Published online: 06 Jan 2014.

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1 This article was downloaded by: [Universite Laval] On: 24 May 2014, At: 07:49 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: Registered office: Mortimer House, Mortimer Street, London W1T 3JH, UK Electric Power Components and Systems Publication details, including instructions for authors and subscription information: Reliability-constrained Based Optimal Placement and Sizing of Multiple Distributed Generators in Power Distribution Network Using Cat Swarm Optimization Deepak Kumar a, S. R. Samantaray a, I. Kamwa b & N. C. Sahoo a a School of Electrical Sciences, Indian Institute of Technology, Bhubaneswar, India b Power System and Mathematics, Hydro-Québec/IREQ, Varennes, QC, Canada Published online: 06 Jan To cite this article: Deepak Kumar, S. R. Samantaray, I. Kamwa & N. C. Sahoo (2014) Reliability-constrained Based Optimal Placement and Sizing of Multiple Distributed Generators in Power Distribution Network Using Cat Swarm Optimization, Electric Power Components and Systems, 42:2, , DOI: / To link to this article: PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the Content ) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at

2 Electric Power Components and Systems, 42(2): , 2014 Copyright C Taylor & Francis Group, LLC ISSN: print / online DOI: / Reliability-constrained Based Optimal Placement and Sizing of Multiple Distributed Generators in Power Distribution Network Using Cat Swarm Optimization Deepak Kumar, 1 S. R. Samantaray, 1 I. Kamwa, 2 andn.c.sahoo 1 1 School of Electrical Sciences, Indian Institute of Technology, Bhubaneswar, India 2 Power System and Mathematics, Hydro-Québec/IREQ, Varennes, QC, Canada CONTENTS 1. Introduction 2. Problem Formulation 3. Base-case Reliability Evaluation 4. Overview of CSO Algorithms 5. Computational Procedure 6. Effects of DG Placement Units on PLR and PTC 7. Numerical Results 8. Conclusion FUNDING References Appendix A Keywords: cat swarm optimization, distributed generators, distributed generators placement and sizing, reliability optimization, particle swarm optimization, genetic algorithm Received 1 February 2013; accepted 5 October 2013 Address correspondence to Dr. S. R. Samantaray, Assistant Professor, School of Electrical Sciences, Indian Institute of Technology, Bhubaneswar, Odisha , India. sbh samant@yahoo.co.in Abstract This article presents optimal placement and sizing of multiple distributed generators to achieve higher overall system reliability in large-scale primary distribution networks using a novel random search algorithm known as cat swarm optimization. A composite reliability index is used as the objective function in the optimization process. Furthermore, the effect of multiple distributed generator units on power transfer capacity and power loss reduction has been observed. Extensive simulations are carried out based on three practical distribution systems to demonstrate the effectiveness of the proposed method. Further, qualitative comparisons are made with adaptive weight particle swarm optimization, particle swarm optimization with constriction factor, and binary-coded genetic algorithm to show the efficacy of the proposed method for optimal placement and sizing of distributed generators in power distribution networks. 1. INTRODUCTION Distributed generators (DGs) play a vital role in modern power systems worldwide. The role of DGs in future smart-grid operations increases aspects of system security, reliability, efficiency, power quality, and system stability [1 4]. The power system, especially at the distribution level, is prone to failures and disturbances due to weather-related issues and human errors. Having distributed generation as an alternative source ensures the reliability of electric power supply. Therefore, distributed generation is expected to play a key role in the residential, commercial, and industrial sectors of the power system. The location of DG placement is of key importance for reliability improvement in the distribution network, as DGs can provide continuity in supply after an outage in the main feeder line or in the primary substation to the electric utility and customers. Thus, selection of proper sizing and location of DGs in a power distribution network is an important issue to be addressed. A common strategy to find the site of DG is to minimize the active power loss of the system [5]. Another method for 149

