GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.

Application Strategies of Virtual Reality Technology in the Teaching Design of Vocational Courses from the Perspective of Learning Transfer Theory

With the rapid development of virtual reality technology,it has been widely used in the field of education.It can promote the development of learning transfer,which is an effective method for learners to learn effectively.Therefore,this paper describes how to use virtual reality technology to achieve learning transfer in order to achieve teaching goals and improve learning efficiency.

Academic Stress Assessment Using Virtual Reality as an Educational Tool in Spine Surgery

Introduction: The evaluation of academic stress in medical students and residents is a topic of significant interest, given the considerable challenges they face during their learning process with traditional teaching methods. The use of technologies like virtual reality presents an opportunity to enhance their skills through simulations and training. The main objective of this study is to qualitatively assess the stress levels experienced by medical students and residents by integrating virtual reality into their current learning methods, aiming to improve their ability to manage stressors in their practice. Material and Methods: A questionnaire was conducted with 12 medical students and 12 Traumatology and orthopedics residents. The purpose of the questionnaire was to evaluate the levels of academic stress using the SISCO inventory. The stress levels were calculated by transforming average values into percentages, and the following criteria were assigned: 0 to 33% for Mild Stress, 34 to 66% for Moderate Stress, and 67 to 100% for Deep Stress. Then, a virtual reality class focused on spine surgery was provided. Both medical students and residents were trained using the Non Nocere SharpSurgeon software platform and Oculus Quest 2 virtual reality glasses. At the end of the session, a second questionnaire related to the practice with virtual reality was conducted with the same evaluation criteria and a comparative analysis was carried out. Results: 12 undergraduate students from Hospital Angeles Mexico, CDMX and 12 traumatology and orthopedics residents at Hospital Santa Fe, Bogota were evaluated. The students in CDMX reported an average qualitative stress of 28.50% during habitual practices, which decreased to an average of 14.67% after virtual reality practice. Residents in Bogotá experienced an average qualitative stress of 30.50% with their current learning methods but this reduced to an average of 13.92% after using virtual reality. These findings indicate that the use of virtual reality has a positive impact on reducing stress levels qualitatively. Conclusions: The use of virtual reality as a learning method for medical students and residents qualitatively improves stress levels. Further studies are required to define the potential uses of Virtual Reality to improve learning methods and emotional state in medical students and residents and for a quantitative assessment to validate the training as certified learning methods.

Teleoperation strategies research for collaborative welding systems based on virtual reality

Teleoperation control strategies for collaborative welding system which is targeting at giving full play to human’s superiority is designed and the fitness for teleoperation welding task of which are studied. During the teleoperation welding process, 6-DOF controller’s signal can be converted into welding torch’s position, velocity or acceleration changing which is being controlled. For welding purposes, control strategies of four modes are designed, which are static position and posture mode, dynamic position and posture mode, velocity mode and acceleration mode. A test and analysis system for testing the tracking accuracy and reliability of control strategy based on virtual reality is developed. The tracking accuracies of the four control strategies are studied in the following tests with straight line trajectory, curve trajectory or space curve trajectory. The results show that the control strategy in dynamic position and posture mode has best stability and strong adaptability which is the most suitable for the teleoperation system.

Teaching mechanism empowered by virtual simulation: Edge computing–driven approach

With the rapid development of network and communication techniques,the teaching forms have become diversified.To enhance the education experience and improve the teaching environment,an increasing number of educational institutions have adopted virtual simulation technology.A typical teaching mechanism is to exploit Virtual Reality(VR)technology,which affords participants an immersive experience.Unquestionably,such a VRbased mode is highly approved.However,the performance of this technology requires further optimization.On one hand,for VR 360video,the current intraframe decision cannot adapt to rapid response demands.On the other hand,the generated data size is considerably large and fast computation may not be realized,depending on the local VR device.Therefore,this study proposes an improved teaching mechanism empowered by edge computing–driven VR,called VE4T,that involves two parts.First,an intraframe decision algorithm for VR 360videos is devised to realize the rapid responses.Second,an edge computing framework is proposed to offload some tasks to an edge server for computation,where a task scheduling strategy is developed to check whether a task needs to be offloaded.Finally,experiments are performed using a practical teaching scenario with some VR devices.The obtained results demonstrate that VE4T is more efficient than existing mechanisms.

