the <i>l</i>₁_,₂ regularization in Enet, this framework can yield the sparse estimator with robustness to collinearity. Due to the early-stopping property and data-driven settings of LARS-Enet, this framework can solve the estimator fast online. Results carried out on the IEEE 300-bus system indicate the potential of the proposed framework for implementing new grid codes associated with power flow sensitivity analysis and advancing the future development of power-flow security-constrained operations in large-scale smart grids.</p>