Voltage over-limit risk assessment of wind power and photovoltaic access distribution system based on day-night segmentation and Gaussian mixture model

The access of distributed power sources such as wind power and photovoltaic (PV) with randomness and uncertainty makes the operation of distribution system more complicated. It is particularly necessary to comprehensively and efficiently evaluate the risk of voltage over-limit in distribution systems with wind power and photovoltaic. Firstly, aiming at the problem of centralized sampling of Monte Carlo method in risk assessment of distribution system, a day-night segmented Monte Carlo (MC) sampling method is proposed to generate a large number of day-night sampling scenarios. Secondly, the Gaussian mixture model is used to fit the probability density of the voltage obtained from probabilistic power flow calculations, concurrently, the utility preference exponential function characterizes the severity of the over-limit, and a comprehensive evaluation model of voltage over-limit risk is constructed. Finally, the effectiveness and accuracy of the proposed method are verified by taking the improved IEEE 33-bus distribution system as an example. These results offer actionable insights for future risk assessments of new energy grid connections. • Day-night Monte Carlo method enhances risk assessment. • Gaussian mixture models improve voltage probability fit. • Utility function quantifies severity of voltage exceedances. • Model validated on the IEEE 33-bus distribution system. • Results inform future new energy grid risk assessments.