Integrated analysis of plasma rotation effect on HL-3 hybrid scenario

The hybrid scenario,which has good confinement and moderate MHD instabilities,is a proposed operation scenario for international thermonuclear experimental reactor(ITER).In this work,the effect of plasma rotation on the HL-3 hybrid scenario is analyzed with the integrated modeling framework OMFIT.The results show that toroidal rotation has no obvious effect on confinement with a high line averaged density of n_(bar)~(7)×10^(19)m^(-3).In this case,the ion temperature only changes from 4.7 keV to 4.4 keV with the rotation decreasing from 10^(5) rad/s to 10^(3) rad/s,which means that the turbulent heat transport is not dominant.While in the scenarios characterized by lower densities,such as n_(bar)~4×10^(19)m^(-3),turbulent transport becomes dominant in determining heat transport.The ion temperature rises from 3.8 keV to 6.1 keV in the core as the rotation velocity increases from 10^(3) rad/s to 10^(5) rad/s.Despite the ion temperature rising,the rotation velocity does not obviously affect electron temperature or density.Additionally,it is noteworthy that the variation in rotation velocity does not significantly affect the global confinement of plasma in scenarios with low density or with high density.

Optimal dispatching method for integrated energy system based on robust economic model predictive control considering source-load power interval prediction

Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.

Optimal Operation Strategy Analysis with Scenario Generation Method Based on Principal Component Analysis,Density Canopy,and K-medoids for Integrated Energy Systems

The operation of integrated energy systems(IESs)is confronted with great challenges for increasing penetration rate of renewable energy and growing complexity of energy forms.Scenario generation is one of ordinary methods to alleviate the system uncertainties by extracting several typical scenarios to represent the original high-dimensional data.This paper proposes a novel representative scenario generation method based on the feature extraction of panel data.The original high-dimensional data are represented by an aggregated indicator matrix using principal component analysis to preserve temporal variation.Then,the aggregated indicator matrix is clustered by an algorithm combining density canopy and K-medoids.Together with the proposed scenario generation method,an optimal operation model of IES is established,where the objective is to minimize the annual operation costs considering carbon trading cost.Finally,case studies based on the data of Aachen,Germany in 2019 are performed.The results indicate that the adjusted rand index(ARI)and silhouette coefficient(SC)of the proposed method are 0.6153 and 0.6770,respectively,both higher than the traditional methods,namely K-medoids,K-means++,and density-based spatial clustering of applications with noise(DBSCAN),which means the proposed method has better accuracy.The error between optimal operation results of the IES obtained by the proposed method and all-year time series benchmark value is 0.1%,while the calculation time is reduced from 11029 s to 188 s,which verifies that the proposed method can be used to optimize operation strategy of IES with high efficiency without loss of accuracy.

Spatiotemporal Characteristics and Future Scenario Simulation of the Trade-offs and Synergies of Mountain Ecosystem Services: A Case Study of the Dabie Mountains Area, China

Mountain ecosystems play an essential role in supporting regional sustainable development and improving local ecological environments. However, economic development in mountainous areas has long been lagging, and multiple conflicts related to resource assurance, ecological protection, and economic development have emerged. An accurate grasp of the current status and evolutionary trends of mountain ecosystems is essential to enhance the overall benefits of ecosystem services and maintain regional ecological security. Based on the In VEST(Integrated Valuation of Ecosystem Services and Trade-offs) model, this study analyzed the spatiotemporal evolution patterns and the trade-offs and synergies among ecosystem services(ES) in the Dabie Mountains Area(DMA) of eastern China. The Markov-PLUS(Patch-generating Land Use Simulation) model was used to conduct a multi-scenario simulation of the area's future development. Water yield(WY) and soil conservation(SC) had overall increasing trends during 2000-2020, carbon storage(CS)decreased overall but slowed with time, and habitat quality(HQ) increased and then decreased. The ecological protection scenario is the best scenario for improving ES in the DMA by 2030;compared to 2020, the total WY would decrease by 3.77 × 10^(8) m^(3), SC would increase by 0.65 × 10^(6) t, CS would increase by 1.33 × 10^(6) t, and HQ would increase by 0.06%. The comprehensive development scenario is the second-most effective scenario for ecological improvement, while the natural development scenario did not have a significant effect. However, as the comprehensive development scenario considers both environmental protection and economic development, which are both vital for the sustainable development of the mountainous areas, this scenario is considered the most suitable path for future development. There are trade-offs between WY, CS, and HQ, while there are synergies between SC, CS, and HQ. Spatially, the DMA's central core district is the main strong synergistic area, the marginal zone is the weak synergistic area, and trade-offs are mainly distributed in the transition zone.

