Research on dynamic robust planning method for active distribution network considering correlation

The universality of load subjects in distribution network brings challenges to the reliability of distribution network planning results. In this paper, a two-stage dynamic robust distribution network planning method considering correlation is proposed. The method evaluates the correlation between random variables using the Spearman rank correlation coefficient, and converts the correlated random variables into mutually independent random variables by Cholesky decomposition and independent transformation; expresses the source-load uncertainty by a bounded interval without distribution, and describes the active distribution network planning as a dynamic zero-sum game problem by combining with the two-phase dynamic robust planning; use the Benders decomposition approach to tackle the issue; mathematical simulation is used to confirm the accuracy and efficacy of the method. The results show that the dynamic robustness planning method of active distribution network taking into account the correlation can accurately simulate the operation of active distribution network with uncertain boundaries, which enhances the reliability and economy of the active distribution network planning results

Interval Power Flow Analysis Considering Interval Output of Wind Farms through Affine Arithmetic and Optimizing-Scenarios Method

Wind power belongs to sustainable and clean energy sources which play a vital role of reducing environment pollution and addressing energy crisis. However, wind power outputs are quite difficult to predict because they are derived from wind speeds, which vary irregularly and greatly all the time. The uncertainty of wind power causes variation of the variables of power grids, which threatens the power grids’ operating security. Therefore, it is significant to provide the accurate ranges of power grids’ variables, which can be used by the operators to guarantee the power grid’s operating security. To achieve this goal, the present paper puts forward the interval power flow with wind farms model, where the generation power outputs of wind farms are expressed by intervals and three types of control modes are considered for imitating the operation features of wind farms. To solve the proposed model, the affine arithmetic-based method and optimizing-scenarios method are modified and employed, where three types of constraints of wind control modes are considered in their solution process. The former expresses the interval variables as affine arithmetic forms, and constructs optimization models to contract the affine arithmetic forms to obtain the accurate intervals of power flow variables. The latter regards active power outputs of the wind farms as variables, which vary in their corresponding intervals, and accordingly builds the minimum and maximum programming models for estimating the intervals of the power flow variables. The proposed methods are applied to two case studies, where the acquired results are compared with those acquired by the Monte Carlo simulation, which is a traditional method for handling interval uncertainty. The simulation results validate the advantages, effectiveness and the applicability of the two methods

Operation Optimization for Integrated System of Wind-PV-Thermal-Storage with CC-P2G

The carbon capture (CC) and power to gas (P2G) devices can utilize the abundant new energy of the system to capture the carbon emissions generated by thermal power combustion and generate usable gas, forming a carbon resource recycling chain. In order to reduce the carbon emission of the power system, promote new energy absorption, and improve the operation flexibility of the power system, an integrated system architecture including CC and P2G is proposed and its optimization operation model is designed. The operational characteristics of power flow and carbon flow in this architecture are mainly discussed. Considering the benefits of carbon emission trading under the quota system, an optimized operation model for the integrated system of wind-PV-thermal-storage with CC-P2G is proposed, aimed at maximizing the comprehensive benefits of the integrated system and taking the operation characteristics of various equipment as constraint conditions. Furthermore, the effectiveness of the CC-P2G system in improving new energy consumption capacity and system operation efficiency is verified. The results show that the participation of the CC-P2G system needs to be effectively coordinated with market mechanisms such as carbon emissions quota trading, which can reduce the overall carbon emissions of the system and improve its operation efficiency

Multidimensional Intelligent Distribution Network Load Analysis and Forecasting Management System Based on Multidata Fusion Technology

In order to improve the work efficiency of load characteristic analysis and realize lean management, scientific prediction, and reasonable planning of the distribution networks, this paper develops a multidimensional intelligent distribution network load analysis and prediction management system based on the fusion of multidimensional data for the application of multidimensional big data in the smart distribution network. First, the framework of the software system is designed, and the functional modules for multidimensional load characteristic analysis are designed. Then, the method of multidimensional user load characterization is introduced; furthermore, the application functions and the design process of some important function modules of the software system are introduced. Finally, an application example of the multidimensional user load characterization system is presented. Overall, the developed system has the features of interoperability of data links between functional modules, information support between different functions, and modular design concept, which can meet the daily application requirements of power grid enterprises and can respond quickly to the issued calculation requirements

Simultaneous detection of influenza virus type B and influenza A virus subtypes H1N1, H3N2, and H5N1 using multiplex real-time RT-PCR

Use of multiplex real-time reverse transcription polymerase chain reaction (RT-PCR) for the simultaneous detection of influenza type B virus and influenza A virus subtypes H5N1, H3N2, and H1N1 has been described. The method exhibited a high specificity and sensitivity of approximately 10(1)-10(2) copies per microliter or 10(-3)-10(-2) TCID50/L for each subtype, as well as a high reproducibility with coefficient of variation (CV) ranging from 0.27% to 4.20%. The assays can be performed commendably on various models of real-time PCR instruments; including ABI7500, ROCH 2.0, and Mx3005p. In an analysis of 436 clinical samples from patients during the year 2009, this detection method has successfully identified 261 positive samples, as compared to only 189 positive samples using the conventional cell culture systems, and at the same time further differentiated them as 35 type B, 21 subtype H1N1, and 205 subtype H3N2. The results indicate that the multiplex real-time RT-PCR method is a potential tool for rapid screening of influenza virus from a large pool of clinical samples during flu pandemics and facilitates early influenza virus identification in most public health laboratories around the world