3 150 Electric Power Components and Systems, Vol. 42 (2014), No. 2 placing DGs is to apply rules that are often used in shunt capacitor placement in distribution systems. A two-thirds was presented by Willis [6] for DG placement on a radial distribution feeder with a uniformly distributed load, and it was suggested to install DG of approximately two-thirds capacity of the incoming generation at approximately two-thirds of the length of line. This rule is simple and easy to use, but it cannot be applied directly to a feeder with other types of load distribution or to a networked system. Wang and Nehrir [7] suggested an analytical approach to identify the location to optimally place a single DG with unity power factor in radial as well as meshed networks to minimize losses. However, in these approaches, the optimal sizing is not considered. Several research works have been reported that address the use of swarm intelligence algorithms to optimize the placement and sizing of DGs [8 19] based on several factors, such as minimization of power loss and improving overall system reliability while retaining the voltage stability margin. DG placement and sizing using mixed-integer non-linear programming with an objective function of improving the voltage stability margin in a distribution system was reported by Al Abri et al. [8]. Shukla et al. [9] proposed a probabilistic-based planning technique for determining the optimal fuel mix of different types of renewable DG units (i.e., wind, solar, and biomass) to minimize the annual energy losses in the distribution system. However, DG units capable of delivering real power only is considered in this research work. Further, Kavousi-fard and Samet [20] presented similar work on optimal placement and sizing of capacitors in a distribution system by using an adaptive modified honey bee mating optimization (HBMO) evolutionary algorithm, which simultaneously considered total power losses, voltage deviation, and cost of both power losses and capacitor investment. However, reliability issues were not considered in that work. Other works, such as Marei and Soliman [21] addressed power system stability issues and showed the impact of DG interface on controlling the active power flow and voltage regulation of the microgrid. Shivarudraswamy and Gaonkar [22] addressed an application of DGs on coordinated voltage regulation of a distribution system using an evolutionary genetic algorithm. The proposed research work considers the aforementioned issues, including the impact of multiple DG units on power transfer capability (PTC), power loss reduction (PLR), voltage profile, and overall system reliability using an efficient swarm intelligence cat swarm optimization (CSO) algorithm. Some existing research works have focused on DG placement in distribution networks considering reliability constraint. Borges and Falcao [23] formulated the relationship between the benefits obtained by the installation of DG units (measured by the cost of reduction of losses) and the investment costs plus operational costs incurred in the installation of the DG units as an objective function, showing the impact of optimal distributed generation allocation on reliability, power losses, and voltage improvement. Most of the existing approaches on the DG placement problem in power distribution networks have considered cost minimization associated with system losses and investment cost as an objective function, including reliability assessment. A few research works presented validation models for calculating reliability indices by Brown and Ochoa [24]. The model determines the component of reliability data so that the predicted values of reliability indices match with the historical data. An ant colony based algorithm [25] is used as a combinatorial optimization solution for proper simultaneous allocation of reclosers and DGs in a distribution network, but this algorithm possesses slower convergence and also requires a number of parameters to be fine-tuned, resulting in more complexity. The proposed research work is based on an efficient swarm intelligence method known as CSO to optimize the placement of multiple DG units considering a composite reliability index as an objective function. Further, the impact of DG unit installation on PTC, PLR, reliability, and voltage profile of radial distribution networks is assessed. CSO is proposed because of its robustness in finding an optimal solution and its ability to provide a near-optimal solution close to the global minima with reduced computational burden. The organization of this article is as follows. Section 2 includes the problem formulation, where the optimum placement of DGs in the distribution network is introduced, and a composite reliability index is defined. In Section 3, the distribution circuit description and base-case reliability evaluation are discussed. In Section 4, the inner working of CSO and its basic steps are discussed in detail. Section-5 includes the computational procedure for solving DG placement using the CSO technique. The effect on PLR and PTC is discussed in Section 6. Simulation results and analysis are given in Section 7, and conclusions are drawn in Section PROBLEM FORMULATION The optimal placement of DGs is formulated as a constrained non-linear integer optimization problem. The objective of multiple DG placement in a radial feeder is to maximize the distribution network reliability under certain constraints. In this article, the system average interruption frequency index (SAIFI) and system average interruption duration index (SAIDI) are typically used to measure the average accumulated duration and frequency of sustained interruptions per customer. These system reliability indices are defined as follows [24]. SAIFI is defined as the average number of interruptions per customer

