Large-scale var optimization and planning by tabu search

A generalization on the tabu search method, recently developed for combinatorial optimization problems, is described in this paper. The novel version of the tabu search method can be used to solve a larger class of combinatorial optimization problems. Application of this method to var optimization and planning, which is formulated as a nonlinear large-scale mixed integer programming problem with non-differentiable objective function, is demonstrated. Judicious engineering judgment which is essential for a successful application of the proposed tabu search is developed. Simulation results of a real-world power system are included. A simulation comparison is done between the proposed method and the simulated annealing method, which is currently one of the most popular method for combinatorial optimization problem.     

Comparative study of modern heuristic algorithms to servicerestoration in distribution systems

This paper presents a comparative study for four modern heuristic algorithms (MHAs) to service restoration in distribution systems: reactive tabu search, tabu search, parallel simulated annealing, and genetic algorithm. Since service restoration is an emergency control in distribution control centers to restore out-of-service areas as soon as possible, it requires fast computation and high quality solutions for customers' satisfaction. The problem can be formulated as a combinatorial optimization problem to divide the out-of-service area to each power source. The effectiveness of the MHAs is compared against each other on typical service restoration problems     

基于改进小生境遗传算法的Pareto多目标配电网重构

配电网重构是一个多目标、多约束的复杂非线性组合优化问题,若采用传统的遗传算法处理此类问题,由于其易于陷入局部最优解和随着配电网规模的扩大搜索效率低的问题,难以得到理想结果。建立了Pareto多目标重构数学模型并提出一种改进小生境遗传算法来处理配电网重构问题。算法主要有以下几种特点：设置个体之间的距离判别标准L为动态函数,保持了种群的多样性;采用最优保存策略,提高了算法的收敛速度;交叉、变异采用自适应规则,避免了算法陷入局部最优的情况。另外,Pareto多目标数学模型的引入也使算法更具实际工程意义,采用国外一个实际的配电网络对算法进行了验证。理论分析和算例表明,该算法具有高收敛性、快实时性和强全局稳定性的优点。     

An application of reactive tabu search for service restoration in distribution systems and its comparison with the genetic algorithm and parallel simulated annealing

Service restoration in distribution systems can be formulated as a combinatorial optimization problem. It is the problem to determine power sources for each load considering various operational constraints in distribution systems. Up to now, the problem has been dealt with using conventional methods such as the branch and bound method, expert systems, neural networks, and fuzzy reasoning. Recently, modern heuristic methods such as genetic algorithms (GA), simulated annealing (SA), and tabu search (TS) have been attracting notice as efficient methods for solving large combinatorial optimization problems. Moreover, reactive tabu search (RTS) can solve the parameter tuning problem, which is recognized as the essential problem of the TS. Therefore, RTS, GA, and SA can be efficient search methods for service restoration in distribution systems. This paper develops an RTS for service restoration and compares RTS, GA, and PSA (parallel SA) for the problem. The feasibility of the proposed methods is shown and compared on a typical distribution system model with promising results. © 2000 Scripta Technica, Electr Eng Jpn, 133(3): 71–82, 2000     

基于Tabu搜索的配电网优化规划

针对常规数学方法求解配电网优化规划问题时速度慢的缺点，引入具有快速搜索能力的Tabu搜索算法。同时引入地理信息系统（GIS）平台使规划结果更加直观。优化模型考虑线路投资的时间价值，以计及投资和损耗费用的年费用最小为目标函数，采用点溶合法求解备选网络的生成树作为Tabu搜索的初始解。算例结果表明，该方法具有一定的可行性和有效性。     

基于Tabu方法的配电电容器投切策略

本文首先给出电网电容器优化投切策略的混合整数规划模型。然后应用Ｔａｕｂ Ｓａｅｒａｃｈ优化方法解决混合整数规划模型。通过对算例的演算证明,Ｔａｂｕ Ｓｅａｒｃｈ方法能够有效地处理整数约束和不可微的目标函数。     

A tabu search approach to the optimal sequential partitions of directed acyclic graphs

Tabu search is a novel technique for solving combinatorial optimization problems. The process in which the tabu search method seeks to transcend local optimality is based on an evaluation function which chooses the highest-evaluation move in terms of objective function and tabu restrictions. This paper presents a tabu search algorithm for finding a minimum-cost partition of the nodes of a directed acyclic graph into subsets of a given size, subject to the constraint that the precedence relations are satisfied. A standard tabu search approach cannot realize good solutions for this problem, because the problem is a complex multiple partitioning problem in which the number of subsets and the number of nodes in each subset are unsettled. For this problem, we use an appropriate data structure for this method and develop effective neighborhood structure and heuristics. We also assess the effectiveness of the developed algorithm. The results show that this algorithm is effective in obtaining a near-optimal solution to this problem. The running time of the procedure is proportional to the number of nodes in the graph. © 1997 Scripta Technica, Inc. Electr Eng Jpn, 119(4): 42–51, 1997     

