一种基于数据驱动多项式混沌展开的微电网爬坡支撑能力评估与增强方法
A Data-Driven Polynomial Chaos Expansion-Based Method for Microgrid Ramping Support Capability Assessment and Enhancement
AI总结：
针对不确定性RES和EVs影响微电网爬坡支撑能力的问题，本文提出基于DDSPCE的评估方法并结合BESS调度增强策略，在IEEE 33节点微电网中实现了准确高效的逐小时RSC评估与增强，耗时不足3分钟。
摘要：
微电网（MGs）被视为在重负荷时段为主电网提供爬坡支撑的有效解决方案。然而，接入微电网的不确定性可再生能源（RES）和电动汽车（EVs）可能影响其爬坡支撑能力（RSC）。为应对这一挑战，本文提出一种基于数据驱动稀疏多项式混沌展开（DDSPCE）的方法，以准确高效地逐小时评估微电网的RSC。进一步利用DDSPCE模型识别最具影响力的随机输入，据此开发电池储能系统（BESS）调度方法以增强微电网的RSC。在修改后的IEEE 33节点微电网中的仿真结果表明，所提方法评估和增强逐小时RSC的耗时不足3分钟。
英文摘要：
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.
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