4 Kumar et al.: Reliability-constrained Based Optimal Placement and Sizing of Multiple Distributed Generators 151 served per time unit: Total number of customer interruptions SAIFI = Total number of customer served K λ i N i i=1 = (Interruptions/system customer), (1) K N i i=1 where λ i is the failure rate of the ith load point, N i is the number of customers connected at the ith load point, and K is the total number of branches. SAIDI is defined as the average interruption duration for customer served per time unit Sum of customer interruption duration SAIDI = Total number of customer served K U i N i i=1 = (hrs/system customer), (2) K N i i=1 where U i is the outage time of the ith load point. For the purpose of optimization, a composite reliability index is defined through a weighted aggregation of these two reliability indexes. The mathematical model of the problem can be expressed as follows [25]: C = W SAIDI SAIDI + W SAIFI SAIFI, (3) SAIDI T SAIFI T where w SAIDI and w SAIFI indicate the weights for the corresponding reliability index, and subscript T indicates the target value. The composite reliability index C defines both reliability SAIDIs and SAIFIs in the objective function. In this formulation, the desired values of both reliability indices are defined and empirically justified. Interruption is known to be one of the major concerns for reliability; i.e., if interruption is less, reliability becomes more. Lower service interruption can be identified by calculating lower values of a set of system reliability indices, such as SAIDI and SAIFI. Thus, the objective of the optimization algorithm used is to minimize the composite reliability index value. The aforementioned optimization problem is defined subject to the following constraints. 1) Bus voltage tolerance limit for all load points: U i min U i U i max where U i is the voltage of the ith bus (p.u.), and U i max, U i min are the upper (1.05 p.u.) and lower bounds (0.95 p.u.) of U i (p.u.), respectively. 2) Distributed energy resource capacity limit constraint: PG i min PG i PG i max, QG i min QG i QG i max, where PG i is the active power generation of the ith DG, PG i max, PG i min are the upper and lower limits of PG i (kw), QG i is the power generation of the ith reactive power source, and QG i max, QG i min are the upper and lower limits of QG i (kvar). 3) Constraints on ith feeder overloading: I i I i,max, where I i is the thermal flow of the ith branch, and I i,max is the upper limit of I i. 3. BASE-CASE RELIABILITY EVALUATION 3.1. System Description and Reliability Evaluation In this work, system 1, as shown in Figure 1, is a 34-bus radial test distribution system with a total real and reactive load of MW and MVAr, respectively [26]. All customer data and basic load point indices for reliability calculation are shown in Appendix A (Tables A1 and A2, respectively). The procedure for calculating the reliability indices was illustrated in [2]. Figure 1 shows that there are no disconnect switches on the line, and in case any section on the distribution line fails, it would result in power outage for all the distributor laterals. Each distributor lateral is considered as one load point. Installing a DG on the distribution feeder in the absence of disconnect switches will not improve the system reliability, because a failed section on the line cannot be isolated. Thus, a key assumption in this study is to add disconnect switches on the distribution line. Once disconnect switches are in place, the failed section can then be isolated, and the rest of the loads can be supplied by both the substation and DG [17]. Figure 2 shows the same circuit with disconnect switch, fuses, and load points. The second system studied is a 16-bus radial test distribution system with a total real and reactive load of 28.7 MW FIGURE 1. A 34-bus radial distribution system with no disconnects on the line.

5 152 Electric Power Components and Systems, Vol. 42 (2014), No. 2 FIGURE 2. A 34-bus radial distribution system with disconnects, fuses, and load points. and 5.9 MVAr, respectively [27]. The 16-bus, 3-feeder electrical power distribution system is shown in Figure 3. The type of conductor employed for the feeder is Mink, and its rated capacity is 234 A. The resistance and reactance of each conductor are considered as given in [27]. Transformers connected to feeders 1, 2, and 3 are each of 10-MVA rated capacity. All customer data and basic load point indices for reliability calculation are given in Appendix A (Tables A3 and A4, respectively). The third system used in this article is an IEEE 69-bus radial test distribution system with a total real and reactive load of 3.80 MW and 2.69 MVAr, respectively, shown in Figure 4 [18]. All customer data and basic load point indices for reliability calculation are given in Appendix A (Tables A5 and A6, respectively) Assumptions Following key assumptions are considered for the analysis: FIGURE 3. A 16-bus radial distribution system with disconnects, fuses, and load points. 1) disconnects, transformers, and fuses are assumed to be 100% available; however, thedg failure rate and repair rate are shown later (Table 3); 2) the failure rate for the sections of the distribution line is assumed to be 0.2 f/km-year; 3) total isolation and switching time is 2 min for DG; and 4) repair time for each line section is 3 hr.

