Research on Coordinated Development and Optimization of Distribution Networks at All Levels in Distributed Power Energy Engineering

The uncertainty of distributed generation energy has dramatically challenged the coordinated development of distribution networks at all levels.This paper focuses on the multi-time-scale regulation model of distributed generation energy under normal conditions.The simulation results of the example verify the self-optimization characteristics and the effectiveness of real-time dispatching of the distribution network control technology at all levels under multiple time scales.

An Approach of Distributed Joint Optimization for Cluster-based Wireless Sensor Networks

Wireless sensor networks(WSNs) are energyconstrained,so energy saving is one of the most important issues in typical applications.The clustered WSN topology is considered in this paper.To achieve the balance of energy consumption and utility of network resources,we explicitly model and factor the effect of power and rate.A novel joint optimization model is proposed with the protection for cluster head.By the mean of a choice of two appropriate sub-utility functions,the distributed iterative algorithm is obtained.The convergence of the proposed iterative algorithm is proved analytically.We consider general dual decomposition method to realize variable separation and distributed computation,which is practical in large-scale sensor networks.Numerical results show that the proposed joint optimal algorithm converges to the optimal power allocation and rate transmission,and validate the performance in terms of prolonging of network lifetime and improvement of throughput.

Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.

Binary Gravitational Search based Algorithm for Optimum Siting and Sizing of DG and Shunt Capacitors in Radial Distribution Systems

This paper presents a binary gravitational search algorithm (BGSA) is applied to solve the problem of optimal allotment of DG sets and Shunt capacitors in radial distribution systems. The problem is formulated as a nonlinear constrained single-objective optimization problem where the total line loss (TLL) and the total voltage deviations (TVD) are to be minimized separately by incorporating optimal placement of DG units and shunt capacitors with constraints which include limits on voltage, sizes of installed capacitors and DG. This BGSA is applied on the balanced IEEE 10 Bus distribution network and the results are compared with conventional binary particle swarm optimization.

Optimal Allocation of Public Transport Hub Based on Load Loss Value and the Economy of Distribution Network

The rapid development of electric buses has brought a surge in the number of bus hubs and their charging and discharging capacities.Therefore,the location and construction scale of bus hubs will greatly affect the operation costs and benefits of an urban distribution network in the future.Through the scientific and reasonable planning of public transport hubs on the premise of meeting the needs of basic public transport services,it can reduce the negative impact of electric bus charging loads upon the power grids.Furthermore,it can use its flexible operation characteristics to provide flexible support for the distribution network.In this paper,taking the impact of public transport hub on the reliability of distribution network as the starting point,a three-level programming optimization model based on the value and economy of distribution network load loss is proposed.Through the upper model,several planning schemes can be generated,which provides boundary conditions for the expansion of middle-level optimization.The normal operation dispatching scheme of public transport hub obtained from the middle-level optimization results provides boundary conditions for the development of lower level optimization.Through the lower level optimization,the expected load loss of the whole distribution system including bus hub under the planning scheme given by the upper level can be obtained.The effectiveness of the model is verified by an IEEE-33 bus example.

Optimization of the Open Degree of Key Valves Based on Relative Entropy and Pipeline Leakage

Based on information entropy theory, the definition of relative entropy, and the relative entropy minimum principle, this study establishes a multi-objective optimization model for a key valve opening of an urban water distribution network(WDN). Each node pressure is taken as the main research object to reduce pipeline leakage. Moreover, genetic algorithm is applied in the proposed model to solve the key valve opening of the actual WDN in a city in southern China. Using the proposed model, the relevant decision variables of a WDN can be optimized to provide a new manner of network dispatching.