An Integrated Modelling Approach Inmountainous Watershed of Heihe River Basin, Northwest China

An integrated modelling approach is developed to simulate ecological economics processes of mountainous watershed of Heihe river basin, northwest China. The objectives are 1) a hydrologic unit model HLM_HMU was developed;2) the impacts of LUCC on the hydrological processes based on different scenarios were simulated respectively;and 3) the Payment for Ecosystem Services (PES) curve of prohibition grazing is given. The hydrological results show that the observed and simulated data have a good fit, in which the Nash–Sutcliffe efficiency, balance error, and explained variance are 0.69, -0.13, and 0.62, respectively;the model can simulate the majority of peaks well;the source code of Spatial Modelling Environment (SME) needs to be deeply understood to improve the algorithms of interpolation and conflux;the PES result shows that prohibition grazing will occur completely when the price of PES reached to 17.42 yuan, and the increased amount of ecosystem services is 2.13 × 108 m3.The main purpose of this paper is to build a better understanding of developing a meaningful integrated model in the study area to solve its ecological and economic problems.

场景理论视域下乡村体育旅游融合的理论逻辑、发展困境与纾解路径

乡村体育旅游作为落实乡村振兴战略的重要组成部分,已成为推动乡村文化、生态、产业等发展的主要内容。运用文献资料法、逻辑分析法等研究方法,从场景理论的真实性、戏剧性、合法性维度探究乡村、体育、旅游三要素融合的理论逻辑、发展困境与纾解路径。构建“新场景—新行为—新价值”乡村体育旅游融合的理论逻辑,阐释乡村体育旅游融合的乡村表达性、符号感知性、融合生产性等多维度困境,并提出场景理论视域下乡村体育旅游融合的纾解路径:深挖文化内涵与乡土特色,感知乡村体育空间;提升具身体验与品牌设计,彰显乡村体育价值;强化创新意识与产业实力,完善乡村体育产业链。

医院多场景融合智能门禁系统的设计与实现

目的充分发挥门禁系统在医院管理中的作用,满足医院多场景应用需求,使门禁系统更智能。方法制定合理流程及相应技术方案,通过门禁系统与医院信息系统、智慧医院微信公众平台、叫号系统、人事系统、教学系统等进行融合,建设多场景融合智能门禁系统。结果实现了医院多场景融合下的全流程自动化、智能化门禁系统。结论多场景数据与门禁数据互通,解决了患者、陪护、职工、学生、第三方人员进出医院的管理问题,提升了患者就诊效率,节约了人力成本,实现了医院的精细化管理。

医学免疫学多学科融合联合场景式教学的设计与实施

本研究结合多学科融合和场景式教学的优点,建立了“多学科融合联合场景式”教学模式,并在包头医学院2021级临床医学专业医学免疫学教学中进行了应用探究。其围绕医学免疫学各章节重难点知识与基础学科、临床学科、其他相关学科融合组建教学内容,联合8种教学场景,通过直接进课堂、远程Wi-Fi视频和播放专家访谈等方式开展多学科融合联合场景式教学。课程结束后,以期末总评成绩和问卷调查结果评价教学效果。该教学模式对促进学生专业知识学习,培养多学科融合思维和临床思维,提升专业素养和职业认识等方面均具有积极作用。

综合能源系统下虚拟储能建模方法与应用场景研究综述及展望

为了促进多能耦合互补和实现可再生能源就地消纳,综合能源系统已成为多领域的研究热点。考虑到能源设备及网络的多样性、复杂性以及多种能源时间尺度的差异性,根据能量平衡原理,对系统内具有可调特性的源、网、荷进行储能化建模,并构成虚拟储能系统参与到综合能源系统的优化调度中。为了准确把握虚拟储能的研究重点,首先介绍了虚拟储能的定义、逻辑架构和技术内涵;其次针对综合能源系统源、网、荷端设备或网络,总结和归纳了4种虚拟储能建模方法和特性指标;然后重点分析了虚拟储能在4种典型场景的具体应用;最后展望了虚拟储能未来的发展方向。

智媒时代应用型高校导播课程教学改革策略研究

智媒时代对传媒类人才培养提出了更高的要求。导播课程作为应用型高校传媒类专业的核心课程,应本着“以学生为中心”的教学理念,从教学模式、教育场景、课程建设等方面切入,紧跟行业一线,鼓励自主学习,推动产教融合,体现地域特色,着力培养掌握全媒体视听节目制作能力的复合型、应用型人才,与行业发展、岗位需求同频共振。

