Novel Affine Power Flow Method for Improving Accuracy of Interval Power Flow Data in Cyber Physical Systems of Active Distribution Networks

A large number of load power and power output of distributed generation in an active distribution network(ADN)are uncertain,which causes the classical affine power flow method to encounter problems of interval expansion and low efficiency when applied to an AND.This then leads to errors of interval power flow data sources in the cyber physical system(CPS)of an ADN.In order to improve the accuracy of interval power flow data in the CPS of an ADN,an affine power flow method of an ADN for restraining interval expansion is proposed.Aiming at the expansion of interval results caused by the approximation error of non-affine operations in an affine power flow method,the approximation method of the new noise source coefficient is improved,and it is proved that the improved method is superior to the classical method in restraining interval expansion.To overcome the decrease of computational efficiency caused by new noise sources,a novel merging method of new noise sources in an iterative process is designed.Simulation tests are conducted on an IEEE 33-bus,PG&E 69-bus and an actual 1180-bus system,which proves the validity of the proposed affine power flow method and its advantages in terms of computational efficiency and restraining interval expansion.

Constraint Learning-based Optimal Power Dispatch for Active Distribution Networks with Extremely Imbalanced Data

Transition towards carbon-neutral power systems has necessitated optimization of power dispatch in active distribution networks(ADNs)to facilitate integration of distributed renewable generation.Due to unavailability of network topology and line impedance in many distribution networks,physical model-based methods may not be applicable to their operations.To tackle this challenge,some studies have proposed constraint learning,which replicates physical models by training a neural network to evaluate feasibility of a decision(i.e.,whether a decision satisfies all critical constraints or not).To ensure accuracy of this trained neural network,training set should contain sufficient feasible and infeasible samples.However,since ADNs are mostly operated in a normal status,only very few historical samples are infeasible.Thus,the historical dataset is highly imbalanced,which poses a significant obstacle to neural network training.To address this issue,we propose an enhanced constraint learning method.First,it leverages constraint learning to train a neural network as surrogate of ADN's model.Then,it introduces Synthetic Minority Oversampling Technique to generate infeasible samples to mitigate imbalance of historical dataset.By incorporating historical and synthetic samples into the training set,we can significantly improve accuracy of neural network.Furthermore,we establish a trust region to constrain and thereafter enhance reliability of the solution.Simulations confirm the benefits of the proposed method in achieving desirable optimality and feasibility while maintaining low computational complexity.

The Online Assessment of Electric Distribution Network Load Capability

To improve the security and reliability of a distribution network, several issues, such as influences of operation con-strains, real-time load margin calculation, and online security level evaluation, are with great significance. In this pa-per, a mathematical model for load capability online assessment of a distribution network is established, and a repeti-tive power flow calculation algorithm is proposed to solve the problem as well. With assessment on three levels: the entire distribution network, a sub-area of the network and a load bus, the security level of current operation mode and load transfer capability during outage are thus obtained. The results can provide guidelines for prevention control, as well as restoration control. Simulation results show that the method is simple, fast and can be applied to distribution networks belonged to any voltage level while taking into account all of the operation constraints.

