Load Distribution of Base Stations in User-Centric Heterogeneous UDN

In ultra-dense networks(UDN),multiple association can be regarded as a user-centric pattern in which a user can be served by multiple base stations(BSs).The data rate and quality of service can be improved.However,BSs in user-centric paradigm are required to serve more users due to this multiple association scheme.The improvement of system performance may be limited by the improving load of BSs.In this letter,we develope an analytical framework for the load distribution of BSs in heterogeneous user-centric UDN.Based on open loop power control(OLPC),a user-centric scheme is considered in which the clustered serving BSs can provide given signal to interference plus noise ratio(SINR)for any typical user.As for any BS in different tiers,by leveraging stochastic geometry,we derive the Probability Mass Function(PMF)of the number of the served users,the Cumulative Distribution Function(CDF)of total power consumption,and the CDF bounds of downlink sum data rate.The accuracy of the theoretical analysis is validated by numerical simulations,and the effect the system parameters on the load of BSs is also presented.

Improvement on Conventional Load Distribution Algorithm in Hot Tandem Mills

Load distribution is a key technology in strip hot rolling process, which influences the coil＇s mierostrueture and performance. Currently, Newton-Raphson algorithm is applied to load distribution of hot tandem mills in many hot rolling plants and has some serious defects such as having a strict restriction on initial iterative calculation value and requiring coefficient matrix of nonlinear equations to be nonsingular. To eliminate these defects and improve the online performance of the process control computer, Newton descendent numeric algorithm is introduced to this field to widen the initial value range and a new model named error conversion algorithm is put forth to deal with special conditions when the coefficient matrix is singular. Furthermore, considering the characteristics of load distribution, a condition of strip thickness distribution abnormality and corresponding solutions are provided which ensure that rolling parameters can be calculated normally. Simulation results show that the improved algorithm has overcome the defects of the Newton-Raphson algorithm and is suitable for online application.

A Distributed Decision Mechanism for Controller Load Balancing Based on Switch Migration in SDN

Software Defined Networking(SDN) provides flexible network management by decoupling control plane from data plane. And multiple controllers are deployed to improve the scalability and reliability of the control plane, which could divide the network into several subdomains with separate controllers. However, such deployment introduces a new problem of controller load imbalance due to the dynamic traffic and the static configuration between switches and controllers. To address this issue, this paper proposes a Distribution Decision Mechanism(DDM) based on switch migration in the multiple subdomains SDN network. Firstly, through collecting network information, it constructs distributed migration decision fields based on the controller load condition. Then we choose the migrating switches according to the selection probability, and the target controllers are determined by integrating three network costs, including data collection, switch migration and controller state synchronization. Finally, we set the migrating countdown to achieve the ordered switch migration. Through verifying several evaluation indexes, results show that the proposed mechanism can achieve controller load balancing with better performance.

Distributed Model Predictive Load Frequency Control of Multi-area Power System with DFIGs

Reliable load frequency control LFC is crucial to the operation and design of modern electric power systems. Considering the LFC problem of a four-Area interconnected power system with wind turbines, this paper presents a distributed model predictive control DMPC based on coordination scheme. The proposed algorithm solves a series of local optimization problems to minimize a performance objective for each control area. The generation rate constraints GRCs, load disturbance changes, and the wind speed constraints are considered. Furthermore, the DMPC algorithm may reduce the impact of the randomness and intermittence of wind turbine effectively. A performance comparison between the proposed controller with and without the participation of the wind turbines is carried out. Analysis and simulation results show possible improvements on closed-loop performance, and computational burden with the physical constraints. © 2014 Chinese Association of Automation.

