Optimal Energy-Storage Configuration for Microgrids Based on SOH Estimation and Deep Q-Network

Energy storage is an important adjustment method to improve the economy and reliability of a power system. Due to the complexity of the coupling relationship of elements such as the power source, load, and energy storage in the microgrid, there are problems of insufficient performance in terms of economic operation and efficient dispatching. In view of this, this paper proposes an energy storage configuration optimization model based on reinforcement learning and battery state of health assessment. Firstly, a quantitative assessment of battery health life loss based on deep learning was performed. Secondly, on the basis of considering comprehensive energy complementarity, a two-layer optimal configuration model was designed to optimize the capacity configuration and dispatch operation. Finally, the feasibility of the proposed method in microgrid energy storage planning and operation was verified by experimentation. By integrating reinforcement learning and traditional optimization methods, the proposed method did not rely on the accurate prediction of the power supply and load and can make decisions based only on the real-time information of the microgrid. In this paper, the advantages and disadvantages of the proposed method and existing methods were analyzed, and the results show that the proposed method can effectively improve the performance of dynamic planning for energy storage in microgrids.     

油品对DMCC甲醛排放的比较研究

为了研究甲醇燃料发动机尾气中甲醛的排放特征,在一台经过改装的组合燃烧发动机上,采用标准测试柴油(TF)和普通市场柴油(MF)进行了实验,比较了燃用不同油料时的甲醛排放特征.实验结果表明;在相同甲醇掺烧比时,两种油料均是在低负荷时甲醛排放最高,在中负荷时最低,在高负荷时甲醛排放居中,三种负荷下MF甲醛排放均比TF高;此外,实验还对比了相同负荷、不同甲醇掺烧比时的甲醛排放,均表现出MF的甲醛排放比TF高,在高负荷下甚至达到2.5倍;实验还表明单纯的依靠氧化催化转化器不能有效降低甲醛排放.     

Green Tea (Camellia sinensis): A Review of Its Phytochemistry,Pharmacology, and Toxicology

Objectives Green tea (Camellia sinensis) is a kind of unfermented tea that retains the natural substance in fresh leaves to a great extent. It is regarded as the second most popular drink in the world besides water. In this paper, the phytochemistry, pharmacology, and toxicology of green tea are reviewed systematically and comprehensively. Key findings Green tea has been demonstrated to be good for human health. Nowadays, multiple pharmacologically active components have been isolated and identified from green tea, including tea polyphenols, alkaloids, amino acids, polysaccharides, and volatile components. Recent studies have demonstrated that green tea shows versatile pharmacological activities, such as antioxidant, anticancer, hypoglycemic, antibacterial, antiviral, and neuroprotective. Studies on the toxic effects of green tea extract and its main ingredients have also raised concerns including hepatotoxicity and DNA damage. Summary Green tea can be used to assist the treatment of diabetes, Alzheimer’s disease, oral cancer, and dermatitis. Consequently, green tea has shown promising practical prospects in health care and disease prevention.     

用化学溶液方法在Ni-5at%W基底上制备La2Zr2O7过渡层的研究

La2Zr2O7(LZO)过渡层以其独特的物理化学性质越来越受到人们的关注。本文以乙酰丙酮镧和乙酰丙酮锆为前驱盐,丙酸为溶剂配置前驱液,用化学溶液方法(CSD)在具有立方织构的Ni-5at%W基底上制备了LZO过渡层薄膜。研究了前驱液成分、性质以及退火温度对LZO成相以及取向的影响。用常规XRD和X射线四环衍射仪分析了LZO薄膜的相成分和织构。结果显示,在1050℃下退火可以获得强立方织构的LZO薄膜,其中(222)峰的Phi扫描半高宽值为8.95°;(400)峰的Chi扫描半高宽值为6.8°。用高分辨扫描电子显微镜(FE-SEM)观察到LZO薄膜表面均匀致密,没有裂纹和空洞。     

Preparation and Characterization of Methyl Jasmonate Microcapsules and Their Preserving Effects on Postharvest Potato Tuber