主动配电网潮流的全纯嵌入计算方法

配电网潮流计算是进行配电网规划和运行的重要基础。目前,配电网潮流求解方法主要有牛拉(Newton Raphson,NR)法和前推回代(forward backward substitution,FBS)法两大类,但NR法和FBS法在潮流收敛性和计算效率等方面均存在一定的不足。全纯嵌入法(holomorphic embedding method,HEM)是一种求解非线性潮流方程的新方法,它可以有效避免NR法和FBS法在配电网潮流求解过程中出现的上述问题,但现有HEM未充分考虑配电网中各类型分布式电源(distributed generation,DG)和智能软开关(soft open point,SOP)控制方式的多样性,且未构建计及负荷电压特性和频率特性的潮流模型,难以适用于不同运行模式下配电网潮流计算。针对上述问题,提出一种可求解含多类型DG和SOP且可运行在并网/孤岛模式的主动配电网统一全纯嵌入潮流计算方法。首先,根据负荷静态电压和频率特性,构建计及配电网频率和节点电压影响的负荷节点全纯嵌入潮流模型;其次,根据DG不同的控制方式以及配电网不同的运行模式,将DG所连节点等效为不同节点类型,构建各类DG的全纯嵌入潮流模型;再次,考虑SOP不同控制方式,构建SOP全纯嵌入潮流模型;从次,依据全纯嵌入潮流模型等式左右两侧同次幂级数系数相等原则,推导配电网各全纯函数幂级数系数的求解递推关系式,进而计算各全纯函数的各项幂级数系数,借助解析延拓理论求取电压和频率逼近值,实现配电网潮流的准确求解;最后,通过修改的IEEE-33节点测试系统和IEEE-123节点测试系统验证所提方法准确性和有效性。

考虑光伏随机性的交直流混合配电网鲁棒机会约束安全域模型

为实现高比例光伏接入下交直流混合配电网的安全评估,构建了交直流混合配电网的鲁棒机会约束安全域模型。首先,提出一种基于区间划分的自适应带宽核密度估计方法,以获取光伏预测误差概率分布,构建光伏出力机会约束不确定集合;其次,采用功率与节点电压幅值比值为状态变量,在已有交流线性化潮流模型的基础上推导一种交直流混合配电网线性化潮流模型;然后,提出交直流混合配电网鲁棒机会约束安全域的概念和模型,结合列与约束生成算法和径向迭代搜索算法生成可观测的鲁棒机会约束安全域空间。最后,算例分析验证了所提模型能够在鲁棒机会约束安全域上准确、直观的反映光伏出力的随机性,进而为交直流混合配电网的准确安全评估提供支撑。

考虑风光出力不确定性及相关性的配电网规划方法

新能源在电力系统中的渗透率日益提高,大大增加了电网中的不确定因素,对配电网的规划运行和控制提出了更高的要求。配电网规划是电力系统安全稳定运行的重要基石,传统配电网规划中各参数都已确定,缺乏对不确定因素的适应性。因此,提出一种基于概率潮流的配电网规划方法,首先对配电网中的不确定因素定量建模,建立源荷出力模型,其次将风速、太阳辐射与用电负荷之间的相关性用秩相关系数矩阵表征,建立计及相关性的半不变量概率潮流计算方法,最后构建以规划成本最小为目标函数,以潮流平衡、馈线容量、节点电压、网架辐射型结构为约束条件的配电网规划模型,并通过改进惯性参数与引入变异操作对粒子群算法进行改进,利用改进后的算法求解规划模型。以33节点系统为例进行仿真,结果验证了该方法能够有效降低网损,减少网络规划费用。

基于电压补偿的双端直流配电网电压就地协调控制

双端直流配电网是一种双端电源供电的网络结构,可为直流负荷提供更加稳定和可靠的电源电压。然而,负荷的功率波动和不平衡使线路电压跌落增大,可能导致直流负荷的电压不平衡度和电压偏差指标越限,影响直流负荷的正常运行。文中基于电压源型换流器(VSC)外环电压控制,考虑中线电压补偿,提出基于电压补偿等效模型的直流配电网电压偏差及不平衡度联合抑制策略,实现直流配电网电压就地协调控制。首先,建立双端直流配电网潮流模型,将VSC电压下垂控制引入潮流模型,分析不同控制策略下的电压偏差和不平衡度的特性。其次,在此基础上,以电压最低点为分点获得双端电源供电回路压降的简化等效模型,并利用最小二乘法实现等效阻抗参数辨识,构建双端直流配电网的电压补偿等效模型。以参数辨识结果作为电压外环控制输入,提出考虑中线电压补偿的双端直流配电网电压就地协调控制策略。最后,在MATLAB/Simulink中搭建仿真模型,验证了双端直流配电网潮流模型的正确性和控制策略的有效性。

