  Microgrids (MGs) are regarded as effective solutions to provide ramping
support to the main grid during heavy-load periods. Nevertheless, the uncertain
renewable energy sources (RES) and electric vehicles (EVs) integrated into an
MG may affect the ramping support capability (RSC) of an MG. To address the
challenge, this paper develops a data-driven sparse polynomial chaos expansion
(DDSPCE)-based method to accurately and efficiently evaluate the hour-by-hour
RSC of an MG. The DDSPCE model is further exploited to identify the most
influential random inputs, based on which a scheduling method of BESS is
developed to enhance the RSC of an MG. Simulation results in the modified IEEE
33-bus MG shows that the proposed method takes less than 3 minutes for
evaluating and enhancing the hourly RSC.
