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  • This article was downloaded by:[National Institute of Technology Calicut]On: 31 March 2008Access Details: [subscription number 773426689]Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

    Electric Power Components andSystemsPublication details, including instructions for authors and subscription information:http://www.informaworld.com/smpp/title~content=t713399721

    Determination of Location and Capacity of PowerFacilities by Genetic AlgorithmTomonobu Senjyu a; Kai Shimabukuro a; Hirohito Yamashiro a; Katsumi Uezato a;Toshihisa Funabashi ba Faculty of Engineering, University of the Ryukyus, Okinawa, Japan.b Meidensha Corporation, Tokyo, Japan.

    Online Publication Date: 01 April 2004To cite this Article: Senjyu, Tomonobu, Shimabukuro, Kai, Yamashiro, Hirohito,Uezato, Katsumi and Funabashi, Toshihisa (2004) 'Determination of Location andCapacity of Power Facilities by Genetic Algorithm', Electric Power Components and

    Systems, 32:4, 1To link to this article: DOI: 10.1080/759369249URL: http://dx.doi.org/10.1080/759369249

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    Electric Power Components and Systems, 32:375390, 2004Copyright c Taylor & Francis Inc.ISSN: 1532-5008 print/1532-5016 onlineDOI: 10.1080/15325000490217470

    Determination of Location and Capacity ofPower Facilities by Genetic Algorithm

    TOMONOBU SENJYUKAI SHIMABUKUROHIROHITO YAMASHIROKATSUMI UEZATOFaculty of EngineeringUniversity of the RyukyusOkinawa, Japan

    TOSHIHISA FUNABASHIMeidensha CorporationTokyo, Japan

    This article presents the determination of optimal location and capacity ofpower facilities by using genetic algorithm (GA) based on reliability, loss ofload and dump power. We determine optimal capacity after optimal location;however, since the optimal location and capacity are closely related, the optimallocation varies with the capacity and vice versa. Hence, we propose the methodthat determines the optimal location and capacity at the same time. Using theproposed method, expected loss of load in faults can be reduced by 32.3% incomparison with that of separate optimization techniques.

    Keywords power system reliability, loss of load, dump power, geneticalgorithm

    1. Introduction

    In recent years, the demand of power has been increasing with rapid industrializa-tion. Since electric power systems play a major role in a modern society, professionalengineers are responsible for proper planning, design, and operation of power sys-tems. Further, the modern power systems are required to have better reliability.Under these circumstances, new generation facilities and expansion of the trans-mission lines must be planned and constructed accordingly to maintain reliabilityof power systems and reduce loss of load during faults. Large reserve can maintainreliability and decrease loss of load during faults. However, this reserve increases

    Manuscript received in nal form on 23 December 2002.Address correspondence to Tomonobu Senjyu, Faculty of Engineering, University

    of the Ryukyus, 1 Senbaru Nishihara-cho Nakagami Okinawa 903-0213 Japan. E-mail:b985542@tec.u-ryukyu.ac.jp

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    376 T. Senjyu et al.

    cost and dump power that indicates the excess electricity. Hence, there is an im-portance of not only location but also capacity on power system planning.

    To solve these optimization problems, algorithms for heuristic techniques havebeen presented. Among the various techniques are Tabu search, Lagrangian relax-ation (LR) methods, and neural network [1]. However, since these methods are localsearch techniques, it is dicult to obtain the optimal solution.

    To achieve global search, we propose a genetic algorithm (GA) solution to theoptimal location and capacity problem. GA is an optimization technique basedon a model of evolutionary adaptation in nature [2, 3, 6]. The objective of theproblem mentioned above is maintenance of reliability and reducing the loss ofload and dump power. Since power system reliability and loss of load have diversedimensions, they cannot be evaluated at the same time. This is the multi-objectiveoptimization problem for that reason.

    In this article, objective functions are incorporated for the evaluation of thesevariables at the same time. Using the proposed method, optimal location andcapacity can be optimized at the same time, and the expected value of loss ofload in faults can be reduced by 32.3% in comparison with the method optimizingthe location and capacity separately.

    2. Power System Reliability

    There are many variations on the denition and evaluation of reliability. We adoptthe following denition of reliability; reliability is the probability of a device per-forming its purpose adequately for the period of time intended under the operatingconditions encountered [4, 5]. Since the power system is playing a major role inmodern society, its reliability evaluation is essential. Figure 1 shows the operatingcondition and failure condition of power facilities. Reliability is decided with theavailability given by equation (1), which includes durability and maintainability ofpower facilities.

    A =operating time

    operating time + failure time(1)

    Equation (1) can be further written as,

    A =MTBF

    MTBF +MTTR(2)

    Figure 1. Power system conditions.

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    Determination of Power Facilities 377

    where MTBF and MTTR are the mean time between failure and mean time torepair, respectively. Here, we dene the new variable,

    =MTTRMTBF

    (3)

    where is the coecient of maintainability.Using the relationship given in equation (3), we obtain the following expression

    for A as,

    A =1

    1 + . (4)

    From equation (4), the availability A increases with decrease the coecient ofmaintainability.

    3. Genetic Algorithm

    The model of power system used in this article is shown in Figure 2, where G, T , N ,L represent power plant, transmission line, transformer, and load point, respectively.Suppose that two generators and two transmission lines are added in Figure 2. Wewould then determine the optimum setup point and capacity using GA. Figure 3shows the owchart of genetic algorithm and each step of the owchart is describedas follows:

    STEP 1 Generating Initial Population

    Initial population is randomly generated using binary strings (0 and 1). The popu-lation strings consists of 14 bits. Figure 4 shows an initial population. Generators

    Figure 2. Power system conguration.

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    Figure 3. GA owchart.

    Figure 4. Initial population.

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    Determination of Power Facilities 379

    1 through 9 in Figure 2 are candidates for new generator setup point. New trans-mission lines are set up in the existing transmission lines. Setup point and capacityof those new facilities are expressed by bit row in Figure 4.

    STEP 2 Evaluation

    Populations are evaluated by the tness function. Fitness function is the mostimportant factor in GA.

    STEP 3 Selection

    The selection creates a new population from the old one. We adopt the elite selectionand roulette selection.

    STEP 4 Crossover

    Crossover is the most important operator in GA and many new solutions aregenerated by crossover. This operator simply combines the parent symbol strings,forming a new chromosome strings that inherits solution characteristic from bothparents. The crossover scheme used in this paper is an one-point crossover. We usevariable crossover rate. The crossover rate is given by

    Pcr = Pc (Fave/Fmax ) (5)where Pc is coecient of crossover rate. Fave and Fmax are the average andmaximum tnesses respectively. An example of the one-point crossover operatoris shown in Figure 5.

    STEP 5 Mutation

    The mutation operator is applied

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