配电网络重构的优化可信度度量的区间方法

提出了一种以能量损耗减小的可信度度量值最大化作为目标函数的配电网络重构区间算法。考虑每时段上负荷的不确定性,采用区间潮流算法,按照区间化的日负荷曲线得到一日之内系统能量损耗的变化区间,然后根据能量损耗减小的可信度度量值来决定一日的网络最优结构。该方法可用于实现以星期或月为周期的配电网络重构。33母线和69母线测试系统的计算实例表明了算法的应用价值。     

含电动汽车和分布式电源的主动配电网动态重构

分布式电源和大量电动汽车充电功率的间歇性和随机性使配电网重构面临新的挑战。在此背景下,提出了一种基于区间数方法的含电动汽车和分布式电源的动态重构策略。文中引入区间数以描述分布式电源出力和电动汽车充电负荷的不确定性,以区间数描述最小化网损为目标函数,提出依据动态降损参数的动态时段划分,建立配电网动态重构的数学模型,在利用KrawczykMoore区间迭代法对潮流方程进行求解的同时引入仿射乘除运算代替区间乘除运算,改善了区间运算过于保守的问题。最后,结合邻域搜索以及克隆选择算法提出了求解上述模型的优化方法,以实现考虑多种不确定因素下主动配电网动态重构。算例分析证明所提方法相较于传统人工智能算法具有优越性。     

Artificial immune algorithm applied to distribution system reconfiguration with variable demand

This paper presents a new methodology to solve the reconfiguration problem of electrical distribution systems (EDSs) with variable demand, using the artificial immune algorithm Copt-aiNet (Artificial Immune Network for Combinatorial Optimization). This algorithm is an optimization technique inspired by immune network theory (aiNet). The reconfiguration problem with variable demand is a complex problem of a combinatorial nature. The goal is to identify the best radial topology for an EDS in order to minimize the cost of energy losses in a given operation period. A specialized sweep load flow for radial systems was used to evaluate the feasibility of the topology with respect to the operational constraints of the EDS and to calculate the active power losses for each demand level. The algorithm was implemented in C++ and was evaluated using test systems with 33, 84, and 136 nodes, as well as a real system with 417 nodes. The obtained results were compared with those in the literature in order to validate and prove the efficiency of the proposed algorithm.     

Multi-Objective Approach for Reconfiguration of Distribution Systems with Distributed Generations

This paper presents a fuzzy multi-objective based heuristic algorithm for network reconfiguration of distribution systems considering distributed generations (DGs). The objectives of reduction of real power loss, branch current carrying capacity limit, maximum and minimum voltage constraints, and feeder load balancing are considered for performance enhancement of the distribution system. Since these objectives are non-commensurable and difficult to solve simultaneously using conventional approaches, they are converted into fuzzy domain and a fuzzy multi-objective function is formulated. A sensitivity analysis based on voltage profile improvement and real power loss reduction is used for obtaining optimal locations of DGs and genetic algorithm is used for optimal sizing of DGs. The proposed reconfiguration algorithm is implemented in two stages, initially in the first stage without incorporating DGs and in the second stage incorporating DGs for obtaining an optimal distribution system network reconfiguration. The advantage of the proposed method is demonstrated through a seventy node four feeders and a sixteen node three feeders distribution systems.     

基于蚁群系统(ACS)的配电网重构

配电网络重构是一个非常复杂的大规模组合优化问题。提出了一种新颖的基于蚁群系统的算法来求解正常运行条件下的配电网络重构问题,以达到损失最小。蚁群系统算法ACS(Ant Colony System)是一种新型通用内启发式算法。结合配电网的特点,应用蚁群算法来解决配电网重构问题,建立了相应的数学模型,并给出求解算法。研究了一个算例系统,并给出了计算结果。结论表明,提出的算法是可行、有效的。     

