Cloud-Edge Collaborative Federated GAN Based Data Processing for IoT-Empowered Multi-Flow Integrated Energy Aggregation Dispatch

The convergence of Internet of Things(IoT),5G,and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing.While generative adversarial networks(GANs)are instrumental in resource scheduling,their application in this domain is impeded by challenges such as convergence speed,inferior optimality searching capability,and the inability to learn from failed decision making feedbacks.Therefore,a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges.The proposed algorithm facilitates real-time,energy-efficient data processing by optimizing transmission power control,data migration,and computing resource allocation.It employs federated learning for global parameter aggregation to enhance GAN parameter updating and dynamically adjusts GAN learning rates and global aggregation weights based on energy consumption constraint violations.Simulation results indicate that the proposed algorithm effectively reduces data processing latency,energy consumption,and convergence time.

A PI+R Control Scheme Based on Multi-Agent Systems for Economic Dispatch in Isolated BESSs

Battery energy storage systems(BESSs)are widely used in smart grids.However,power consumed by inner impedance and the capacity degradation of each battery unit become particularly severe,which has resulted in an increase in operating costs.The general economic dispatch(ED)algorithm based on marginal cost(MC)consensus is usually a proportional(P)controller,which encounters the defects of slow convergence speed and low control accuracy.In order to solve the distributed ED problem of the isolated BESS network with excellent dynamic and steady-state performance,we attempt to design a proportional integral(PI)controller with a reset mechanism(PI+R)to asymptotically promote MC consensus and total power mismatch towards 0 in this paper.To be frank,the integral term in the PI controller is reset to 0 at an appropriate time when the proportional term undergoes a zero crossing,which accelerates convergence,improves control accuracy,and avoids overshoot.The eigenvalues of the system under a PI+R controller is well analyzed,ensuring the regularity of the system and enabling the reset mechanism.To ensure supply and demand balance within the isolated BESSs,a centralized reset mechanism is introduced,so that the controller is distributed in a flow set and centralized in a jump set.To cope with Zeno behavior and input delay,a dwell time that the system resides in a flow set is given.Based on this,the system with input delays can be reduced to a time-delay free system.Considering the capacity limitation of the battery,a modified MC scheme with PI+R controller is designed.The correctness of the designed scheme is verified through relevant simulations.

Optimization dispatching strategy for an energy storage system considering its unused capacity sharing

In renewable energy systems,energy storage systems can reduce the power fluctuation of renewable energy sources and compensate for the prediction deviation.However,if the renewable energy prediction deviation is small,the energy storage system may work in an underutilized state.To efficiently utilize a renewable-energy-sided energy storage system(RES),this study proposed an optimization dispatching strategy for an energy storage system considering its unused capacity sharing.First,this study proposed an unused capacity-sharing strategy for the RES to fully utilize the storage’s unused capacity and elevate the storage’s service efficiency.Second,RES was divided into“deviation-compensating energy storage(DES)”and“sharing energy storage(SES)”to clarify the function of RES in the operation process.Third,this study established an optimized dispatching model to achieve the lowest system operating cost wherein the unused capacity-sharing strategy could be integrated.Finally,a case study was investigated,and the results indicated that the proposed model and algorithm effectively improved the utilization of renewable-energy-side energy storage systems,thereby reducing the total operation cost and pressure on peak shaving.

Energy Economic Dispatch for Photovoltaic-Storage via Distributed Event-Triggered Surplus Algorithm

This paper presents a novel approach to economic dispatch in smart grids equipped with diverse energy devices.This method integrates features including photovoltaic(PV)systems,energy storage coupling,varied energy roles,and energy supply and demand dynamics.The systemmodel is developed by considering energy devices as versatile units capable of fulfilling various functionalities and playing multiple roles simultaneously.To strike a balance between optimality and feasibility,renewable energy resources are modeled with considerations for forecasting errors,Gaussian distribution,and penalty factors.Furthermore,this study introduces a distributed event-triggered surplus algorithm designed to address the economic dispatch problem by minimizing production costs.Rooted in surplus theory and finite time projection,the algorithm effectively rectifies network imbalances caused by directed graphs and addresses local inequality constraints.The algorithm greatly reduces the communication burden through event triggering mechanism.Finally,both theoretical proofs and numerical simulations verify the convergence and event-triggered nature of the algorithm.