考虑风电不确定性和储能灵活性的有源配电网有功无功协同鲁棒优化

提出考虑风电不确定性和储能灵活性的有源配电网有功无功协同鲁棒优化方法。首先建立计及储能四象限功率可调特性的有源配电网有功无功协同优化模型;其次给出储能约束条件和交流潮流方程的线性化表征方式;然后利用盒式集描述风电出力的不确定性,建立有源配电网有功无功协同鲁棒优化模型,并通过对偶变换得到混合整数线性规划形式的两阶段鲁棒主、子问题,采用列约束生成算法交互迭代求解;最后以IEEE33节点系统为例进行仿真验证。仿真结果表明,所提方法能够克服模型高度非凸非线性对其收敛精度和求解效率的影响,充分利用储能的无功调节能力,实现有源配电网有功无功协同鲁棒优化运行。

Interval optimal power flow applied to distribution networks under uncertainty of loads and renewable resources

Optimal power flow(OPF) has been used for energy dispatching in active distribution networks.To satisfy constraints fully and achieve strict operational bounds under the uncertainties from loads and sources, this paper derives an interval optimal power flow(I-OPF)method employing affine arithmetic and interval Taylor expansion.An enhanced I-OPF method based on successive linear approximation and second-order cone programming is developed to improve solution accuracy.The proposed methods are benchmarked against Monte Carlo simulation(MCS) and stochastic OPF.Tests on a modified IEEE 33-bus system and a real 113-bus distribution network validate the effectiveness and applicability of the proposed methods.

分布式电源对配电网潮流计算的影响研究

配电中接入的分布式电源的增多,潮流计算时,传统的前推回代法无法满足精度要求。对系统研究发现,分布式电源的出力和接入位置对配电网的网损有不同的影响,阐述了分布式电源潮流计算模型,并引入了一种带有PV节点无功修正的分布式电源配电网潮流计算方法。通过对IEEE14节点系统进行仿真验证,证明了所提算法的有效性和收敛性。研究结果显示:新提出的方案和技术能够更加高效地解决带有分布式电源的配电网潮流问题。

Deep Reinforcement Learning Based Approach for Optimal Power Flow of Distribution Networks Embedded with Renewable Energy and Storage Devices

This study proposes a deep reinforcement learning(DRL)based approach to analyze the optimal power flow(OPF)of distribution networks(DNs)embedded with renewable energy and storage devices.First,the OPF of the DN is formulated as a stochastic nonlinear programming problem.Then,the multi-period nonlinear programming decision problem is formulated as a Markov decision process(MDP),which is composed of multiple single-time-step sub-problems.Subsequently,the state-of-the-art DRL algorithm,i.e.,proximal policy optimization(PPO),is used to solve the MDP sequentially considering the impact on the future.Neural networks are used to extract operation knowledge from historical data offline and provide online decisions according to the real-time state of the DN.The proposed approach fully exploits the historical data and reduces the influence of the prediction error on the optimization results.The proposed real-time control strategy can provide more flexible decisions and achieve better performance than the pre-determined ones.Comparative results demonstrate the effectiveness of the proposed approach.

Coordination Control of Power Flow Controller and Hybrid DC Circuit Breaker in MVDC Distribution Networks

The two main challenges of medium voltage direct current(MVDC)distribution network are the flexible control of power flow(PF)and fault protection.In this paper,the power flow controller(PFC)is introduced to regulate the PF and inhibit the fault current during the DC fault.The coordination strategy of series-parallel PFC(SP-PFC)and hybrid DC circuit breaker(DCCB)is proposed.By regulating the polarity and magnitude of SP-PFC output voltage during the fault,the rising speed of fault current can be suppressed so as to reduce the breaking current of hybrid DCCB.The access mode of SP-PFC to the MVDC distribution network and its topology are analyzed,and the coordination strategy between SP-PFC and hybrid DCCB is investigated.Moreover,the emergency control and bypass control strategies of SP-PFC are developed.On this basis,the mathematical model of SP-PFC in different fault stages is derived.With the equivalent model of SP-PFC,the fault current of the MVDC distribution network can be calculated accurately.A simulation model of the MVDC distribution network containing SP-PFC is established in MATLAB/Simulink.The fault current calculation result is compared with the simulation result,and the effectiveness of the proposed coordination strategy is verified.