6 Kumar et al.: Reliability-constrained Based Optimal Placement and Sizing of Multiple Distributed Generators 153 FIGURE 4. IEEE 69-bus radial distribution system with disconnects, fuses, and load points. 4. OVERVIEW OF CSO ALGORITHMS 4.2. Basic Principle CSO algorithm is based on the behavior of cats with exceptionally vigorous vitality of curiosity toward moving objects and possessing good hunting skills. Chu and Tsai [28] proposed a new optimization algorithm that imitates the natural behavior of cats. Even though cats spend most of their time resting, they always remain alert and move very slowly. Cats have a very high level of alertness; this alertness does not desert them when they are resting. Hence, what appears to be a cat lazing around upon closer examination will show large wide eyes observing their surroundings. Cats appear to be lazy, when they are actually very smart and deliberate creatures. When the presence of prey is sensed, they chase it very quickly, spending a large amount of energy. These two characteristics of resting with slow movement and chasing with high speed are represented by seeking and tracing, respectively. In CSO, these two modes of operations are mathematically modeled for solving complex optimization problems.these modes are termed the seeking mode and the tracing mode. A combination of these two modes allows CSO better performance. memory of each cat. SRD declares the mutative ratio for the selected dimensions. While in seeking mode, if a dimension is selected for mutation, the difference between the old and new values should not be out of range, and the value of range is defined by the SRD percentage. CDC tells how many of the dimensions will be varied. All of these factors play important roles in seeking mode. Seeking mode is now described. Step 1: Make j copies of the present position of cat k, where j = SMP-1, and retain the present position as one copy. Step 2: For each copy, according to CDC, randomly plus or minus SRD percent from the present values and replace the old ones. Step 3: Calculate the fitness values of all candidate points. Movement Tracing Mode Tracing mode is the sub-model for modeling the case of the cat in tracing targets. Once a cat goes into tracing mode, it moves according to its own velocities for each dimension. The action of tracing mode can be described as follows Basic Steps Rest and Alert Seeking Mode This sub-mode is used to model the cat during a period of resting but being alert, looking around its environment for its next move. Seeking mode has three essential factors, which are designed as follows: seeking memory pool (SMP), seeking range of the selected dimension (SRD), and counts of dimension to change (CDC). SMP is used to define the size of seeking Step 1: Update the velocities for every dimension (v k, d) according to Eq. (4): V j+1 k = w V j k + r ( GB j X j k ), (4) where w is the inertia weight, and r is a random number uniformly distributed in the range [0, 1]. GB j represents the global best position in the entire population at the jth iteration, and X j k is the position of the kth cat in the jth iteration.

7 154 Electric Power Components and Systems, Vol. 42 (2014), No. 2 Step 2: Check if the velocities are in the range of maximum velocity; in case the new velocity is over-range, set its value equal to the maximum limit. Step 3: Update the position of cat k according to Eq. (5): X j+1 k = X j k + V j+1 k. (5) 5. COMPUTATIONAL PROCEDURE The computational procedure of the proposed optimization algorithm in the form of a flowchart is shown in Figure 5, which is now described in detail. 1) Randomly initialize the initial set of cats of size N pop, where each cat is of dimension D, Xi = (Xi 1, Xi 2,..., Xi D ), and each dimension represents one location size pair; for example, a cat with N dimension represents one cat with N DG location size pairs. 2) Initialize the velocity of each cat, i.e., the velocity of cat i in the D-dimensional space as Vi = (Vi 1, Vi 2,..., Vi D ). 3) Evaluate the fitness of each cat and keep the position of the cat that has the highest fitness value. 4) According to a parameter mixing ratio (MR), cats are randomly distributed to seeking and tracing modes. 5) If cat k is in seeking mode, then a) create SMP-1 copies of the kth cat and retain the present position as one copy; b) for each copy according to CDC, randomly select the dimension to be mutated; c) for the dimension selected, for each copy, randomly add or subtract the SRD percent of the present value; d) calculate the fitness value of all copies; and e) replace original cat k with the copy having best fitness value. 6) If cat k is in tracing mode, then a) update the velocity for every dimension of the kth cat: V j+1 k = w V j k + r ( GB j X j k ) ; b) check if the velocities are in the range of maximum velocity; in case the new velocity is over range, set it equal to the maximum limit; c) update the position for every dimension of the kth cat: X j+1 k = X j k + V j+1 k ; d) constrain the position of the cat so that it does not exceed the limits of interest; e) evaluate the fitness of each cat; f) after evaluation of fitness values of all cats, store the position of the cat that has the best fitness value; and g) compare the previous global best value with the current best value and update the current best value accordingly. 7) Check if the maximum pre-specified number of iterations is reached, which is used as the termination criterion; if yes, terminate the program; else, go to Step EFFECTS OF DG PLACEMENT UNITS ON PLR AND PTC 6.1. PLR [26] To clarify the concept of DG, it is necessary to define the relative size of the DG units to the total power of the load in the same area. The penetration level (PL) of DG can be defined in two ways as or PL = P DG P load 100% (6) P DG PL = 100%, (7) P DG + P load where P DG is the total active power of all DG units installed in a network, and P load is the total active power of the load in a network. In this article, Eq. (6) is used for PL calculation of DGs. For calculation of active PLR by DG units, the following relation has been used: PLR DG = P loss Ploss DG 100%, (8) P loss where Ploss DG is the total active power loss with DG units, and P loss is the total active power loss component of the distribution system PTC of Distribution Network [26] Distributed generation systems provide new alternatives in expanding the distribution network capacity of existing distribution lines. DG units can independently set and control the real and reactive power flow on a distribution network to maximum utilization of the line, available system capacity, and minimizing reactive current flow, which in turn minimizes distribution lines losses. DGs provide a direct and rapid bus voltage control that enhances PTC in distribution lines and are used to change directly the power flow by controlling injected power to the system.

