Power Allocation for SE Maximization in Uplink Massive MIMO System Under Minimum Rate Constraint

In this paper,we optimize the spectrum efficiency(SE)of uplink massive multiple-input multiple-output(MIMO)system with imperfect channel state information(CSI)over Rayleigh fading channel.The SE optimization problem is formulated under the constraints of maximum power and minimum rate of each user.Then,we develop a near-optimal power allocation(PA)scheme by using the successive convex approximation(SCA)method,Lagrange multiplier method,and block coordinate descent(BCD)method,and it can obtain almost the same SE as the benchmark scheme with lower complexity.Since this scheme needs three-layer iteration,a suboptimal PA scheme is developed to further reduce the complexity,where the characteristic of massive MIMO(i.e.,numerous receive antennas)is utilized for convex reformulation,and the rate constraint is converted to linear constraints.This suboptimal scheme only needs single-layer iteration,thus has lower complexity than the near-optimal scheme.Finally,we joint design the pilot power and data power to further improve the performance,and propose an two-stage algorithm to obtain joint PA.Simulation results verify the effectiveness of the proposed schemes,and superior SE performance is achieved.

Synthesis Scheme for Low Power Designs Under Timing Constraints

To minimize the power consumption with resources operating at multiple voltages a time-constrained algorithm is presented.The input to the scheme is an unscheduled data flow graph (DFG),and timing or resource constraints.Partitioning is considered with scheduling in the proposed algorithm as multiple voltage design can lead to an increase in interconnection complexity at layout level.That is,in the proposed algorithm power consumption is first reduced by the scheduling step,and then the partitioning step takes over to decrease the interconnection complexity.The time-constrained algorithm has time complexity of O(n 2),where n is the number of nodes in the DFG.Experiments with a number of DSP benchmarks show that the proposed algorithm achieves the power reduction under timing constraints by an average of 46 5%.

Joint Power Control and Spectrum Allocation for Cognitive Radio with QoS Constraint

Spectrum sharing with quality of service (QoS) requirement and power constraint on cognitive users is studied. The objective is to maximize the system throughput. This problem is modeled as a mixed integer nonlinear programming problem and then transformed to a continuous nonlinear programming problem through eliminating integer variables. We propose the joint power control and spectrum allocation algorithm based on particle swarm optimization to obtain the global optimal solution. Simulation results show that the proposed method can achieve higher system throughput and spectrum utilization under the constraints of transmit power and QoS requirement.

OPTIMAL POWER ALLOCATION FOR COGNITIVE RADIO UNDER INTERFERENCE OUTAGE CONSTRAINT

Cognitive radio is able to share the spectrum with primary licensed user,which greatly improves the spectrum efficiency.We study the optimal power allocation for cognitive radio to maximize its ergodic capacity under interference outage constraint.An optimal power allocation scheme for the secondary user with complete channel state information is proposed and its approxi-mation is presented in closed form in Rayleigh fading channels.When the complete channel state in-formation is not available,a more practical transmitter-side joint access ratio and transmit power constraint is proposed.The new constraint guarantees the same impact on interference outage prob-ability at primary user receiver.Both the optimal power allocation and transmit rate under the new constraint are presented in closed form.Simulation results evaluate the performance of proposed power allocation schemes and verify our analysis.

Grey Wolf Optimizer to Real Power Dispatch with Non-Linear Constraints

A new and efficient Grey Wolf Optimization(GWO)algorithm is implemented to solve real power economic dispatch(RPED)problems in this paper.The nonlinear RPED problem is one the most important and fundamental optimization problem which reduces the total cost in generating real power without violating the constraints.Conventional methods can solve the ELD problem with good solution quality with assumptions assigned to fuel cost curves without which these methods lead to suboptimal or infeasible solutions.The behavior of grey wolves which is mimicked in the GWO algorithm are leadership hierarchy and hunting mechanism.The leadership hierarchy is simulated using four types of grey wolves.In addition,searching,encircling and attacking of prey are the social behaviors implemented in the hunting mechanism.The GWO algorithm has been applied to solve convex RPED problems considering the all possible constraints.The results obtained from GWO algorithm are compared with other state-ofthe-art algorithms available in the recent literatures.It is found that the GWO algorithm is able to provide better solution quality in terms of cost,convergence and robustness for the considered ELD problems.

Taking into Account of Functional Constraints in Optimization of Modes of Power Systems by Genetic Algorithms

The development of the capabilities of computational tools has created up new possibilities for the effective use of a number of classical mathematical methods and algorithms for solving many important problems in the power engineering. In particular, a set of algorithms are developed to optimize the modes of electric power systems based on genetic algorithms. At the same time, the issues of taking into account functional constraints in solving such problems by genetic algorithms need to be improved. In accordance with it in this article the problems of taking into account of different constraints in optimization of modes of power systems using genetic algorithms are considered. The algorithm of optimization by genetic algorithm taking into account of functional constraints in forms of equality and inequality by penalty functions is proposed. The results of research of proposed algorithm’s efficiency in example of optimization of mode of power system with 8 buses, 4 thermal power plants and 3 transmission lines with controlled power flow are presented.

