基于适应性权重遗传算法的配电网多目标综合优化

针对配电网多目标综合优化问题的复杂性,以及网络重构、电容器投切的离散控制变量优化问题,基于遗传算法从全局角度来求解此优化问题,研究了网络重构、电容器投切的编码方案,以及两者的综合编码方案,并研究给出合理的选择、交叉、变异以及保留操作策略。针对线损、电压质量以及线路负载多目标的各个目标权重难以确定问题,进一步引入适应性权重遗传算法,随着遗传代数的进化,算法自适应地给出各个目标权重。仿真算例验证了所提算法用于配电网多目标综合优化的合理性。     

Oppositional krill herd algorithm for optimal location of capacitor with reconfiguration in radial distribution system

The delivery of power from sources to the consumer points is always accompanied of power losses. Basically, active losses in distribution systems can be reduced by optimal reconfigurations of the network. Optimal capacitor allocation problem in reconfigured distribution network is a challenge of researchers for several decades. This paper presents a computationally efficient methodology namely, krill herd (KH) algorithm to find optimal location of capacitor and optimal reconfiguration in order to minimize real power loss of radial distribution systems. Moreover, the opposition based learning (OBL) concept is integrated with KH algorithm for improving the convergence speed and simulation results. In order to show the usefulness and supremacy, the conventional KH and proposed oppositional KH (OKH) algorithms are tested on 33-bus and 69-bus radial distribution networks. The simulation results of the proposed methods are compared with fuzzy multi-objective approach and non dominated sorting genetic algorithm (NSGA). The solution results show that OKH technique could generate better quality solutions and better convergence characteristics than those obtained by conventional KH algorithm and other existing optimization techniques available in the literature. Results also show the robustness of the proposed methodology to solve reconfigured distribution network (RDN) problems.     

Reconfiguration of distribution network for loss reduction and reliability improvement based on an enhanced genetic algorithm

Electrical distribution network reconfiguration is a complex combinatorial optimization process aimed at finding a radial operating structure that minimizes the system power loss or/and maximizes the system reliability while satisfying operating constraints. In this paper, a distribution network reconfiguration method is presented for both the indices of power loss reduction and reliability improvement. The enhanced genetic optimization algorithm is used to handle the reconfiguration problem so as to determine the switch operation schemes. Based on the information of a single loop caused by closing a normally open switch, we improve the algorithm on crossover and mutation operations of original Genetic Algorithms. The effectiveness of the proposed method is demonstrated on 33-bus, 69-bus, and 136-bus radial distribution systems.     

基于PSO的配网重构与电容器投切综合优化算法

配电网络重构和电容器投切是配电网络优化的两个重要内容,简单地迭加难以反映二者之间的联系和影响,不能到达整体最优的目的。将二者融为一体,提出了一种基于PSO的综合优化算法。算法借鉴了遗传算法的变异思想,对具有集聚倾向的粒子进行速度变异,避免了“早熟”现象;并结合配电网络综合优化问题的特点,适当地改进更新规则来同时处理不同类型的变量,引入“飞回”策略处理越限问题。算例结果表明所提算法不依赖于网络的初始状态,不易陷入局部最优,优化效果好。     

改进的支路交换算法在配电网重构中的应用

提出了一种改进的配电网重构支路交换算法.该方法中用近似网损代替精确网损,对每个联络开关依次进行环网的重构优化;并通过最佳转移负荷与各支路负荷之间的距离确定打开的分段开关,且在重构过程中无需进行潮流计算,进一步提高了处理效率.算例结果验证了该方法是有效的     

模糊遗传算法在配电网络综合优化中的应用

网络重构和电容器投切是配电自动化的两个重要内容,是配电网降低网损、安全经济运行的重要措施。两者的综合优化问题比较复杂,肝传统算法难以同时进行。提出用遗传算法从全局角度进行求解,将开关状态变量和电容器投切组数变量作为控制变量。为加快回收敛速度,根据种群进化情况对交叉率和变异率进行在线模糊控制。算例表明该方法不依赖于网络初始状态,易于处理整数约束问题,优化效果好。另外,对配电网络进行综合优化,能大幅度地降低网损,改善电压质量,提高供电的经济性和安全性。     

