Methodology for allocation of remotely controlled switches in distribution networks based on a fuzzy multi-criteria decision making algorithm

Continuity in power supply for the consumers is a permanent concern from the utilities, pursued with the development of technological solutions in order to improve the performance of network restoration conditions. Using remotely controlled switches corresponds to one possible approach to reach such an improvement and giving some convenient remote resources such as the fault detect, isolation and transfer loads. This paper presents a methodology implemented in a computer programming language for allocation these devices in electric distribution systems based on multi-criteria fuzzy analysis. The main contributions are focus on considering the impact of installing remote-controlled switches in the reliability indexes and algorithm of fuzzy multi-criteria decision making for the switches allocation. The effectiveness of the proposed algorithm is demonstrated with case studies involving actual systems of the AES Sul utility located in the south of Brazil.     

基于AHP群决策法的配电网规划方案综合决策

配电网规划方案的综合决策过程不可避免地会受到主观意识左右,从而影响决策结果的可靠性,因此尽量降低主观因素对配电网规划方案综合决策的影响对指导电网的合理建设和改造具有重要意义。基于层次分析法引入群决策理论,通过对各决策组成员意见进行一致性分析,合理反馈,以得到各成员动态权重,进而形成客观的、具有代表性的聚合决策意见,为最终的方案排序提供了依据,增强了决策过程的客观公正性,提高了决策结果精度和可靠度。实例计算表明了该方法的正确性,且简洁易实现,具有较好的现实意义。     

基于协同进化算法的配电网络重构方案

提出了一种基于协同进化算法的配电网络重构方案,将配电网络重构分解为各个回路优化的子问题,采用分开进化、定期协同的思想来处理各个子问题,即生成与回路数目相等的种群,在确保全局最优解为优化方向的前提下,各种群独立进化对应各回路的优化过程。特殊的基因操作减少了不可行解的数量,提高了优化效率;使用"确定树"方法进行不可行解的修复,降低了陷入局部最优解的概率。算例结果表明所提算法具有较好的计算结果和较高的计算效率。     

Reliability-oriented distribution network reconfiguration considering uncertainties of data by interval analysis

This paper presents a new reliability-oriented reconfiguration method to enhance distribution system performance. The major purpose is to deal with imprecision and ambiguity in reliability inputs, electrical parameters and load data. The presented method uses the interval analysis techniques to enclose data uncertainties and to maximize the possibility of reliability improvement and/or loss reduction. Case studies have demonstrated the effectiveness and efficiency of the proposed reconfiguration method.     

An improved distribution network reconfiguration method based on minimum spanning tree algorithm and heuristic rules

This paper presents an improved distribution network reconfiguration method with the goal to minimize active power loss. The proposed method combines the minimum spanning tree (MST) algorithm and improved heuristic rules. It consists of three procedures. The first procedure calculates the branch (edge) weights with bus (vertex) voltages, and then carries out preliminary optimization with MST algorithm to get a local optimal solution. The second procedure gets alternative optimal solution based on the improved heuristic rules. Then during the third procedure, the optimal solution can generally be obtained through correcting the results. The algorithm does not rely on the initial network topology. The local optimal solution, solved by MST algorithm, provides a favorable initial condition for the subsequent optimization procedures. Further with the improved heuristic rules, the amount of the candidate switches can be significantly reduced. Two typical test systems, 33-bus system and 69-bus system, and a real 210-bus MV utility distribution system verified the feasibility and effectiveness of the proposed method. The method has higher efficiency and can be used to the large distribution systems.     

Effect of network reconfiguration on power quality of distribution system

Effect of network reconfiguration on power quality issues of distribution system has been investigated. The problem of network reconfiguration is reformulated with an objective to improve the power quality of the distribution system. Along with the traditional objective of loss minimization, power quality related objectives such as minimization of harmonic distortion of the voltage waveform, minimization of voltage unbalances at the nodes and maximization of sag voltages are identified as the objectives of reconfiguration. Branch exchange technique has been used to establish each of the objectives. The problem has also been formulated as a multi-objective optimization problem. The multiple objectives are, however, incorporated into a single objective using weighting multipliers and branch exchange technique has been judicially applied to take care of all the objectives. It is found that network reconfiguration can be used as an effective tool to improve the power quality of distribution system. Besides, the distributed energy sources also have great impacts on distribution network, as their size and locations are found to have great importance on the power loss, voltage sag, voltage harmonic distortion and unbalance. The effectiveness of the network reconfiguration on power quality issues have been studied on 25-bus network and IEEE 33-bus network with and without presence of distributed generation and VAr sources.     

模糊遗传算法在网络重构中的应用

采用模糊遗传算法 ,从全局出发寻找配网馈线上开关状态的优化组合 ,在安全可靠供电的情况下 ,使得网损最小 ,以实现配网重构。通过算例进行分析比较 ,说明该方法是行之有效的     

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.     

