Optimal sizing and siting of renewable energy resources in distribution systems considering time varying electrical/heating/cooling loads using PSO algorithm

Distributed Generation (DG) sources based on Renewable Energy (RE) can be the fastest growing power resources in distribution systems due to their environmental friendliness and also the limited sources of fossil fuels. In general, the optimal location and size of DG units have profoundly impacted on the system losses in a distribution network. In the present article, the Particle Swarm Optimization (PSO) algorithm is employed to find the optimal location and size of DG units in a distribution system. The optimal location and size of DG units are determined on the basis of a multi-objective strategy as follows: (i) the minimization of network power losses, (ii) the minimization of the total costs of Distributed Energy Resources (DERs), (iii) the improvement of voltage stability, and (iv) the minimization of greenhouse gas emissions. The related distribution system was assumed to be composed of the fuel cells, wind turbines, photovoltaic arrays, and battery storages. The electrical, cooling, and heating loads were also considered in this article. The heating and cooling requirements of the system consist of time varying water heating load, space heating load, and space cooling load. In this study, the waste and fuel cell were used to produce the required heating and cooling loads in the distribution system. In addition, the absorption chiller was used to supply the required space cooling loads. A detailed performance analysis was carried out on 13 bus radial distribution system to demonstrate the effectiveness of the proposed methodology.     

A numerical model for drying of spherical object in an indirect type solar dryer and estimating the drying time at different moisture level and air temperature

A numerical model for simultaneous heat and mass transfer was developed for solar drying of spherical objects and the object considered is green peas. Solar collector outlet temperature is assumed as drying chamber temperature and justified through energy balance equations. Assumptions are imposed on heat and mass transfer governing equations without losing the physics of the problem. Discretization is performed by finite difference method with implicit scheme. To generalize, the governing equation and boundary conditions are non-dimensionalized. The set of finite difference equations was solved by Tridiagonal Matrix Algorithm and a computer code in MATLAB was developed to solve them. The drying curves showed two stages of drying, initial, and secondary drying stage. At all drying temperatures and drying time, the center moisture was maximum and it was minimum at the boundary. A percentage of 85.67 surface moisture content and 25.33% center moisture was eliminated in the first 1 hr at 348 K. The product should be dried up to 7.45, 4.74, and 3.74 hr at air drying temperatures of 318, 333, and 348 K respectively, to maintain 10% of the product’s initial moisture content. The result is compared with the experimental result from literature and they are found to be in good agreement.     

Optimal Management of a Multispecies Shorebird Flyway under Sea‐Level Rise

Every year, millions of migratory shorebirds fly through the East Asian–Australasian Flyway between their arctic breeding grounds and Australasia. This flyway includes numerous coastal wetlands in Asia and the Pacific that are used as stopover sites where birds rest and feed. Loss of a few important stopover sites through sea‐level rise (SLR) could cause sudden population declines. We formulated and solved mathematically the problem of how to identify the most important stopover sites to minimize losses of bird populations across flyways by conserving land that facilitates upshore shifts of tidal flats in response to SLR. To guide conservation investment that minimizes losses of migratory bird populations during migration, we developed a spatially explicit flyway model coupled with a maximum flow algorithm. Migratory routes of 10 shorebird taxa were modeled in a graph theoretic framework by representing clusters of important wetlands as nodes and the number of birds flying between 2 nodes as edges. We also evaluated several resource allocation algorithms that required only partial information on flyway connectivity (node strategy, based on the impacts of SLR at nodes; habitat strategy, based on habitat change at sites; population strategy, based on population change at sites; and random investment). The resource allocation algorithms based on flyway information performed on average 15% better than simpler allocations based on patterns of habitat loss or local bird counts. The Yellow Sea region stood out as the most important priority for effective conservation of migratory shorebirds, but investment in this area alone will not ensure the persistence of species across the flyway. The spatial distribution of conservation investments differed enormously according to the severity of SLR and whether information about flyway connectivity was used to guide the prioritizations. With the rapid ongoing loss of coastal wetlands globally, our method provides insight into efficient conservation planning for migratory species. Gestión Óptima de una Ruta Migratoria de Múltiples Especies de Aves Costeras Sometida a Incremento del Nivel del Mar     

矿井通风系统风流控制的改进算法

介绍了矿井通风系统风流控制的基本数学模型和计算方法,指出了常规计算方法的不足,提出了一种能够避免大量矩阵运算、适用于实际通风网络的新算法,程序简单,计算时间省,并结合实例阐述了算法原理。     

Applying deep learning to right whale photo identification

Photo identification is an important tool for estimating abundance and monitoring population trends over time. However, manually matching photographs to known individuals is time-consuming. Motivated by recent developments in image recognition, we hosted a data science challenge on the crowdsourcing platform Kaggle to automate the identification of endangered North Atlantic right whales (Eubalaena glacialis). The winning solution automatically identified individual whales with 87% accuracy with a series of convolutional neural networks to identify the region of interest on an image, rotate, crop, and create standardized photographs of uniform size and orientation and then identify the correct individual whale from these passport-like photographs. Recent advances in deep learning coupled with this fully automated workflow have yielded impressive results and have the potential to revolutionize traditional methods for the collection of data on the abundance and distribution of wild populations. Presenting these results to a broad audience should further bridge the gap between the data science and conservation science communities.     

