输配电网一体化分布式潮流计算方法

随着大量分布式电源和电动汽车接入配电网,传统的输配电网相互独立的潮流计算已不再适合。考虑到输配电网由不同层级控制中心监控和管理,提出了基于主从分裂的输配电网一体化分布式潮流计算方法。该方法以输配全局电网作为研究对象,将一体化潮流问题分成输电网潮流计算、多个配电网潮流计算和边界节点协调子问题,其中,输、配电网潮流计算子问题分别采用牛顿法和前推回代法来解决,并通过边界节点电压和等值功率的交换实现输配电网一体化分布式潮流计算。最后,对IEEE-30节点输电系统和IEEE-33节点三相不平衡配电系统构造的全局电力系统进行仿真,结果表明该方法的有效性。     

基于广义主从分裂的输配电网一体化分布式无功优化方法

大量分布式电源接入配电网后,输、配电网间无功电压关系更加密切,传统输、配电网无功优化孤立进行已不再合适。根据输、配电网运行管理的独立性,提出了一种基于广义主从分裂思想的输配电网一体化分布式无功优化方法。输配全局无功优化问题分解为输电网优化主子问题、各配电网优化从子问题及边界一致性判别问题。各子网无功优化子问题采用对偶规划类算法求解,离散变量采用罚函数法处理以保持增广拉格朗日函数的可微性。通过由对偶乘子构造的边界灵敏度实现输、配电网子问题间的解耦,输配电网控制中心间通过传递边界变量及其灵敏度信息实现分布式协调。对IEEE 30节点系统(输电网)和含多种分布式电源的IEEE 33节点系统(配电网)进行仿真,验证了所提方法的有效性。     

多能耦合三相不平衡主动配电网与输电网交互随机模糊潮流方法

随着能源互联网建设推进,含能量枢纽和高比例风电主动配电网与输电网交互更加紧密,输配全局潮流计算面临新挑战。该文基于体系(system of systems,SoS)概念,定义输电网和其接入的多个主动配电网为既独立又交互的体系系统,考虑含随机模糊性风电能量枢纽与配电网有功功率双向交互,获取主动配电网与输电网交互状态;进一步考虑输电网规模化风电接入和配网负荷三相不平衡,建立含风电和能量枢纽等的多能耦合三相不平衡主动配电网与输电网交互全局随机模糊潮流So S模型;提出结合随机模糊模拟、输配交互状态、前推回代和牛顿拉夫逊法的模型并行求解算法。改进的IEEE39节点输电网和IEEE33节点配电网算例仿真结果表明文中方法正确有效。     

计及不确定性的输配电网协调恢复全并行分布式优化方法

极端事件频发导致大停电，在此期间协调优化输、配电网的恢复进程，对于提高恢复速度、减少恢复成本至关重要。然而，输、配电网分属于不同运营商，之间交互信息有限。为了在负荷恢复阶段实现二者协调运行，该文提出计及不确定性的输配电网协调恢复全并行分布式优化方法。以恢复成本最小为目标函数构建输配电网恢复运行模型，考虑风电不确定性引入模糊理论和可信性理论将确定性约束转化为模糊机会约束，并采用目标级联分析法将全局输配电网恢复问题分解为输电网恢复与配电网恢复两类子问题进行协调优化。在此基础上，引入对角二次近似对耦合变量一致性约束的惩罚函数松弛处理，从而全并行求解各子问题，提高每次迭代的计算效率。以IEEE 30节点输电网和IEEE 33节点配电网构成的输配系统为例，验证了所提方法的有效性、收敛性以及计算效率。     

基于广义主从分裂理论与配电网相协同的输电网规划方法

基于广义主从分裂理论提出与配电网相协同的输电网规划方法。以输电网投资成本、输电网运行成本与配电网运行成本之和最小为目标函数,考虑配电网网络约束及分布式电源,基于Karush-Kuhn-Tucker(KKT)最优性条件对输配耦合约束条件解耦,将原问题分解为输电网优化子问题与配电网优化子问题。引入计及电压幅值的直流潮流方程,使得在基于直流潮流的输电网规划模型中也能计算电压幅值,从而支撑输配协同优化计算时的变量交互。采用Benders分解混合异质分解算法的有效求解策略。以中国某省级输配电网为算例,对比传统输电网规划方法,验证了所提规划方法的有效性与优越性。     

