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Recent reforms in the power industry include the promotion of ‘dual carbon’ targets, the development of large-scale and high-penetration, renewable energy and grid-connected consumption, and deep integration of digital technology and power services. The ‘renewable energy+energy storage’ combined innovation is the important direction of business model innovation for energy power enterprises. The data-driven, intelligent empowerment, green and low-carbon are all interesting items. This paper analyzed smart green business model innovation for the motivation, essential connotation and basic structure of ‘renewable energy+ energy storage’. Combining application scenarios, analyzing typical cases and practical effects of sustainable business model innovation were analyzed. the mechanism of digital technology empowering smart green energy business model innovation is explored. Finally, the general rules of sustainable business model innovation of smart green energy are summarized. The study will help power enterprises to promote the green and low-carbon transformation of energy and help to provide actionable guidance for achieving the goal of ‘double carbon’.

In recent years, heavy rainfall events have occurred frequently in the Qinba region. Forecasting and predicting heavy rainfall in the Qinba region is difficult due to the unique underlying terrain and complicated mechanisms involved. One significant weather system that might bring significant rainfall to the region is the southwest vortex (SWV); however, its different positions, intensities, and interaction with other weather systems might result in precipitation with different intensities and distributions. In this study, ERA-5 reanalysis data, FY-4A satellite data, and conventional observation data were used to examine heavy rainstorms that occurred in the Qinba region in the periods of 3–4 September 2021 (referred to as Stage I) and 4–5 September 2021 (referred to as Stage II), while the SWV was in effect. During Stage I, the northwest vortex (NWV) and SWV generated a mesoscale shear line and mesoscale convective complex (MCC) in the Qinba region. This led to a considerable area of heavy rainfall, with a maximum hourly precipitation of 129 mm and heavy precipitation at 15 stations. During Stage II, a mesoscale convective system (MCS) influenced by the SWV was initiated by a low-level jet, resulting in a localized heavy downpour with a maximum hourly precipitation of 72 mm. Significant topography-forced uplift was found in both Stages I and II in the high-altitude Qinba region. Furthermore, the rainfall was stronger during Stage I due to the secondary circulation that developed in the middle and lower levels. These findings will improve our capability to predict rainstorms and prevent disasters in the Qinba region. [ABSTRACT FROM AUTHOR]

When nuclear power plants (NPPs) are in a state of failure, they may release radioactive material into the environment. The safety of NPPs must thus be maintained at a high standard. Online monitoring and fault detection and diagnosis (FDD) are important in helping NPP operators understand the state of the system and provide online guidance in a timely manner. Here, to mitigate the shortcomings of process monitoring in NPPs, five-level threshold, qualitative trend analysis (QTA), and signed directed graph (SDG) inference are combined to improve the veracity and sensitivity of process monitoring and FDD. First, a three-level threshold is used for process monitoring to ensure the accuracy of an alarm signal, and candidate faults are determined based on SDG backward inference from the alarm parameters. According to the candidate faults, SDG forward inference is applied to obtain candidate parameters. Second, a five-level threshold and QTA are combined to determine the qualitative trend of candidate parameters to be utilized for FDD. Finally, real faults are identified by SDG forward inference on the basis of alarm parameters and the qualitative trend of candidate parameters. To verify the validity of the method, we have conducted simulation experiments, which comprise loss of coolant accident, steam generator tube rupture, loss of feed water, main steam line break, and station black-out. This case study shows that the proposed method is superior to the conventional SDG method and can diagnose faults more quickly and accurately.