Collaborative robust dispatch of electricity and carbon under carbon allowance trading market

The launch of the carbon-allowance trading market has changed the cost structure of the power industry.There is an asynchronous coupling mechanism between the carbon-allowance-trading market and the day-ahead power-system dispatch.In this study,a data-driven model of the uncertainty in the annual carbon price was created.Subsequently,a collaborative,robust dispatch model was constructed considering the annual uncertainty of the carbon price and the daily uncertainty of renewable-energy generation.The model is solved using the column-and-constraint generation algorithm.An operation and cost model of a carbon-capture power plant(CCPP)that couples the carbon market and the economic operation of the power system is also established.The critical,profitable conditions for the economic operation of the CCPP were derived.Case studies demonstrated that the proposed low-carbon,robust dispatch model reduced carbon emissions by 2.67%compared with the traditional,economic,dispatch method.The total fuel cost of generation decreases with decreasing,conservative,carbon-price-uncertainty levels,while total carbon emissions continue to increase.When the carbon-quota coefficient decreases,the system dispatch tends to increase low-carbon unit output.This study can provide important guidance for carbon-market design and the low-carbon-dispatch selection strategies.

Economic Power Dispatching from Distributed Generations: Review of Optimization Techniques

In the increasingly decentralized energy environment,economical power dispatching from distributed generations(DGs)is crucial to minimizing operating costs,optimizing resource utilization,and guaranteeing a consistent and sustainable supply of electricity.A comprehensive review of optimization techniques for economic power dispatching from distributed generations is imperative to identify the most effective strategies for minimizing operational costs while maintaining grid stability and sustainability.The choice of optimization technique for economic power dispatching from DGs depends on a number of factors,such as the size and complexity of the power system,the availability of computational resources,and the specific requirements of the application.Optimization techniques for economic power dispatching from distributed generations(DGs)can be classified into two main categories:(i)Classical optimization techniques,(ii)Heuristic optimization techniques.In classical optimization techniques,the linear programming(LP)model is one of the most popular optimization methods.Utilizing the LP model,power demand and network constraints are met while minimizing the overall cost of generating electricity from DGs.This approach is efficient in determining the best DGs dispatch and is capable of handling challenging optimization issues in the large-scale system including renewables.The quadratic programming(QP)model,a classical optimization technique,is a further popular optimization method,to consider non-linearity.The QP model can take into account the quadratic cost of energy production,with consideration constraints like network capacity,voltage,and frequency.The metaheuristic optimization techniques are also used for economic power dispatching from DGs,which include genetic algorithms(GA),particle swarm optimization(PSO),and ant colony optimization(ACO).Also,Some researchers are developing hybrid optimization techniques that combine elements of classical and heuristic optimization techniques with the incorporation of droop control,predictive control,and fuzzy-based methods.These methods can deal with large-scale systems with many objectives and non-linear,non-convex optimization issues.The most popular approaches are the LP and QP models,while more difficult problems are handled using metaheuristic optimization techniques.In summary,in order to increase efficiency,reduce costs,and ensure a consistent supply of electricity,optimization techniques are essential tools used in economic power dispatching from DGs.

Decision model and algorithm for traffic rescue resource dispatching on expressway

In order to solve the problems of potential incident rescue on expressway networks, the opportunity cost-based method is used to establish a resource dispatch decision model. The model aims to dispatch the rescue resources from the regional road networks and to obtain the location of the rescue depots and the numbers of service vehicles assigned for the potential incidents. Due to the computational complexity of the decision model, a scene decomposition algorithm is proposed. The algorithm decomposes the dispatch problem from various kinds of resources to a single resource, and determines the original scene of rescue resources based on the rescue requirements and the resource matrix. Finally, a convenient optimal dispatch scheme is obtained by decomposing each original scene and simplifying the objective function. To illustrate the application of the decision model and the algorithm, a case of the expressway network is studied on areas around Nanjing city in China and the results show that the model used and the algorithm proposed are appropriate.

量化测试高性能L5-Dispatcher

主要内容是关于HTTPSplicing的性能评测 HTTPSplicing是针对TCPSplicing的缺陷而提出的 ,TCPSplicing现被广泛应用在L5 Dispatcher(即基于内容的Web交换机 )中 测试内容包括开销测试、性能测试、与L4 Dispatcher的对比测试、与TCPSplicing的对比测试和扩展性测试 所获得的结论为 :当尽力 (besteffort)访问 1KB大小的文件时 ,HTTPSplicing的性能要比TCPSplicing至少快 3 5倍 ,而当尽力访问 10KB大小的文件时 ,HTTPSplicing的性能比TCPSplic ing的性能高 2 2 % 当尽力访问 30 7KB大小的文件时 ,HTTPSplicing的一种实现———WebPatcher———的最好性能值为5 5 5 6 8replies/s ,超过了ArrowPoint公司的CS 10 0 (1193replies/s)和Alteon公司的ACEdirector 3(v6 0 2 5 ) (135 6replies/s).

