Description: Distributed generation is a flexible and effective way to utilize renewable energy. The dispersed generators are quite close to the load, and pose some power quality problems such as harmonic current emissions. This paper focuses on the harmonic propagation and interaction between a small-scale wind farm and nonlinear loads in the distribution grid. Firstly, by setting the wind turbines as P – Q(V) nodes, the paper discusses the expanding Newton-Raphson power flow method for the wind farm. Then the generalized gamma mixture models are proposed to study the non-characteristic harmonic propagation of the wind farm, which are based on Gaussian mixture models, improved phasor clustering and generalized Gamma models. After the integration of the small-scale wind farm, harmonic emissions of nonlinear loads will become random and fluctuating due to the non-stationary wind power. Furthermore, in this paper the harmonic coupled admittance matrix model of nonlinear loads combined with a wind farm is deduced by rigorous formulas. Then the harmonic propagation and interaction between a real wind farm and nonlinear loads are analyzed by the harmonic coupled admittance matrix and generalized gamma mixture models. Finally, the proposed models and methods are verified through the corresponding simulation models in MATLAB/SIMULINK and PSCAD/EMTDC.

Description: As a critical subsystem in electric vehicles and smart grids, a battery energy storage system plays an essential role in enhancement of reliable operation and system performance. In such applications, a battery energy storage system is required to provide high energy utilization efficiency, as well as reliability. However, capacity inconsistency of batteries affects energy utilization efficiency dramatically; and the situation becomes more severe after hundreds of cycles because battery capacities change randomly due to non-uniform aging. Capacity mismatch can be solved by decomposing a cluster of batteries in series into several low voltage battery packs. This paper introduces a new analysis method to optimize energy utilization efficiency by finding the best number of batteries in a pack, based on capacity distribution, order statistics, central limit theorem, and converter efficiency. Considering both battery energy utilization and power electronics efficiency, it establishes that there is a maximum energy utilization efficiency under a given capacity distribution among a certain number of batteries, which provides a basic analysis for system-level optimization of a battery system throughout its life cycle. Quantitative analysis results based on aging data are illustrated, and a prototype of flexible energy storage systems is built to verify this analysis.

Description: In this paper, an adaptive collaborative Gaussian Mixture Probability Hypothesis Density (ACo-GMPHD) filter is proposed for multi-target tracking with automatic track extraction. Based on the evolutionary difference between the persistent targets and the birth targets, the measurements are adaptively partitioned into two parts, persistent and birth measurement sets, for updating the persistent and birth target Probability Hypothesis Density, respectively. Furthermore, the collaboration mechanism of multiple probability hypothesis density (PHDs) is established, where tracks can be automatically extracted. Simulation results reveal that the proposed filter yields considerable computational savings in processing requirements and significant improvement in tracking accuracy.

Description: The Open Geospatial Consortium (OGC) Geography Markup Language (GML) standard provides basic types and a framework for defining geo-informational data models such as CityGML and IndoorGML, which provide standard information models for 3D city modelling and lightweight indoor network navigation. Location information, which is the semantic engine that fuses big geo-information data, is however, discarded in these standards. The Chinese national standard of Indoor Multi-Dimensional Location GML (IndoorLocationGML) presented in this study can be used in ubiquitous indoor location intelligent applications for people and robots. IndoorLocationGML is intended as an indoor multi-dimensional location information model and exchange data format standard, mainly for indoor positioning and navigation. This paper introduces the standard’s main features: (1) terminology; (2) indoor location information model using a Unified Modeling Language (UML) class diagram; (3) indoor location information markup language based on GML; and (4) use cases. A typical application of the standard is then discussed. This standard is applicable to the expression, storage, and distribution of indoor multi-dimensional location information, and to the seamless integration of indoor–outdoor location information. The reference and basis are therefore relevant to publishers, managers, users, and developers of indoor navigation and location-based services (LBS).

Description: To investigate the dynamics and relative drivers of cellulose degradation during litter decomposition, a field experiment was conducted in three individual ecosystems (i.e., forest floor, stream, and riparian zone) of an alpine forest meta-ecosystem on the eastern Tibetan Plateau. Four litter species (i.e., willow: Salix paraplesia, azalea: Rhododendron lapponicum, cypress: Sabina saltuaria, and larch: Larix mastersiana) that had varying initial litter chemical traits were placed separately in litterbags and then incubated on the soil surface of forest floor plots or in the water of the stream and riparian zone plots. Litterbags were retrieved five times each year during the two-year experiment, with nine replicates each time for each treatment. The results suggested that foliar litter lost 32.2%–89.2% of the initial dry mass depending on litter species and ecosystem type after two-year’s incubation. The cellulose lost 60.1%–96.8% of the initial mass with degradation rate in the order of stream > riparian zone > forest floor. Substantial cellulose degradation occurred at the very beginning (i.e., in the first pre-freezing period) of litter decomposition. Litter initial concentrations of phosphorus (P) and lignin were found to be the dominant chemical traits controlling cellulose degradation regardless of ecosystems type. The local-scale environmental factors such as temperature, pH, and nutrient availability were important moderators of cellulose degradation rate. Although the effects of common litter chemical traits (e.g., P and lignin concentrations) on cellulose degradation across different individual ecosystems were identified, local-scale environmental factors such as temperature and nutrient availability were found to be of great importance for cellulose degradation. These results indicated that local-scale environmental factors should be considered apart from litter quality for generating a reliable predictive framework for the drivers of cellulose degradation and further on litter decomposition at a global scale.

