基于改进禁忌搜索的配电网重构

在配电网的运行中,常需根据负荷的变化情况及时调整网络中的开关状态,以达到整个网络消除过载、损耗最小的目的。为了解决配电网优化重构问题,提出了一种改进禁忌搜索(TS)方法。该方法根据配电网在正常运行的情况下呈辐射状的特点,将同步开关法的思想用于TS优化编码策略中,以系统有功网损最小为目标函数对配电网进行重构。通过对典型系统的计算,结果表明该算法具有更高的搜索效率和可靠性。     

基于改进禁忌搜索的配电网重构

为解决配电网的重构问题,提出了一种改进的禁忌搜索(Tabu search)方法.该方法结合配电网络的闭环设计和开环运行的特点,将遗传算法中的优化编码技术引入到Tabu搜索算法中,并以有功网损最小为目标函数对配电网络进行优化.对3个典型IEEE测试系统进行了优化计算,将其结果与模拟退火算法和遗传算法的计算结果进行比较证实,改进的Tabu搜索算法比模拟退火算法和遗传算法具有更高的搜索效率.     

基于启发式搜索降低配电网网损的配网重构算法

配电网网络重构是配电网自动化系统的一个重要组成部分,给电力的供应提供了可靠性,给电力操作提供了灵活性.配电网网络重构的解是一组开关的组合,网络重构的目标多种多样,在实际应用中如何选取适合不同的配电网络重构方法是研究的目的.重点在启发式搜索和前推回代的配电网潮流计算方法的基础上以系统网损最小为目标函数来优化配网结构.首先生成一个1×N阶描述配电网开关通断的矩阵,然后以网损不能再减少为目的对所有的联络开关进行搜索,而寻找全局最优解.     

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。     

基于遗传和禁忌搜索混合算法的配电网重构

根据遗传和禁忌搜索算法的特点,提出了适用于配电网重构的混合算法。根据电力系统配电网重构的特点就混合算法的具体步骤进行了详细的描述,并提出了混合算法中早熟识别的具体方法、对按环编码变异进行了改进。该算法用于IEEE69节点系统的计算结果表明,混合算法用于配电网重构是可行的、有效的。     

基于遗传和禁忌搜索混合算法的配电网重构

根据遗传和禁忌搜索算法的特点,提出了适用于配电网重构的混合算法.根据电力系统配电网重构的特点就混合算法的具体步骤进行了详细的描述,并提出了混合算法中早熟识别的具体方法、对按环编码变异进行了改进.该算法用于IEEE69节点系统的计算结果表明,混合算法用于配电网重构是可行的、有效的.     

基于改进蚁群算法的配电网重构

配电网重构可以提高配电网运行的安全性、经济性和供电质量,对于当前国内配电自动化系统的建设和应用具有重要意义。以网络损耗最小为目标,提出了一种改进的蚁群算法来求解正常运行条件下配电网络的重构问题。针对基于蚁群算法在求解过程中存在搜索易陷入局部最优解和收敛到全局最优解的时间较长的缺点,从蚁群算法的转移概率和信息素动态更新2方面进行了改进,提高了蚁群算法全局搜索能力与收敛到最优解的速度。最后通过对69节点配电系统算例的仿真,取得了较好的效果,证明了该算法的有效性和可行性。     

基于并行禁忌搜索算法的配电网重构

为更好地实现配电网重构,引入了分治法,进而提出了采用并行禁忌搜索算法实现重构的方法.将配电网简化为只含支路组的环网,并给出了配电网重构的必要条件.重构时,首先选择断开的支路组,采用深度优先搜索算法形成父子链表;然后引入可降低复杂度的分治思想,以可断开支路组为解空间进行分组,应用多处理器进行并行禁忌搜索.算例结果验证了该方法的有效性.     

含分布式发电的改进混合整数差分算法的配电网重构

提出采用改进混合整数差分算法解决含分布式发电以降低网损为目标的配电网重构的新方法。该方法将网损最小作为目标函数,通过引入加速操作和迁移操作,克服了种群规模较小时传统差分算法易早熟的问题,而且提高了全局搜索能力。同时提出以环路为基础的整数编码方法,减少了编码长度,大大提高了可行解的比例,从而进一步提高其计算效率。最后讨论了减少计算量的措施。计算结果表明,在含有分布式发电的情况下,该方法不仅可以有效地降低网损,还能够达到提高供电电压质量的目的。以IEEE33节点为例,验证了该方法的可行性。     