Randomized MILP framework for Securing Virtual Machines from Malware Attacks

Cloud computing involves remote server deployments with public net-work infrastructures that allow clients to access computational resources.Virtual Machines(VMs)are supplied on requests and launched without interactions from service providers.Intruders can target these servers and establish malicious con-nections on VMs for carrying out attacks on other clustered VMs.The existing system has issues with execution time and false-positive rates.Hence,the overall system performance is degraded considerably.The proposed approach is designed to eliminate Cross-VM side attacks and VM escape and hide the server’s position so that the opponent cannot track the target server beyond a certain point.Every request is passed from source to destination via one broadcast domain to confuse the opponent and avoid them from tracking the server’s position.Allocation of SECURITY Resources accepts a safety game in a simple format as input andfinds the best coverage vector for the opponent using a Stackelberg Equilibrium(SSE)technique.A Mixed Integer Linear Programming(MILP)framework is used in the algorithm.The VM challenge is reduced by afirewall-based controlling mechanism combining behavior-based detection and signature-based virus detection.The pro-posed method is focused on detecting malware attacks effectively and providing better security for the VMs.Finally,the experimental results indicate that the pro-posed security method is efficient.It consumes minimum execution time,better false positive rate,accuracy,and memory usage than the conventional approach.

比较优势视角下我国虚拟水流动归因分析

虚拟水是近年来水资源领域的研究热点,而相关研究成果主要集中于虚拟水转移的规模核算及其特征分析,对虚拟水转移的动力成因较少讨论,所以借助比较优势理论中的区位商、增长商指数,量化解析农林牧渔、工业、建筑业、批发零售、交通运输、住宿餐饮、金融、房地产、其他服务业9大行业的发展优势及增长潜力,并分析这9大行业虚拟水流动格局与行业比较优势的相关关系。研究结果表明:各行业虚拟水流动格局与行业比较优势具有明显关联,2002—2017年间我国72%的虚拟水会从具有比较优势的地区向不具备优势的地区转移,71%的虚拟水转移量会受到行业发展速度影响。研究成果预期可为我国水资源宏观管理及节水政策制定提供理论依据及决策参考。决策参考。

基于NSGA-Ⅱ的飞轮-火电联合二次调频最优负荷分配策略

为了在提升飞轮-火电联合二次调频性能的同时控制调频成本、减少飞轮损耗,面向飞轮-火电联合系统,提出一种考虑自动发电控制(AGC)指令及电网频率偏差的基于多目标遗传算法的负荷分配策略。该策略以电网调频偏差、飞轮-火电出力能量和、飞轮损耗为三层最小化目标,考虑火电机组爬坡率及其负荷备用、飞轮荷电状态及其功率等约束条件,基于改进的非支配排序遗传算法(NSGA-Ⅱ),通过AGC功率指令变化速度及频率偏差动态调整飞轮与火电出力权重系数,以频率偏差及其变化方向调整飞轮虚拟下垂、惯性系数,求解飞轮-火电出力比例、飞轮下垂系数和飞轮惯性系数。结合中国宁夏某电厂飞轮储能系统辅助火电机组调频现场AGC指令等历史数据,在双区域两机系统模型上进行仿真实验,结果表明该负荷分配策略有效地减小了火电二次调频偏差和调节时间,提升了调频性能,同时也有效地控制了系统出力能量及飞轮损耗。

基于新型VLMZVM的ANPC型三电平并网逆变器环流抑制方法

为解决广泛应用在大功率分布式发电系统中的多机有源中点钳位(ANPC)型三电平并网逆变器间的低频零序环流问题,提出一种基于虚拟大矢量的新型十三矢量调制(VLMZVM)策略。通过建立多机ANPC型三电平并网逆变器零序环流模型,分析零序环流产生机理和影响因素,通过检测零序环流状态,实时选择虚拟大矢量或实际大矢量合成方法来抑制零序环流,并提出区分奇偶扇区的矢量分配方法快速选择合成矢量。实验结果验证了新型VLMZVM策略能在电流突变、参数不一致、全功率因数范围都具有很好的环流抑制效果。

我国虚拟现实科技创新与产业高质量发展的推进策略

随着虚拟现实技术的日益成熟与融合应用步伐的加快,虚拟现实产业正迎来“元宇宙”这一新的发展浪潮.针对当前虚拟现实科技创新与产业发展面临的基础设施不完善、产品开发成本高昂、复合型人才匮乏以及产业生态尚待优化等问题,提出以下建议:其一,强化政策扶持力度,夯实产业发展的基础;其二,加强科技创新,攻克关键技术难题;其三,积极培养和引进人才,汇聚产业发展力量;其四,加大推广力度,拓宽产品应用范围;其五,优化产业生态,着力培育科技创新骨干企业.采取这些措施,有助于推动虚拟现实产业迈向新的发展阶段.

