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Microgrids (MGs) are regarded as effective solutions to provide rampingsupport to the main grid during heavy-load periods. Nevertheless, the uncertainrenewable energy sources (RES) and electric vehicles (EVs) integrated into anMG may affect the ramping support capability (RSC) of an MG. To address thechallenge, this paper develops a data-driven sparse polynomial chaos expansion(DDSPCE)-based method to accurately and efficiently evaluate the hour-by-hourRSC of an MG. The DDSPCE model is further exploited to identify the mostinfluential random inputs, based on which a scheduling method of BESS isdeveloped to enhance the RSC of an MG. Simulation results in the modified IEEE33-bus MG shows that the proposed method takes less than 3 minutes forevaluating and enhancing the hourly RSC.

A Data-Driven Polynomial Chaos Expansion-Based Method for Microgrid Ramping Support Capability Assessment and Enhancement