动态最优潮流的预测/校正解耦内点法

从动态最优潮流中动静态变量的弱耦合关系出发,深入分析了原对偶内点法的解耦思想及其产生的根本原因,然后将该解耦策略推广应用于预测/校正环节的线性方程求解,提出动态最优潮流的预测/校正解耦内点法.该算法利用预测/校正原对偶内点法的优势,提高了动态最优潮流的迭代计算效率.同时,针对线性修正方程组常数项的特点,进一步提出了一种分组解耦同步迭代策略,使动态变量和各时段静态变量在预测/校正环节中实现同步解耦计算,从而进一步提高了动态最优潮流的解耦计算效率.通过典型算例的仿真分析与对比,验证了该算法的有效性.     

电力系统内-外点优化潮流算法

提出了利用内-外点算法(IEPM)求解电力系统最优潮流问题。内点法(IPM)具有全局收敛性好的优点,但有时在最优点附近其收敛速度会降低,而外点法(EPM)在满足二阶最优条件时具有1.5Q的超线性局部收敛速度。IEPM将原对偶内点法和外点法结合,利用内点法寻找全局最优点所在的邻域,当内点法收敛到全局最优点邻域时,转到外点算法继续进行优化潮流数值计算。内-外点优化潮流算法结合了内点法和外点法的各自优点,具有快速的全局收敛性和超线性局部收敛性。对4个IEEE标准测试系统和一个实际某区域685节点系统仿真结果表明,该方法能够保证优化潮流计算的全局收敛性,且收敛速度快,迭代次数少。     

基于最优乘子快速解耦法的交直流混合系统潮流计算

快速解耦法具有程序设计简单、计算速度快、内存占用量小、收敛性好等优点,因此成为高电压等级网络优先采用的潮流计算方法。但对一些病态系统,往往会出现计算过程振荡甚至不收敛的现象。在快速解耦法中引入最优乘子调节变量的修正步长,应用到交直流混合系统的潮流计算中,提高了计算速度,并有效地解决了病态交直流混合系统的潮流计算问题。通过算例验证了该方法的有效性。     

电力系统潮流计算的一种新算法

电力系统潮流计算有多种方法 ,而快速解耦法是电力系统潮流计算广泛使用的一种方法 ,这是因为快速解耦法的程序设计较牛顿法简单 ,同时其计算速度快 ,内存占用量小且具有较好的收敛性。但快速解耦法假设条件不满足 ,可能会出现迭代次数增加或不收敛 ,而一些病态系统 ,如重负荷系统、且有梳子状放射结构网络的系统以及具有邻近多跟运行条件的系统 ,会出现计算过程的振荡或不收敛的情况。本文将快速解耦法和最小潮流算法结合起来 ,形成一种新的潮流计算方法——带有最优乘子的快速解耦法。它将两者的优点结合起来并克服了各自的缺点。1　最优…     

七种最优潮流分解协调算法的性能比较

目前应用于最优潮流领域的诸多分解协调算法缺乏统一的比较基础,为此,将7种最优潮流分解协调算法用于求解同一系统,力求获得更为客观公正的结论。文中首先采用区域划分的方式将以现代内点理论为基础的7种最优潮流分解协调算法分成4类,即母线撕裂类、边界重叠类、边界分区类和节点解耦类,并简要阐述各算法的模型和计算过程。然后以IEEE 300节点系统为基础,分别构造了2区域600节点和4区域1 200节点系统,比较了各最优潮流算法的收敛性、计算速度和通信量等性能,探讨了参数变化对算法稳定性的影响。最后,以MATLAB并行实验室为平台,进一步测试了各算法的并行性能。测试结果表明,在7种分解协调算法中,近似牛顿方向法在计算速度和通信量方面表现最佳,分解协调内点法的收敛性和稳定性最好。     

最优潮流改进简化梯度法的研究及应用

研究和改进电力系统最优潮流问题的简化梯度法 ,并将改进简化梯度法应用于求解无功优化问题。在算法上结合电力系统的PQ解耦特性 ,采用简化梯度法和共轭梯度法的组合算法解算优化潮流问题 ,改进了传统意义上的简化梯度法和共轭梯度法 ,进一步提高了计算速度 ,获得了良好的收敛性 ,尤其是在接近最优点处。无功优化问题的计算实例证明了本文算法是有效的、可靠的。     

