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The electric power grid is changing from a traditional power system to a modern, smart, and integrated power system. Microgrids (MGs) play a vital role in combining distributed renewable energy resources (RESs) with traditional electric power systems. Intermittency, randomness, and volatility constitute the disadvantages of distributed RESs. MGs with high penetrations of renewable energy and random load demand cannot ignore these uncertainties, making it difficult to operate them effectively and economically. To realize the optimal scheduling of MGs, a real-time economic energy management strategy based on deep reinforcement learning (DRL) is proposed in this paper. Different from traditional model-based approaches, this strategy is learning based, and it has no requirements for an explicit model of uncertainty. Taking into account the uncertainties in RESs, load demand, and electricity prices, we formulate a Markov decision process for the real-time economic energy management problem of MGs. The objective is to minimize the daily operating cost of the system by scheduling controllable distributed generators and energy storage systems. In this paper, a deep deterministic policy gradient (DDPG) is introduced as a method for resolving the Markov decision process. The DDPG is a novel policy-based DRL approach with continuous state and action spaces. The DDPG is trained to learn the characteristics of uncertainties of the load, RES output, and electricity price using historical data from real power systems. The effectiveness of the proposed approach is validated through the designed simulation experiments. In the second experiment of our designed simulation, the proposed DRL method is compared to DQN, SAC, PPO, and MPC methods, and it is able to reduce the operating costs by 29.59%, 17.39%, 6.36%, and 9.55% on the June test set and 30.96%, 18.34%, 5.73%, and 10.16% on the November test set, respectively. The numerical results validate the practical value of the proposed DRL algorithm in addressing economic operation issues in MGs, as it demonstrates the algorithm’s ability to effectively leverage the energy storage system to reduce the operating costs across a range of scenarios.

Supplemental material, Appendix_-_new for School Bullying Among Vocational School Students in China: Prevalence and Associations With Personal, Relational, and School Factors by Shuqing Xu, Jun Ren, Fenfen Li, Lei Wang and Shumei Wang in Journal of Interpersonal Violence

Individuals differ. This seemingly trivial statement has nevertheless led to paradigm shifts, as three different fields of organismal biology have seen a marked change in key concepts over the past few decades. In animal behaviour, it has increasingly been realised that behavioural differences among individuals can be stable over time and across contexts, giving rise to the concept of animal personalities. In ecology, an increasing focus is likewise on the considerable variation in the ecological niche realised by species, populations, and individuals, giving rise to the concept of niche specialisation or individual niche variation. In evolutionary biology, where individual variation has always been central, there is an increasing awareness of the complexity with which individuals interact with the environment in producing unique phenotypes. Recent theoretical and empirical work has highlighted that the fitness landscape is rather complex, with multiple fitness peaks. It depends on the individual with its genotype, in interaction with its specific environment, which local or global fitness peak is attainable. Over the past 15 years, the need for more integrated conceptual frameworks transcending disciplines has been voiced ever more strongly. Whereas initially the ecological time scale was deemed to be fundamentally different from the evolutionary one, this notion has recently been replaced by a more integrative one, where evolution can indeed happen over ecological time scales. While in each of the three fields behaviour, ecology, and evolution, the concept of individualisation has contributed to major scientific progress, sufficient cross-fertilisation is lacking. Here, we propose a new level of conceptual unification: the individualised niche. By merging the niche concept with the fitness and animal personality concepts, new explanatory power for both ecological and evolutionary processes emerges.

Population decline among Asian horseshoe crabs in Asia is increasingly reported, but knowledge of their population and ecological status in China is limited. We conducted community interviews in 30 fishing villages around Beibu Gulf in Guangxi, China, to collect distribution information about the potential spawning/nursery grounds of Tachypleus tridentatus and Carcinoscorpius rotundicauda, and any imminent threats to their populations. Based on the results from 400 respondents we identified 45 potential spawning/nursery grounds distributed widely along the shores of Beibu Gulf. We visited 10 of these sites and verified the presence of juvenile horseshoe crabs by field surveys. Nearly all respondents reported an overall depletion in horseshoe crab populations from these 45 sites, which they attributed mainly to unsustainable fishing practices. Respondents who reported having seen horseshoe crab mating pairs on shores were mostly older people, which may suggest a considerable reduction in horseshoe crabs coming to the shores to spawn in recent years. The mean daily harvest of adult T. tridentatus offshore, as indicated by fishers, has declined from c. 50–1,000 in the 1990s to 0–30 individuals during 2011–2016. Our Wisdom of Crowds approach, supported by confirmatory field surveys, is a cost-effective method for assessing the population status of horseshoe crabs, and the level of threat they face. Similar approaches with other species are likely to be particularly valuable in the Asia–Pacific region, where well-structured population monitoring is largely unaffordable.