A Numerical Convex Lens for the State-Discretized Modeling and Simulation of Megawatt Power Electronics Systems as Generalized Hybrid Systems

Modeling and simulation have emerged as an indispensable approach to create numerical experiment platforms and study engineering systems.However,the increasingly complicated systems that engineers face today dramatically challenge state-of-the-art modeling and simulation approaches.Such complicated systems,which are composed of not only continuous states but also discrete events,and which contain complex dynamics across multiple timescales,are defined as generalized hybrid systems(GHSs)in this paper.As a representative GHS,megawatt power electronics(MPE)systems have been largely integrated into the modern power grid,but MPE simulation remains a bottleneck due to its unacceptable time cost and poor convergence.To address this challenge,this paper proposes the numerical convex lens approach to achieve state-discretized modeling and simulation of GHSs.This approach transforms conventional time-discretized passive simulations designed for pure-continuous systems into state-discretized selective simulations designed for GHSs.When this approach was applied to a largescale MPE-based renewable energy system,a 1000-fold increase in simulation speed was achieved,in comparison with existing software.Furthermore,the proposed approach uniquely enables the switching transient simulation of a largescale megawatt system with high accuracy,compared with experimental results,and with no convergence concerns.The numerical convex lens approach leads to the highly efficient simulation of intricate GHSs across multiple timescales,and thus significantly extends engineers’capability to study systems with numerical experiments.

Applying Convex Optimal Power Flow to Combined Economic and Emission Dispatch

This paper addresses the problem of reducing CO2 emissions by applying convex optimal power flow model to the combined economic and emission dispatch problem. The large amount of CO2 emissions in the power industry is a major source of global warming effect. An efficient and economic approach to reduce CO2 emissions is to formulate the emission reduction problem as emission dispatch problem and combined with power system economic dispatch (ED). Because the traditional optimal power flow (OPF) model used by the economic dispatch is nonlinear and nonconvex, current nonlinear solvers are not able to find the global optimal solutions. In this paper, we use the convex optimal power flow model to formulate the combined economic and emission dispatch problem. The advantage of using convex power flow model is that global optimal solutions can be obtained by using mature industrial strength nonlinear solvers such as MOSEK. Numerical results of various IEEE power network test cases confirm the feasibility and advantage of convex combined economic and emission dispatch (CCEED).

Computation Rate Maximization in Multi-User Cooperation-Assisted Wireless-Powered Mobile Edge Computing with OFDMA

In the era of Internet of Things(Io T),mobile edge computing(MEC)and wireless power transfer(WPT)provide a prominent solution for computation-intensive applications to enhance computation capability and achieve sustainable energy supply.A wireless-powered mobile edge computing(WPMEC)system consisting of a hybrid access point(HAP)combined with MEC servers and many users is considered in this paper.In particular,a novel multiuser cooperation scheme based on orthogonal frequency division multiple access(OFDMA)is provided to improve the computation performance,where users can split the computation tasks into various parts for local computing,offloading to corresponding helper,and HAP for remote execution respectively with the aid of helper.Specifically,we aim at maximizing the weighted sum computation rate(WSCR)by optimizing time assignment,computation-task allocation,and transmission power at the same time while keeping energy neutrality in mind.We transform the original non-convex optimization problem to a convex optimization problem and then obtain a semi-closed form expression of the optimal solution by considering the convex optimization techniques.Simulation results demonstrate that the proposed multi-user cooperationassisted WPMEC scheme greatly improves the WSCR of all users than the existing schemes.In addition,OFDMA protocol increases the fairness and decreases delay among the users when compared to TDMA protocol.

计及UPFC最优配置的电力系统鲁棒调度协同优化策略

统一潮流控制器(UPFC)应用于潮流调控时,计及UPFC调控参数的交流潮流计算是非凸、非线性问题,且多台装置间的非线性交叉耦合特性也会直接影响优化配置方案。为此,基于UPFC的潮流调控特性,构建了计及UPFC的松弛型交流潮流二阶锥规划模型;计及风电的不确定性,协同考虑UPFC的规划和电力系统的调度问题,建立了计及UPFC最优配置的电力系统鲁棒协同优化模型,并采用列和约束生成算法进行求解。以IEEE RTS-24节点系统为算例进行仿真分析,结果表明所提协同优化策略有效提升了UPFC配置方案的适应性,提高了系统运行经济性和风电消纳能力,增强了系统运行调控的灵活性。