Optimal dispatching method of traffic incident rescue resource for freeway network

An optimal resource dispatching method is proposed to solve the multiple-response problem under the conditions of potential incidents on freeway networks.Travel time of the response vehicle is selected instead of route distance as the weight to reflect the impact of traffic conditions on the decisions of rescue resources.According to the characteristics of different types of rescue vehicles the dispatching decision-making time is revised to show the heterogeneity among different rescue vehicle dispatching modes. The genetic algorithm is used to obtain the solutions to the rescue resources dispatching model. A case study shows that the proposed method can accurately reveal the impact of potential incidents on the costs of rescues according to the variations in the types and quantities of rescue resources and the optimal dispatching plan with respect to potential incidents can be obtained.The proposed method is applicable in real world scenarios.

高性能L5-Dispatcher的性能评测

为支持基于请求内容调度的策略 ,提出并实现了一个集中调度、分布路由的可扩展路由机制 与通常的单前端机不同 ,提出的可扩展路由结构由多个前端机组成 一个前端机执行集中调度任务 (即调度器 ) ,其余的前端机执行分布路由任务 (即转发器 ) 测试数据表明 ,一台运行调度器程序的PⅢ 933主机的吞吐量为 4 5 0 0 0 0conns/s,可满足 80台转发器同时请求调度服务 换言之 ,有 80台转发器可同时转发请求数据或响应数据 ,即分布式前端总共可提供

基于请求内容的高性能L5-Dispatcher

提出了一种实现高性能 L5 - Dispatcher的方法—— HTTP Splicing.其特点有 :1脱离正常操作系统的TCP实现 ,而根据 L5 - Dispatcher的需要实现一个功能精简的 TCP(称为 RTCP) ;2 HTTP请求的解析工作不再需第 3方程序 (如 Proxy应用程序 )的干预 ,而直接实现在 RTCP中 ;3在 IP层转发 HTTP包 ;4完全的 I/ O零拷贝 ;5支持 HTTP1.1协议 .与已有的 TCP Gateway和 TCP Splicing相比 ,HTTP Splicing方法没有打断客户端和后端服务器之间的 TCP可靠传输机制 ,因此具有更好的性能 .经测试 ,用 HTTP Splicing技术实现的 L5 - Dispatcher的性能与 L inux操作系统自带的 L4 - Dispatcher—— ip vs——性能几乎完全相等 ,且比现有的基于 TCP Splicing技术的 L5 - Dispatcher的性能好约 2 0 % .

DISPATCH系统在我国大型露天矿山中的应用

本文首先介绍ＤＩＳＰＡＴＣＨ系统在国内外的应用情况，然后详细地介绍了德兴铜矿购买的ＤＩＳＰＡＴＣＨ系统，包括系统配置、组成、主要原理、优化算法、数据库、网络、系统组成等，最后介绍了取得的经济效益。

利用ORACLE PL/SQL PACKAGE实现DISPATCHER设计模式

以ORACLEPACKAGE语言及支撑环境为基础,有效地利用其对面向对象技术的支持,实现DISPATCHER设计模式。

利用Oracle PL/SQL Package实现Dispatcher设计模式

Dispatcher设计模式可以使用各种技术来实现。以社会保险业务处理为基础,有效地利用ORACLEPL/SQLPac-kage语言及支撑环境对面向对象技术的支持,通过定义消息ID、传递消息ID、撰写处理函数、连接消息ID和处理函数来实现Dispatcher设计模式,并且构造出能处理各项社会保险管理业务的Package组件。

