Harnessing Adaptive Sparsity: Data-Driven Control for Solar PV Generation

This paper introduces a novel statistical learning method using adaptive regulated sparsity promotion for data-driven modeling and control of solar photovoltaic (PV) generation in smart grids. Unlike traditional data-driven modeling approaches that may encounter computational challenges with an expanding pool of candidate functions, we propose an innovative algorithm called adaptive regulated sparse regression (ARSR). The proposed ARSR dynamically adjusts the hyperparameter weights of candidate functions to effectively capture the dynamics of PV systems. Leveraging this algorithm, we derive open-loop and closed-loop models of single-stage PV systems from measurements, facilitating a data-driven control design for PVs in smart grid.