8 Kumar et al.: Reliability-constrained Based Optimal Placement and Sizing of Multiple Distributed Generators 155 FIGURE 5. Flowchart of computational procedure for proposed algorithm.

9 156 Electric Power Components and Systems, Vol. 42 (2014), No. 2 The PTC of distribution network by using of DG units can be defined by Eq. (9): PTC DG = P slack Pslack DG + P loss Ploss DG 100%, (9) P slack where P slack = P loss + P load, P loss = P G P load, P DG loss = PDG G P load, P slack is the total active power of the slack bus, Pslack DG is the total active power of the slack bus with DG units, P G is the total active power of the generation units, PG DG is the total active power of the generation units with DG units. 7. NUMERICAL RESULTS 7.1. Test Systems The proposed methodology is tested on three test radial distribution systems (RDSs). The first system is a 34-bus radial test distribution system with a total real and reactive load of MW and MVAr, respectively [26]; the second system is a 16-bus radial test distribution system with a total real and reactive load of 28.7 MW and 5.9 MVAr, respectively [27]. The third test system is an IEEE 69-bus RDS with a total real and reactive load of 3.80 MW and 2.69 MVAr, respectively [18]. The maximum number of DG units is three, with the size each varying from 250 kw to the total load plus loss, and the maximum DG penetration is 100%. In this work, DG units are modeled as PQ nodes. These units can be classified into four types based on real and reactive power delivering capability as follows: Type 1 is active power supply only, Type 2 is reactive power supply only, Type 3 is active and reactive power supply, and Type 4 is active power supply and reactive power consumption. Type 1 DG units could be photovoltaic, microturbines, and fuel cells that are interfaced to the grid by a power electronics interface. Typical synchronous compensators are units of Type 2. All those units use a synchronous machine as a generator, fall in Type 3, and those units with asynchronous generators (wind farms and mini hydro) belong to Type 4. The power factor of DG units depends on operating conditions and type of DG. With the proposed methodology, it is possible to handle four different types of DGs. However, in this work, the DG units are modeled as Type 3 with a power factor of 0.9. For comparison purposes, an adaptive weight particle swarm optimization (AWPSO), particle swarm optimization (PSO) with constriction factor (PSO-CF) [29], and binary-coded genetic algorithm (BCGA) [9] are developed for this problem. The computational procedure of the AWPSO and PSO-CF for handling the target problem primarily includes the following steps. Step 1: Read the input system data, set termination criterion. Step 2: Initially, generate a random number of particles and initialize its position and velocity. Particles have initialized according to the limits of each generating unit. These initial particles must be the candidate solutions that satisfy the practical operating constraints. Step 3: Set an iteration counter as 1. Step 4: Evaluate the fitness function F for each individual set of particles based on the objective function defined in Eq. (3). Step 5: Evaluate the values of personal best and global best of each particle. Step 6: Update the particle positioning vector and velocity vector of each particle. Update the personal best for each particle. Step 7: Update the global best according to the objective value of the previous global best. Step 8: Construct a new set of population, and if the maximum number of iterations is reached, terminate the program; else, increment the counter by one and return to Step 4. The candidate with the highest fitness value is the final solution to the target problem. For comparison purposes, a BCGA is also developed to derive solutions for this problem, which has turned out to be very effective in various engineering optimization applications. In the BCGA, each candidate solution is considered as a chromosome, and the stochastic search is carried out based on a population of chromosomes. The defined composite reliability index is to be minimized, and its value is an indicator of fitness for each chromosome. The higher the fitness value, the higher the chromosome s chance to survive for the next generation. The computational procedure of the BCGA for handling the target problem primarily includes the following steps. Step 1: Initially, a set of chromosomes is created in a random fashion, and each chromosome has D dimension, where each dimension represents one location size pair. Step 2: The fitness of each chromosome is evaluated based on the objective function defined in Eq. (3). Step 3: Based on the fitness value of each chromosome, different genetic operators including reproduction, crossover, and mutation are applied in the entire