基于近似牛顿方向的多区域无功优化解耦算法

针对多区域电力系统的无功优化问题，提出了基于近似牛顿方向和GMRES算法的无功优化解耦算法。该算法以非线性原对偶内点法为基础，在迭代计算过程中构造近似牛顿方向，实现弱耦合系统的完全解耦，保证算法具有局部线性收敛特性，且其计算速度要比非线性原对偶内点法快。对于不能实现解耦的强耦合系统，以近似牛顿方向为初值和解耦对角阵作为预处理器，采用GMRES法求解，使算法具有良好的收敛性和较快的计算速度。以708节点系统作为试验系统验证所提算法的正确性和有效性，得到了满足所有等式和不等式约束的最优可行解。并以树型子系统分解法对其进行分解，对不同分解方案的计算结果进行了比较分析。     

基于快速解耦的电力系统连续潮流并行计算方法

为提高大型区域互联系统连续潮流的计算效率,提出一种改进的基于快速解耦电力系统连续潮流并行计算方法.通过在校正阶段采用快速解耦法求解潮流方程,根据系统的阻抗参数和功率增长方向构造修正方程组的系数矩阵,对潮流方程修正方程组进行预处理,并采用基于CPU-GPU混合架构加速的稳定双共轭梯度法进行求解.基于IEEE-118节点系统、Case13802等多个不同规模测试系统的算例分析表明,该改进算法有效提高了连续潮流的计算速度.     

基于内点法的安全约束经济调度

在分析仿射变换内点法的基础上，导出了改进的仿射变换内点法，这种方法特别适合电力系 统 最优潮流问题的求解；在此基础上建立了基于内点法的电力系统最优潮流问题——安全约束 的经济调度 的数学模型，并给出了其算法与实现；最后，对新算法以实例进行了验证。算例表明，所提 的优化方法具有较快的计算速度。     

求解暂态稳定约束最优潮流模型的递推降阶解耦算法

利用微分代数方程离散化处理后其差分方程所具有的递推特性,结合内点算法修正方程计算,通过对内点修正方程的递推降阶解耦,把含多变量高维微分代数方程约束的电力系统暂态稳定约束最优潮流模型(transient stability constraints optimal power flow,TSCOPF)完全等价地转化为与传统最优潮流模型相同规模的问题来求解。新算法可以方便地应用于并行和分布式计算环境,从而极大地平衡原模型的计算量,提高求解速度。大规模TSCOPF算例结果显示了所提方法的有效性,可提高计算速度数十倍。     

快速实时电价算法

本文首先说明了内点法OPF在电力市场环境下计算和分解实时电价上的优势，同时指出内点法OPF在每次迭代中都要重新计算修正方程系数矩阵及其因子表，不利于实时电价的快速在线计算。为弥补这一缺陷，本文在内点法OPF的基础上通过合理的近似使修正方程系数矩阵常数化，在每次失代时不必重新计算，使计算速度显著提高，从而得到既具有在计算和分解实时电价上的优势，又利于快速在线计算的快速实时电价算法。通过IEEE30节点系统的计算并和内点法进行比较，表明本文方法和内点法的计算结果近似相等，但计算速度却大大提高。     

Interior-point based algorithms for the solution of optimal power flow problems

Interior-point method (IPM) is a very appealing approach to the optimal power flow (OPF) problem mainly due to its speed of convergence and ease of handling inequality constraints. This paper analyzes the ability of three interior-point (IP) based algorithms, namely the pure primal-dual (PD), the predictor–corrector (PC) and the multiple centrality corrections (MCC), to solve various classical OPF problems: minimization of overall generation cost, minimization of active power losses, maximization of power system loadability and minimization of the amount of load curtailment. These OPF variants have been formulated using a rectangular model for the (complex) voltages. Numerical results on three test systems of 60, 118 and 300 buses are reported.     

电力市场环境中常规潮流与最优潮流在网损分摊中的应用

潮流计算是电力系统最重要的运算之一,为此,通过算例对BX型快速解耦潮流和最优潮流进行了分析.算例使用基于合作博弈论中的核仁理论的网损分摊方法对这两种潮流计算方法所计算的网损进行了分摊,计算结果表明,采用最优潮流计算网损优于使用常规潮流计算网损,并且基于最优潮流的网损分摊结果更易于被市场成员接受.     