论DISPATCH系统在大型露天矿的作用

本文全面介绍了露天矿计算机卡车调度系统（ＤＩＳＰＡＴＣＨ）的原理和功能，分析了使用的效益和风险，论证了露天矿采用该系统指挥和管理生产的必要性。

祁连山区DISPATCH、多元回归降尺度方法及SMAP产品的应用对比

土壤水分对山区水文过程具有重要意义,遥感土壤水分产品能够长时间序列地提供山区流域的土壤水分空间分布数据,但分辨率较粗,无法直接应用,因此需要在山区进行降尺度研究.本文采用DISPATCH(disaggregation base on physical and theoretical scale change)方法和多元回归方法对SMAP(soil moisture active passive)36 km×36 km遥感土壤水分产品进行降尺度,进而选取SMAP (9 km×9 km)的高精度遥感土壤水分产品和实测土壤水分数据,利用R(相关系数)、ERMS(均方根误差)和Ebias(偏差)指标评估降尺度结果.评估结果表明:由于2种降尺度方法的函数关系和反演过程存在差异,DISPATCH方法降尺度结果的数据趋势拟合效果较好,而多元回归方法降尺度结果的数据精度较好;在季节尺度对比中,不同季节山区温度和土壤水分的时空变化,导致多元回归方法降尺度效果春季最好,秋季次之,而夏季最差;DISPATCH方法降尺度效果秋季最好,夏季次之,而春季最差;亮温数据和SMAP表层土壤温度数据在山区的质量,导致2种方法降尺度结果的精度均比SMAP (9 km×9 km)产品好,但趋势拟合效果较差.

基于自适应变异粒子群算法的风光储微网调度

为克服传统粒子群算法在求解时容易形成局部最优,求解精度低的不足,提出了一种基于自适应变异粒子群优化的微电网调度求解方法。惯性权重采用自适应正态分布递减,随着迭代次数的增加更新粒子位置的移动策略,并且在算法后期引入变异环节。为验证算法的有效性,文章与其他算法进行收敛性能对比,并对两种典型天气情况下的微网运行成本模型仿真求解,得到最优调度。算例结果表明,改进算法能够对粒子全局最优搜索优化,效果优于其他算法,可合理调配分布式电源出力时段,具有良好的可行性。

Modified Cuckoo Search Algorithm to Solve Economic Power Dispatch Optimization Problems

A modified cuckoo search(CS) algorithm is proposed to solve economic dispatch(ED) problems that have nonconvex, non-continuous or non-linear solution spaces considering valve-point effects, prohibited operating zones, transmission losses and ramp rate limits. Comparing with the traditional cuckoo search algorithm, we propose a self-adaptive step size and some neighbor-study strategies to enhance search performance.Moreover, an improved lambda iteration strategy is used to generate new solutions. To show the superiority of the proposed algorithm over several classic algorithms, four systems with different benchmarks are tested. The results show its efficiency to solve economic dispatch problems, especially for large-scale systems.

Pre-recorded instructional audio vs. dispatchers' conversational assistance in telephone cardiopulmonary resuscitation: A randomized controlled simulation study

BACKGROUND:To assess the effectiveness of the telephone chest-compression-only cardiopulmonary resuscitation(CPR)guided by a pre-recorded instructional audio when compared with dispatcher-assisted resuscitation.METHODS:It was a prospective,blind,randomised controlled study involving 109 medical students without previous CPR training.In a standardized mannequin scenario,after the step of dispatcher-assisted cardiac arrest recognition,the participants performed compression-only resuscitation guided over the telephone by either:(1)the pre-recorded instructional audio(n=57);or(2)verbal dispatcher assistance(n=52).The simulation video records were reviewed to assess the CPR performance using a 13-item checklist.The interval from call reception to the first compression,total number and rate of compressions,total number and duration of pauses after the first compression were also recorded.RESULTS:There were no significant differences between the recording-assisted and dispatcher-assisted groups based on the overall performance score(5.6±2.2 vs.5.1±1.9,P>0.05)or individual criteria of the CPR performance checklist.The recording-assisted group demonstrated provided(170.2±48.0 vs.156.2±60.7).CONCLUSION:When provided by untrained persons in the simulated settings,the compression-only resuscitation guided by the pre-recorded instructional audio is no less efficient than dispatcher-assisted CPR.Future studies are warranted to further assess feasibility of using instructional audio aid as a potential alternative to dispatcher assistance.

Dispatch coordination between high-speed and conventional rail systems

Cross-line trains, as a link between high-speed and conventional rail networks, will increase the complexity of transport organization and lead to significant challenges in dispatch coordination between the two systems. Based on the characteristics of high-speed transport organization, this paper deals with the necessity of dispatch coordination between high-speed and conventional lines from the following two perspectives： the operation of cross-line trains and work coordination in connection stations. An adjustment model for the operation of high-speed trains, taking cross-line trains into account, is established. Finally, the dispatch system is described in terms of construction and process. Methods for organizing dispatch are proposed, and the processes of coordination adjustment under normal and unexpected situations are analyzed. The discussion in this paper may serve as a theoretical basis for the development of high-speed rail dispatch systems.