基于潮流计算的配电网重构方法

为减小配电网的能量损失,提出了一种基于潮流计算的配电网重构方法。在单环网潮流计算的基础上,通过计算环网中电压最低的节点两侧开关分别断开后产生的网损增加量得到了最优解环开关。在配电网所有开关闭合的情况下,逐次求得各个环中的最优断开开关,然后使网络恢复成辐射状,然后逐次闭合各断开开关形成单环网,求得各单环网中的最优打开开关,直至得到整个网络的最优结果为止。采用该方法对2个测试系统进行重构,结果表明采用该方法可以较快地得到重构结果,与其它方法的比较结果也验证了该方法的正确性和优越性。     

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     

An improved TS algorithm for loss-minimum reconfiguration in large-scale distribution systems

This paper presents an improved tabu search (ITS) algorithm for loss-minimization reconfiguration in large-scale distribution systems. TS algorithm is an efficient meta-heuristic searching algorithm. It has advantages of both high local search efficiency of hill-climbing method and global search ability of intelligent algorithm. But tabu lengths and candidate neighborhood are two key parameters affecting searching performance of TS algorithm, and these two parameters are hard to be effectively determined in advance. In ITS algorithm, mutation operation, a main operator used in genetic algorithm, is introduced to weaken the dependence of global search ability on tabu length. In addition, the candidate neighborhood, which only contains several optimal switch exchanges in each tie switch associated loop network, is designed to improve local search efficiency and to save a large amount of computing time. The proposed ITS algorithm is applied to the sample system and numerical results well demonstrate the validity and effectiveness of the proposed ITS algorithm.     

An improved tabu search for economic dispatch with multiple minima

This paper develops an improved tabu search algorithm (ITS) for economic dispatch (ED) with noncontinuous and nonsmooth cost functions. ITS employs a flexible memory system to avoid the entrapment in a local minimum and developed the ideal of "distance" to the fitness to accelerate optimization. The new approach extends simple tabu search algorithm (STS) to real valued optimization problem and applies parallelism to weaken the dependence of the convergence rate of modified tabu search algorithm (NITS) on the initial condition. Effectiveness of the method was compared with many conventional methods. Results show that the proposed algorithm can provide accurate solutions with reasonable performance and has a great potential for other applications in the power system     

Novel FGbSA: Fuzzy-Galaxy-based search algorithm for multi-objective reconfiguration of distribution systems

Reconfiguration according to different criteria is an important problem in distribution systems. This paper presents a new method for optimal multi-objective reconfiguration of distribution system based on the Galaxy-based Search Algorithm (GbSA). To avoid the convergence problem, the input and output data are normalized in the same range using fuzzy sets. The main objectives of the proposed algorithm have been considered as power loss reduction, voltage profile improvement and increase of the system load balancing. The proposed technique has been investigated using the IEEE 33-bus test system and a real distribution network i.e. Tai-Power 11.4-kV distribution system. The obtained results revealed the superiority of the proposed fuzzy-GbSA method in terms of accuracy compared to the GbSA and other intelligent search algorithms such as Genetic Algorithm (GA) or Particle Swarm Optimization (PSO). Furthermore, the proposed algorithm efficiently converged to the optimum solution compared to the other intelligent counterpart algorithms.     

基于改进和声搜索算法的多目标配电网重构优化

为了减少有源配电网的网络损耗、提高系统运行的稳定性,建立了以网络损耗、开关动作数、负荷和电压平衡度为目标的数学模型。针对传统和声搜索算法全局数据依赖性较差、寻优的后半段速率变慢和易陷于早熟困境等问题,将其自适应参数、取值机制和越界逸出进行了改进。对无功进行随机优化补偿,利用场景分析法确定风机的出力值,采用定时模式的电动汽车模型对日负荷进行削峰填谷。基于欧式距离的K-Means方法对一天内含分布式电源的配电网络进行分段,在IEEE33电力结构中进行了动静态两重测试。在算法收敛上与传统和声、自适应和声搜索算法对比,减少了算法的无效运行次数,提高了算法的运行速度和全局寻优能力。在重构优化结果上与改进蚁群算法对比,减少了网络损耗和开关动作数,提高了系统运行的经济性与可靠性。     

基于改进粒子群动态搜索算法的配电网络重构研究

提出一种基于改进粒子群动态搜索算法的网络重构方法,算法把初始粒子群按照适应度的大小分为两个互不交叉,且具有不同分工的子群,并进行动态搜索。通过引入了交叉和禁忌思想,减少了解陷入局部最优的可能性。与遗传、禁忌搜索算法重构的结果进行比较,表明本文算法具有更高的搜索效率,更容易找到全局最优解。     

基于改进粒子群动态搜索算法的配电网络重构研究

提出一种基于改进粒子群动态搜索算法的网络重构方法,算法把初始粒子群按照适应度的大小分为两个互不交叉,且具有不同分工的子群,并进行动态搜索.通过引入了交叉和禁忌思想,减少了解陷入局部最优的可能性.与遗传、禁忌搜索算法重构的结果进行比较,表明本文算法具有更高的搜索效率,更容易找到全局最优解.