一种实用的配网重构方法

本文提出了一种基于虚拟流法的实用配网重构方法,将重构问题划分为离线的季度性全网重构和在线的实时局部网络重构,实时网络重构不仅考虑了平衡馈线负荷,减少过载,而且考虑平衡三相负荷,相应的提出了一种实时平衡三相负荷的实用算法。     

考虑多种负荷方式的配电网络重构

提出了考虑多种负荷方式的配电网络重构算法.用最大负荷、最小负荷和一般负荷方式模拟研究时段内负荷的变化,首先按照上述三种负荷方式,采用改进支路交换法对配电网络分别进行重构,得到每种负荷方式下的最优重构方案;然后计算出每种最优重构方案下考虑三种负荷方式的加权平均有功损耗;具有最小平均有功损耗的重构方案即为该研究时段内的最优重构方案.所提算法避免了只考虑单一负荷方式的片面性,算例结果表明了该算法的有效性和可行性.     

Real-Time Cascading Failures Prevention Through MAS Algorithm and Immune System Reinforcement Learning

The current options power engineers have during a pending CFs event have been limited to those that require load shedding. In this work, a novel algorithm is proposed that avoids loss incurrence as it does not use load shedding and involves real-time power redispatch from the generators through an appropriate combination. This method requires that the system possesses MAS capabilities to intelligently effect the power changes. A tool is needed to select the right combination for each system state as the use of lookup table and heuristics would be ineffective for large systems. The innovative use of AIS, as a reinforcement learning tool and not an optimization tool, is proposed for selecting the appropriate combination. The results obtained when lookup table, heuristics, and AIS are used to select the combination for a real-time experimental system are compared. The test system is the generation and transmission side of IEEE 30-bus system; and the CFs prevention algorithm is activated once the system is in an N-1 contingency state. IEEE 118-Bus System was also used to test the efficacy of AIS-MAS algorithm on large systems. The AIS-MAS algorithm proposed showed great promise for preventing CFs.     

基于时间区间的配电网重构

提出了基于时间区间的静态重构算法和动态重构算法.算法以最优流法为基础,以一段时间内的能量损耗最小为目标函数.首先利用配电网负荷模式的特点对基于时间区间的最优流计算方法进行了改进,然后利用该计算方法求解配电网的静态重构问题.动态重构中需要考虑开关操作次数的约束,为此,动态重构算法中先利用降损估算公式对开关操作在整个时间区间内的降损效果进行评估和比较,然后提出了合理的启发式规则来确定开关的操作顺序,形成优化方案.69节点配电网的算例结果表明,提出的静态重构和动态重构算法是可行和有效的.     

改进自适应遗传算法在网络重构中的应用

研究了大停电事故后输电系统的重构优化问题,提出一种求解最优目标网的改进自适应遗传算法。将网络重构问题表示为以恢复负荷总量最高为目的的非线性优化问题,在求解目标网时考虑了负荷的重要性、网络连通性、电网所需满足的各种安全和运行约束等问题。采用改进的自适应遗传算法对问题进行求解,通过适应值函数的计算得到了系统允许条件下的最大允许恢复负荷量。该算法在求解电网重构问题时,编码容易且能方便地处理网络连通问题,求解效率高,用IEEE30节点算例验证了本文方法的有效性。     

虚拟负荷法及其在配电网络动态优化中的应用

本文提出考虑负荷变化的电力系统动态优化的有效算法──虚拟负荷法。该算法根据最优网损曲线的变化，将时空分布的动态优化问题按时间分段等值为几个空间分布的静态优化问题，大大简化了计算，提高了算法的灵活性。该算法已成功地用于配电网络重构和电容器投切的动态优化中。     

提高系统可靠性的配电网络多目标重构区间方法

提出了基于可能度概念的区间数大小比较通用数值计算方法。将区间分析应用于提高配电系统可靠性的措施中,提出并实现了以提高系统可靠性为目标的配电网络重构的区间方法。区间算法以特定的系统可靠性指标或系统停电损失费用与网损费用的加权和作为综合评价值,以综合评价值减少的可能度最大化为目标函数。算法考虑可靠性参数与网络动态变化的不确定因素,采用三相区间潮流和区间可靠性算法,进行隐含并行寻优,不仅求得了优化的网络结构,并且给出了网络结构的优化程度,最大可能地降低决策风险。算例分析表明该算法实用、有效。配套软件成为配电系统规划及运行的灵活、易用的工具。     