An optimization-oriented modeling approach using input convex neural networks and its application on optimal chiller loading

Optimization for the multi-chiller system is an indispensable approach for the operation of highly efficient chiller plants.The optima obtained by model-based optimization algorithms are dependent on precise and solvable objective functions.The classical neural networks cannot provide convex input-output mappings despite capturing impressive nonlinear fitting capabilities,resulting in a reduction in the robustness of model-based optimization.In this paper,we leverage the input convex neural networks(ICNN)to identify the chiller model to construct a convex mapping between control variables and the objective function,which enables the NN-based OCL as a convex optimization problem and apply it to multi-chiller optimization for optimal chiller loading(OCL).Approximation performances are evaluated through a four-model comparison based on an experimental data set,and the statistical results show that,on the premise of retaining prior convexities,the proposed model depicts excellent approximation power for the data set,especially the unseen data.Finally,the ICNN model is applied to a typical OCL problem for a multi-chiller system and combined with three types of optimization strategies.Compared with conventional and meta-heuristic methods,the numerical results suggest that the gradient-based BFGS algorithm provides better energy-saving ratios facing consecutive cooling load inputs and an impressive convergence speed.

Multi-source coordinated stochastic restoration for SOP in distribution networks with a two-stage algorithm

After suffering from a grid blackout, distributed energy resources(DERs), such as local renewable energy and controllable distributed generators and energy storage can be used to restore loads enhancing the system’s resilience. In this study, a multi-source coordinated load restoration strategy was investigated for a distribution network with soft open points(SOPs). Here, the flexible regulation ability of the SOPs is fully utilized to improve the load restoration level while mitigating voltage deviations. Owing to the uncertainty, a scenario-based stochastic optimization approach was employed,and the load restoration problem was formulated as a mixed-integer nonlinear programming model. A computationally efficient solution algorithm was developed for the model using convex relaxation and linearization methods. The algorithm is organized into a two-stage structure, in which the energy storage system is dispatched in the first stage by solving a relaxed convex problem. In the second stage, an integer programming problem is calculated to acquire the outputs of both SOPs and power resources. A numerical test was conducted on both IEEE 33-bus and IEEE 123-bus systems to validate the effectiveness of the proposed strategy.

Joint Optimization Strategy for Video Transmission over Distributed Cognitive Radio Networks

A novel joint optimization strategy for the secondary user( SU) was proposed to consider the short-term and long-term video transmissions over distributed cognitive radio networks( DCRNs).Since the long-term video transmission consisted of a series of shortterm transmissions, the optimization problem in the video transmission was a composite optimization process. Firstly,considering some factors like primary user's( PU's) collision limitations,non-synchronization between SU and PU,and SU's limited buffer size, the short-term optimization problem was formulated as a mixed integer non-linear program( MINLP) to minimize the block probability of video packets. Secondly,combining the minimum packet block probability obtained in shortterm optimization and SU's constraint on hardware complexity,the partially observable Markov decision process( POMDP) framework was proposed to learn PU's statistic information over DCRNs.Moreover,based on the proposed framework,joint optimization strategy was designed to obtain the minimum packet loss rate in long-term video transmission. Numerical simulation results were provided to demonstrate validity of our strategies.

Optimal Design of the Modular Joint Drive Train for Enhancing Cobot Load Capacity and Dynamic Performance

Automation advancements prompts the extensive integration of collaborative robot(cobot)across a range of industries.Compared to the commonly used design approach of increasing the payload-to-weight ratio of cobot to enhance load capacity,equal attention should be paid to the dynamic response characteristics of cobot during the design process to make the cobot more flexible.In this paper,a new method for designing the drive train parameters of cobot is proposed.Firstly,based on the analysis of factors influencing the load capacity and dynamic response characteristics,design criteria for both aspects are established for cobot with all optimization design criteria normalized within the design domain.Secondly,with the cobot in the horizontal pose,the motor design scheme is discretized and it takes the joint motor diameter and gearbox speed ratio as optimization design variables.Finally,all the discrete values of the optimization objectives are obtained through the enumeration method and the Pareto front is used to select the optimal solution through multi-objective optimization.Base on the cobot design method proposed in this paper,a six-axis cobot is designed and compared with the commercial cobot.The result shows that the load capacity of the designed cobot in this paper reaches 8.4 kg,surpassing the 5 kg load capacity commercial cobot which is used as a benchmark.The minimum resonance frequency of the joints is 42.70 Hz.

