Tabu search algorithm Distribution system reconfiguration using a modified Tabu Search algorithm

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<ul><li><p>8/13/2019 Tabu search algorithm Distribution system reconfiguration using a modified Tabu Search algorithm</p><p> 1/11</p><p>Electric Power Systems Research 80 (2010) 943953</p><p>Contents lists available at ScienceDirect</p><p>Electric Power Systems Research</p><p> j o u rn a l h o me p ag e : www.e l sev i e r. co m/ l o ca t e / ep s r</p><p>Distribution system reconguration using a modied Tabu Search algorithm</p><p>A.Y. Abdelaziz , F.M. Mohamed, S.F. Mekhamer, M.A.L. BadrElectrical Power and Machines Department, Faculty of Engineering, Ain Shams University, 1 Elsarayat Street, Abdo Basha Square Abbassia, Cairo, Egypt </p><p>a r t i c l e i n f o</p><p> Article history:Received 31 March 2008Received in revised form 30 October 2009Accepted 3 January 2010Available online 20 January 2010</p><p>Keywords:Distribution system recongurationPower loss reductionModied Tabu Search</p><p>a b s t r a c t</p><p>This article presents an efcient meta-heuristic method for reconguration of distribution systems. Amodied Tabu Search (MTS) algorithm is used to recongure distribution systems so that active powerlosses are globally minimized with turning on/off sectionalizing switches. TS algorithm is introduced</p><p>with some modications 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 unexploredregions. The Kirchhoff algebraic method is adopted to check the radial topology of the system. A salientfeature of the MTS method is that it can quickly provide a global optimal or near-optimal solution to thenetwork reconguration problem. To verify theeffectiveness of theproposed approach,the effectof loadvariation is taken into consideration and comparative studies are conducted on three test systems withrather encouraging results. The obtained results, using the proposed MTS approach, are compared withthat obtained using other approaches in the previous work.</p><p> 2010 Elsevier B.V. All rights reserved.</p><p>1. Introduction</p><p>The subject of minimizing distribution systems losses hasgained a great deal of attention due to the high cost of electri-cal energy and therefore, much of current research on distributionautomation has focused on the minimum-loss conguration prob-lem. There are many alternatives available for reducing losses atthe distribution level: reconguration, capacitor installation, loadbalancing, and introduction of higher voltage levels. This researchfocuses on the reconguration alternative.</p><p>Network reconguration is the process of changing the topol-ogy of distribution systems by altering the open/closed statusof switches. Because there are many candidate-switching com-binations in the distribution system, network reconguration isa complicated combinatorial, non-differentiable constrained opti-mization problem. Two types of switches are used in primarydistribution systems. There are normally closed switches (section-alizing switches) and normally open switches (tie switches). Thosetwo types of switches are designed for both protection and con-guration management. The change in network conguration isachieved by opening or closing of these two types of switches insuch a way that the radiality of the network is maintained.</p><p>The reconguration algorithms can be classied by the solutionmethods that they employ: those based upon a blend of heuristicsand optimization methods, those making use of heuristics alone,</p><p>Corresponding author. Tel.: +20 101372930.E-mail addresses: ,</p><p>(A.Y. Abdelaziz).</p><p>and those using some from of articial intelligence (AI). Numerousresearchers advocate the use of a blend of heuristics and optimiza-tion techniques. The blend of the two types of technique permitsthe problem to retain a certain degree of accuracy, while assuringconvergence and an acceptable solution time.</p><p>InRef. [1] , a branch exchange method thatconsidered the onoff conditions of the sectionalizing switches in discrete numbers wasdeveloped [1] . Since themethod is based on heuristics, it is noteasyto take a systematic way to evaluate an optimal solution.</p><p>Two different methods with varying degree of accuracy toapproximate power ow in systems were proposed in Ref. [2] . Thesearch method has an acceptableconvergencecharacteristic. How-ever, it can get stuck in local minimum. The method is very timeconsuming due to the complicated combinations in large-scale sys-tems.</p><p>An expert system for feeder reconguration, based upon exten-sions of the rules of Ref. [1] was presented in Ref. [3], with thepotential of handling realistic operating constrains. The approachtaken is set up a decision tree to represent the various switch-ing operations available. This strategy is efcient for trees that arenot too large. However, as a search tree becomes larger, a greatamount of time can be spent searching for the optimal solution. Toguarantee an optimal solution an exhaustive tree search should beused.