基于混合算法的油田配电网负荷均衡化

提出了基于遗传算法和网络分割算法以均衡负荷为目标的配电网优化方法。将配电网优化问题表示为以负荷均衡指标最小为目标函数的非线性优化问题,针对配电网运行的结构特点对配电网拓扑结构模型进行了简化,然后再通过遗传算法对已分割的网络开关进行优化。算例分析结果表明能够有效解决配电网负荷均衡化的问题,计算速度快,并且收敛性好。     

基于改进二进制粒子群优化算法的负荷均衡化配电网重构

提出了基于改进的二进制粒子群优化算法、以均衡负荷为目标的配电网重构方法。将配电网重构问题表示为以负荷均衡指标最小为目标函数的非线性优化问题,针对配电网开环运行的结构特点对配电网拓扑结构模型进行了简化,并对二进制粒子群优化算法加以改进,以保证配电网的辐射状结构,同时大大减少迭代次数。算例分析结果表明,该方法能够有效解决负荷均衡化的配电网重构问题,计算速度快,收敛性好。     

Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm

This paper proposes a reconfiguration methodology based on a cuckoo search algorithm (CSA) for minimizing active power loss and the maximizing voltage magnitude. The CSA method is a new meta-heuristic algorithm inspired from the obligate brood parasitism of some cuckoo species which lay their eggs in the nests of other host birds of other species for solving optimization problems. Compared to other methods, CSA method has fewer control parameters and is more effective in optimization problems. The effectiveness of the proposed CSA has been tested on three different distribution network systems and the obtained test results have been compared to those from other methods in the literature. The simulation results show that the proposed CSA can be an efficient and promising method for distribution network reconfiguration problems.     

Simultaneous optimization of phase balancing and reconfiguration in distribution networks using BF–NM algorithm

Rephasing strategy is one of the main methods used for phase balancing and neutral current reduction in electrical distribution networks and the reconfiguration technique is an effective method for network loss reduction. In this paper, a new method for the simultaneous implementation of reconfiguration and phase balancing strategies is presented as a combinational strategy. In order to solve the proposed optimization problem, Nelder Mead algorithm combined with a bacterial foraging algorithm (BF–NM) is used based on a fuzzy multi-objective function. The proposed method allows for the simultaneous execution of reconfiguration and phase balancing while minimizing the interruption cost of rephasing in addition to eliminating network unbalancing and reducing neutral current and network losses. To demonstrate the efficiency of the BF–NM algorithm, its performance is compared with bacterial foraging (BF), particle swarm optimization (PSO), genetic and immune algorithms (GA and IA). The proposed method is applied to the IEEE 123-bus test network for evaluation. The simulation results confirm the efficiency of the method in reducing the system costs and network phase balancing.     

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     

基于功率矩和邻域搜索的有源配电网两层重构算法

针对传统的含有分布式电源的配电网重构算法所存在的寻优速度和最终解的质量难以兼顾的问题,提出一种基于功率矩和邻域搜索的配电网两层重构算法。利用功率矩算法对各环路进行第一层重构优化,确定开断支路集,在此基础上采用具有方向的邻域搜索算法进行第二层重构。通过对当前解点的邻域搜索获取更优的解点位置,直至目标函数值不再下降,从而得到最终的重构方案。含有分布式电源的IEEE 33和IEEE 69节点典型测试系统的仿真结果验证了所提算法的有效性。     

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。     

基于量子粒子群算法多目标优化的配电网动态重构

为保证配电网动态重构后系统安全稳定的运行,提出了以网损和节点电压稳定性为目标函数的量子粒子群算法的配电网动态重构。针对配电网动态重构过程中时段划分问题,提出以负荷曲线的单调性和幅值变化大小为依据初步划分时间段落。采用整数型量子粒子群算法进行动态重构,重构过程中以相邻时段的网损变化值的关系获取最佳重构段落,然后综合考虑配电网网损最小和节点电压值最大且波动最小为目标寻找最佳重构结构。以IEEE33配电系统为例验证了所提方法的有效性和实用性。     

改进遗传模拟退火算法在配电网络重构中的应用

对遗传模拟退火算法中的交叉、变异操作进行了改进,并实施了最优保留策略,形成了改进遗传模拟退火算法.以网损最小为目标函数,以配电网电压降的限制、线路电流量的限制等为约束条件,建立了配电网络重构优化模型.在考虑配电网自身特点的基础上,利用改进遗传模拟退火算法求解.重构算例说明,该优化方法有效、实用.     