Virtual Synchronous Generator Adaptive Control of Energy Storage Power Station Based on Physical Constraints

The virtual synchronous generator(VSG)can simulate synchronous machine’s operation mechanism in the control link of an energy storage converter,so that an electrochemical energy storage power station has the ability to actively support the power grid,from passive regulation to active support.Since energy storage is an important physical basis for realizing the inertia and damping characteristics in VSG control,energy storage constraints of the physical characteristics on the system control parameters are analyzed to provide a basis for the system parameter tuning.In a classic VSG control,its virtual inertia and damping coefficient remain unchanged.When the grid load changes greatly,the constant control strategy most likely result in the grid frequency deviation beyond the stable operation standard limitations.To solve this problem,a comprehensive control strategy considering electrified wire netting demand and energy storage unit state of charge(SOC)is proposed,and an adaptive optimization method of VSG parameters under different SOC is given.The energy storage battery can maintain a safe working state at any time and be smoothly disconnected,which can effectively improve the output frequency performance of energy storage system.Simulation results further demonstrated the effectiveness of the VSG control theoretical analysis.

Intelligent Power Grid Load Transferring Based on Safe Action-Correction Reinforcement Learning

When a line failure occurs in a power grid, a load transfer is implemented to reconfigure the network by changingthe states of tie-switches and load demands. Computation speed is one of the major performance indicators inpower grid load transfer, as a fast load transfer model can greatly reduce the economic loss of post-fault powergrids. In this study, a reinforcement learning method is developed based on a deep deterministic policy gradient.The tedious training process of the reinforcement learning model can be conducted offline, so the model showssatisfactory performance in real-time operation, indicating that it is suitable for fast load transfer. Consideringthat the reinforcement learning model performs poorly in satisfying safety constraints, a safe action-correctionframework is proposed to modify the learning model. In the framework, the action of load shedding is correctedaccording to sensitivity analysis results under a small discrete increment so as to match the constraints of line flowlimits. The results of case studies indicate that the proposed method is practical for fast and safe power grid loadtransfer.

有色金属-新能源产业链企业垂直一体化对其竞争力的影响研究

随着市场竞争加剧,产业链合纵连横已成为企业提升竞争力的重要手段。本文以2015~2023年中国有色金属-新能源产业链上市企业为研究样本,实证检验垂直一体化对企业竞争力的影响以及融资约束的门槛效应。研究发现,有色金属和新能源企业一体化程度均呈现大幅上升的趋势,并且一体化企业的竞争力水平明显高于同行业未一体化企业。垂直一体化对企业竞争力有显著的正向影响,有色金属企业效率提高和新能源企业市场规模扩大是其竞争力提升的主要原因。融资约束在一体化与企业竞争力的正向关系中存在单一门槛效应,当融资约束指数大于门槛值时,一体化促进企业竞争力;当融资约束指数小于门槛值时,有色金属企业后向一体化抑制竞争力,新能源企业前向一体化对竞争力的正向影响不再显著。异质性分析发现,非国有、大规模企业一体化对竞争力的促进效果更加显著。研究结论为提升我国有色金属与新能源产业及其他战略性新兴产业的国际竞争力提供重要理论和实践依据。

基于模型预测控制的水浴锅温度控制策略

针对水浴锅加热具有的复杂时间特性、控制物料温度难度大、部分物料达到稳定温度需要较长时间的问题,提出基于物理模型的模型预测控制策略,首先建立系统模型,考虑系统的加热功率,通过加权操作调整输出功率,动态控制系统;然后通过监测物料温度与设定温度的偏差,调整加热管功率的增量,迅速改变加热功率,从而缩短达到设定温度所需的时间。仿真结果显示:该策略能够有效跟踪设定温度,提升水浴锅加热效率,降低能源消耗,增强加热过程的稳定性和可控性。

基于Power图求解容量限制P-中值问题

针对稠密需求下连续域上的容量P-中值问题,提出基于质心的容量限制Power图(CCCPD)理论,对连续P-中值问题进行近似建模,并加快计算过程。扩展Balzer试位法构造Power图,施加质心限制满足P-中值要求,施加容量限制满足需求密度下的容量要求。实验结果表明所提算法可快速得到近似可行解,同Alper Murata方法相比,计算效率高;同质心容量限制Voronoi图(CCCVT)相比,具有容量限制精确度高等优点,并能适应各种复杂需求密度函数。

容量限制Power图快速计算算法（英文）

与Voronoi图不同,Power图有着精确限容的特性,可应用于众多领域.在普通Power图上,通过给每个站点增加权重来添加容量限制约束,即得到容量限制Power图.现有容量限制Power图的生成算法时间复杂度高,速度较慢.本文首先对Power图权值对于容量的单调性进行理论证明,然后提出了一种Power图权值的快速计算方法,相比于现有的试位法,能大幅提升容量限制Power图的生成性能.实验结果表明,文中算法具有速度快,精确度高,便于计算,密度适应性强等优点.

