Real-time scheduling of renewable power systems through planning-based reinforcement learning

The growing renewable energy sources have posed significant challenges to traditional power scheduling. It is difficult for operators to obtain accurate day-ahead forecasts of renewable generation, thereby requiring the future scheduling system to make real-time scheduling decisions aligning with ultra-short-term forecasts. Restricted by the computation speed, traditional optimization-based methods can not solve this problem. Recent developments in reinforcement learning (RL) have demonstrated the potential to solve this challenge. However, the existing RL methods are inadequate in terms of constraint complexity, algorithm performance, and environment fidelity. We are the first to propose a systematic solution based on the state-of-the-art reinforcement learning algorithm and the real power grid environment. The proposed approach enables planning and finer time resolution adjustments of power generators, including unit commitment and economic dispatch, thus increasing the grid's ability to admit more renewable energy. The well-trained scheduling agent significantly reduces renewable curtailment and load shedding, which are issues arising from traditional scheduling's reliance on inaccurate day-ahead forecasts. High-frequency control decisions exploit the existing units' flexibility, reducing the power grid's dependence on hardware transformations and saving investment and operating costs, as demonstrated in experimental results. This research exhibits the potential of reinforcement learning in promoting low-carbon and intelligent power systems and represents a solid step toward sustainable electricity generation.Comment: 12 pages, 7 figure

Validation of the Chinese Version of the 12-Item Child and Youth Resilience Measure

Recent reviews call for developing methodologically sound measures of child and youth resilience. Taking into account cultural dynamics, Ungar and colleagues developed the Child and Youth Resilience Measure (CYRM). Validation of the CRYM beyond the English-speaking context is limited. This study validates the simplified Chinese version of the 12-item CYRM. First, through a meticulous process of translation and back-translation, the language appropriateness of the simplified Chinese1 version of the CYRM-12 was ensured. Second, a sample of 437 Grades Four to Nine students was drawn from two provinces in China. Cronbach's alpha of the translated CYRM-12 obtained from this sample is 0.92. Exploratory Factor Analysis results in a one-factor solution, explaining 54.26% of the variance of the 12 translated items. Next, a second sample of 2632 Grades Four to Nine students was drawn from seven provinces. Confirmatory Factor Analysis validates a one-factor, 12-item measurement model (CFI = 0.96, IFI = 0.96, NFI = 0.95, RFI = 0.94, TLI = 0.95, RMSEA = 0.06). Finally, multiple group analysis was conducted to test the measurement invariance of the translated CYRM-12

Heritage Language learning for Chinese Australians: the role of habitus

The relationship between Heritage Language and ethnic identity has gained significant research ground in social psychological and poststructural scholarship, with empirical evidence largely emerging from the North American settings. There is little pertinent sociological work conducted outside North America. To fill this gap, this sociological study sets its scene in an Australian context. Drawing on Bourdieu’s notion of habitus, the study examines the contribution of Chinese Australians’ Chineseness to their Chinese Heritage Language proficiency. Two hundred and thirty young Chinese Australians completed the online survey. Results from multiple regression indicate that habitus of Chineseness is one of the significant predictors for the Chinese Heritage Language proficiency of these young people. The study makes a theoretical contribution to investigate ethnic identity – Heritage Language link through the notion of habitus and makes a methodological contribution to quantify this habitus