Management of Charging Load of Electric Vehicles for Optimal Capacity Utilisation of Distribution Transformers

A de-centralised load management technique exploiting the flexibility in the charging of Electric Vehicles (EVs) is presented. Two charging regimes are assumed. The Controlled Charging Regime (CCR) between 16:30 hours and 06:00 hours of the next day and the Uncontrolled Charging Regime (UCR) between 06:00 hours and 16:30 hours of the same day. During the CCR, the charging of EVs is coordinated and controlled by means of a wireless two-way communication link between EV Smart Charge Controllers (EVSCCs) at EV owners’ premises and the EV Load Controller (EVLC) at the local LV distribution substation. The EVLC sorts the EVs batteries in ascending order of their states of charge (SoC) and sends command signals for charging to as many EVs as the transformer could allow at that interval based on the condition of the transformer as analysed by the Distribution Transformer Monitor (DTM). A real and typical urban LV area distribution network in Great Britain (GB) is used as the case study. The technique is applied on the LV area when its transformer is carrying the future load demand of the area on a typical winter weekday in the year 2050. To achieve the load management, load demand of the LV area network is decomposed into Non-EV load and EV load. The load on the transformer is managed by varying the EV load in an optimisation objective function which maximises the capacity utilisation of the transformer subject to operational constraints and non-disruption of daily trips of EV owners. Results show that with the proposed load management technique, LV distribution networks could accommodate high uptake of EVs without compromising the useful normal life expectancy of distribution transformers before the need for capacity reinforcement.

State-of-charge Balance Control and Safe Region Analysis for Distributed Energy Storage Systems with Constant Power Loads

This paper presents a fully distributed state-of-charge balance control (DSBC) strategy for a distributed energy storage system (DESS). In this framework, each energy storage unit (ESU) processes the state-of-charge (SoC) information from its neighbors locally and adjusts the virtual impedance of the droop controller in real-time to change the current sharing. It is shown that the SoC balance of all ESUs can be achieved. Due to virtual impedance, voltage deviation of the bus occurs inevitably and increases with load power. Meanwhile, widespread of the constant power load (CPL) in the power system may cause instability. To ensure reliable operation of DESS under the proposed DSBC, the concept of the safe region is put forward. Within the safe region, DESS is stable and voltage deviation is acceptable. The boundary conditions of the safe region are derived from the equivalent model of DESS, in which stability is analyzed in terms of modified Brayton-Moser's criterion. Both simulations and hardware experiments verify the accuracy of the safe region and effectiveness of the proposed DSBC strategy.

Coordinated control of coastal multi-source multi-load system with desalination load: a review

Traditional seawater desalination requires high amounts of energy, with correspondingly high costs and limited benefits, hindering wider applications of the process. To further improve the comprehensive economic benefits of seawater desalination, the desalination load can be combined with renewable energy sources such as solar energy, wind energy, and ocean energy or with the power grid to ensure its effective regulation. Utilizing energy internet(EI) technology, energy balance demand of the regional power grid, and coordinated control between coastal multi-source multi-load and regional distribution network with desalination load is reviewed herein. Several key technologies, including coordinated control of coastal multi-source multi-load system with seawater desalination load, flexible interaction between seawater desalination and regional distribution network, and combined control of coastal multi-source multi-load storage system with seawater desalination load, are discussed in detail. Adoption of the flexible interaction between seawater desalination and regional distribution networks is beneficial for solving water resource problems, improving the ability to dissipate distributed renewable energy, balancing and increasing grid loads, improving the safety and economy of coastal power grids, and achieving coordinated and comprehensive application of power grids, renewable energy sources, and coastal loads.