Differences in genome characters and cell tropisms between two chikungunya isolates of Asian lineage and Indian Ocean lineage

Abstract Background Chikungunya virus (CHIKV) is a mosquito-transmitted alphavirus within the family Togaviridae, which has attracted global attention due to its recent re-emergence. In one of our previous studies, we successfully isolated two CHIKV virus strains, SZ1050 and SZ1239, from the serum samples of two imported patients in 2010 and 2012, respectively. However, the differences in their genome characters and cell tropisms remain undefined. Methods We extracted the RNA of two CHIKV isolates and performed PCR to determine the sequence of the whole viral genomes. The genotypes were classified by phylogenetic analysis using the Mega 6.0 software. Furthermore, the cell tropisms of the two CHIKV isolates were evaluated in 13 cell lines. Results The lengths of the whole genomes for SZ1050 and SZ1239 were 11,844 nt and 12,000 nt, respectively. Phylogenetic analysis indicated that SZ1050 belonged to the Indian Ocean lineage (IOL), while SZ1239 was of the Asian lineage. Comparing to the prototype strain S27, a gap of 7 aa in the nsP3 gene and missing of one repeated sequence element (RSE) in the 3’ UTR were observed in SZ1239. The E1-A226V mutation was not detected in both strains. SZ1050 and SZ1239 could infect most of the evaluated mammalian epithelial cells. The K562 cells were permissive for both SZ1050 and SZ1239 while the U937 cells were refractory to both viruses. For Aedes cell lines C6/36 and Aag-2, both SZ1050 and SZ1239 were able to infect and replicate efficiently. Conclusions Compared to the prototype S27 virus, some deletions and mutations were found in the genomes of SZ1050 and SZ1239. Both viruses were susceptible to most evaluated epithelia or fibroblast cells and Aedes cell lines including C6/36 and Aag-2 in spite of marginal difference

Fabrication of Microlens Arrays with Controlled Curvature by Micromolding Water Condensing Based Porous Films for Deep Ultraviolet LEDs

Microlens arrays (MLAs) have attracted wide attention due to their crucial applications in optics, optoelectronics, and biochemistry. In this paper, we present a simple and green approach for the economical fabrication of MLAs with controlled curvature based on water condensing. By controlling the input current and working time of initiative cooling and the viscoelasticity of UV-curable polymer, uniform porous films with adjustable morphology were prepared. MLAs with aspect ratios of 1.41, 1.01, and 0.69 were fabricated by micromolding the porous film templates. Furthermore, the fluoropolymer encapsulations with the MLAs were applied for the packaging of deep ultraviolet light-emitting diodes (DUV-LEDs). Consequently, the light output powers of DUV-LEDs are enhanced by 7.1%, 10.2%, and 15.4%, respectively, by using these MLAs at the driving current of 350 mA

Effects of canopy gaps on N2O fluxes in a tropical montane rainforest in Hainan of China

Background and aims: Tropical montane rainforests play an important role in increasing atmospheric N2O concentration. Although accurate estimations of N2O fluxes in tropical montane rainforests are critical for predicting global climate change, there are still considerable uncertainties about the spatial and temporal variability of the emissions. This study aims to investigate the effects of canopy gap caused by typhoons on N2O emissions, a key factor for understanding the spatial heterogeneity and supporting environmental regulations. Methods: N2O fluxes were measured monthly using static chambers both inside and outside two large canopy gaps in the tropical montane rainforest of the Jianfengling National Natural Reserve on Hainan Island, south of China, from August 2012 to July 2013. Results: Mean annual N2O emissions were 2.19 +/- 0.43 kg N2O-N ha(-1) yr(-1) inside canopy gaps, and 1.19 +/- 0.29 kg N2O-N ha(-1) yr(-1) outside canopy gaps, revealing substantial differences in N2O emissions resulting from forest structure. Moreover, N2O emission rates within canopy gaps during the wet season (2.89 kg N2O-N ha(-1) yr(-1)) were significantly higher than those during the dry season (1.34 kg N2O-N ha(-1) yr(-1)), suggesting strong regulation of soil moisture and precipitation in controlling soil N dynamics. However, there were significant nonlinear relationships between N2O fluxes and water filled pore space, and soil temperature within canopy gaps, but no significant relationships were found under the closed canopy. Conclusions: Contribution of canopy gaps should be considered to avoid underestimation of N2O emission rates from disturbed forests. Interestingly, emissions from gaps are more strongly coupled with climate drivers (moisture and temperature), with important implications for climate change projections. Therefore, the further research is needed to study the biogeochemical processes and mechanisms behind such phenomenon. (C) 2017 Elsevier B.V. All rights reserved