</p><p>A linear programming method using transportation techniquesanda new heuristic search method forcomparison with previouslydeveloped heuristic techniques which are based on an optimalload ow analysis were presented in Ref. [4] . This study indicatesthat linear programming, in the form of transportation algorithms,is not suitable for application to feeder reconguration since the</p><p>0378-7796/$ see front matter 2010 Elsevier B.V. All rights reserved.</p><p>doi: 10.1016/j.epsr.2010.01.001</p></li><li><p>8/13/2019 Tabu search algorithm Distribution system reconfiguration using a modified Tabu Search algorithm</p><p> 2/11</p><p>944 A.Y. Abdelaziz et al. / Electric Power Systems Research 80 (2010) 943953</p><p>power loss function is not linear whilst heuristic approaches,although not optimal, can provide substantial saving if properlyformulated.</p><p>Based on partitioning the distribution network into groups of load buses, the line section losses between the groups of nodes areminimized [5] . By dividing the distribution network into groups of busses, the combinatorial nature of the reconguration problem isovercome, while simultaneously minimizing losses.</p><p>In recent years, meta-heuristic methods have been studied forsolving combinatorial optimization problems to obtain an opti-mal solution of global minimum. Typical meta-heuristic methodsinclude Simulated Annealing (SA), Genetic Algorithm (GA), andTabu Search (TS).</p><p>A two-stage solution methodology based on a modied simu-lated annealing technique for solving the reconguration problemof distribution systems was proposed in Ref. [6] . In Ref. [7] , a mod-ied SA technique for network reconguration for loss reductionin distribution systems was presented. An efcient perturba-tion scheme and an initialization procedure determining a betterstarting temperature for the simulated annealing approach wereproposed. This method can get a solution better than that obtainedusing the method presented in Ref. [5]. This solution algorithmgives a near-optimal solution but this method does not work sowell in the case of load variation.</p><p>A GA based method for feeder reconguration was proposed inRef. [8] . Strings which represent switch status, a tness functionconsisting of total system losses, and penalty values of volt-age drop limit and current capacity limit were formed. Sampleresults demonstrate that, although the minimal loss solutions wereobtained, solution time was prohibitive.</p><p>An articial neural network based method for feeder recong-uration was presented in Ref. [9]. However, such technique canencounter difculties, such as getting trapped in local minima,increased computational complexity, and not being applicable tocertain objectivefunctions. This ledto theneed of developing a newclass of solution methods that can overcome these shortcomings.</p><p>A parallel Tabu Search (PTS) based method for feeder recong-</p><p>uration has been proposed in Ref. [10] . PTS introduces two parallelschemes. One is the decomposition of the neighborhood with par-allel processors to reduce computational efforts. The other is themultiplicity of the tabu length to improve the solution accuracy.PTS algorithm gives results better than results obtained by SA, par-allelSimulatedAnnealing(PSA), GA,and parallel Genetic Algorithm(PGA).InRef. [11] , a TSalgorithmforsolvingthe problem ofnetworkrecongurationin distributionsystems in order to reducethe resis-tive line losses under normal operating conditions was presented.A method for checking system radiality based on an upward-nodeexpression, which has been developed in solving the problem of restorative planning of power systemwas proposed. In Ref. [12] , anefcienthybrid algorithmof SA andTS method for feederrecongu-ration to improve the computation time and convergence property</p><p>was proposed. In Ref. [13] , a modied Tabu Search (MTS) basedalgorithmfor reconguration of distribution systems has been pro-posed. The TS algorithm was introduced with some modicationssuch as using a tabu list with variable size to prevent cycling andto escape from local minimum. Also, a constrained multiplicativemove wasused in thesearch process to diversifythe searchprocesstoward unexplored regions.</p><p>Zhang et al. [14] presented an Improved Tabu Search (ITS)algorithm for loss-minimization reconguration in large-scale dis-tribution systems. In ITS algorithm, mutation operation, a mainoperator used in genetic algorithm, is introduced to weaken thedependence of global search ability on tabu length. In addition,the candidate neighborhood, which only contains several optimalswitch exchanges in each tie switch associated loop network, is</p><p>designed to improve local search efciency and to save a large</p><p>Fig. 1. 16-Node distribution system.</p><p>amount of computing time. The ITS algorithm in Ref. [14] wasapplied to the 119-node system and gave an optimal solution.