模糊遗传算法和蚁群算法相结合的配电网络重构

通过开关的优化组合可以提高配电系统运行的可靠性、电能质量和经济性。为改善配电网络重构模糊遗传算法的优化速度,提出了一种模糊遗传算法和蚁群算法相结合的方法。该方法将总的种群分为两部分进行搜索,一方面通过选择算子寻找总的种群中较优个体作为模糊遗传算法的子种群进行交叉、变异操作;另一面通过设定适应度函数阈值筛选总的种群中优秀个体,并将其适应度函数值对网络信息矩阵进行全局更新,用蚁群搜索另一部分子种群。该方法设定适应度函数阈值改进了蚁群算法的信息素更新机制;把模糊遗传算法和蚁群算法的子种群融合构成总的新种群,并用     

基于量子编码的离散粒子群算法配电网重构

阐述了配电网重构数学模型、二进制粒子群算法、量子编码的基本理论,对量子粒子群算法配电网重构进行了研究,将量子编码应用到离散粒子群算法中,用量子比特概率表示离散粒子的状态,根据二进制粒子群速度更新公式更新粒子的状态,改变开关开合状态进行网络重构.量子比特概率能够表征丰富的信息量,保证粒子的多样性和全局搜索能力.通过2个算...     

基于改进自适应遗传算法的配电网络重构

提出了一种用于配电系统网络重构的改进型自适应遗传算法。给出了网络重构问题的数学模型及改进的自适应遗传算法。在应用遗传算法时结合配电网自身的特点,提出以环路开关号为基因、系统环路数为染色体长度的编码方法,在优化过程中采用自适应调整的交叉率和变异率,结合一定的禁忌规则．较好地提高了算法在网络重构方面的效率。在IEEE16节点、33节点、69节点3个不同规模的算例系统上进行了测试,计算结果表明,所提出的方法缩短了染色体长度,较好地抑制了不可行解的产生．无论是在收敛性、稳定性还是在计算效率上都取得了比较满意的结果。     

基于十进制编码的差分进化算法在配电网重构中的应用

提出了一种基于差分进化的配电网重构方法。采用基于十进制环状编码方法解决了二进制编码产生大量不可行解的问题。给出了十进制环状编码的变异、交叉和选择策略;提出了一种在计算的同时调整支路节点顺序的算法,解决了配电网重构后部分支路电流流向颠倒的问题。应用差分进化算法对IEEE 33节点网络进行配电网重构仿真,结果表明该方法能有效降低配电网损耗。     

An interpretative structural modeling based network reconfiguration strategy for power systems

The network reconfiguration is an important stage of restoring a power system after a complete blackout or a local outage. Reasonable planning of the network reconfiguration procedure is essential for rapidly restoring the power system concerned. An approach for evaluating the importance of a line is first proposed based on the line contraction concept. Then, the interpretative structural modeling (ISM) is employed to analyze the relationship among the factors having impacts on the network reconfiguration. The security and speediness of restoring generating units are considered with priority, and a method is next proposed to select the generating unit to be restored by maximizing the restoration benefit with both the generation capacity of the restored generating unit and the importance of the line in the restoration path considered. Both the start-up sequence of generating units and the related restoration paths are optimized together in the proposed method, and in this way the shortcomings of separately solving these two issues in the existing methods are avoided. Finally, the New England 10-unit 39-bus power system and the Guangdong power system in South China are employed to demonstrate the basic features of the proposed method.     

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。     

基于改进灰狼优化算法的配电网动态重构

为了更好地解决含分布式电源(Distributed Generation,DG)的配电网重构问题,建立了考虑负荷需求与DG出力时变特性的配电网动态重构模型.首先采用K-means++聚类算法对日负荷进行时段划分.然后以系统损耗、电压偏离量为目标函数,并利用改进灰狼优化算法进行寻优计算.针对传统灰狼优化算法中存在的初始种...     

兼顾均匀性的多目标配电网重构方法

提出了一种兼顾均匀性的配电网多目标重构方法。分析了不同均匀性网架结构下可靠性与网损的关系,给出了网架结构均匀性的评价指标。建立了兼顾均匀性的多目标配电网重构模型(distribution network reconfiguration based on system homogeneity,DSH),并采用改进多目标和声搜索算法来求解DSH模型。通过对IEEE 33节点测试系统的仿真分析,验证了DSH模型求解算法的有效性与合理性,给出了考虑可靠性和均匀性的配网重构问题的最优解集,供决策者根据实际情况选择。     

基于改进MOBPSO算法的含分布式电源的多目标配电网重构

提出一种含多类分布式电源的多目标配电网重构模型。在重构时采用Pareto准则的多目标二进制粒子群优化(Multi-Objective Binary Particle Swarm Optimization, MOBPSO)算法,对算法进行了3点改进：基于环的编码方式用以降低不可行解的产生概率;动态变化的惯性因子和异步变化的学习因子用以提高算法的调节适应能力;在最优粒子的更新方式上提出小生境共享机制和比例选择算子相结合的策略。最后还给出了一种根据决策者偏好信息,从优化解集中选择相应重构方案的评价机制。算例结果表明：改进的MOBPSO算法在寻优效率和稳定性上均有提升;所提模型、改进算法以及评价机制互相配合,能够为决策者提供一种有效的配电网重构方案。     

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。     

基于混合粒子群智能遗传算法的配电网络重构优化研究

将二进制粒子群优化算法的惯性因子进行了动态化自适应改进,设计了区别于标准遗传操作的高频交叉算子和随机自回馈变异算子,基于此提出了一种新算法——混合粒子群智能遗传算法(PGA)应用与配网的重构。在新型编码方案下,PGA应用两个遗传算子使种群保持多样性,避免陷入局部最优,同时结合PSO的快速群体智能寻优指导染色体的进化方向,能够使种群信息共享的同时提高算法的收敛速度,算例结果验证了新算法的可行性。