High-penetration renewable energy power systems introduce significant volatility and uncertainty,exposing the power system to operational risks associated with inadequate flexibility.Assessing the risk of insufficient flexibility under uncertain conditions is crucial for controlling the operational risk levels of power systems and evaluating the merits of planning scenarios.This study explores quantitative assessment methods for the risk of inadequate flexibility in renewable energy power system regulation resources and proposes a risk evaluation index system for this risk.Firstly,a data-driven modeling approach for source-load uncertainty is introduced based on kernel density estimation and order optimization theory.To enhance the adequacy of source-load sample data in power systems,a reconstruction method for low-probability risk sample sets in power systems based on cloud modeling is proposed,enabling cost-free and flexible acquisition of training samples.Secondly,a quantitative assessment method for the risk of inadequate flexibility in renewable energy power system regulation resources is developed from two aspects:ramping capability and regulation depth.Finally,case studies validate the effectiveness and feasibility of the proposed methods.
关键词
高比例新能源电力系统/调节资源灵活性/风险评估指标/数据驱动建模/风险样本重构
Key words
high penetration renewable energy grid/regulate resource flexibility/risk assessment indicators/data-driven modeling/reconstruction of risk samples
维普
万方数据