基于智能电表数据的低压配电网拓扑与线路参数联合辨识

在低压配电网中,用户频繁地扩建和改接导致台账存在信息更新滞后、数据缺失等问题,难以获取当前运行状态下的拓扑及线路参数。针对无相角量测信息且含未知数量零注入功率“隐节点”的低压配电网,提出一种基于智能电表数据的低压配电网拓扑与线路参数联合辨识方法。基于低压配电网的电气特性以及辐射状网络的结构特点,推导出一种功率-电压比形式的线性逆潮流模型。通过线性回归求解得到阻抗距离矩阵,再利用无判定阈值形式的改进分组递归算法实现拓扑与线路参数的联合辨识。最后,在IEEE欧洲低压测试馈线和中国南京市某地区的实际低压配电网中对所提算法进行数值仿真,验证了所提算法的有效性。

基于区域间场景等值的配电网随机潮流加速算法

针对配电网随机潮流算法中需要对节点功率注入进行大量模拟所引起的计算效率低的问题,提出一种基于区域间场景等值的配电网随机潮流加速算法。首先,从配电网的拓扑连接规律出发,将各区域内节点功率注入对其他区域潮流的影响转换为对边界量的影响,并由此建立了基于边界量的多区域潮流模型;其次,提出一种区域间场景等值方法,快速等值引起其他区域相同潮流变化的功率注入场景;再次,设计了一种随机潮流的分布式求解算法,在求解过程中各区域通过交换边界等值场景集并结合局部功率注入信息重新构造潮流方程,分布式地求解各区域内的随机潮流;最后,通过IEEE 118节点测试系统进行算例分析,验证了所提方法的准确性,且该方法能够显著提高计算效率。

基于动态平衡布局的输电网潮流图自动生成方法

为解决输电网潮流图自动生成中厂站布局与线路布线问题,提出基于动态平衡布局的输电网潮流图自动生成方法。针对厂站布局,首先,在力导向算法的基础上引入基于图纸中心点的惩罚函数,以解决力导向算法布局不均匀的问题,提高布局的均布度;然后,利用厂站分布率参数控制算法平衡次数,引导厂站向图纸边界运动,解决布局自动适应给定图幅大小的问题;最后,通过位置映射变换将围绕图纸中心点的椭圆分布转换为矩形分布,解决引入惩罚函数布局均匀但不符合传统输电网潮流图绘制习惯的问题。针对线路布线提出以构建通道树为中心的布线方法,有效降低布线交点个数,提升布线效果。所提布局与布线算法在实际工程应用中取得了良好的效果,通过案例验证了输电网潮流图自动生成的正确性和有效性。

算力网络环境下基于区块链的自适应联邦学习

算力网络旨在深度融合算力资源与网络资源,实现对泛在离散部署的海量数据和异构资源的高效利用。为了应对算力网络中复杂的多中心计算协同和数据隐私安全的要求,联邦学习在隐私保护和边缘算力资源利用等方面有着先天的优势。然而,基于算力边缘服务器的高度异构和广泛分布,算力网络环境下的联邦学习仍然存在一定的困难。一方面,算力网络中海量边缘服务器之间存在的数据异质性会引起非独立同分布问题,导致联邦学习中局部模型的更新偏离全局最优值。另一方面,由于不同边缘服务器的数据质量存在差异,生成低质量的局部模型会显著影响训练效果。为了解决上述问题,提出了基于区块链的自适应联邦学习(AWFL-BC,adaptiveweightfederatedlearning-blockchain)方案。通过智能合约计算不同边缘服务器的数据分布距离,生成相似度矩阵指导聚合。同时,设计了一种自适应权重聚合算法,以减轻由数据质量差异引起的模型性能和稳定性下降,从而提升模型的准确率,加速模型收敛。最后,结合区块链技术加强了安全保障机制,可有效防止投毒攻击与推理攻击。在3个公有标准数据集上进行的综合实验表明,与最先进的方法相比,AWFL-BC实现了更高的模型准确率,且模型收敛速度更快。

