Distribution Pattern Scale of Natural Toona ciliata Population

In order to research the population distribution pattern of endangered species Toona ciliata Roem., the sampling quadrats of 5 mx5 m and 3 m×3 m in size, accurate to 1 mxl m, were established in 2 newly-found only existing T. cili- ata Roem. communities （T1 and T2） with contiguous grid quadrate method, in the Nanhe River valley, Gucheng County. By X2 test, t-test of distribution coefficient Cx method, and F test of Morisita pattern index Iδ whether the distribution patterns of the T. ciliata Roem. populations conformed to Poisson distribution were checked. The results indicated that, population T1 was in Poisson distribution under 5 m×5 m and 3 m×3 m in size by Cx and 16 methods, but in clumped distribution pattern un- der 5 m×5 m in size by the Chi-square test; however, the population was in Pois- son distribution under the dimension of 3 m×3 m. Population T2 under human dis- turbance had higher population density, indicating clumped distribution under 3 above-mentioned tests. If Chi-square test is satisfied, a distribution pattern is in Poisson distribution, and size and quantity of sampling quadrats should be given pri- ority to; and if df is greater, both theoretical values and observed values tend to- wards normal distribution more probably, and the test of distribution pattern, there- fore will be more dependable.

Joint modelling of location and scale parameters of the skew-normal distribution

Joint location and scale models of the skew-normal distribution provide useful ex- tension for joint mean and variance models of the normal distribution when the data set under consideration involves asymmetric outcomes. This paper focuses on the maximum likelihood estimation of joint location and scale models of the skew-normal distribution. The proposed procedure can simultaneously estimate parameters in the location model and the scale model. Simulation studies and a real example are used to illustrate the proposed methodologies.

Variable Selection for Robust Mixture Regression Model with Skew Scale Mixtures of Normal Distributions

In this paper, we propose a robust mixture regression model based on the skew scale mixtures of normal distributions (RMR-SSMN) which can accommodate asymmetric, heavy-tailed and contaminated data better. For the variable selection problem, the penalized likelihood approach with a new combined penalty function which balances the SCAD and l2 penalty is proposed. The adjusted EM algorithm is presented to get parameter estimates of RMR-SSMN models at a faster convergence rate. As simulations show, our mixture models are more robust than general FMR models and the new combined penalty function outperforms SCAD for variable selection. Finally, the proposed methodology and algorithm are applied to a real data set and achieve reasonable results.

Spatial Distribution Pattern of Populations of Isoetes sinensis Palmer, an Endangered Quillwort in China

The distribution pattern of Isoetes sinensis populations was investigated in relation to its biological characteristics such as age structure and regeneration mode, and the microenvironment. Contagious distribution pattern was found in populations of all sizes. Under the same climatic and site conditions, the heterogeneity of mieroenvironment resulting from the different depths of water and soil erosion significantly affecred the type and scale of distribution patterns of Isoetes sinensis populations. Even at the same site, the center and the edges of the water body had different effects on the distribution pattern of the plants. As one of the co-dominants, the distribution of Isoetes sinensis in communities was affected by other dominant species, especially by the evergreen grass species. Water is the major factor determining the distribution pattern of lsoetes sinensis in various communities. The pattern of contagious distribution was observed in different size of populations of I.sinensis, however the Lloyd indices obtained for the populations of various sizes differed significantly.

Scaling p_T Distribution in Small Rapidity Region and Pseudorapidity Asymmetry for Charged Hadron in d+Au Collisions at (S_(NN))^(1/2)=200 GeV

With the experimental data from STAR program on the centrality dependence of the PT spectra of charged hadron produced in d＋Au collisions at √^8NN=200 GeV, we show that there also exists a scaling distribution in the backward rapidity regions. Using this scaling distribution, we get the theoretical psedorapidity asymmetry which is basically in agreement with STAR data in the regions of 0≤｜η｜≤0.5 and 0.5 ≤｜η｜≤1. No theoretical models are used in any part of this phenomenological analysis.

Scaling of weighted spectral distribution in weighted small-world networks

Many real-world systems can be modeled by weighted small-world networks with high clustering coefficients. Recent studies for rigorously analyzing the weighted spectral distribution（W SD） have focused on unweighted networks with low clustering coefficients. In this paper, we rigorously analyze the W SD in a deterministic weighted scale-free small-world network model and find that the W SD grows sublinearly with increasing network order（i.e., the number of nodes） and provides a sensitive discrimination for each input of this model. This study demonstrates that the scaling feature of the W SD exists in the weighted network model which has high and order-independent clustering coefficients and reasonable power-law exponents.