基于改进粒子群优化算法的配电网络重构

提出了一种求解配电网络重构的改进粒子群优化（PSO）算法。结合配电网络的特点改进了PSO算法粒子位置的更新规则，提高了迭代过程中有效解的产生概率；并结合禁忌（Tabu）搜索的记忆功能和藐视准则，克服了PSO算法的早熟问题。算，其结果与最优解吻合，证实了算法的有效性，并与较，表明了算法具有更好的搜索效率。最后对3个典型IEEE测试系统进行优化计Tabu搜索算法和遗传算法的计算结果相比     

Determination of loss‐minimum configuration with mathematical optimality in a three‐sectionalized three connected distribution feeder network

In a distribution system, in order to enhance the reliability of power supply, the distribution feeder is divided into several sections by installing sectionalizing switches, and then each of the sectionalized sections is connected to a different feeder. For example, one feeder is divided into three sections by two sectionalizing switches, and then each of the divided sections is connected to the other feeder through sectionalizing switch. Since a distribution system with many feeders has many sectionalizing switches, the system configuration is determined by states (opened or closed) of sectionalizing switches. Usually, a power utility tries to obtain distribution loss‐minimum configuration among large numbers of configuration candidates. However, it is very difficult to determine the loss‐minimum configuration such that the mathematical optimality is guaranteed, because it is well known that determination of a distribution system's configuration is to decide whether each sectionalizing switch is opened or closed by solving a combinatorial optimization problem. In this paper, the authors propose a determination method of loss‐minimum configuration by which the mathematical optimality is guaranteed for a three‐sectionalized three‐connected distribution feeder network. A problem to determine the loss‐minimum configuration is formulated as a combinatorial optimization problems with four operational constraints ( feeder capacity, voltage limit, radial structure, and three‐sectionalization). In the proposed method, after picking up all partial configurations satisfied with radial structure constraint by using enumeration method, optimal combination of partial configurations is determined under the other operational constraints by using conventional optimization method. Numerical simulations are carried out for a distribution network model with 140 sectionalizing switches in order to examine the validity of the proposed algorithm in comparison with one of conventional meta‐heuristics (tabu search). © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 167(1): 56– 65, 2009; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20530     

Method for minimum‐loss reconfiguration of distribution system by tabu search

This paper proposes a loss minimum reconfiguration method by tabu search for an open‐loop radial distribution system with distributed generators. The problem is to find the optimal normal open sectionalizing switch positions which minimize the total distribution line losses subjected to the line/transformer capacity constraints and voltage constraint. Generally, the problem is mathematically formulated as a complex combinatorial optimization problem or mixed integer programming problem, and is solved by using mathematical programming method, heuristic algorithm, intelligent method, and so on. However, a satisfactory algorithm for power companies has not yet been attained in both computational burden and solution accuracy. Thus, in this paper, the authors propose a method to solve the above problem by using tabu search (TS). Reverse power flow caused by distributed generators can be included in the solution algorithm. TS is one of the meta‐heuristic algorithms, and sometimes has been evaluated to be better than genetic algorithm (GA) or simulated annealing (SA) from viewpoints of both computational speed and solution accuracy. In order to evaluate the validity and efficiency of the algorithm, several numerical examples are shown in this paper. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 152(2): 18–25, 2005; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20086     

基于禁忌BP神经网络的动态测量误差预测研究

本文针对BP算法在神经网络参数学习中局部性能好但易陷入局部极小值而禁忌搜索算法拥有良好的全局性能的特点,提出了神经网络的综合训练方法（禁忌BP算法,TSBP Algorithm）即首先利用禁忌搜索算法对神经网络参数进行金局搜索训练,然后再利用BP算法对参数进行局部学习。设计了一种多样化和集中化并行搜索的禁忌搜索算法,一定程度上解决了传统禁忌算法局部寻优和拓展搜索空间之间的矛盾。最后利用仿真实例验证了TSBP神经网络较之BP神经网络和TS神经网络在动态误差预测方面的优越性,得出了相关结论。     

基于改进蚁群算法的输电网络扩展规划

输电网络扩展规划问题是一个复杂的组合优化问题。蚁群算法是用于解决组合优化问题的一种高效的随机化内启发式全局搜索技术。文中结合单阶段输电网络扩展规划问题的特点，应用改进的蚁群算法来解决输电网络扩展规划问题，建立了相应的数学模型，并给出求解算法。2个算例系统的计算结果表明了这种方法可有效减小搜索空间，并具有计算速度快和收敛性好等优点。     

现代启发式算法在电网规划中应用的比较

分析了以遗传算法、模拟退火算法和禁忌搜索算法为代表的现代启发式算法应用于电网规划这类非线性组合优化问题时存在的缺陷。在传统遗传算法的基础上,结合模拟退火算法概率性的突跳搜索机制和禁忌搜索算法能避免迂回的邻域搜索机制提出了一种混合算法,并以地理信息系统为平台来求解电网规划问题。实际应用结果表明,采用文中的混合算法可提高计算速度、收敛性能和计算效率。     