Metaheuristic Based Resource Scheduling Technique for Distributed Robotic Control Systems

The design of controllers for robots is a complex system that is to be dealt with several tasks in real time for enabling the robots to function independently.The distributed robotic control system can be used in real time for resolving various challenges such as localization,motion controlling,mapping,route planning,etc.The distributed robotic control system can manage different kinds of heterogenous devices.Designing a distributed robotic control system is a challenging process as it needs to operate effectually under different hardware configurations and varying computational requirements.For instance,scheduling of resources(such as communication channel,computation unit,robot chassis,or sensor input)to the various system components turns out to be an essential requirement for completing the tasks on time.Therefore,resource scheduling is necessary for ensuring effective execution.In this regard,this paper introduces a novel chaotic shell game optimization algorithm(CSGOA)for resource scheduling,known as the CSGOA-RS technique for the distributed robotic control system environment.The CSGOA technique is based on the integration of the chaotic maps concept to the SGO algorithm for enhancing the overall performance.The CSGOA-RS technique is designed for allocating the resources in such a way that the transfer time is minimized and the resource utilization is increased.The CSGOA-RS technique is applicable even for the unpredicted environment where the resources are to be allotted dynamically based on the early estimations.For validating the enhanced performance of the CSGOA-RS technique,a series of simulations have been carried out and the obtained results have been examined with respect to a selected set of measures.The resultant outcomes highlighted the promising performance of the CSGOA-RS technique over the other resource scheduling techniques.

Load distribution strategy for multi-lift system with helicopters based on power consumption and robust adaptive game control

It is of great significance to reasonably distribute the slung load to each helicopter while considering difference in power consumption,relative position and interaction comprehensively.Therefore,the load distribution strategy based on power consumption and robust adaptive game control is proposed in this paper.The study is on a"2-lead"multi-lift system of four tandem heli-copters carrying a load cooperatively.First,based on the hierarchical control,the load distribution problem is divided into two parts:the calculation of expected cable force and the calculation of the anti-disturbance cable force.Then,aimed at minimizing the maximum equivalent power of heli-copter,an optimization problem is set up to calculate the expected cable force.Specially,the agent power model is trained by BP neural network,the safe distance constraint between helicopters is set to 2.5 rotor diameters to reduce aerodynamic interference,and the helicopters with different perfor-mance can be considered by introducing the equivalent power factor into the objective function.Next,considering the difference and interaction between helicopters,the robust adaptive differen-tial game control is proposed to calculate the anti-disturbance cable force.Particularly,to solve the coupled Hamiltonian equations,an adaptive solving method for value function is proposed,and its stability is proved in the sense of Lyapunov.The simulation results indicate that the proposed load distribution method based on power consumption is applicable to the entire flight trajectory even there are differences between helicopters.The game control can consider interaction between heli-copters,can deal with different objective functions,and has strong robustness and small steady-state error.Based on the entire strategy,the cable force can be reasonably allocated so as to resist disturbance and improve the flight performance of the whole system.

含电动汽车和光伏的配电网演化动态博弈调度策略

针对大规模电动汽车的无序充电行为可能导致电网出现的三相不平衡问题,基于演化动态博弈模型,提出一种考虑三相负荷平衡和光伏消纳的电动汽车充电调度策略。首先,基于电动汽车接入时段的主观性以及光伏出力和负荷的不确定性,引入游牧集群和定居集群之间的演化动态博弈方程;然后,考虑用户侧和电网侧双方利益关系,设计以最小化电动汽车充电费用、三相负荷不平衡度和无功需求为目标的调度优化模型;最后,通过对某商业区配电网算例进行仿真求解,对比分析三相有功曲线、三相无功曲线以及荷电状态曲线的变化趋势。算例结果表明,该调度策略可有效降低三相不平衡度,满足无功补偿的需求以及改善用户充电成本。

区域一致趋同的分布式负荷频率控制方法研究

随着可再生能源发电比例的上升,电力系统呈现源荷双重不确定性,对频率稳定产生不利影响。提出一种结合多智能体一致算法与动态面控制的分布式负荷频率控制方法,以增强电力系统的频率调节性能。首先,给出频率控制模型结构,并设计系统框架与分散式区域观测器;其次,考虑通信时滞的影响,设计出各区域控制器结构;最后,基于互联电力系统算例验证了方法的有效性与优越性。与传统控制方法相比,该控制方法改善了电力系统负荷频率控制性能,提高了区域间频率调节的一致性。

