Effect of uneven noise source and/or station distribution on estimating the azimuth anisotropy of surface waves

With the development of the dense array,the surface wave velocity and azimuthal anisotropy under the array can be directly obtained by beamforming the noise cross-correlation functions(NCFs). However, the retrieval of the Green’s function by cross-correlating the seismic noise requires that the noise source has a uniform distribution. For the case with uneven noise source, the azimuthal dependence on the sources in the expression for the spatial coherence function, which corresponds to the NCF in the time domain,has the same form as the azimuthal dependence of the surface wave velocity in weakly anisotropic media. Therefore, the uneven noise source will affect the surface wave anisotropy extraction. In this study, three passive seismic methods, i.e.,beamforming, SPAC(spatial autocorrelation), and NCF, are compared to demonstrate that an uneven source distribution and uneven station distribution have equivalent effects on the outcome from each method. A beamforming method is proposed to directly extract the velocity and azimuthal anisotropy of surface waves. The effect of uneven noise source and/or station distribution on estimating the azimuth anisotropy of surface waves was investigated using data from the ChinArray Phase Ⅱ. A method for correcting the apparent anisotropy in beamforming results caused by an uneven station distribution is suggested.

Research on Short-Term Load Forecasting of Distribution Stations Based on the Clustering Improvement Fuzzy Time Series Algorithm

An improved fuzzy time series algorithmbased on clustering is designed in this paper.The algorithm is successfully applied to short-term load forecasting in the distribution stations.Firstly,the K-means clustering method is used to cluster the data,and the midpoint of two adjacent clustering centers is taken as the dividing point of domain division.On this basis,the data is fuzzed to form a fuzzy time series.Secondly,a high-order fuzzy relation with multiple antecedents is established according to the main measurement indexes of power load,which is used to predict the short-term trend change of load in the distribution stations.Matlab/Simulink simulation results show that the load forecasting errors of the typical fuzzy time series on the time scale of one day and one week are[−50,20]and[−50,30],while the load forecasting errors of the improved fuzzy time series on the time scale of one day and one week are[−20,15]and[−20,25].It shows that the fuzzy time series algorithm improved by clustering improves the prediction accuracy and can effectively predict the short-term load trend of distribution stations.

Evaluation of Multi-Temporal-Spatial Scale Adjustment Capability and Cluster Optimization Operation Method for Distribution Networks with Distributed Photovoltaics

Themassive integration of high-proportioned distributed photovoltaics into distribution networks poses significant challenges to the flexible regulation capabilities of distribution stations.To accurately assess the flexible regulation capabilities of distribution stations,amulti-temporal and spatial scale regulation capability assessment technique is proposed for distribution station areas with distributed photovoltaics,considering different geographical locations,coverage areas,and response capabilities.Firstly,the multi-temporal scale regulation characteristics and response capabilities of different regulation resources in distribution station areas are analyzed,and a resource regulation capability model is established to quantify the adjustable range of different regulation resources.On this basis,considering the limitations of line transmission capacity,a regulation capability assessment index for distribution stations is proposed to evaluate their regulation capabilities.Secondly,considering different geographical locations and coverage areas,a comprehensive performance index based on electrical distance modularity and active power balance is established,and a cluster division method based on genetic algorithms is proposed to fully leverage the coordination and complementarity among nodes and improve the active power matching degree within clusters.Simultaneously,an economic optimization model with the objective of minimizing the economic cost of the distribution station is established,comprehensively considering the safety constraints of the distribution network and the regulation constraints of resources.This model can provide scientific guidance for the economic dispatch of the distribution station area.Finally,case studies demonstrate that the proposed assessment and optimization methods effectively evaluate the regulation capabilities of distribution stations,facilitate the consumption of distributed photovoltaics,and enhance the economic efficiency of the distribution station area.