考虑空载损失的非集中式共同配送订单分派及路径优化研究

物流活动的空载率居高不下源于路径规划不合理及企业间缺少合作,共同配送是降低空载损失的有效模式,但非集中式共同配送下物流企业可能基于被分派的订单选择自身成本最小的配送路径,从而导致共同配送联盟的空载损失变大。本文研究考虑空载损失的非集中式共同配送订单分派及路径优化,首先提出空载损失定义,权衡整个配送过程的成本最小和空载损失最小两个目标,基于非集中式共同配送的特征设计订单分派策略,进而建立订单分派及路径优化模型。设计了基于ε约束法的精确算法、改进的MOPSO(multiple objective particle swarm optimization)算法、多项式时间快速算法进行求解,并结合算例验证算法的有效性。数值分析结果表明,即使物流企业均追求自身成本最小化,提出的订单分派策略也可得到与全局优化相近的结果。

Venlo型温室柱脚螺栓节点力学性能

为研究连栋温室柱脚节点尺寸对节点承载力的影响,依托珠海某Venlo型温室项目,基于《混凝土结构设计规范》《化工设备基础设计规定》以及《混凝土结构构造手册》对中柱基础短柱和边柱柱脚节点的构造进行设计,通过数值模拟和节点试验研究了中柱基础短柱柱脚节点的抗弯性能、边柱柱脚节点的抗剪性能以及破坏机理。结果表明:2种节点的屈服荷载和极限荷载随着节点构造尺寸的减小而降低,其破坏过程可划分为3个阶段:弹性阶段、屈服阶段、极限承载力阶段。中柱基础短柱柱脚节点破坏模式为受拉侧混凝土锥形破坏,边柱柱脚节点的破坏模式为混凝土楔形体破坏,研究结果可为连栋温室柱底地脚螺栓节点设计提供参考。

面向季节性短时高峰负荷的虚拟配电馈线功能架构及协同配置

传统线路增容方式面对季节性短时高峰负荷时存在线路整体利用率低、紧急功率支撑能力差等问题。因此,文中以功能虚拟化为核心,研究了传统配电馈线与储能元件融合的虚拟配电馈线功能架构,利用双端储能元件搭建虚拟电能传输通道,以应对季节性短时高峰负荷、延缓传统线路增容改造工程,并在非高峰时期提供紧急功率支撑和辅助服务功能,提高储能元件整体利用率。为充分挖掘虚拟配电馈线的灵活传输和紧急功率支撑潜力,提出了配电网线路增容优化配置模型,在保证经济性和满足高峰负荷需求的前提下,优化虚拟配电馈线储能配置。算例研究表明,虚拟配电馈线能够有效延缓传统线路增容改造工程、应对季节性短时高峰负荷;对于线路负荷增长较稳定、线路长度较长和设计延缓增容年限较短的配电区域,虚拟配电馈线的优势更加凸显。

考虑净负荷不确定性的配电网负荷恢复仿射可调鲁棒优化方法

为应对负荷恢复过程中源荷双重不确定性问题,提出了基于仿射可调鲁棒优化的配电网负荷恢复调整策略。第1阶段基于净负荷预测场景,建立一个以最大化加权负荷恢复量为目标函数的优化模型,决策最优的负荷恢复量;第2阶段采用仿射策略,利用预算不确定集来描述净负荷的不确定性,构建考虑净负荷误差的负荷恢复调整方案。在求解过程中,对双线性变量进行对偶变换得到可快速求解的线性规划模型。最后,在改进33节点和123节点配电系统进行仿真验证,结果表明:相比于传统的鲁棒优化方法,该方法降低了优化结果的保守性,有助于加快负荷恢复,减小停电损失。

基于遗传算法的时间敏感网络调度方法

随着网络技术的进步,车载网、工业物联网以及5G超高可靠低时延通信(uRLLC)等应用都需要时间敏感网络(TSN)来保证超低延时的确定性数据传输。TSN流量调度需要快速且精确的调度算法,现有的精确式求解方法复杂度高,在大规模联合调度时无法满足实时性。文中设计了一种性能更优的路由优化遗传算法(Routing-GA),结合路由和流量调度约束,能通过优化路由来提高调度算法求解效率,为链路负载均衡调度提供服务。该策略增加了调度的求解空间以及求解灵活性,具备元启发式算法的快速求近最优解特点,能够简单有效地处理大规模TSN路由约束联合调度问题。Routing-GA以时间敏感流最小端到端时延作为优化目标,联合考虑路由和TSN约束,并针对TSN传输问题特性提供一种低复杂度、高效率和高拓展性的遗传算法编码方式。此外,为了提高调度算法的性能,提出针对路由长度及链路负载均衡进行优化的交叉变异机制。实验结果表明所实现的Routing-GA能有效减少端到端时延,显著提高求解质量,进化率可以达到24.42%,平均只需要传统遗传算法(GA)迭代运行时间的12%,从而有效提高了算法的求解性能,满足TSN调度的约束要求。