Distribution system reconfiguration using a modified Tabu Search algorithm

This article presents an efficient meta-heuristic method for reconfiguration of distribution systems. A modified Tabu Search (MTS) algorithm is used to reconfigure distribution systems so that active power losses are globally minimized with turning on/off sectionalizing switches. TS algorithm is introduced with some modifications such as using a tabu list with variable size according to the system size. Also, a random multiplicative move is used in the search process to diversify the search toward unexplored regions. The Kirchhoff algebraic method is adopted to check the radial topology of the system. A salient feature of the MTS method is that it can quickly provide a global optimal or near-optimal solution to the network reconfiguration problem. To verify the effectiveness of the proposed approach, the effect of load variation is taken into consideration and comparative studies are conducted on three test systems with rather encouraging results. The obtained results, using the proposed MTS approach, are compared with that obtained using other approaches in the previous work.     

Optimal loss reduction of distributed networks

A novel algorithm for the network reconfiguration of power distribution systems is presented. An optimal loss reduction is accomplished to maintain acceptable voltage at customer loads as well as to assure sufficient conductor and substation current capacity to handle load requirements. The success of the algorithm depends directly upon the straightforward and highly-efficient solution of the quadratic cost transshipment problem. The proposed algorithm completely eliminates the need for matrix operations and executes all operations directly on a graph of the distribution system     

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.     

计及不确定性的配电网模糊优化重构

实际配电网的负荷变化具有不确定性,使得配电网重构难以用传统的模型来描述。为此,提出根据负荷变化的模糊特性,建立以供电质量最优和网络损耗最小为目标的模糊化多目标配电网重构模型。采用量子进化算法对模型进行求解,以提高配电网络的经济性和供电质量;计算过程中采用支路前推回代法得到通应度函数的模糊区间,并通过区间评价函数进行模糊区间的比较来衡量方案的优劣。采用含分布式电源的IEEE33节点测试系统进行仿真计算,证明该方法的有效性。     

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.     

多负荷水平下含风电接入的配电网无功优化

由于风电输出功率的随机性,风电机组的大量接入给配电网无功优化带来更多不确定性因素。为了提高配电网无功优化对风力发电并网的适应能力,建立了多负荷水平下基于场景分析的考虑风电接入的多目标无功优化模型。该模型综合考虑了节省电能损失费用和节点电压偏差2个指标,将2个指标进行模糊化,采用最大化模糊满意度指标法将多目标优化问题转换为单目标优化问题,然后采用自适应遗传算法进行求解。并以IEEE 33节点测试系统为例,计算和分析了在不同场景时最大负荷、一般负荷和最小负荷3种负荷水平下,电容器投切、系统有功损耗、节点电压以及节省电能费用情况。计算结果表明,所提出的无功模糊优化方法,在不同负荷水平、不同场景下改善电压质量和降损节能效果显著,适合多负荷水平下含风电机组的配电网无功优化需要。     

Mathematical representation of radiality constraint in distribution system reconfiguration problem

Distribution systems are most commonly operated in a radial configuration for a number of reasons. In order to impose radiality constraint in the optimal network reconfiguration problem, an efficient algorithm is introduced in this paper based on graph theory. The paper shows that the normally followed methods of imposing radiality constraint within a mixed-integer programming formulation of the reconfiguration problem may not be sufficient. The minimum-loss network reconfiguration problem is formulated using different ways to impose radiality constraint. It is shown, through simulations, that the formulated problem using the proposed method for representing radiality constraint can be solved more efficiently, as opposed to the previously proposed formulations. This results in up to 30% reduction in CPU time for the test systems used in this study.     

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.