柔性互联输配一体化电网有损潮流的精细化建模及应用

多端柔性互联输配一体化电网内部的运行方式复杂、网损特性差异显著,导致传统一刀切的网损补偿方法难以准确刻画网损的实际情况,影响了输(配)电交直流有损潮流模型的应用潜力和输配边界潮流的一致性。为此,该文提出一种基于双层定点迭代的精细化有损潮流模型。针对网损补偿方法不准确的问题,综合考虑网络类型、数据属性和物理特征等因素,为输、配电网及交、直流电网的精准有损潮流分析建立了差异化的网损补偿模型。针对输配边界潮流的不一致问题,将输配一体化有损潮流建模为一个双层定点迭代问题,并采用主从分裂法实现协同求解。在多个算例中分析了不同有损潮流模型的性能差异及其原因,讨论了所提模型在求解精度、收敛速度和计算效率等方面的优势,验证了所提模型在不同控制方式和优化目标下的鲁棒性和通用性。

基于虚拟阻抗的低压配电网拓扑识别方法

当前拓扑识别技术难以反映潮流特性对拓扑识别的影响,基于配电网现有量测数据,通过分析节点间的电气距离,提出了虚拟阻抗的概念。将节点间具备电气意义的且与电气距离成正相关的连续变量定义为虚拟阻抗,并提出了一种基于虚拟阻抗的低压配电网拓扑识别方法。首先,构建以节点间虚拟阻抗为因变量的多元线性回归方程。然后,通过岭回归计算每一个单相电表与关口电表构成的回归方程的虚拟阻抗,根据计算结果快速判别出拓扑关系异常的电气设备。最后,建立基于导数动态时间弯曲(derivative dynamic time warping,DDTW)距离的校验模型,重新构建得到电气设备的正确拓扑关系,实现低压配电网拓扑关系的修正。以实际的低压配电网台区样本数据为依据,验证了所提方法的有效性。

基于线性化潮流模型的配电网电压无功控制策略

针对现有基于二阶锥松弛的电压无功控制模型无法以电压波动为目标且求解速度较慢等问题,文中提出了一种基于线性化潮流模型的配电网电压无功控制策略。基于支路潮流方程建立了包含有载调压分接头、电容器组和光伏逆变器等调控手段的电压无功控制模型,并将其离散变量松弛为连续变量,以形成二阶锥规划。进一步以二阶锥规划所得结果为基准点构造线性化潮流模型,并以电压波动最小为目标,将连续变量还原为离散变量。最后基于改进的IEEE-33节点测试系统进行了仿真,仿真结果表明所提出的线性化潮流模型具有较高的精度和更快的求解速度,能够适用于配电网实时电压无功控制。

基于数字孪生的配电网潮流网络模型构建研究

由于配电网中导线重复接地对潮流计算收敛性的影响,无法获取精准配电网潮流求解结果,为此,提出了基于数字孪生的配电网潮流网络模型构建研究。根据配电网数字孪生技术架构,计算配电网鲁棒最优潮流。依据配电网对电机出力和负荷需求,构建基于鲁棒优化的鲁棒约束函数,引入保守度调节参数使调控安全性与经济性达到平衡状态。分析配电网潮流网络拓扑结构,结合预设的特征数据类型与特征关联信息之间关系,确定配电网潮流方向。结合数字孪生技术迭代处理注入电压,完成数字孪生潮流网络模型构建。实验结果表明,该模型收敛次数较多,最短收敛时长为1.30 s,具有良好的收敛性。