Potato tubers tend to sprout during long-term storage, resulting in quality deterioration and shortened shelf life. Restrictions on the use of chlorpropham, the major potato sprout suppressant, have led to a need to seek alternative methods. In this study, the effects of methyl jasmonate (MeJA) solutions and MeJA microcapsules on sprouting and other key quality attributes of the potato tuber were investigated. The results showed that the MeJA solution was most effective at 300 μmol L⁻¹ according to TOPSIS analysis. To prepare MeJA microcapsules, the optimal formulation is with 0.04% emulsifier, 2.5% sodium alginate, 0.5% chitosan and 3% CaCl₂. Compared to 300 μmol L⁻¹ MeJA solution, MeJA microcapsules consumed a lower dose of MeJA but demonstrated a better retaining effect on the overall quality attributes of potato tubers. MeJA microcapsules are promising agents for the preservation of postharvest potato tubers.     

Hard X-ray one dimensional nano-focusing at the SSRF using a WSi2 /Si multilayer Laue lens

The multilayer Laue lens (MLL) is a novel diffraction optics which can realize nanometer focusing of hard X-rays with high efficiency. In this paper, a 7.9 μm-thick MLL with the outmost layer thickness of 15 nm is designed based on dynamical diffraction theory. The MLL is fabricated by first depositing the depth-graded multilayer using direct current (DC) magnetron sputtering technology. Then, the multilayer sample is sliced, and both cross-sections are thinned and polished to a depth of 35-41 μm. The focusing property of the MLL is measured at the Shanghai Synchrotron Facility (SSRF). One-dimensional (1D) focusing resolutions of 205 nm and 221 nm are obtained at E=14 keV and 18 keV, respectively. It demonstrates that the fabricated MLL can focus hard X-rays into nanometer scale.     

第一性原理对SinMg(n=1-12)团簇结构、稳定性与电子性质的研究

运用密度泛函理论(DFT)的杂化密度泛函B3LYP方法,在6-311G基组水平上对 团簇进行了构型优化、频率分析与电子性质计算.同时讨论了团簇的平均结合能、能级间隙、二阶能量差分、自然电子布居、极化率.研究结果表明： 团簇的基态绝大多数为立体结构. 时,体系的基态为自旋三重度, 时,则为单重态.镁原子的掺入使得主团簇的电子性质发生了明显的变化,掺杂使体系的平均结合能降低,能隙减小,化学硬度减小,电子亲和能增大.电子总是从 原子向 原子转移.团簇中原子之间的成键相互作用随n的增大 而增强,团簇的电子结构随n的增大而趋于紧凑.     

钢丝的切变模量与扭转角度关系

研究了钢丝的切变模量随其扭转角度变化而变化的情况,观察到扭转角大于某一角度时,切变模量趋于定值的规律,得出扭摆实验准确测量的方法.     

An Edge Server Placement Method Based on Reinforcement Learning

In mobile edge computing systems, the edge server placement problem is mainly tackled as a multi-objective optimization problem and solved with mixed integer programming, heuristic or meta-heuristic algorithms, etc. These methods, however, have profound defect implications such as poor scalability, local optimal solutions, and parameter tuning difficulties. To overcome these defects, we propose a novel edge server placement algorithm based on deep q-network and reinforcement learning, dubbed DQN-ESPA, which can achieve optimal placements without relying on previous placement experience. In DQN-ESPA, the edge server placement problem is modeled as a Markov decision process, which is formalized with the state space, action space and reward function, and it is subsequently solved using a reinforcement learning algorithm. Experimental results using real datasets from Shanghai Telecom show that DQN-ESPA outperforms state-of-the-art algorithms such as simulated annealing placement algorithm (SAPA), Top-K placement algorithm (TKPA), K-Means placement algorithm (KMPA), and random placement algorithm (RPA). In particular, with a comprehensive consideration of access delay and workload balance, DQN-ESPA achieves up to 13.40% and 15.54% better placement performance for 100 and 300 edge servers respectively.