基于分布鲁棒联合机会约束的光储充电站滚动优化调控模型

随着电动汽车的普及和清洁能源装机容量的日益增加,光储充电站展现出广阔的应用前景。针对电动汽车随机接入影响下光储充电站功率难以精准调控的问题,提出基于分布鲁棒联合机会约束(DRJCC)的光储充电站滚动优化调控模型。将基于混合整数规划(MIP)的精确DRJCC模型、基于CVaR-Slim凸松弛的DRJCC模型、基于Bonferroni不等式的DRJCC模型进行对比,通过算例分析验证了所提优化调控模型的有效性。算例分析表明,所提基于CVaR-Slim凸松弛的DRJCC模型能够同时满足更多电动汽车的充电需求。

Comparative Study on Distributed Waste Pyrolysis Cold Emission Energy Station and Incineration Power Generation

The proper terminal disposal of organic solid waste such as domestic waste is a worldwide issue.Landfill covers a large area,with limited capacity,and a single landfill will be filled one day;incineration is costly to build and operate.These methods all need to transfer and centralized treatment,and secondary pollution is difficult to control,against the purification law of the nature."NIMBY effect"is very serious,and the social cost of treatment is increasing,becoming a heavy financial burden."The Distributed Waste Pyrolysis Cold Emission Energy Station"developed by Hunan Zhongzhou Energy-Saving Technology Co.,Ltd.overcomes these disadvantages and constructs a more appropriate environmental economic industrial chain for the treatment of organic solid waste such as urban and rural household waste.Based on its technical characteristics,this paper compares it with waste incineration power generation project in the aspects of secondary pollution control,treatment effect,energy utilization,investment and operation economy,etc.

基于分布鲁棒优化的电热综合能源配网系统与氢能源站协同优化

氢能源站(Hydrogen Fueling Station,HFS)对氢气生产和供应至关重要。然而HFS通常在午夜和凌晨生产氢气,同时由于热负荷处于高峰,热电联产(Combined-Heat-and-Power,CHP)机组的灵活性降低。在可再生能源比例较高的电热综合能源配网系统(Integrated Electricity-Heat Energy System,IEDS)中,CHP机组缺乏灵活性将不可避免地影响IEDS和HFS的经济和安全运行。先前的研究侧重于配电网系统和HFS的协同运行,而没有考虑灵活性问题。论文旨在提出IEDS和HFS的协同分布鲁棒协同调度模型,以实现协同优化运行,从而降低运营成本和安全运营。在该模型中,HFS中产生的废热被回收后注入热网系统。利用历史数据对风电场景进行聚类,构建风电不确定性集。并相应地提出了具有更好收敛速度的改进Benders分解算法,从而进一步以并行和分散式求解所考虑的优化模型。此外,基于综合能源测试系统实例进行了算例仿真,在考虑HFS协同调度的前提下,所提出的协同调度模型能够使运行成本降低7.8%,并分析了所提出的协同调度模型在处理风电不确定性方面的有效性。最后,相比经典Benders算法,论文提出的改进Benders分解算法求解时间降低了70%,迭代次数减少了50%,验证了分散式优化具有良好计算性能。

考虑新能源、电动汽车充电站与储能协调优化的分布鲁棒规划方法研究

新能源出力的不确定性和电动汽车的无序充电给电力系统安全性带来极大挑战。首先建立确定性的风电场、光伏电站、储能设备、负荷需求响应及电动汽车充电站协调规划模型,以总成本最小化为目标。基于此,提出考虑新能源不确定性的电动汽车充电站与储能协调优化两阶段分布鲁棒规划模型,第1阶段最小化基础场景的投建与运行成本,第2阶段则最小化考虑不确定场景的切负荷惩罚成本期望,通过列与约束生成CCG(col⁃umn and constraint generation)算法将该两阶段模型分解成主问题和子问题反复迭代求解。最后通过算例分析验证了所提模型的有效性与实用性。

考虑极端灾害下系统韧性约束的气电联合配网分布鲁棒扩展规划

近年来,在未来多能互补、综合能源系统的背景下,气电联合配网得到迅速的发展。然而,当前全球各地极端灾害频发,给能源系统的安全性带来了严峻的挑战。针对极端灾害对系统造成的失负荷影响,为兼顾系统的经济性与安全性,提出了一种考虑极端灾害下系统韧性约束的气电联合配网分布鲁棒扩展规划模型。首先,以规划成本与运行成本最小化为目标,建立考虑电动汽车充电站、分布式燃气机组和储能设备的气电联合配网扩展规划模型;然后,考虑极端灾害的作用,建立考虑韧性约束的气电联合配网分布鲁棒扩展规划模型,该模型包括基础场景与极端灾害最坏场景,其中基础场景考虑系统的经济性,以规划成本与基础场景运行成本最小为目标,极端灾害最坏场景则通过韧性约束保证系统的安全性,所提模型通过主问题–子问题迭代求解。最后,算例仿真发现:通过增加电动汽车充电站、分布式燃气机组和储能设备的规划投建,系统的经济性得到明显提升;而在面对极端灾害的影响下,通过分布式燃气机组的发电和电动汽车、储能设备的放电,系统失负荷情况得到减少,但随着系统韧性的增大所需单位规划成本会大幅增加。本文为在规划层面提升系统韧性提供了一种有效与实用的方法,为未来减小极端灾害对系统造成的影响的方案提供了一定参考。