基于凸包算法的极端运行方式提取方法

在电力系统规划与运行的安全稳定计算中,一般选择对安全稳定最不利的运行方式进行校验,但随着新能源渗透率的增加,电网越发复杂多变,人工选择场景主观性较强,针对此问题,提出了一种基于快速凸包算法的极端运行方式提取,从而满足场景选取进行安全校核需求。从电源规划、发输电协调规划两个角度,考虑发电充裕性、火电爬坡需求、网络传输安全3个方面,分析影响规划的极端运行方式;基于凸包算法分别从上述3个方面构建坐标系进行极端运行方式提取;采用中国西北地区电网为算例进行极端运行方式提取,并与基于K-means算法的极端场景提取做对比,结果表明:所提方法能为场景选取提供有效数据支撑,满足电网规划要求。

无人机辅助无线供能通信系统优化算法研究

文章研究了无人机辅助无线供能数据收集系统的资源分配优化方案,其中无人机发送射频信号能量,装配能量收集电路的地面节点从无人机发送的射频信号中收集能量,并使用收集到的能量发送一定量的数据至无人机。针对此系统,通过联合优化能量传输区间、信息传输区间以及无人机在这两个区间的飞行速度和时间,在满足无人机数据收集约束条件下,最小化无人机飞行时间。文章建立的优化问题是难以求解的非凸优化问题,为求解此问题,将其分解为与能量传输区间和信息传输区间分别对应的且易于解决的两个子问题,并结合迭代优化方法,提出了一种高效的系统优化算法。仿真结果表明,与已有的基准方案相比,文章提出的无人机辅助通信系统优化方案可以显著地减少无人机飞行时间。

无人机辅助传感器网络中吞吐量与节点能量优化方法

无人机凭借其高机动性可以对传感节点进行无线数据采集和无线供能,然而无线数据采集与无线供能是相互耦合的,如何对两者的资源分配和无人机飞行轨迹进行优化是无人机辅助无线供能传感器网络性能的关键。为此,提出了一种联合优化方案,通过交替优化节点接入、无人机三维飞行轨迹和能量数据传输时间分配,最大化最小节点效用。针对无线供能传感器网络模型中最大化最小节点效用问题提出了一个联合优化方案。由于该问题为非凸问题,提出了一个最大化最小节点效用算法(Maximized the Minimum Node Utility Algorithm,MMNUA),采用块坐标下降法将原始问题转化成4个子问题,并利用连续凸近似技术在每次迭代时依次优化4个子问题,使最大化最小节点效用逐渐收敛。与基础无人机方案相比,MMNUA使系统性能分别提升了14.1%、567.5%和177.0%,有效提高了无人机的工作效率,同时所提算法具有良好的收敛性和较小的复杂度。

基于凸包理论的含风电电力系统负荷恢复方案优化

大停电事故后,合理的负荷恢复策略对加快电力系统恢复、减少停电损失具有重要意义。针对标准交流潮流方程的非凸性导致负荷恢复模型求解效率低的问题,提出了基于凸包理论的含风电电力系统负荷恢复方案优化方法。首先,简要介绍了凸包理论以及非线性函数的凸包构建方法。其次,针对风电出力的不确定性,采用条件风险价值理论处理风电波动带来的风险,进而建立了直角坐标系下基于凸包理论的电力系统负荷恢复二阶锥优化模型,其中,交流潮流方程中的非线性项采用凸包表达。针对无法精确确定凸包参数的问题,提出了基于迭代更新的参数优化方法,减少因凸包引入的松弛误差。所提出的模型为混合整数二阶锥规划模型,可采用商业求解器高效求解。最后,采用IEEE 39节点系统和改进的广东电力系统验证了所提出模型的有效性。

基于点估计仿射可调鲁棒优化的含储能电力系统实时调度

为应对大规模风电的接入,建立一种机组与储能联合参与自动发电控制的电力系统实时调度模型,并提出一种点估计仿射可调鲁棒优化来处理风电不确定性。不同于传统仿射可调鲁棒调度优化基准运行成本,点估计仿射可调鲁棒调度优化期望运行成本以提高系统经济性。提出利用确定性的点估计法来实现对期望运行成本的快速、精确评估。所提模型为一混合整数双线性约束问题,采用一种“预估-矫正”的凸化方法来求解该难题,预估阶段对储能的状态变量进行松弛,而矫正阶段直接对其状态变量进行矫正。最后,引入一种凸函数差优化进一步凸化2个阶段的双线性约束问题,以提高含储能实时调度的求解质量。在IEEE39、118以及300节点3个系统的仿真验证了所提模型及方法的有效性。