基于OPC UA的分布式数据采集处理系统架构研究

随着物联网与互联网融合的不断深化,感知层与应用层之间的互联互通要求不断提高。针对现有数据采集系统存在的规范性、扩展性和适应性不足的问题,文章提出一套基于对象连接与嵌入过程控制统一架构(object linking and embedding for process control unified architecture,OPC UA)协议的分布式数据采集处理系统架构。对数据采集处理系统各个模块进行功能解耦和架构重组,分别介绍该系统硬件和软件架构;针对发布订阅模式下负载的特异性,提出一种改进的适用于OPC UA分布式订阅的负载均衡算法;最后在某车企实例验证该系统架构。结果表明,基于OPC UA的分布式数据采集处理系统数据采集处理效果良好,证明了该系统架构的可行性及有效性。

基于储能有功功率与OLTC协调的配电网电压控制

高比例光伏系统接入配电网会引起电压越限,传统无功电压调整方案在高阻抗比配网中失效,利用储能系统有功功率实现电压控制的技术方案被广泛采用,但现有技术方案中的储能系统在电压控制过程中可能产生过度充放电问题。针对这些问题,提出了一种基于储能有功功率与有载调压变压器(on-loadtapchanger,OLTC)协调的配电网电压控制策略。在分析光伏接入对配电网电压影响的基础上,利用测量得到的电压数据控制储能有功功率,实现对接入节点电压水平的快速调控;同时在OLTC控制环节加入储能荷电状态参考量,利用节点电压信息与储能荷电状态共同控制OLTC分接头动作,实现对配电网整体电压水平调控的同时避免了储能系统过度充放电。最后在IEEE 13节点系统中进行仿真验证,结果表明所提策略能够解决配电网内电压越限问题,避免储能系统过度充放电,从而延长储能系统的使用寿命,实现对配电网内资源的合理利用。

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.

含废旧矿井抽蓄电站的源-荷-储协同控制调频研究

针对新能源在电网中渗透率不断提高导致系统调频容量和频率响应能力不足的问题,提出一种含废旧矿井抽蓄电站的源-荷-储协同控制调频方法。首先,建立了电解铝工业负荷、废旧矿井抽蓄电站和超级电容频率响应模型。然后,基于模型预测控制理论,以系统状态量和控制量的加权函数最小为目标,设计区域信息互动的分布式模型预测控制器,并提出含废旧矿井抽蓄电站的源-荷-储协同控制调频方法。最后,将分布式模型预测控制器应用在所提源-荷-储协同控制方法中,并在改进的IEEE标准两区域LFC模型中进行仿真。结果表明所提方法可以缩短频率恢复时间,减小最大频差值,改善系统调频动态性能。

基于深度学习的配电网三相不平衡负载自动控制方法

常规的配电网三相不平衡负载控制,更多采用负载监测进行控制,导致控制结果的网损降低量较小。为此,提出了基于深度学习的配电网三相不平衡负载自动控制方法。分析配电网三相拓扑结构的电流补偿能力,并计算三相不平衡度及负载率,根据计算结果判定配电网的三相状态,采用深度学习预测三相状态,并根据期望回报计算负载控制的补偿值,实现自动控制。结果表明,应用该方法后得出的控制结果表现出的网损降低量较高,均值为0.376 kW,控制效果较优,满足配电网的现实运行需求。