冷能梯级利用的港口多能微网双层不确定性经济调度

为有效挖掘港口液化天然气(LNG)冷能利用的低碳灵活性潜力,充分发挥多时间尺度协同优化效应,提出一种考虑LNG冷能梯级利用的港口多能微网(MEMG)鲁棒-随机双层不确定性经济调度模型。首先,考虑LNG深冷-中冷-浅冷等各个温区的低碳灵活性潜力,建立低温碳捕集-冷能发电-直接冷却的冷能梯级利用模型,并以此为基础形成捕集-存储-利用协同的碳处理流程。其次,根据等概率逆变换生成考虑预测误差时序相关性的风电场景,并基于Wasserstein距离的0-1规划模型进行场景削减。再次,针对风电预测误差随时间尺度增加而增大的特性,构建多时间尺度优化的鲁棒-随机双层不确定性经济调度模型,上层通过分布鲁棒优化保证日前预调度决策鲁棒性,下层通过随机优化保证日内滚动调度决策经济性。最后,仿真结果表明,所提考虑冷能梯级利用的鲁棒-随机双层调度模型在解决日前长时间尺度预测精度低与日内短时间尺度易陷入局部最优矛盾的同时,可赋予港口MEMG更多经济性、低碳性及供电灵活性。

基于分布鲁棒模型预测控制的微电网多时间尺度优化调度

源荷多元不确定性给“源荷储”一体化微电网优化调度带来了诸多挑战,但传统方案存在优化模型过于片面极端和时间尺度单一导致调度不合理的问题,无法兼顾可靠性和经济性,并且难以协调不确定性分析方法与不同时间尺度之间的配合关系。文中基于数据驱动的多离散场景分布鲁棒方法,提出一种微电网两阶段分布鲁棒日前优化调度模型,使用列和约束生成算法进行求解。结合改进分布鲁棒优化的不确定方法、多时间尺度调度策略和模型预测控制理论,通过逐级细化调度时间尺度和减小预测周期的长度来提高精度,以最小化发电成本以及调节成本等为目标建立日前-日内多时间尺度滚动优化调度模型,较大程度上抵抗系统不确定性因素的影响。结合算例仿真分析,进一步说明了所提模型能够有效消纳新能源、降低运行成本同时兼顾安全性和经济性。

计及精细化氢能利用的综合能源系统多时间尺度鲁棒优化策略

能源结构改革背景下,构建以氢能驱动、电-热为主体的综合能源系统(HEH-IES)具有重要意义。为提高含电氢耦合单元的IES运行灵活性,同时减小功率波动对系统的影响,提出含多元储能与综合需求响应的氢能精细化利用两阶段调度方法。首先,分析电制氢(P2H)两阶段运行过程与电氢耦合单元运行特性,对氢能的用能过程与设备进行精细化建模;其次,计及多元储能与综合需求响应提出了日前-日内两阶段多时间尺度优化策略,日前阶段充分考虑能源系统的不确定性,构建数据驱动的分布鲁棒优化(DRO)模型,日内阶段遵从日前计划,考虑多能流在灵活性调节时间尺度上的差异,通过多时间尺度的滚动优化降低功率波动的影响;最后,仿真算例证明了所提模型与策略对提升IES的运行灵活性具有积极作用。

考虑灵活性的储能容量多阶段分布鲁棒规划

储能作为一种灵活性资源,具有促进新能源消纳和电力系统安全稳定运行的作用。然而受储能投资成本的制约,难以仅依靠大规模的储能满足系统的灵活性要求。为此,提出了一种考虑灵活性的电力系统储能容量规划模型,计及现有可调节的传统发电机组对灵活性的影响,并采用分布鲁棒机会约束描述新能源出力的不确定性;根据系统不同时间尺度的运行特性,综合考虑储能的投资成本、新能源出力的不确定性和系统运行的灵活性,建立了多时间尺度下的储能配置模型;采用多阶段迭代线性优化的方法提高求解效率。基于IEEE-RTS 24节点系统进行算例分析,结果验证了所提方法在求解储能配置容量方面具有较好的经济性和鲁棒性。