基于ADMM的多区域直流系统完全分布式最优潮流算法

针对传统集中式最优潮流算法扩展性差的问题,提出一种适用于求解大规模分布式电源接入的直流系统最优潮流的完全分布式算法。首先,利用凸松弛方法将非凸的直流系统最优潮流模型转化为二阶锥规划模型;然后,基于ADMM(交替方向乘子法)建立求解该模型的分布式算法,再去除算法中的一致性变量,从而将其改进成为直流系统最优潮流的完全分布式算法;最后,在改进的IEEE13节点和118节点系统算例上进行的仿真验证表明,该算法无需中央协调单元协同各区域的边界一致性信息,通过各区域的并行优化、相邻区域间少量边界节点信息的交换,即可得出辐射式以及环网拓扑的直流系统最优潮流问题的全局最优解。

一种地外天体着陆凸轨迹曲率滑模制导方法

针对复杂地外天体表面安全着陆问题,提出了凸轨迹曲率滑模制导方法。该方法考虑着陆轨迹形状对着陆安全性的影响,将曲率约束引入到滑模面设计中,实现着陆器系统状态位于滑模面上时,轨迹形状为凸。首先,针对曲率约束进行等效转化和松弛,提出了曲率松弛项,并利用曲率松弛项设计了曲率滑模面,满足三维凸轨迹条件。其次,推导了曲率滑模反馈制导律,滑模状态可在有限时间内收敛到零,在该制导律作用下,着陆器将以凸轨迹状态完成精准软着陆。最后,通过蒙特卡洛仿真验证了所提算法的有效性和鲁棒性。

一种防空服务质量模型下的集中式多输入多输出雷达三维机动跟踪功率分配方法

针对防空作战中现有多功能雷达功率资源利用率低的问题,提出一种基于服务质量(Quanlity of Service,QoS)模型的三维机动跟踪功率分配方法以差异化标准提升多目标跟踪性能。将目标三维机动模型建立为自适应当前统计模型,通过将加速度协方差与估计误差协方差矩阵相关联以实现自适应调整。在此基础上,对三维跟踪下的贝叶斯克拉美罗下界进行推导,并将其作为跟踪误差衡量指标。通过构建关于目标威胁度与期望跟踪精度的函数关系,建立防空QoS模型下的闭环功率优化分配机制。证明所构建功率优化分配模型是凸优化问题,并进一步转化为半正定规划问题进行求解。仿真结果表明,相对于传统功率分配方法,所提方法能显著提高全局跟踪效能。

Deterministic and Robust Volt-var Control Methods of Power System Based on Convex Deep Learning

Volt-var control(VVC)is essentially a non-convex optimization problem due to the non-convexity of power flow(PF)constraints,resulting in the difficulty in obtaining the optimum without convexity conversion.The existing second-order cone method for the convexity conversion often leads to a sharp increase in PF constraints and optimization variables,which in turn increases the optimization difficulty or even leads to optimization failure.This paper first proposes a deterministic VVC method based on convex deep learning power flow(DLPF).This method uses the input convex neural network(ICNN)to establish a single convex mapping between state parameters and node voltage to complete the convexity conversion while the optimization variables only correspond to reactive power equipment,which can ensure the global optimum with extremely fast computation speed.To cope with the impact brought by the uncertainty of distributed energy and omit the additional worst scenario search of traditional robust VVC,this paper proposes robust VVC method based on convex deep learning interval power flow(DLIPF),which continues to adopt ICNN to establish another convex mapping between state parameters and node voltage interval.Combining DLIPF with DLPF,this method decreases the modeling and optimization difficulty of robust VVC significantly.Test results on 30-bus,118-bus,and 200-bus systems prove the correctness and rapidity of the proposed methods.

几个n元平均的积的Schur-p阶幂凸性

设α>0,A(a)、G(a)、H(a)分别为a=(a1,a2,…,an)∈R+n的算术平均、几何平均和调和平均.本文研究了Aα(a)G(a)和Aα(a)H(a)的Schur-p阶幂凸性.