</p><p>In this article, an enlarged version of Ref. [13] is introduced tosolve the reconguration problem.The proposedmethod is appliedto large-scale networks to show the effectiveness of the modiedTabu Search algorithm. In comparison with Ref. [14] in which themutation operation of GA is used to weaken the dependence of </p><p>global search ability on tabu length, on the other side, we use adynamic tabu list with variable size according to the system sizeand a multiplicative move is applied to diversify the search processand improve the local search efciency of Tabu Search to reach theglobalsolution. Also, theeffect of variationof load is taken into con-sideration to show the capability of the proposed algorithm (MTS)to work at different load levels.</p><p>To verify the effectiveness of the proposed method, compar-ative studies are conducted on three test systems with ratherencouraging results. The proposed method is applied to a 16-nodesystem, a 69-node system, and a 119-node system. The results,obtained using the proposed MTS approach, are compared with</p><p>Fig. 2. Flow chart of Tabu Search algorithm.</p></li><li><p>8/13/2019 Tabu search algorithm Distribution system reconfiguration using a modified Tabu Search algorithm</p><p> 3/11</p><p> A.Y. Abdelaziz et al. / Electric Power Systems Research 80 (2010) 943953 945</p><p>Fig. 3. Flow chart for checking system radiality.</p><p>results obtained using other modern techniques to examine theperformance of the proposed approach.</p><p>2. Problem formulation</p><p>Generally, there are two types of switches in distribution sys-tems: tie switch and sectionalizing switch. As shown in Fig. 1,switches in dotted branches connecting nodes(1014), (511), and(716) are tie switches, and switches in other continuous branchesare sectionalizing switches. The tie switches are normally open</p><p>and the sectionalizing switches are normally closed. When theoperating conditions have been changed, feeder reconguration isperformed by the opening/closing of these two types of switchesto reduce resistive line losses.</p><p>That is,a tieswitch may be closedfor thepurpose of transferringloads to different feeders, and, at the same time, a sectionalizingswitchshould be openedto maintain theradial structureof thedis-tribution network. For example, in Fig. 1, when the loads of feeder2 become heavy under normal operating conditions, the tie switchconnecting nodes (511) may be closed to transfer the load at node11fromfeeder 2 tofeeder1 and atthe sametimethe sectionalizingswitch connecting nodes (911) must be opened to maintain theradial structure of the network.</p><p>The objective of the reconguration is to minimize the dis-</p><p>tribution losses with turning on/off sectionalizing switches. The</p><p>reconguration problem has the following constrains:</p><p>1. Power ow equations.2. Upper and lower bounds of nodal voltages.3. Upper and lower bounds of line currents.4. Feasible conditions in terms of network topology.</p><p>Mathematically, the problem can be formulated as follows:Cost function:</p><p>Min Z =L</p><p>i= 1</p><p>r i P 2i + Q </p><p>2i</p><p>V 2i(1)</p><p>Subject to:</p><p> g ( x) = 0 (2)</p><p>V mini &lt; V i &lt; V maxi (3)</p><p>I mini &lt; I i &lt; I maxi (4)</p><p>det( A) = 1 or 1 radial system (5)</p><p>det( A) = 0 not radial (6)</p><p>where Z : objective function (kW); L: no. of branches; P i: active</p><p>power loss at sending endof branch i; Q i: reactive power at sending</p></li><li><p>8/13/2019 Tabu search algorithm Distribution system reconfiguration using a modified Tabu Search algorithm</p><p> 4/11</p><p>946 A.Y. Abdelaziz et al. / Electric Power Systems Research 80 (2010) 943953</p><p>Fig. 4. Optimal conguration of the 16-node system.</p><p>end of branch i; V i: voltage at sending end of branch i; I i: line cur-rent at branch i; g ( x): power ow equations; V mini : lower voltagelimit (taken to be 0.9 p.u); V maxi : upper voltage limit (taken to be1 p.u); I mini : lower current limit; I </p><p>maxi : upper current limit; A: bus</p><p>incidence matrix; r i: resistance of branch i.</p><p>3. Tabu Search</p><p>Tabu Search is one of the modern heuristic search methodsfor combinatorial optimization problems, based on neighborhoodsearch with local optimaavoidance, which modelshuman memoryprocesses. Tabu Search was initially proposed by Glover and manyother authors have applied similar ideas to various classical prob-lems [15,16] . Tabu Search (TS) canbe considered as a neighborhoodsearch method which is more elaborate than the descent method.</p><p>Like any local search method (LS), TS needs three basic compo-nents: a conguration structure, a neighborhood function denedon the conguration structure, and neighborhood examinationmechanism. The rst component denes the search space of S of the application, the second associates with each point of the searchspace which is a subset of S, while the third one prescribes the wayof going from one...</p></li></ul>