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

## TL;DR

This paper introduces a data-driven polynomial chaos expansion method to evaluate and enhance the microgrid's ramping support capability amidst renewable energy and electric vehicle uncertainties, enabling quick assessment and optimization.

## Contribution

It develops a novel DDSPCE-based approach for accurate RSC evaluation and proposes a BESS scheduling method to improve RSC in microgrids.

## Key findings

- Evaluation takes less than 3 minutes.
- Method accurately assesses RSC under uncertainties.
- BESS scheduling effectively enhances RSC.

## Abstract

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.