10 Kumar et al.: Reliability-constrained Based Optimal Placement and Sizing of Multiple Distributed Generators 157 Parameter Value of range Data Substation Feeder data DG data MR 2% SRD 30% CDC 1 c r 1 [0, 1] Failure rate (f/km year) Repair rate (hr) TABLE 3. Reliability data table for all test cases TABLE 1. Parameter settings for CSO population in order to produce the next generation of chromosomes. Step 4: Repeat Steps 2 and 3 until any stopping criterion is satisfied. The chromosome with the highest fitness value is the final solution to the target problem. The effectiveness of the proposed methodology is tested on three widely used 34-bus, 16-bus, and 69-bus radial test distribution systems. The simulation program was coded for five different algorithms, namely CSO, AWPSO, PSO-CF, BCGA, and backward/forward load flow program using MAT- LAB R2010a (The MathWorks, Natick, Massachusetts, USA), with a system configuration of an Intel core i3-380m processor, 2.53 GHz and RAM of 3 GB (Sony Corporation, Tokyo, Japan). In the simulations, the parameters of CSO is depicted in Table 1, and those of PSO-CF and AWPSO are shown in Table 2. The parameters for BCGA are population size of 100, cross-over rate of 0.65, and mutation rate of The elitist strategy is used to preserve the best solutions found in each iteration, and the elite count is 2. Table 3 represents the reliability data table for all test cases. The maximum number of iterations is 100, which is used as the stopping criterion for all test cases. Initially forward backward sweep load flow is conducted for 34-, 16-, and 69-bus test systems to obtain initial total real power loss, composite reliability index value, and bus voltages. Furthermore, sizes and locations of DGs corresponding to global solution are determined by using the proposed algorithm as described in Section 5. Parameter AWPSO PSO-CF Initial weight Final weight c c r 1 [0, 1] [0, 1] r 2 [0, 1] [0, 1] Constriction factor TABLE 2. Parameter settings for AWPSO and PSO-CF 7.2. Simulation Results and Analysis Test Case 1: 34-bus Test Distribution System The proposed CSO-based algorithm is applied to minimize the objective function (Eq. (3)) for the RDS 34-bus test system. The system has 34 buses having 1 substation and 33 lines. The system bus data were also given in [26]. The customer data and basic load point indices for Test Case 1 is shown in Appendix A (Tables A1 and A2). The basic load point indices and customer data are required to calculate the system reliability SAIDIs and SAIFIs. Note that the developed method works at any system power factor. The reliability index weights are chosen as W SAIFI = 0.33 and W SAIDI = The target values of the reliability indices are set as SAIFI T = 1 and SAIDI T = 2.2. The reliability index weights and target values of the reliability indices are chosen same for all the three test cases. These values are empirically justified and indicate a satisfactory level of reliability. Table 4 presents the simulation results of placing DG units by various techniques. The results of the base case (no DG unit placed) and three cases with DG number ranging from one to three are compared. The results include optimal sizes and locations of DG units with respect to composite reliability index value and total real power losses. The PLR, PTC, and computational time are also presented in Table 4. It is shown that the optimal placement of DG unit in the system causes an improvement in overall system reliability, PLR, and PTC. As observed from Table 4, for the single-dg case with CSO, the optimal location of DG is at the 14th bus with an optimum size of 0.70 MW, yielding the composite reliability index value (percentage improvement over base case), PLR, and PTC of 3.25, 38, and 15.25%, respectively. The condition results the optimal DG location on the 34th bus with optimum size of 0.66 MW using both AWPSO and PSO-CF, resulting an improvement on composite reliability index value, PLR, and PTC of only 1.65, 25, and 15.15%, respectively. The performance of AWPSO and PSO-CF is the same for all cases. While comparing with the BCGA, the optimal location is at the 34th bus with an optimum size of MW, resulting in improvement on composite reliability index value, PLR, and PTC of only 1.65, 25, and 15.05% respectively. Similarly for two DG units with CSO, optimal locations are at the 14th and 11th bus with an optimal size of 0.929