输配协同的配电网态势快速感知方法

智能配电网的发展呈现更加灵活多变的趋势,使得输配电网间的相互作用日趋显著,对配电网态势感知提出更高的要求。为提升配电网态势感知能力,文中从输配协同的角度,提出了配电网态势快速感知的方法。首先,针对预估的配电网负荷变化计算输电网的状态;其次,从配电网根节点处将输电网进行戴维南等值并辨识等值参数;最后,将戴维南等值后的输电网接入配电网感知配电网负荷变化后的态势。该方法无需多次交互输配电网信息,感知速度快,计算结果精度高。在该方法的基础上,可通过对未来状态的多步预估,预测配电网静态电压稳定裕度指标轨迹,然后根据该轨迹评估配电网静态电压稳定态势。仿真算例验证了所提方法的可行性和有效性。     

基于电网拓扑优化的电压稳定预防控制方法

电压稳定是电力系统安全稳定运行的重要内容之一,然而某些故障或扰动的发生可能导致系统的负荷裕度大幅度降低,甚至造成电压失稳。为增强系统的电压稳定性,提出采用改变母线运行方式的方法优化电网拓扑结构,从而提高电力系统的负荷裕度,确保基态系统和预想事故下的电压稳定性。提出了计及预想事故的电压稳定预防控制模型,并发展了一套阶段式的母线运行方式优化求解方法。首先利用线路参数的灵敏度指标对母线运行方案进行预筛选,快速筛选出可以提高负荷裕度的备选方案集,以减小计算规模;随后在此集合上基于look-ahead裕度指标对运行方案排序,并以连续潮流法的分析结果确定有效母线运行方案;最后,通过IEEE 118和1648节点系统的仿真结果验证了所提方法的有效性。     

计及主网潮流响应的主动配电网故障恢复研究

随着传统配电网向含高分布式电源(DG)渗透率的主动配电网转变,传统的配电网故障恢复孤立进行的做法已不再适用。提出了一种计及主网潮流响应的主动配电网故障恢复方法。通过考虑主配全局潮流实现了主配网的协调,配电网侧DG、可中断负荷和网络重构资源的合理配合,保证了故障恢复策略的准确性和经济性。对IEEE30节点主网和IEEE33节点配网组成的全局电网算例进行测试,结果表明,该方法较传统的配电网孤立故障恢复方法更具有效性。     

一种用于电力系统静态稳定性分析的故障筛选与排序方法

提出了一种用于电力系统静态稳定性分析的故障筛选和排序方法,该方法分为两个阶段:在第1阶段采用基于灵敏度的负荷裕度预估方法和通过两次潮流迭代预估系统电压降的方法从全部故障中筛选出严重故障集;在第2阶段采用改进的二次曲线预估方法对严重故障进行负荷裕度的预估和排序,同时应用故障连续潮流对失稳故障进行识别和排序.利用该方法开发的电力系统静态稳定分析与控制软件在国外某实际电力系统中的成功运行表明了该方法的有效性.     

考虑新能源发电不确定性的静态电压稳定故障筛选与排序方法

为了有效地分析电力系统设备故障和新能源发电不确定性对系统静态电压稳定的影响,提出了一种考虑新能源发电不确定性的静态电压稳定故障筛选与排序方法。假设各新能源发电出力为均值,建立N-1故障场景下确定性的电压稳定临界点模型,根据所得的故障后负荷裕度筛选得到严重故障集。考虑风电和光伏发电的随机分布,建立故障场景下的电压稳定概率评估模型,采用随机响应面法求解获得故障场景的负荷裕度累积概率分布。根据负荷裕度累积概率分布,设计了2种故障后系统电压稳定性的排序指标,确定严重故障集的排序。IEEE 118节点标准系统的计算结果表明,所提故障筛选与排序方法是有效的,可以甄别和排序各故障场景下系统的概率静态电压稳定性;根据故障排序结果构建的电力系统概率电压稳定域有助于对概率稳定边界的分析和研究。     