危化品道路运输事故应急救援中心选址模型研究

为快速提高危化品道路运输事故应急救援能力,降低事故后果,文章基于目前应急救援中心选址模型的不足,提出了一种新的双目标决策救援中心选址模型。通过综合考虑应急响应时间及应急处理能力对救援能力的影响,建立了救援中心点的最大网络覆盖模型;并采用较优的邻域搜索算法求解救援中心点的合理位置。最后以某地危化品运输路网为例,利用所建模型对事故应急救援中心的合理布局计算求解。结果表明:考虑应急响应时间及应急处理能力限制的应急救援中心选址模型,能够有效提高救援中心的综合应急能力,为有关部门合理规划危化品道路运输事故应急救援中心布局提供理论参考和决策依据。     

基于成本-效益准则的高层RC框架结构优化设计(Ⅰ)——方法

成本-效益准则是基于性能的抗震设计的重要原则,该准则在设计中除了考虑技术要素外,还考虑经济、社会等诸多因素,它所追求的设计目标为结构在其寿命周期内的总造价最小.因此,结构全寿命造价评估是基于性能的抗震设计的基本问题.在已有全寿命造价优化模型的基础上,提出了三目标函数全寿命造价优化模型.与已有的全寿命造价模型相比,该模型除考虑结构初始造价、损失期望外,还考虑了反映结构设计与施工复杂性的构件截面类型数量;另外,还将结构在其运行期间的检查、维护费用及由于优化导致建筑使用面积变化所引起的租金收益影响也列入到结构全寿命造价中.最后,基于所提出的优化模型,给出了以多目标遗传算法为搜索引擎、运用MATLAB语言开发的高层RC框架结构基于成本-效益准则的自动优化程序流程图.     

Metric-topological interaction model of collective behavior

Many simulations of collective behavior have been presented in recent years. Recently, a pioneering study by [Ballerini et al., 2008a] and [Ballerini et al., 2008b] suggested that the interactions of birds in a flock should be modeled using the topological distance rather than the metric distance. The concept of topological distance is deemed important for explaining collective behavior. However, few studies have discussed the distinctions between the metric and topological distances. In this paper, we clarify the difference between models based on the topological and metric distances and propose a new hybrid model of these models. The agent of our model switches between these two interactions by tuning the threshold parameters. We show that this hybrid flocking model has the medium property between the metric and the topological distance. In other words, the agents of our hybrid model can dramatically make and divide flock by tuning their neighborhoods. This result suggests that making and dividing flock would be deeply connected with animal's cognition.     

Inverse modeling for effective dispersal: Do we need tree size to estimate fecundity?

The estimation of the dispersal kernel for the seedling and sapling stages of the recruitment process was made possible through the application of inverse modeling to dispersal data. This method uses the spatial coordinates of adult trees and the counts of seedlings (or saplings) in small quadrats to estimate the dispersal kernel. The unknown number of recruits produced by an adult tree (the fecundity) is estimated - simultaneously with the dispersal kernel - via an allometric linear model relating the unknown quantity with a (easily) measured characteristic of the adult tree (usually the basal area). However, the allometric relation between tree size and reproductive success in the sapling (or seedling) stage may not be strong enough when numerous, well-documented, post-dispersal processes (such as safe-site limitation for recruitment) cause large post-dispersal seedling mortality, which is usually unrelated to the size of the tree that dispersed them. In this paper we hypothesize that when tree size and reproductive success in the seedling/sapling stage are not well correlated then the use of allometry in inverse modeling is counter-productive and may lead to poor model fits. For these special cases we suggest using a new model for effective dispersal that we term the unrestricted fecundity (UF) model that, contrary to allometric models, makes no assumptions on the fecundities; instead they are allowed to vary freely from one tree to another and even to be zero for trees that are reproductively inactive. Based on this model, we examine the hypothesis that when tree size and reproductive success are weakly correlated and the fecundities are estimated independently of tree size the goodness-of-fit and the ecological meaning of dispersal models (in the seedling or sapling stage) may be enhanced. Parameters of the UF model are estimated through the EM algorithm and their standard errors are approximated via the observed information matrix. We fit the UF model to a dataset from an expanding European beech population of central Spain as well as to a set of simulated dispersal data were the correlation between reproductive success and tree size was moderate. In comparisons with a simple allometric model, the UF model fitted the data better and the parameter estimates were less biased. We suggest using this new approach for modeling dispersal in the seedling and sapling stages when tree size (or other adult-specific covariates) is not deemed to be in strong relation to the reproductive success of adults. Models that use covariates for modeling the fecundity of adults should be preferred when reproductive success and tree size guard a strong relationship.     

基于改进飞蛾扑火算法的应急资源调度研究*

为解决多灾害点应急资源调度问题,构建不同路况下运输消耗成本最低、装车时间最少的多目标应急资源调度模型,基于双自适应因子改进飞蛾扑火算法,克服传统飞蛾扑火算法存在的局部最优而早熟收敛、种群多样性低而全局寻优性能差的问题,提高算法的寻优性能和求解精度,并求解应急资源调度问题,获得各灾害点高效应急资源配置方案。研究结果表明:与经典飞蛾扑火优化（MFO）算法以及基于Levy飞行的飞蛾扑火优化（LMFO）算法相比,基于双自适应因子的改进MFO（DAMFO）算法模型求解精度更高,研究结果可为制定合理高效的应急资源调度方案提供依据。