In recent years, extreme events have frequently occurred, and the extreme uncertainty of the source-demand side of high-ratio renewable energy systems poses a great challenge to the safe operation of power systems. Accurately generating extreme scenarios related to the source-demand side under a high percentage of new power systems is vital for the safe operation of power systems and the assessment of their reliability. However, at this stage, methods for extreme scenario generation that fully consider the correlation between wind power, solar power, and load are lacking. To address these problems, this paper proposes a method for extreme scenario generation based on information-maximizing generative adversarial networks (InfoGANs) for high-proportion renewable power systems. The example analysis shows that the method for extreme scenario generation proposed in this paper can fully explore the correlation between historical wind–solar–load data, greatly improve the accuracy with which extreme scenarios are generated, and provide effective theories and methodologies for the safe operation of a new type of power system.

With the rapid development of distribution networks and increasing demand for electricity, the pressure of power supply for medium- and low-voltage distribution networks (M&LVDNs) is increasingly significant, especially considering the large scale of customers at the low-voltage (LV) level. In this paper, an outage sequence optimization method for low-voltage distribution networks (LVDNs) that considers the importance of users is proposed. The method aims to develop an optimal outage sequence strategy for LV customers in case of medium-voltage (MV) failure events. First, a multi-dimensional importance indicator system for LV users is constructed, and the customers are ranked using a modified Analytic Hierarchy Process–Entropy Weight (AHP-EW) method to determine their priorities during outages. Then, an elastic net regression-based method is used to identify the topology of the LV network. Finally, an outage sequence optimization model based on the user importance is proposed to reduce the load-shedding level. Extensive case studies are conducted in the modified LV distribution network. The results show that the proposed method results in fewer outage losses throughout the restoration periods than traditional methods and effectively improves the reliability of the power supply to LV users.

The reliability of the power supply in low-voltage distribution networks plays a crucial role in efficient power system operation. Faced with the growing demand for electricity and the diverse usage patterns of users, existing management approaches struggle to meet the varying needs of different groups. This paper proposes a reliability assessment model that is based on user demands and integrates the Delphi method and gray relational analysis to provide an innovative approach for low-voltage distribution network tiered classification management. The study focuses on the distribution network of a certain area in China. In terms of reliability assessment methods, this study creatively introduces the equivalent series method to simplify the reliability evaluation, enabling a more efficient and intuitive reliability analysis. Through actual substation case studies, this research not only assesses low-voltage distribution network reliability but also identifies weak links within the system and the key factors affecting power supply reliability via a chain tracing method, providing a scientific basis for future management strategies.