面向密集场景的空天地网络资源分配算法

空天地网络具有覆盖范围大、吞吐量高、弹性强等优点。该文针对大量用户并发接入、网络负载不均衡所引发的网络拥塞、服务质量恶化等问题,提出一种面向密集场景的资源分配算法。首先以用户需求为中心,根据不同类型用户任务的偏好来构建用户效用函数,然后基于匹配博弈的网络选择算法和结合对偶上升法的功率控制算法来实现负载均衡,优化资源分配方案。实验表明,相较于传统策略,所提策略整体用户接入率至少提高35%,时延和吞吐量方面性能提升超过50%;在密集场景下,能更有效地均衡负载,提升网络性能。

高职院校融合现实数字文旅实训室建设探索与实践

在当前新时代旅游新常态下,产业亟须具备信息化素养和创意发展思维的新时代旅游人才。融合现实数字文旅实训室通过融合现实情境设计、双维度人才培养思路、实训教学平台搭建,满足工学结合、产教融合的理实一体化教学需要。文章以南京旅游职业学院融合现实数字文旅实训室建设为例,分析其建设必要性,重点阐述实训室建设原则及思路,为高职院校建设高水平数字文旅实训室提供了一定参考。

融入科技特派员制度的“三融三维”卓越农林人才培养研究

在对科技特派员制度与卓越农林人才培养的耦合逻辑进行分析的基础上,以科技特派员制度为原点,以丰富基于情景融合的课程教学资源、搭建基于产教融合的实践教学平台、重构基于思专融合的协同育人体系等三方面融合为方法,构建起具有服务区域社会经济发展的“知识—能力—素质”三个维度的卓越农林人才培养培养体系,并从建立稳定运行机制、完善协同培养机制、健全师生激励机制、优化评价反馈机制等方面构建融入科技特派员制度的卓越农林人才长效机制。

5G关键技术与应用场景融合探究

在分析国内外5G发展现状的基础上,研究5G新技术如高速率、低延迟、高可靠性、更大连接密度等技术特点,揭示5G在未来发展潜力。探究5G关键技术与智慧城市、智能交通等应用场景的融合,梳理5G技术标准发展梯次与应用场景需求不匹配、行业应用进入瓶颈期、企业专网建设发展缓慢等5G应用场景融合发展过程中所面临的挑战。为确保我国在5G应用方面全球领先地位,提出以需求驱动推动5G技术标准的制定,从产业应用体系、产业技术体系及生态体系三个方面加速产业发展,通过出台支持产业政策、实施安全保障、扩大应用拓展建立多层级管理体系,加速企业专网建设。

考虑“响应-碳排放”责任耦合交易的IDR策略研究

综合需求响应(integrated demand response,IDR)作为区域综合能源系统(regional integrated energy system,RIES)维持供需平衡、实现分布式能源就地消纳的重要途径,已成为当下的研究热点。但现有IDR研究存在3个方面不足:综合能源服务商(integrated energy service provider,IESP)制定激励策略时,未考虑区域内用户的响应疲劳特性;在实施IDR的复杂场景下,未考虑区域IESP间的响应责任交易;忽略响应责任与碳排放责任的耦合关系。为此,首先分析用户响应意愿随响应次数的演化特性,通过引入响应疲劳函数实现对用户响应疲劳特性的有效建模;在此基础上,根据IESP的响应任务类型,设置多个IESP参与IDR的复杂场景。进一步,为实现各IESP参与IDR的经济性与低碳性,在考虑复杂场景下IESP间响应责任交易时的能源转移与碳排放责任转移基础上,建立响应责任-碳排放责任耦合交易机制。最终,采用基于自适应迭代步长的议价方法以确定最优交易价格。通过仿真算例验证所提模型的有效性:考虑用户响应疲劳特性的改进模型使用户的总收益提升27%;所提交易机制不仅使各场景下IESP的总成本分别降低15.8%、9.8%、94.1%,还使典型场景下IESP的碳排放量降低17.8%,提高IESP参与IDR的经济性与低碳性,实现IESP与用户的共赢。