Convexification of Hybrid AC-DC Optimal Power Flow with Line-Commutated Converters

Line-commutated converter (LCC)-based high-voltage DC (HVDC) systems have been integrated with bulk AC power grids for interregional transmission of renewable power. The nonlinear LCC model brings additional nonconvexity to optimal power flow (OPF) of hybrid AC-DC power grids. A convexification method for the LCC station model could address such nonconvexity but has rarely been discussed. We devise an equivalent reformulation for classical LCC station models that facilitates second-order cone convex relaxation for the OPF of LCC-based AC-DC power grids. We also propose sufficient conditions for exactness of convex relaxation with its proof. Equivalence of the proposed LCC station models and properties, exactness, and effectiveness of convex relaxation are verified using four numerical simulations. Simulation results demonstrate a globally optimal solution of the original OPF can be efficiently obtained from relaxed model.

基于序列凸优化的带动力飞行器再入轨迹优化

对序列凸优化方法在带动力飞行器再入轨迹优化中的应用进行了研究。首先,针对原始优化问题中控制量高度耦合且非线性的问题,通过引入新的控制量并建立与原始控制量的映射关系,得到关于控制量为线性的状态方程。其次,对原始优化问题中的非线性动力学方程、性能指标、过程约束与控制量约束进行松弛和线性化处理,通过附加状态变量的信赖域约束,并在性能指标中加入速度方位角的积分项以保证松弛和线性化的合理性。进一步,通过离散技术,将原始问题转化为一个凸优化问题。最后,通过数值仿真进行了算法验证。结果表明,本文算法对于带动力再入轨迹优化问题具有较高的求解精度,序列凸优化求解结果满足原始非线性约束,在一定精度范围内优化解可以作为原始问题的可行解。

计及压缩机运行域的动态最优电-气能流凸化算法

压缩机是电-气互联系统的关键元件,其运行工作点是否位于运行域内关系到系统的安全稳定运行,同时其模型非凸性大幅增加了最优电-气能流的求解难度。为此,该文在压缩机准确建模的基础上,针对电力和天然气系统迥异的传输特性,提出一种计及压缩机运行域的动态最优电-气能流凸化算法。首先,根据压缩机运行参数与状态变量间的映射关系,构建压缩机三维运行域模型,并利用超平面实现运行域的凸包络松弛。其次,考虑差异化传输特性建立动态最优电-气能流模型,实现模型的混合整数二阶锥凸重构,并提出一种基于动态线性化与紧缩凸包络的凸化算法。最后,算例验证所提模型的有效性,表明所提算法在计算精度和效率上均优于连续锥规划算法。

有源RIS辅助SWIPT网络公平性资源分配算法

针对有源可重构智能表面(reconfigurable intelligent surface,RIS)辅助的同步无线信息与能量传输(simultaneous wireless information and power transfer,SWIPT)系统,提出了一种考虑公平性的能量资源采集分配算法,以解决因乘性衰落导致的公平性能量采集性能较差的问题。在有源RIS辅助的SWIPT系统采用功率切割架构实现信息与能量的同步传输,构建了以所有用户中最小的采集能量最大化为目标函数,用户信干噪比、有源RIS和基站发射功率、功率划分因子等满足需求为约束条件的联合资源分配问题。利用交替优化、半正定松弛、连续凸近似、罚函数等技术将不能直接解决的非凸问题转换成标准凸问题,提出了一种交替迭代的公平性采集能量算法。数值仿真结果表明,所提优化算法能够显著提高用户中能量资源分配最少的用户处采集到的能量值,保障通信网络中能量资源分配的公平性。

凸域内的平均随机弦长及其极值

为研究在不同随机意义下平面凸域内的平均随机弦长问题,以圆域、正三角形域、矩形域、正方形域为例,用定义法得到这些凸域内的各平均随机弦长。用弦幂积分及其不等式,讨论了任意凸域内5种平均随机弦长的极值问题,并建立了相应的不等式。在此基础上,提出了2个猜想。

STAR-RIS辅助无线供能通信网络的优化

提出一种基于可同时透射和反射的可重构智能表面(STAR-RIS)辅助的无线供能通信网络,其中两个传感器节点组(SNG)利用供电站(PS)的无线能量信号采集能量,并利用所获取的能量将其信息发送至接入点(AP). STAR-RIS部署在两个SNG的中间,为无线能量和信息传输提供帮助.通过联合优化STAR-RIS的相移参数和通信资源分配以实现系统吞吐量最大化.由于优化问题的非凸性,采用块坐标下降法对问题进行求解.通过引入辅助变量并利用连续凸逼近和罚函数等方法将原问题转化为凸问题进行求解.实验结果表明,提出的通信方案可以为系统提供更高的性能增益.