计及源荷时序特性的低压台区分布式光伏接入分布鲁棒优化方法

为提高低压配电台区中分布式光伏(DPV)的接入容量,促进光伏消纳,本文提出一种计及源荷时序特性的低压台区DPV接入分布鲁棒优化方法.首先,针对低压台区中分布式光伏出力和负荷需求的不确定性,提出一种基于优化聚类的源-荷联合时序场景生成方法;其次,计及电压约束、线路容量约束、逆变器无功补偿约束及光伏消纳约束等,构建低压台区分布式光伏接入分布鲁棒优化模型,在保证各典型场景最恶劣概率分布下的弃光率期望值符合要求的情况下,最大化低压台区中分布式光伏接入容量;然后,建立低压台区分布式储能接入的数学模型,以研究储能接入及其充放电机制对低压台区分布式光伏接入的影响;最后,以实际配电台区为例进行仿真计算,验证了本文模型的有效性.

A Novel Technology for Generation of Electricity and Cold by Using Energy Potential of Transmission Line＇s High Pressure Gas

The main objective of this paper is to develop a novel technology for combined generation of electricity and cold by using energy potential of transmission line＇s high pressure gas. For this purpose, the reduction of high pressure of the gas in gas distribution station instead of useless expansion throttling process is suggested to realize by adiabatic expansion, which is executed in a gas expanding turbine. Herewith, the gas distribution station is turned into energy and cold generating plant. Simultaneous operation of energy and cold generating plant is described. A method and appropriate formulas for determination of design characteristics of considered plant are suggested. A new method for reveres order of calculation and design of the cold store based on the use of expanded cold gas as cooling agent is developed. Calculations and analysis prove high energy efficiency of suggested technology, the wide use of which will provide significant production of cheap electricity and cold and as well as reduction of fossil fuel consumption.

Precise orbit determination for a large LEO constellation with inter-satellite links and the measurements from different ground networks:a simulation study

Geodetic applications of Low Earth Orbit(LEO)satellites requires accurate satellite orbits.Instead of using onboard Global Navigation Satellite System observations,this contribution treats the LEO satellite constellation independently,using Inter-Satellite Links and the measurements of different ground networks.Due to geopolitical and geographical reasons,a ground station network cannot be well distributed.We compute the impact of different ground networks(i.e.,global networks with different numbers of stations and regional networks in different areas and latitudes)on LEO satellite orbit determination with and without the inter-satellite links.The results are based on a simulated constellation of 90 LEO satellites.We find that the orbits determined using a high latitude network is worse than using a middle or low latitude network.This is because the high latitude network has a poorer geometry even if the availability of satellite measurements is higher than for the other two cases.Also,adding more stations in a regional network shows almost no improvements on the satellite orbits if the number of stations is more than 16.With the help of ISL observations,however,the satellite orbits determined with a small regional network can reach the same accuracy as that with the global network of 60 stations.Furthermore,satellite biases can be well estimated(less than 0.6 mm)and have nearly no impact on satellite orbits.It does thus not matter if they are not physically calibrated for estimating precise orbits.

Dynamics of an autonomous Gilpin-Ayala competition model with random perturbation

This paper discusses the dynamics of a Gilpin-Ayala competition model of two interact­ing species perturbed by white noise.We obtain the existence of a unique global positive solution of the system and the solution is bounded in pth moment.Then,we establish sufficient and necessary conditions for persistence and the existence of an ergodic sta­tionary distribution of the model.We also establish sufficient conditions for extinction of the model.Moreover,numerical simulations are carried out for further support of present research.