基于改进蚁群算法的"N-1"安全输电网优化规划

满足N-1安全准则的输电网络扩展规划是一个复杂的非线性组合优化问题。蚁群算法是一种解决组合最优化问题的高效的启发式方法,但容易出现停滞和陷入局部最优。为此,结合满足N-1安全准则的输电网络规划问题的特点,用罚函数方法建立了网络扩展规划性能指标。通过改变蚁群算法中信息增量的选择方法,有效地减小了算法的全局搜索能力和计算效率之间的矛盾。该方法不需初始可行解,在提高计算效率的同时加大了找到全局最优解的概率。通过对IEEE-6节点和IEEE-24节点两个算例的测试,验证了这种方法能有效地解决直接求解满足N-1安全检验的输电网最优扩展规划问题。     

Identification of interacting bad data in the framework of the weighted least square method

The identification of multiple interacting bad data, arising in the framework of static state estimation, is commonly handled by the largest normalized residual criterion. However, this technique may lead to faulty results when the bad data are of the conforming type. In the present work, the identification problem is formulated as a non-linear optimization with mixed variables. Its solution is found by means of combinatorial optimization methods such as branch-and-bound, genetic algorithms and tabu search techniques. All these approaches consist of three successive steps: generation of a tentative bad data identification, solution of the corresponding state estimation problem and memorization of already considered cases. To speed up the state estimation solution, the possible use of sensitivity techniques is also considered. It is shown that the efficient storage of solved cases and the breadth of the search play a critical role in determining the efficiency of the procedures. The proposed approaches were applied to the identification of multiple interacting bad data with reference to the IEEE test systems as well as to an actual network of Italian origin.     

Network Expansion Planning Using Improved Controlled NSGA‐II

This paper presents the application of multiobjective optimization methods to network expansion planning. Distribution network expansion planning minimizes system cost and distribution loss while satisfying the constraints. Problem formulation yields combinatorial optimization problems that are difficult to solve due to their complexity. This research applies a genetic algorithm, which is a meta‐heuristics method. The present study proposes a new method of multiobjective optimization: NSGA‐II, SPEA2, and Controlled NSGA‐II are assumed to be the best methods now. The proposed method introduces the concept of a linkage identification genetic algorithm, enabling more efficient searching than methods hitherto known. In the past, most research on network expansion planning did not include the load curve. This research demonstrates that the investigation must include the load curve. It also proposes a new method of search including the load curve.     

改进单亲遗传算法在电源规划中的应用

电源规划问题本质上是一多阶段组合优化问题,使用常规遗传操作会产生大量无效解,从而降低了求解效率.文章提出了一种基于单亲遗传算法的电力系统电源规划模型,并采用分段编码法解决了单亲遗传算法用于电源规划的编码问题.该模型容易计及电源规划中需要考虑的各种约束条件.文中还研究了精英保留算子和扰动算子对收敛的影响.算例仿真表明:该模型可靠有效,既能获得最优解,也能获得次优解;加入特殊算子改进后,可进一步提高精度,加快收敛速度.     

基于蚁群禁忌搜索混合算法的配电网重构

介绍了以最小化配电网网损为目标函数,以网络拓扑约束、潮流约束、运行约束以及辐射状约束为约束条件,在基本蚁群算法基础上的改进型蚁群禁忌混合算法在配电网重构中的应用,在IEEE 69节点系统的计算中,蚁群禁忌混合算法与禁忌算法相比提高了优化效果,从而证明了蚁群禁忌混合算法的实用性。     

SA-PSO在水火电混合电力系统电源规划中的应用

电源规划是电力系统电源布局的战略决策,在电力系统规划中处于十分重要的地位。其核心问题是要确定在规划期内随着负荷的增长,系统应在何时、何地、建什么类型、多大容量的电厂。由于其本身的的复杂性,用传统的优化方法求解需采取简化措施,寻求一个满足各种约束条件和可靠性指标及环保要求的最优电源建设方案,以满足系统负荷发展的需要。为此,提出一种粒子群算法与模拟退火算法结合的模拟退火粒子群算法,并将其用于求解复杂的、非线性的水火电混合电力系统(包含核电)电源规划问题。该组合算法在粒子群算法中引入了模拟退火算法成功的提高了基本粒子群算法的全局搜索能力。算例结果表明:该算法能可靠、快速的收敛到全局最优解,特别适合于大型电力系统的中长期电源规划。     

人口迁移算法在“N-1”安全输电网规划中的应用

提出了一种改进人口迁移算法求解输电网扩展规划的大规模组合优化问题。针对标准人口迁移算法在求解过程中搜索容易陷入局部最优解和后期收敛时间较长等缺点,对算法的迭代初始化、种群生成策略以及参数设置进行了改进,将遗传算法的最优保留思想引入到算法中,提高了算法搜索全局最优解的能力与收敛到最优解的速度。将改进后的算法应用到满足"N-1"安全准则的输电网扩展规划问题中,建立了输电网扩展规划的数学模型,设计了相应的算法。比较该算法与粒子群算法、标准人口迁移算法对IEEE Garver6节点系统和IEEE Garver18节点系统的仿真计算结果,证明了改进人口迁移算法能有效地解决满足"N-1"安全检验的输电网扩展规划优化问题。