利用数字媒体和多元化途径助推中国文化遗产的国际传播

随着全球化的发展,文化交流已成为连接不同国家和民族的重要桥梁。文化遗产作为一个国家文化软实力和展示国际形象的载体,在国际交流中发挥着举足轻重的作用。传统的推广方式已无法满足今天的时代发展,数字媒体为文化遗产的海外传播提供了新的平台和可能。同时,文化艺术交流、教育交流、旅游合作等多种方式也有助于扩大文化遗产的国际影响力。通过对国内外相关文化遗产的案例研究,分析数字媒体和多元化方式在传递价值观、增强国际认同感和激发创新思维方面的应用,探讨传播过程中存在的问题和挑战,采取多维策略确保文化遗产传播的积极影响,有利于提升中国文化遗产的国际传播效果。

基于虚拟阻抗自适应的孤岛型微电网并联逆变器下垂控制策略

当前微电网并联逆变器下垂控制机制一般设定为独立式,控制的范围较难扩展,导致控制补偿差增加。为此,提出对基于虚拟阻抗自适应的孤岛型微电网并联逆变器下垂控制策略的设计与实践。先进行下垂控制特性提取,采用交互的方式,扩大控制的范围,设计交互控制机制。在此基础上,构建虚拟阻抗自适应微电网并联逆变器下垂控制模型,采用补偿核验的方式确保下垂控制效果。针对选定的5个测试逆变器,经过2个周期的测定得出的控制补偿差被较好地控制在1.05以下,说明此次在虚拟阻抗自适应原理的辅助下,所设计的微电网并联逆变器下垂控制方法更为高效,具有实际的应用价值。

元宇宙应用引发的人文风险与应对策略

通过深入探究元宇宙应用带来的人文风险,为元宇宙的数字治理准则阐发提供崭新视角和有效方法。从元宇宙关键技术出发,详细论述了元宇宙带来的主要包括自由风险、平等风险、公正风险和法治风险在内的群体风险,以及主要包括主体性弱化风险、个体能力损伤风险、权利遭侵犯风险和身心健康风险在内的个体风险,重点阐述了元宇宙技术的全周期中包括技术研发、技术应用和技术分配的三个关键阶段,并基于我国现有的元宇宙治理方案,针对这三个技术维度的风险特征提出多样多元的风险防控策略。

基于主从博弈的虚拟电厂参与多元竞争市场投标策略研究

虚拟电厂(virtual power plant,VPP)可以聚合多元异构分布式能源(distributed energy resource,DER)灵活参与电力市场,但受市场多元主体投标行为不确定性的影响,VPP在日前电力市场面临着潜在的投标需求流标风险。为解决多元竞争电力市场中电价电量不确定性影响下VPP的优化申报问题,提出一种VPP灵活分段投标策略。首先,基于分布式能源运行特性构建了虚拟电厂聚合可调节能力评估方法,在考虑电力平衡需求的基础上,提出按可调节能力划分区间的VPP灵活分段投标策略。然后,构建了虚拟电厂参与日前电力现货市场投标的主从博弈模型,以实现VPP收益及社会效益的最大化。最后,采用强对偶理论和大M法将该均衡约束规划问题(equilibrium problems with equilibrium constraints,EPEC)转化为混合整数线性规划问题(mixed integer linear program,MILP)求解。算例结果表明,VPP采用灵活分段投标策略参与日前电力市场,可以充分利用其可调节能力,保障其投标需求有效中标,有效提升了VPP收益及社会效益。