11 158 Electric Power Components and Systems, Vol. 42 (2014), No. 2 Objective Power DG DG size function Percent loss Time Cases Methods location (MW) value (C) improvement (MW) PLR PTC (sec) No DG CSO One DG PSO-CF AWPSO GA CSO 14, , Two DG units PSO-CF 34, , AWPSO 34, , GA 34, , CSO 14, 11, , 0.70, Three DG units PSO-CF 34, 33, , 0.68, AWPSO 34, 33, , 0.68, GA 34, 33, , 0.44, Bold indicates results obtained by using CSO. and MW, respectively. Composite reliability index value with CSO is increased by 3.51% over AWPSO and PSO-CF and 4.20% over the BCGA. Also, the total real power losses were reduced to 37.50% with significant improvements of 3.12 and 2.50%, and PTC was increased by 27.52% with significant improvements of and 13.87% over AWPSO and BCGA, respectively. Furthermore, the computational time of CSO for deriving the solution is about sec compared to and sec consumed by AWPSO and PSO-CF, respectively. The BCGA is found to be more computationally intensive compared to other methods. Similar observations can also be made for the three-dg case. Figure 6 shows the voltage profile curve for the typical 34-bus test system for different DG combinations. Figures 7 and 8 show the PLR and PTC profile, respectively, for different DG cases. It clearly observed that TABLE 4. Simulation result for Test Case 1 CSO provides improved performance compared to AWPSO, PSO-CF, and BCGA. It is further observed from the simulation results that for an optimum capacity of DGs, the proposed CSO approach outperforms the AWPSO, PSO-CF, and BCGA methods. The proposed CSO-based approach results in better sizing and locations that are capable of achieving higher system reliability, higher PTC of the line, and higher PLR; also, the computational efficiency of CSO is significantly higher than that of AWPSO, PSO-CF, and BCGA. Test Case 2: 16-bus Test Distribution System The customer data and basic load point indices for Test Case 2 is given in Appendix A (Tables A3 and A4, respectively). Table 5 presents the simulation results of placing multiple DG 60 PLR Profile 50 PLR Values One DG unit Two DG units Three DG units CSO AWPSO PSO-CF GA FIGURE 6. Voltage profile of 34-bus RDS (in p.u.) (color figure available online). FIGURE 7. Performance comparison of PLR profile of 34-bus RDS with different approaches (color figure available online).

12 Kumar et al.: Reliability-constrained Based Optimal Placement and Sizing of Multiple Distributed Generators 159 PTC Values PTC Profile One DG unit Two DG units Three DG units CSO AWPSO PSO-CF GA FIGURE 8. Performance comparison of PTC profile of 34-bus RDS with different approaches (color figure available online). FIGURE 9. Voltage profile of 16-bus RDS (in p.u.) (color figure available online). units for different design scenarios by various techniques. The results of the base case and all three cases with DG numbers from one to three are compared. It is observed from Table 5 that the proposed CSO method leads to a completely optimal solution as compared to AWPSO, PSO-CF, and BCGA, resulting in better locations and sizing of DGs for achieving higher system reliability. Furthermore, the computational time for deriving the solution by the proposed approach is about 6.1 sec in different design scenarios, while that of AWPSO and PSO-CF requires about 95 sec in performing the same task. Thus, the computational efficiency of the proposed method is significantly higher than that of AWPSO, PSO-CF, and BCGA. Moreover, the effect of DG placement shows that PTC and PLR are significantly improved as DGs provide a direct and rapid bus voltage control that enhances PTC and PLR in distribution lines. As seen from Table 5, the obtained values of PLR and PTC for each DG combination using the proposed CSO method are improved compared with AWPSO, PSO-CF, and BCGA. Figure 9 shows the voltage profile curve for the typical 16-bus test system for different DG combinations. Figures 10 and 11 show the PLR and PTC profile with respect to different DG cases. Test Case 3: IEEE 69-bus Test Distribution System Customer data and basic load point indices for the IEEE 69-bus system are given in Appendix A (Tables A5 and A6, respectively). Table 6 presents the results on the optimal sizes and locations of DG units by various techniques. For all cases, the CSO method leads to a global optimal solution compared to AWPSO, PSO-CF, and BCGA. Furthermore, the computational time for deriving the solution by the proposed approach is about 21 sec in different design scenarios, while that of AW- PSO and BCGA requires about 188 and 210 sec, respectively, Objective Power DG DG function Percent loss Time Cases Methods location size value (C) improvement (MW) PLR PTC (sec) No DG CSO One DG PSO-CF AWPSO GA CSO 15, , Two DG units PSO-CF 16, , AWPSO 16, , GA 16, , CSO 15, 10, , 0.26, Three DG units PSO-CF 16, 15, , 0.26, AWPSO 16, 15, , 0.26, GA 16, 15, , 0.80, TABLE 5. Simulation result for Test Case 2