Description: Sustainability, Vol. 10, Pages 1748: The Evolution of Renewable Energy Price Policies Based on Improved Bass Model: A System Dynamics (SD) Analysis Sustainability doi: 10.3390/su10061748 Authors: Xin-gang Zhao Yu-zhuo Zhang Yan-bin Li Many countries in the world have implemented many price support policies to promote the development of renewable energy, and there are evolutionary processes between different policies at different stages of national development. Existing literature has less research on the internal mechanism and alternative process of renewable energy price policies’ evolution process. In view of this, this paper innovatively introduces the classic model of innovation diffusion theory, the Bass model, into the renewable energy price mechanism, and improves it on the basis of the traditional Bass model, and then proposes a system dynamics (SD) simulation based on the improved Bass model to study the evolution process of the renewable energy price policies. This paper mainly studies the evolution process of the policies from feed-in tariff (FIT) to renewable portfolio standard (RPS), and takes China’s wind power industry as an example to simulate the model. The results show that FIT can effectively and quickly evolve to RPS based on the internal influence of the interaction among power generation enterprises and the external influence of government behaviors. All the power generation enterprises will implement RPS, and the amount of green power enterprises eventually grows steadily and slowly. In addition, increasing the decline rate of FIT subsidy and RPS unit fine can effectively promote the evolution of RPS policy, and also improve the amount of green power enterprises and the activity of the tradable green certificates (TGC) trading market.

Description: IJERPH, Vol. 15, Pages 1102: Particulate Matter and Gaseous Pollutions in Three Metropolises along the Chinese Yangtze River: Situation and Implications International Journal of Environmental Research and Public Health doi: 10.3390/ijerph15061102 Authors: Mao Mao Xiaolin Zhang Yan Yin The situation of criteria atmospheric pollutants, including particulate matter and trace gases (SO2, NO2, CO and O3), over three metropolises (Chongqing, Wuhan, and Nanjing), representing the upstream, midstream and downstream portions of the Yangtze River Basin from September 2015 to August 2016 were analyzed. The maximum annual mean PM2.5 and PM10 concentrations were 61.3 and 102.7 μg/m3 in Wuhan, while highest annual average gaseous pollutions occurred in Nanjing, with 49.6 and 22.9 ppb for 8 h O3 and NO2, respectively. Compared to a few years ago, SO2 and CO mass concentrations have dropped to well below the qualification standards, and the O3 and NO2 concentrations basically meet the requirements though occasionally is still high. In contrary, about 13%, 25%, 22% for PM2.5, and 4%, 17%, 15% for PM10 exceed the Chinese Ambient Air Quality Standard (CAAQS) Grade II. Particulate matter, especially PM2.5, is the most frequent major pollutant to poor air quality with 73%, 64% and 88% accounting for substandard days. Mean PM2.5 concentrations on PM2.5 episode days are 2–3 times greater than non-episode days. On the basis of calculation of PM2.5/PM10 and PM2.5/CO ratios, the enhanced particulate matter pollution on episode days is closely related to secondary aerosol production. Except for O3, the remaining five pollutants exhibit analogous seasonal patterns, with the highest magnitude in winter and lowest in summer. The results of back trajectories show that air pollution displays synergistic effects on local emissions and long range transport. O3 commonly demonstrated negative correlations with other pollutants, especially during winter, while moderate to strong positive correlation between particulate matter and NO2, SO2, CO were seen. Compared to pollutant substandard ratios over three megacities in eastern China (Beijing, Shanghai, and Guangzhou), the situation in our studied second-tier cities are also severe. The results in this paper provide basic knowledge for pollution status of three cities along Chinese Yangtze River and are conductive to mitigating future negative air quality levels.

Description: Mobile autonomous systems are very important for marine scientific investigation and military applications. Many algorithms have been studied to deal with the computational efficiency problem required for large scale Simultaneous Localization and Mapping (SLAM) and its related accuracy and consistency. Among these methods, submap-based SLAM is a more effective one. By combining the strength of two popular mapping algorithms, the Rao-Blackwellised particle filter (RBPF) and extended information filter (EIF), this paper presents a Combined SLAM—an efficient submap-based solution to the SLAM problem in a large scale environment. RBPF-SLAM is used to produce local maps, which are periodically fused into an EIF-SLAM algorithm. RBPF-SLAM can avoid linearization of the robot model during operating and provide a robust data association, while EIF-SLAM can improve the whole computational speed, and avoid the tendency of RBPF-SLAM to be over-confident. In order to further improve the computational speed in a real time environment, a binary-tree-based decision-making strategy is introduced. Simulation experiments show that the proposed Combined SLAM algorithm significantly outperforms currently existing algorithms in terms of accuracy and consistency, as well as the computing efficiency. Finally, the Combined SLAM algorithm is experimentally validated in a real environment by using the Victoria Park dataset.