电压稳定分析中支路型故障筛选及排序算法研究

提出了一种用于电压稳定性分析的支路型故障筛选和排序的快速算法，该方法分为4个阶段：①依据正常运行状态下的潮流解从所有预想事故中筛选出严重事故；②通过分析线路被置换后系统雅克比矩阵的最小奇异值，从所筛选出的事故中再次筛选出更严重的事故；③在指定的负荷水平下解潮流方程，从第二次筛选出的事故中再次筛选出最严重的事故；④对每个最严重的事故进行1次潮流计算，估计其最大负荷能力。通过New-England 39节点系统和IEEE 118节点系统算例表明，此算法计算量少，简单有效，易于实现。     

静态电压稳定分析的故障筛选和排序方法

提出一种新的故障筛选和排序方法。首先,定义一种负荷裕度阈值,用连续潮流法计算此裕度下的潮流工况,在此工况下,利用最优乘子法依次求解所有的开断潮流,将有解的大部分开断作为安全故障筛选掉,少量无解的则为不安全故障。然后,在基态下利用最优乘子法依次求解不安全故障的开断潮流。有解则为危险故障,并用连续潮流法求解负荷裕度,按裕度给出排序;无解则为失稳故障,结合最小二乘潮流解失配量中隐含的校正控制灵敏度信息,使用序列线性规划法给出使潮流恢复有解的最小切负荷代价,据此代价来排序失稳故障。新英格兰10机39节点系统的仿真结果验证了所提出的方法能快速、可靠、全面地实现电压稳定的故障筛选和排序。     

ANN based integrated security assessment of power system using parallel computing

This paper presents the application of cascade neural network (CANN) based approach for integrated security (voltage and line flow security) assessment. The developed cascade neural network is a combination of one screening module and two ranking modules, which are Levenberg–Marquardt Algorithm based neural networks (LMANNs). All the single line outage contingency cases are applied to the screening module, which is 3-layered feed-forward ANN having two outputs. The screening module is trained to classify them either in critical contingency class or in non-critical contingency class from the viewpoint of voltage/line loading. The screened critical contingencies are passed to the corresponding ranking modules, which are developed simultaneously by using parallel computing. Parallel computing deals with the development of programs where multiple concurrent processes cooperate in the fulfillment of a common task. For contingency screening and ranking, two performance indices: one based on voltage security of power system (VPI) and other based on line flow (MWPI) are used. Effectiveness of the proposed cascade neural network based approach has been demonstrated by applying it for contingency selection and ranking at different loading conditions for IEEE 30-bus and a practical 75-bus Indian system. The results obtained clearly indicate the superiority of the proposed approach in terms of speedup in training time of neural networks as compared to the case when the two ranking neural networks were developed sequentially to estimate VPI and MWPI.     

基于电压稳定及FACTS配置的输电能力的研究

输电能力是电网技术的重要指标,也是反映电网输电容量的市场信号。介绍了考虑电压稳定性后,采用线路电压稳定指标快速筛选出严重预想事故集,并用连续潮流法计算输电能力;同时叙述了采用临界状态下线路电压稳定指标来评估FACTS配置安装地点对输电能力的影响,以快速捕获最佳安装位置。通过IEEE5节点和IEEE30节点标准系统验证了此方法的有效性。     

基于灰色关联分析法的电压稳定故障筛选与排序

故障筛选与排序是电力系统电压稳定评估的重要部分。针对以往基于潮流计算筛选方法的单一性,提出了一种新的故障筛选与排序方法。首先结合感应电动机特性、负荷大小、电源支撑及输电线路情况构建一套故障筛选指标体系;其次针对单一指标反映故障严重程度的局限性,采用灰色关联分析法计算综合指标,综合多方面因素评估故障严重程度。采用存在电压稳定问题的实际电网数据对所提方法进行验证,结果表明基于灰色关联分析法的电压稳定故障筛选与排序方法是合理和有效的。     