基于信赖域内点法的最优潮流算法

在电力市场环境下 ,诸多问题 (例如实时电价、网络阻塞管理和可用传输能力的计算等 )都需要最优潮流 ( OPF)作为理想的工具。文中基于信赖域的思想提出了求解 OPF的新算法。该算法连续求解线性规划 ( LP)子问题 ,通过信赖域决定线性化步长的选取 ,由多步中心校正原—对偶内点法求解信赖域 LP子问题 ,并采用了一个物理策略以改善 OPF算法的稳定性。对国外一个 662节点实际电力系统进行了数值计算 ,结果表明该算法是快速、鲁棒的 ,具有实用意义     

Multi-objective harmony search algorithm for optimal power flow problem

This paper proposes a multi-objective harmony search (MOHS) algorithm for optimal power flow (OPF) problem. OPF problem is formulated as a non-linear constrained multi-objective optimization problem where different objectives and various constraints have been considered into the formulation. Fast elitist non-dominated sorting and crowding distance have been used to find and manage the Pareto optimal front. Finally, a fuzzy based mechanism has been used to select a compromise solution from the Pareto set. The proposed MOHS algorithm has been tested on IEEE 30 bus system with different objectives. Simulation results are also compared with fast non-dominated sorting genetic algorithm (NSGA-II) method. It is clear from the comparison that the proposed method is able to generate true and well distributed Pareto optimal solutions for OPF problem.     

An interior point nonlinear programming for optimal power flowproblems with a novel data structure

This paper presents a new interior point nonlinear programming algorithm for optimal power flow problems (OPF) based on the perturbed KKT conditions of the primal problem. Through the concept of the centering direction, the authors extend this algorithm to classical power flow (PF) and approximate OPF problems. For the latter, CPU time can be reduced substantially. To efficiently handle functional inequality constraints, a reduced correction equation is derived, the size of which depends on that of equality constraints. A novel data structure is proposed which has been realized by rearranging the correction equation. Compared with the conventional data structure of Newton OPF, the number of fill-ins of the proposed scheme is roughly halved and CPU time is reduced by about 15% for large scale systems. The proposed algorithm includes four kinds of objective functions and two different data structures. Extensive numerical simulations on test systems that range in size from 14 to 1047 buses, have shown that the proposed method is very promising for large scale application due to its robustness and fast execution time     

On a nonlinear multiple-centrality-corrections interior-pointmethod for optimal power flow

Large scale nonlinear optimal power flow (OPF) problems have been efficiently solved by extensions from linear programming to nonlinear programming of the primal-dual logarithmic barrier interior-point method and its predictor-corrector variant. Motivated by the impressive performance of the nonlinear predictor-corrector extension, in this paper we extend from linear programming to nonlinear OPF the efficient multiple centrality corrections (MCC) technique that was developed by Gondzio. The numerical performance of the proposed MCC algorithm is evaluated on a set of power networks ranging in size from 118 buses to 2098 buses. Extensive computational results demonstrate that the MCC technique is fast and robust, and outperforms the successful predictor-corrector technique     

A decentralized solution to the DC-OPF of interconnected power systems

This paper presents a new method for the decentralized solution of the DC optimal power flow (OPF) problem in large interconnected power systems. The method decomposes the overall OPF problem of a multiarea system into independent OPF subproblems, one for each area. The solutions of the OPF subproblems of the different areas are coordinated through a pricing mechanism until they converge to the global OPF solution. The prices used for the coordination of the subproblem solutions are the prices of electricity exchanges between adjacent areas. Test results from the application of the method to the three-area RTS-96 and the Balkan power system are reported.     

基于强度Pareto进化算法的最优潮流

为更好地解决电力系统最优潮流问题,分析了当前多目标优化算法存在的缺陷,将强度Pareto进化算法(SPEA)应用于最优潮流中.SPEA是一种新型的多目标进化算法,具有收敛速度快,参数设置少,全局搜索能力强,所求的Pareto最优解分布均匀等优点.通过对IEEE30节点测试系统运用SPEA和混沌粒子群方法(CpSO)的计...     

Blackstart capability planning for power system restoration

Blackstart capability is essential for power system restoration following a blackout. System restoration planners determine the restoration sequences to provide cranking power from blackstart units (BSUs) to non-blackstart units (NBSUs), pick up critical loads, and energize the necessary transmission paths. This paper proposes a new algorithm for optimization of the restoration actions. An optimal search algorithm is proposed to determine the plan to crank NBSUs through the selected paths of transmission lines. Assuming that the generation capability of a BSU is constant, the method is used to optimize the overall system MWHr generation capabilities from NBSUs. To reduce the computational complexity of system restoration planning, a new generator model is proposed that results in a linear integer programming (IP) formulation. Linearity of the IP problem formulation ensures that the global optimality is achieved. The optimal power flow (OPF) is used to examine the feasibility of planned restoration actions. Test cases from the IEEE 39-bus system, ISO New England system, and Duke-Indiana system are used to validate the proposed algorithm. Numerical simulations demonstrate that the proposed method is computationally efficient for real-world power system cases.