多中心开放式电动货车冷链物流配送路径优化

为了实现物流企业的降本增效和绿色发展,考虑载重、电量、时间窗约束和电池动态耗电率、产品新鲜度损耗、增加配送中心充电功能和多中心联合配送等因素,提出了开放式多配送中心联合配送的电动货车冷链物流配送路径问题。以总成本最小为目标函数,建立该问题的混合整数规划模型,设计改进的遗传算法进行求解,优化电动货车冷链物流配送路径和充电方案。结果表明:开放式多中心联合配送能更好地满足客户时间窗约束并降低物流运营成本;增加配送中心的充电功能可以降低充电站短缺对物流企业运营的影响;考虑车辆载重动态影响耗电率能准确反映出配送途中车辆电量消耗;改进遗传算法求解算例成本更低,充电方案和路径规划更优。

换热站并联水泵分布式优化控制

针对现有换热站并联水泵优化算法在集中式架构下控制适应性不足的问题,本文提出了一种改进的分布式并联水泵优化算法.首先,建立了并联水泵的分布式控制系统,并对该优化问题的数学模型进行描述,在目标函数中引入自适应非线性因子;然后,设计了改进的分布式果蝇优化算法,在该算法中每台水泵的控制器仅通过与邻居控制器交互信息即可完成并联水泵的优化;并且,在嗅觉搜索阶段,使用正弦余弦策略替代赋予个体距离与方向的随机策略;最后,以两个实际换热站中不同并联水泵系统为例对算法进行仿真验证,并基于仿真结果进行性能分析.结果表明,相较于传统算法,改进的分布式果蝇优化算法能得到更优的控制策略,有着收敛速度快、稳定性好和鲁棒性强的特点;并且该算法适用于不同系统的并联水泵优化问题,具有可扩展性.在实际工程验证中相较于集中式算法,该算法在总功率和计算时间上分别平均降低了5.47%和29.90%,因此,能够满足实际换热站中对并联水泵热负荷优化分配的需求.

面向分布式数据库的算子并行优化策略

随着网络技术的不断发展,数据规模呈现爆发式增长,使得传统的单机数据库逐步被分布式数据库所取代。分布式数据库采用节点协同工作方式解决了大规模数据存储问题,但由于增加了节点间通信开销,查询效率却不如单机数据库。分布式架构下,存储节点的数据仅用作多备份的冗余,为系统故障时提供数据恢复,并未被利用起来改善查询效率。针对上述问题,提出了一种面向分布式数据库的算子并行优化策略,通过对关键物理算子进行拆分,将拆分后的子请求均匀分配到存储层多个节点,由多个节点并行处理,从而减少查询响应时间。上述策略已经在分布式数据库CBase上进行了应用,实验表明,提出的并行优化策略可显著缩短SQL请求查询时间,并提高系统资源利用率。

接缝和主梁损伤对装配式多主梁桥荷载横向分布规律的影响研究

为了验证不同损伤对装配式多主梁桥荷载横向分布规律的影响,并探讨基于横向分布规律的桥梁损伤识别方法,考虑接缝损伤程度、主梁局部损伤程度、损伤位置和损伤区域长度的影响,采用刚接板梁法对接缝和主梁损伤情况下装配式多主梁桥的荷载横向分布规律进行了研究。结果表明,接缝损伤后其两侧主梁的横向分布影响线与损伤前的影响线有交点;有损伤的主梁其影响线整体下降,且刚度剩余系数越小、损伤位置距跨中越近、损伤范围越大,下降程度越大;主梁与接缝损伤同时发生时,可通过与敏感梁单独损伤时的影响线进行对比来进行分析。根据损伤前后敏感主梁的横向分布影响线变化规律,可对桥梁的损伤类别进行识别,但无法识别具体损伤位置。