13 160 Electric Power Components and Systems, Vol. 42 (2014), No. 2 Objective Power DG DG size function Percent loss Time Cases Methods location (MW) value (C) improvement (MW) PLR PTC (sec) No DG CSO One DG PSO-CF AWPSO GA CSO 60, , Two DG units PSO-CF 60, , AWPSO 60, , GA 60, , CSO 60, 11, , , Three DG units PSO-CF 60, 11, , 0.645, AWPSO 60, 11, , 0.645, GA 60, 11, , 0.78, showing improved computational efficiency of the proposed method. Moreover, the effect of DG placement shows that PTC and PLR are significantly improved as DGs provide a direct and rapid bus voltage control that enhances PTC and PLR in distribution lines and directly change the power flow by controlling injected power to the system. As observed from Table 6, similar to the 34- and 16-bus systems, the PLR and TABLE 6. Simulation result for Test Case 3 PTC that resulted for each DG combination by the proposed CSO method outperformed the AWPSO, PSO-CF, and BCGA methods. A performance comparison of the CSO method with other existing methods is shown in Table 7, which clearly shows that for an optimum DG capacity, the proposed CSO approach outperforms the other existing methods. The proposed approach results in better sizing and locations that are Real Loss Simulation Optimal DG size power reduction case Methodology location (MW) loss (kw) (kw) CSO Fuzzy-based genetic algorithm [12] Weighted sum method [12] Genetic algorithm [9] Modified teaching-learning based optimization algorithm (MTLBO) [18] DG unit Gozel and Hocaoglu [13] Alrashidi and AlHajri [15] Loss sensitivity [10] Analytical approach [10] Repeated load flow [10] CSO 60, , DG units Alrashidi and AlHajri [15] 21, , MTLBO [18] 17, , 1, CSO 60, 11, , Alrashidi and AlHajri [15] 21, 61, , 1.278, DG units MTLBO [18] 11, 18, , , Fuzzy-based genetic algorithm [12] 4, 11, 60 1, 1.25, Weighted sum method [12] 11, 49, , 1.25, Genetic algorithm [9] 61, 11, , 0.343, TABLE 7. Performance comparison of proposed method with other existing methods for 69-bus RDS

14 Kumar et al.: Reliability-constrained Based Optimal Placement and Sizing of Multiple Distributed Generators 161 PLR Values PLR Profile One DG unit Two DG units Three DG units CSO AWPSO PSO-CF GA PLR Values PLR Profile One DG unit Two DG units Three DG units CSO AWPSO PSO-CF GA FIGURE 10. Performance comparison of PLR profile of 16- bus RDS with different approaches (color figure available online). FIGURE 13. Performance comparison of PLR profile of IEEE-69 bus system with different approaches (color figure available online). PTC Values PTC Profile 0 One DG unit Two DG units Three DG units CSO AWPSO PSO-CF GA FIGURE 11. Performance comparison of PTC profile of 16- bus RDS with different approaches (color figure available online). capable of achieving higher PLR. Figure 12 shows the voltage profile for the typical 69-bus test system for different DG combinations. Figures 13 and 14 show the PLR and PTC profile for different DG cases. FIGURE 12. Voltage profile of IEEE-69 bus system (in p.u.) (color figure available online). PTC Values PTC Profile One DG unit Two DG units Three DG units CSO AWPSO PSO-CF GA FIGURE 14. Performance comparison of PTC profile of IEEE-69 bus system with different approaches (color figure available online). 8. CONCLUSION In this article, a new powerful swarm intelligence method known as CSO is developed for optimal placement and sizing of multiple DG units in the traditional RDSs, improving overall system reliability. The effect of the number of DG installation locations on PLR and PTC is also assessed. The simulation results from three test distribution systems (IEEE 16-, 34-, and 69-bus systems) confirm the effectiveness of the proposed method. The result shows that PLR and PTC percentages improve when the number of DG installations is increased from one to three. The proposed CSO approach is found to provide improved performance compared with other approaches, such as AWPSO, PSO-CF, and BGCA, providing better locations and sizing for each DG placement and capable of achieving higher system reliability, including significant improvement in computational efficiency.

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