配电网络重构的快速支路交换算法

提出了一种配电网重构的快速支路交换算法.该方法中用近似网损替代精确网损,对每个联络开关依次进行对应环网的重构优化,根据最佳转移负荷的符号和大小确定环网的最大降损开关交换,避免了使用启发式规则,且在重构过程中无需进行潮流计算,从而显著提高了网络重构的计算速度.算例结果验证了该方法的正确性和快速性.     

计及分布式电源动态行为的配电网重构优化策略

解决分布式电源高渗透率地区电压越限的根本方法是实施合理的配电网规划。现有方法存在着人工智能算法初始域搜索半径过大以及动态时段划分与拓扑寻优分离的问题。引入半不变量法计算随机潮流来处理分布式电源动态行为带来的不确定性,并采用改进的NSGA-II算法实现配电网重构,提出了一种计及分布式电源动态行为的配电网重构概率约束优化策略和求解方法。特别提出了在网架寻优的基础上再在时间层面上依据优压参数再次整合优化来构建配电系统动态重构数学模型,最后采用嵌套基因回路搜索策略改进NSGA-Ⅱ算法并求解上述优化模型,以解决传统配电网规划中对强相关性随机变量考虑较少的不足。IEEE-33节点配电系统算例验证了所提方法的有效性。     

配电网重构的自适应和声搜索算法

采用和声搜索算法（ＨＳＡ）搜索最优方案进行配电网重构时,会受初始参数值的影响。为此,提出自适应和声搜索算法（ＡＨＳＡ）。在和声搜索算法中加入局部搜索进行改进,增强了算法的寻优性能。建立以降低网络的有功和无功功率损耗为目标的配电网重构的数学模型,采用基于节点分层的前推回代法对网络结构进行潮流计算和分析。对IEEE69节点配电网进行仿真,与和声搜索算法,遗传算法（ＧＡ）的结果进行对比分析,表明该算法的迭代次数少,有较好的收敛效果。在配电网重构的算例应用中,证明了自适应和声搜索算法是可行的。     

计及过程安全性的配电网重构开关顺序优化

配电网重构过程应采用合理、可靠的开关操作顺序以避免对配电网的稳定运行产生冲击。采用改进的二进制粒子群算法求取配电网重构解集,对迭代过程中的无效粒子进行随机性的坐标修正,提升搜索效率;提出配电网重构实施过程合环、解环应满足的安全性约束,并应用戴维南等效模型计算合环冲击电流;提出用于求解计及过程安全性的配电网重构开关顺序的递归算法,搜索过程遵循贪心策略;针对递归过程中的无效中间解,以二进制编码方式记录,避免重复计算。IEEE 33节点系统、PGE 69节点系统算例结果验证了所提方法的有效性。     

Electric distribution network reconfiguration based on a fuzzy multi-criteria decision making algorithm

Topological reconfiguration is an important tool for the planning and operation of electric power distribution systems. The amount of time that an algorithm spends to obtain an alternative topological status for the system is not a primary concern, as the main goals of distribution operation planning are the reduction of power losses, the enhancement of the voltage profile, and the increase of reliability levels. The utility can use multiple criteria regarding the observation of regulation policies and public awareness to drive the topological reconfiguration. Several researchers are looking for new optimization methods, as the complexity of this combinatorial issue is high in large systems and the classic optimization methods are failing to address the problem reasonably. Therefore, a new fuzzy multi-criteria decision making algorithm for the proper processing of the information sources available at the utilities in the context of distribution network reconfiguration is proposed. The algorithm is evaluated through the use of a proof-of-concept implementation in a set of case studies based on actual distribution systems.