针对温控负载变化的虚拟电厂控制策略研究

温控负载是一种变化剧烈、波动频繁、高峰负荷比例较高的负载,对电网的安全产生了极大的影响。为了应对这一问题,系统的输出功率往往会超过90%负荷最大值的调峰限制。针对虚拟电厂(VPP)系统内的超限过程进行了研究,并提出了2种控制策略,以降低超限总电量或者利用超限电量的同时获取收益。在验证策略的控制效果时,选取了2种由温控负载引起的功率需求的典型变化。研究结果表明,温控负载引起的功率需求峰值的变化对策略具有重要影响。在安全模式下,超限比例与峰值的增加呈反比,而与平均功率的增加呈正比;在收益模式下,峰值变化会影响储能放电过程,平均功率变化则会影响储能充电过程。随着功率需求的增加,收益逐渐减小,收益范围为3.62万~3.25万元。

基于“虚拟碳储存”的需求侧电-碳耦合交易机制

“双碳”目标指引下,电力系统低碳转型势在必行。结合时变价格信号的低碳需求响应可以引导用户调整用电行为降低碳排放。然而,不同用户灵活性差异较大,仅通过价格信号的变化难以引导低灵活性用户实现碳减排;同时,部分高灵活性用户的调节潜力并未被完全释放。为了通过用户间碳排放责任的交易进一步释放需求侧的碳减排潜力,该文提出需求侧电-碳耦合交易机制,旨在通过碳排放责任的再分配进一步释放高灵活性用户的碳减排潜力。但根据比例共享定理,碳排放责任与用电量成固定比例,限制其在用户间的灵活转移,提出“虚拟碳储存”,使储能中电的碳排放强度成为可控变量,实现碳排放责任在用户间的自由转移。以地区电力系统为研究场景,综合考虑电费成本、碳排放成本以及负荷调节成本,引入近端策略优化算法求解用户的最佳用电策略。仿真结果显示,相比用户仅参与传统电力市场,参与电-碳耦合市场并考虑“虚拟碳储存”,可减少31.9%的碳排放,同时显著降低了用户用电成本。

考虑碳交易下虚拟电厂参与现货市场竞价和调度策略

以虚拟电厂参与电力现货市场竞争的博弈过程为研究对象,求解以虚拟电厂总收益最大为目标的虚拟电厂参与现货市场的最优购、售电策略;并在日前市场成交电量的基础上,设计以偏差考核成本最小为目标的虚拟电厂参加实时市场的优化调度策略。最后通过算例验证了售电公司采用虚拟电厂聚合方式能够通过对分布式能源调度控制来增加在购售电决策时的灵活性,从而提高自身运营收益,为售电公司组合资源提供了技术支撑。

惯量阻尼自适应虚拟直流发电机控制策略

直流微网无需考虑频率、相位等因素,拓扑结构简单且易于控制,但基于大量电力电子变换器接口的直流微网惯性较低,严重时会影响微网的安全稳定运行。针对此问题,文中通过分析扰动时电压波动各阶段系统对惯性的需求,以及惯量阻尼参数对系统惯性的影响,提出了一种附加动态调节系数的惯量阻尼自适应控制策略,可以根据电压变化率与电压偏差灵活调节系统惯性,减小功率波动对母线电压的影响。建立了系统小信号模型,利用阻抗比判据分析了惯量阻尼参数的小信号稳定性。最后利用PSCAD/EMTDC仿真软件建立了直流微网仿真模型进行分析,验证了控制策略的有效性。

基于模糊PID的考虑风速变化的虚拟惯性控制策略

为充分利用风电功率支撑能力,有效缓解机组调频压力,提升电网频率稳定性,提出基于模糊PID的考虑风速变化的虚拟惯性控制策略。构建风机的数学模型,依据该模型分析风机各个组成部分的运行特性,并构建风速模型。在考虑风速影响下,分析风速变化对于风机虚拟惯性控制的影响,设计模糊PID控制器,自适应动态调整虚拟惯性系数,实现频率控制;为避免虚拟惯性的控制结果发生超调控制,采用粒子群算法优化PID控制器的控制参数,设计粒子群优化的模糊PID控制器,完成虚拟惯性控制,保证电网频率稳定。测试结果显示:考虑风速变化完成虚拟惯性系数调整具有较好的合理性,能够实现该系数随着风速的动态变化进行自适应调整;在故障扰动和负荷突变两种工况下,电网频率的控制结果较好,稳定性较高,满足控制需求。