Fast real power contingency ranking using counter propagation network: feature selection by neuro-fuzzy model

In deregulated operating regime power system security is an issue that needs due consideration from researchers in view of unbundling of generation and transmission. Real power contingency ranking is an integral part of security assessment. The objective of contingency screening and ranking is to quickly and accurately shortlist critical contingencies from a large list of credible contingencies and rank them according to their severity for further rigorous analysis. In the present work, modified counter propagation network (CPN) with neuro-fuzzy (NF) feature selector is used for real power contingency ranking of the transmission system. The CPN is trained to estimate the severity of a series of contingencies for given pre-contingencies line-flows. But for larger size system it becomes rather difficult to cope with the increased size of input pattern and network as well. And it adversely affected the performance of the network and computational overhead. The proposed NF feature selector prunes the size of input pattern by exploring the individual power of features to characterize/discriminate different clusters. The reduced set of discriminating inputs not only ensures saving in training time but also improves estimation accuracy and execution time and these are the deciding parameters in evaluating the performance of particular contingency ranking technique. The effectiveness of proposed approach is demonstrated on IEEE 30-bus test system and practical 75-bus Indian system.     

On-line voltage stability assessment using radial basis function network model with reduced input features

In recent years, voltage instability has become a major threat for the operation of many power systems. This paper presents an artificial neural network (ANN)-based approach for on-line voltage security assessment. The proposed approach uses radial basis function (RBF) networks to estimate the voltage stability level of the system under contingency state. Maximum L-index of the load buses in the system is taken as the indicator of voltage stability. Pre-contingency state power flows are taken as the input to the neural network. The key feature of the proposed method is the use of dimensionality reduction techniques to improve the performance of the developed network. Mutual information based technique for feature selection is proposed to enhance overall design of neural network. The effectiveness of the proposed approach is demonstrated through voltage security assessment in IEEE 30-bus system and Indian practical 76 bus system under various operating conditions considering single and double line contingencies and is found to predict voltage stability index more accurate than feedforward neural networks trained by back propagation algorithm and AC load flow. Experimental results show that the proposed method reduces the training time and improves the generalization capability of the network than the multilayer perceptron networks.     

A novel collapse prediction index for voltage stability analysis and contingency ranking in power systems

Voltage instability is a serious phenomenon that can occur in a power system because of critical or stressed conditions. To prevent voltage collapse caused by such instability, accurate voltage collapse prediction is necessary for power system planning and operation. This paper proposes a novel collapse prediction index (NCPI) to assess the voltage stability conditions of the power system and the critical conditions of lines. The effectiveness and applicability of the proposed index are investigated on the IEEE 30-bus and IEEE 118-bus systems and compared with the well-known existing indices (Lmn, FVSI, LQP, NLSI, and VSLI) under several power system operations to validate its practicability and versatility. The study also presents the sensitivity assumptions of existing indices and analyzes their impact on voltage collapse prediction. The application results under intensive case studies prove that the proposed index NCPI adapts to several operating power conditions. The results show the superiority of the proposed index in accurately estimating the maximum load-ability and predicting the critical lines, weak buses, and weak areas in medium and large networks during various power load operations and contingencies. A line interruption or generation unit outage in a power system can also lead to voltage collapse, and this is a contingency in the power system. Line and generation unit outage contingencies are examined to identify the lines and generators that significantly impact system stability in the event of an outage. The contingencies are also ranked to identify the most severe outages that significantly cause voltage collapse because of the outage of line or generator.     

A novel fuzzy index for steady state voltage stability analysis and identification of critical busbars

In this paper, a novel fuzzy index is proposed for the prediction of steady state voltage stability conditions in transmission networks. The uncertainties in the input parameters are efficiently modeled in terms of fuzzy sets by assigning trapezoidal and triangular membership functions. The results include fuzzy load flow solutions for the base case and critical conditions with and without contingencies. The proposed fuzzy voltage stability index clearly indicates the location and status of critical busbars. Case studies have been conducted on standard test systems (IEEE 14-bus, 30-bus, and 57-bus) with proper validation of the results.