考虑风光不确定性的综合能源系统容量-成本两阶段规划优化研究

针对综合能源系统规划面临可再生能源波动性强、出力不确定性等问题,文章提出了一种考虑风光不确定性的综合能源系统容量-成本两阶段规划优化方法。首先,运用拉丁超立方抽样生成基础风、光场景集,并基于改进的k-means算法进行场景削减;其次,以运行成本最低、碳减排最优、污染物减排最优构建多目标优化模型;最后,提出系统容量-成本两阶段规划优化求解策略,并选取南方某商业园区进行规划仿真。仿真结果表明,文章所构建的综合能源系统两阶段规划模型能够在保证系统经济性的同时兼顾环保性,并满足用户多种用能需求。

严重遮挡场景下AOA-ENN辅助列车定位的方法研究

铁路周边卫星遮挡情况复杂多变,当列车在隧道等严重遮挡场景下运行时,北斗卫星导航系统/捷联惯性导航系统(BDS/SINS)列车组合定位系统无法接收到卫星信号,导致列车定位误差累积甚至定位失效。为提高列车在严重遮挡场景下的定位精度,提出阿基米德优化算法优化的Elman神经网络(AOA-ENN)辅助BDS/SINS列车组合定位系统进行列车定位的方法。首先,在无迹卡尔曼滤波算法中引入新息理论得到自适应无迹卡尔曼滤波算法(AUKF),将其作为BDS/SINS列车组合定位系统的信息融合算法。其次,基于模糊C均值聚类算法(FCM)建立列车运行场景识别模型,依据环境特征参数对列车运行场景进行自主识别。最后根据场景识别模型的输出结果,当列车在开阔、低遮挡、高遮挡场景运行时,通过AUKF对BDS和SINS解算的定位信息进行融合来完成列车定位,同时将采集的列车定位数据加入训练集,对AOA-ENN进行在线训练;当列车在严重遮挡场景下运行时,BDS无法正常接收信号,利用训练好的AOA-ENN辅助列车组合定位系统进行定位,利用AUKF对AOA-ENN的预测信息和SINS解算的信息进行融合后输出定位结果。实验结果表明:在严重遮挡场景下,AOA-ENN辅助列车组合定位系统得到的定位成功率达到98.2%;通过不同优化算法和神经网络的仿真对比实验,验证了AOA-ENN在辅助列车组合定位系统定位时的优越性。所得成果为优化列车在隧道等严重遮挡场景下的定位精度提供了参考。

基于信息间隙决策理论的含碳捕集−电转气综合能源系统优化调度

【目的】为科学统筹综合能源系统运行经济性、稳定性和低碳性优化目标,采用何种技术手段以提升能源转化效率,减少系统能源浪费和区域环境污染,是当下综合能源系统合理优化的主要问题。为此,提出一种基于场景生成与信息间隙决策理论的含碳捕集与封存(carbon capture and storage,CCS)—两段式电转气(power to gas,P2G)综合能源系统低碳优化策略。【方法】在技术层面,通过对电P2G两阶段精细化建模,提高氢能利用效率,建立热电联产(combined heating and power,CHP)-CCS-P2G耦合模型;在市场机制层面,引入阶梯型碳交易模型以降低系统中CO_(2)排放量。最终,基于信息间隙决策理论(IGDT)构建不同风险偏好下的优化调度模型。【结果】以典型综合能源系统进行算例分析,仿真结果表明所提模型可提高风光消纳率,实现系统低碳、经济、稳定运行。【结论】该优化策略可有效帮助决策者根据其风险偏好制定风险规避与风险追求策略下的调度方案,实现系统不确定性与经济性的平衡。

一种面向多场景的轨迹相似度计算框架

轨迹相似度计算作为轨迹数据分析中的一个研究热点,主要用来衡量两个移动对象轨迹相似程度。已有的轨迹相似度计算工作主要根据不同类型的轨迹数据和应用场景提出了大量轨迹相似性度量方法,但随着轨迹数据采集手段的逐渐丰富和应用需求的发展,在轨迹相似性计算方面却缺少一个统一的框架以支持多样化轨迹数据下的相似轨迹分析。为此,本文在综合分析已有相似轨迹度量方法共性特征基础上,提出了一个兼容多种轨迹相似度度量方法的集成计算框架(Multiple Scenarios Similarity Trajectory Framework,MSSTJ),并通过一种统一的轨迹数据分区索引方法来优化不同相似轨迹度量方法的计算效率,从而使得用户可以通过参数化配置的简单方式来实现不同场景下多样化的轨迹相似度高效计算的需求。通过基于两类不同类型轨迹数据集的相关实验表明,MSSTJ可以帮助用户快速实现不同的轨迹相似度计算算法,并且实现的算法具有较好的性能表现。

我国交通运输业碳达峰时间预测

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