基于“先无功–后有功”功率补偿的配电网电压协同控制策略

整县分布式光伏电源的大量开发与利用及分布式电源规模化并网对实现“碳达峰、碳中和”与“乡村振兴”两大国家战略具有重要的现实意义。由于分布式电源发电尖峰和低谷阶段与负荷峰谷时常不同步,进而导致配电系统过/欠电压问题,严重影响了电能质量,甚至威胁系统安全。针对上述问题,本文提出一种基于“先无功–后有功”功率补偿的配电网层级式电压协同控制策略。首先,建立基于“先无功–后有功”功率补偿的配电网层级式电压协同控制框架。其次,结合多微电网节点注入电流与电压的方程与功率–电压灵敏度分析,根据多节点电压越限程度,提出一种基于“先无功–后有功”功率补偿的多智能体协同调节策略,该策略先通过无功补偿器进行无功调节,当电压尚未得到有效治理时,再采用多微电网进行有功调节。再次,为了进一步满足各节点微电网有功功率调节需求,并考虑电网内部源–荷–储运行成本与污染排放,提出一种基于多目标优化的微电网内部分布式源–荷–储优化协调功率控制策略。最后,在MATLAB平台中设计了3种仿真场景以及IEEE测试系统模型对所提控制策略进行了验证。结果表明,所提出的电压协同控制方法在最经济的情况下可以实现各节点电压的综合高效治理,同时,所提出的微电网优化协调功率控制策略能使源–荷–储在绿色经济的供电模式下实时精准地满足电压治理目标下微电网有功调节的需求。仿真实验结果验证了本文所提控制策略的有效性。

曹娥江流域水环境容量与污染负荷分配研究

以曹娥江流域为研究对象,计算水环境容量,将其分配至各控制单元并细化到污染源类型,提出各控制单元污染物的削减量,以期提升曹娥江水环境质量。结果表明:1)2016—2021年,曹娥江流域内的屠家埠、汤曹汇合口和曹娥江大闸闸前3个监测断面的COD、氨氮、TP均达到《地表水环境质量标准》(GB 3838—2002)Ⅲ类要求。2)COD、氨氮、TN、TP入河量分别为16013.20、959.10、3658.53、416.05 t/a,且主要以非点源为主。3)基于满足曹娥江流域水体监测断面和入海断面水质达标的约束条件下,COD、氨氮、TN、TP水环境容量分别为47945.18、2435.81、6863.19、474.98 t/a。流域内COD还有剩余水环境容量,不需削减,氨氮、TN、TP分别需削减6.91、501.52、128.34 t/a。

基于MPC-MRAC的用于子母式AUV投放的子AUV控制算法

针对水下搜救、水下集群作业等场合对负载能力、响应速度和多体协同的特殊要求,提出一种子母式自主水下航行器(AUV)概念。针对子母式AUV中的子AUV平台进行投放过程设计与分析,并对不同运动模式下的子AUV进行受力分析和建模。根据子AUV运动特性设计一套基于模型预测控制-模型参考自适应控制算法(MPC-MRAC)的子AUV控制算法,并进行仿真。结果显示:该算法具有较快的响应速度和控制稳定性,适用于子AUV投放后的自主运动控制;其响应速度比反演滑模控制算法快42.48%,比比例积分微分(PID)控制算法快87.05%;控制精度比反演滑模控制算法高77.54%,比PID控制算法高76.19%。针对该系统设计制作样机并开展湖上试验,结果表明,子AUV控制算法具有良好的可行性和可靠性,可顺利实现子AUV投放后运行的安全可靠。子AUV在出舱后经过短时间姿态变化,成功与母AUV分离并顺利进行定深任务,最终达到近似等距分布的效果。研究成果可为用于子母式AUV投放的子AUV提供控制算法基础。

配电网多端柔性互联协调控制策略

随着社会经济的快速发展和新型电力系统建设的加快推进,柔性互联逐渐成为配电网网架升级和灵活调控能力提升的重要技术手段。针对多端柔性互联系统的功率控制需求,提出了一种面向工程应用的功率协调控制策略,包括在部分馈线重载时合理分配功率的重载限制控制,以及在所有馈线重载时优化潮流分布的功率均衡控制。基于所提策略开发了柔性互联协调控制装置并应用于实际工程。基于电网真实数据的案例分析和工程实测数据验证了所提策略能够应对不同负荷/电源特性的配电网柔性互联场景,有效解决配电网中馈线重载和光伏倒送的问题,提高配电网的供电效率和安全性。