考虑风电爬坡限幅及频率安全约束的高比例风电电力系统多层级优化调度

近年来,具有强不确定性、弱支撑能力的风电机组大规模并网,对新型电力系统的频率安全及稳定运行造成了严重冲击。为保障高比例风电电力系统安全、优质、经济运行,提出了一种考虑风电爬坡限幅与频率安全约束的电力系统多层级优化调度策略。首先,考虑风电预测误差与风电场并网要求,对相邻调度时段风电爬坡进行几何描述与特征分析。其次,提出日前-日内-实时多阶段风电场站-电力系统级联合调度架构,进一步建立考虑风电爬坡限幅约束的场站级优化调度模型与计及频率安全约束的系统级优化调度模型。最后,基于改进IEEE 39节点系统开展算例分析,验证了所提策略的有效性。

高比例可再生能源电力系统的动态频率安全约束机组组合

随着高比例可再生能源的并网,电力系统的惯量响应和一次调频能力有所下降,因此在机组组合规划中需纳入动态频率安全约束条件。首先,提出了一种简单实用的动态频率安全约束建模方法,通过将频率响应分为两段,对备用容量不完全充足时的频率响应进行深入分析,充分考虑了惯量、备用容量以及单位调节功率对系统频率最小值的影响。其次,进一步将频率响应分为两种情况,以构建模频率最小值的安全约束模型。将所构建的动态频率安全约束应用在机组组合中,为使优化模型便于求解,对其中的非线性部分进行线性化变换。最后,通过算例验证了所提模型的有效性,结果表明该模型能够保证在发生最严重意外事件的任意时段内,均能确保故障后的频率响应符合频率安全要求。通过与无频率安全约束的机组组合对比,展示了频率安全约束的作用机理。与两种常用的频率安全约束对比,所提频率安全约束能在保证频率安全的情况下,找到相对更优的调频资源配置方案。

考虑FACTS设备和可再生能源的多目标优化调度

随着高比例可再生能源接入电网,增加了电网调度和运行的难度。针对这一问题,本文提出一种基于自适应罚函数的多目标竞争粒子群算法,用于解决包含随机性可再生能源和多种柔性交流输电系统设备的多目标优化问题。首先,构建完整的交流潮流模型与多样化连续/离散控制变量约束,以降低燃料成本、电压偏差、输电损耗和排放量为优化目标;然后,探讨不同柔性交流输电系统设备配置下这些目标的平衡;最后,通过修改的IEEE 30节点系统分析结果得出,综合考虑可再生能源和柔性交流输电系统设备的优化配置,可以显著降低排放和燃料成本,提高电网的可靠性和输电效率。与现有方法相比,所提出的算法在多目标优化问题上表现出更高的解质量和稳定性。

考虑多约束的电源接入端最大承载力分段算法

10千伏配电网环境中,电力载荷分布不均是电源接入端分段承载力超限的主要原因。为避免上述情况的发生,研究考虑多约束的电源接入端最大承载力分段算法。分析10千伏配电网结构的连接关系,计算可靠性规划向量;考虑多约束情况,进行配电网电源接入端规划。自动化地分配电量信号,根据最大承载力功率谱,确定电量分段参数的实际取值范围,完成电源接入端的最大承载力分段。实验结果表明,该方法可使电力载荷分布密度保持在0.75~1.60 W/m^(2)之间,较好地解决了电力载荷分布不均的问题,在10千伏配电网环境中,可以避免电源接入端出现分段承载力超限的情况,符合实际应用需求。

基于Column-and-constraint的鲁棒配电网重构实用算法

网络重构是配电自动化的重要功能之一,随着智能电网的迅速发展,也越发体现其重要性。但是负荷的波动性和随机性给传统的静态配电网重构带来了严峻的挑战,改进的两阶段鲁棒优化模型可以很好地解决配电网重构中存在的这一问题。第一阶段配置辐射状配电网络,第二阶段寻求满足需求的最优潮流。该策略采用Column-and-constraint实用算法,其主问题和子问题采用整数二阶锥规划算法(mixed-integer second-order cone programming algorithm)来解决。33节点、69节点等典型算例实验结果表明,即使在负荷不确定的情况下,鲁棒配电重构表现出比传统配电网络功率损耗低,可靠性高的特点。实验验证了方法的有效性和良好的计算性能。

Neural-Network-Based Terminal Sliding Mode Control for Frequency Stabilization of Renewable Power Systems

This paper addresses a terminal sliding mode control(T-SMC) method for load frequency control(LFC) in renewable power systems with generation rate constraints(GRC).A two-area interconnected power system with wind turbines is taken into account for simulation studies. The terminal sliding mode controllers are assigned in each area to achieve the LFC goal. The increasing complexity of the nonlinear power system aggravates the effects of system uncertainties. Radial basis function neural networks(RBF NNs) are designed to approximate the entire uncertainties. The terminal sliding mode controllers and the RBF NNs work in parallel to solve the LFC problem for the renewable power system. Some simulation results illustrate the feasibility and validity of the presented scheme.