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Hc2512901294

  1. 1. K.Srinivasa Rao, M.Nageswara Rao / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.1290-1294 Optimal Placement Of Multiple Distributed Generator By Hs Algorithm K.Srinivasa Rao 1 M.Nageswara Rao 2 1 University College of engineering, JNTU, Kakinada, India. 2 University College of engineering, JNTU, Kakinada, India.Abstract This paper presents a methodology for some or all of the required power without the needmultiple distributed generator (DG) placement in for increasing the existing traditional generationprimary distribution network for loss reduction. capacity or T&D system expansion. DG capital costOptimal location for distributed generator (DG) is not large due to its moderate electric size andis selected by Analytical expressions and the modular behavior as it can be installedoptimal DG size calculated by IA method and incrementally unlike installing new substations andHarmony search algorithm. These two methods feeders, which require large capital cost to activateare tested on two test systems 33-bus and 69-bus the new expanded distribution system [12]. Theradial distribution systems. The final results technical benefits include improvement of voltage,showed that harmony search algorithm gives loss reduction, relieved transmission andsame loss reduction and minimum voltage in the distribution congestion, improved utility systemsystem with less DG size than obtained in IA reliability and power quality [6] and increasing themethod. durability of equipment, improving power quality,Index Terms—IA method, Harmony search, total harmony distortion networks and voltageoptimal location, Optimal DG size, Multiple DG, stability by making changes in the path throughAnalytical Expressions, Distributed generation. which power passes [9].These benefits get the optimum DG size and location is selected.I. INTRODUCTION Distributed system planning using distributed The central power plants are thermal, generation [12]. If the DG units are improperly sizednuclear or hydro powered and their rating lies in the and allocated leads to real power losses increasesrange of several hundred MW‟s to few GW‟s [2]. than the real power loss without DG and reversecentral power plants are economically unviable in power flow from larger DG units. So, the size ofmany areas due to diminishing fossil fuels, distribution system in terms of load (MW) will playincreasing fuel costs, and stricter environmental important role is selecting the size of DG. Theregulations about acid deposition and green house reason for higher losses and high capacity of DGgas emission[3].smaller power plants with a few can be explained by the fact that the distributiondozens of MW‟s, instead of few GW‟s, became system was initially designed such that power flowsmore economical[2]. Also, generators with from the sending end (source substation) to the loadrenewable sources as wind or solar energy became and conductor sizes are gradually decreased frommore economically and technically feasible. This the substation to consumer point. Thus withouthas resulted in the installation of small power plants reinforcement of the system, the use of highconnected to the distribution side of the network, capacity DG will lead to excessive power flowclose to the customers and hence referred to as through small sized conductors and hence results in“embedded” or “distributed” generation (DG). higher losses.[7]Sometimes it is also called “dispersed generation” or Different techniques are proposed by“decentralized generation”. [4]. authors the techniques are, a technique for DG Distributed generation technologies are placement using “2/3 rule” which is traditionallyrenewable and nonrenewable. Renewable applied to capacitor allocation in distributiontechnologies include solar, photovoltaic or thermal, systems with uniformly distributed loads has beenwind, geothermal, ocean. Nonrenewable presented. Although simple and easy to apply, thistechnologies include internal combustion engine, technique cannot be applied directly to a feeder withice, combined cycle, combustion turbine, micro other types of load distribution or to a meshedturbines and fuel cell. [5] Most of the DG energy distribution system. The genetic algorithm (GA)sources are designed using green energy which is based method has been presented to determine theassumed pollution free [6]. size and location of DG. GA is suitable for multi- Installing DGs at the load centers will objective problems and can lead to a near optimalprevent the new transmission lines extension to solution, but demand higher computational time. Anenergize new substation, DG is capable of providing analytical approach based on an exact loss formula has been presented to find the optimal size and 1290 | P a g e
  2. 2. K.Srinivasa Rao, M.Nageswara Rao / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.1290-1294 location of single DG. A probabilistic-based without updating the 𝛼 and 𝛽 the results are same planning technique has been proposed for [1]. determining the optimal fuel mix of different types B. Optimal DG size selection: of renewable DG units (i.e., wind, solar, and The Distributed generator is placed at the biomass) in order to minimize the annual energy optimum location. The optimum DG size is selected losses in the distribution system [1]. by varying the DG in small steps up to the point where real power loss is minimum. The real power II. PROBLEM FORMULATION loss is calculated by “back ward forward sweep” This section describes to find the optimum load flow algorithm. size and location of distributed generator. A. Selection of Location: III. IA METHOD Find the best bus for the placement of DG, The computational procedure of IA method The DG sizes at each bus is calculated by using is as follows: (2).The DG‟s are placed at each bus and calculate Step 1: Enter the number of DG units to be the real power loss by (1).The bus which has installed. minimum real power loss is selected as best location Step 2: Run load flow for the base case and find for placement of DG. losses using (1). The real power loss in a system can be calculated by Step 3: Find these optimal location of DG using the (1). This is also called as “Exact loss formula” [13]. following steps. 𝑁 𝑁 a) Calculate the optimal size of DG at each𝑃𝐿 = [𝛼 𝑖𝑗 𝑃𝑖 𝑃𝑗 + 𝑄 𝑖 𝑄 𝑗 + 𝛽𝑖𝑗 𝑄 𝑖 𝑃𝑗 − 𝑃𝑖 𝑄 𝑗 ] (1) bus using (2) and (3). 𝑖=1 𝑗 =1 b) Place the DG with the optimal size as Where mentioned earlier at each bus, one at a 𝑟 𝑖𝑗 𝛼 𝑖𝑗 = cos 𝛿 𝑖 − 𝛿𝑗 ; time. Calculate the approximate loss for 𝑣𝑖 𝑣𝑗 𝑟 𝑖𝑗 each case using (1). 𝛽𝑖𝑗 = sin 𝛿 𝑖 − 𝛿𝑗 ; c) Locate the optimal bus at which the loss 𝑣𝑖 𝑣𝑗 is at minimum. 𝑟𝑖𝑗 + 𝑗𝑥 𝑖𝑗 = 𝑍 𝑖𝑗 ijth element of [Zbus] impedance Step 4: Find the optimal size of DG and calculate matrix; losses using the following steps. N is number of buses a) Place a DG at the optimal bus obtained Where Pi and Qi are Real and Reactive power in step 4, change this DG size in small injections at node „i‟ respectively. step, and calculate the loss for each case Real power injection is the difference between Real using “Back ward forward” load flow. power generation and the real power demand at that b) Select and store the optimal size of the node. DG that gives the minimum loss. 𝑃𝑖 = (𝑃 𝐷𝐺𝑖 − 𝑃 𝐷𝑖 ) Step 5: Update load data after placing the DG with 𝑄 𝑖 = (𝑄 𝐷𝐺𝑖 − 𝑄 𝐷𝑖 ) the optimal size obtained in step 5 to Where, 𝑃 𝐷𝐺𝑖 and 𝑄 𝐷𝐺𝑖 is the real power injection allocate the next DG. and reactive power injection from DG placed at Step 6: Stop if either the following occurs node i respectively. 𝑃 𝐷𝑖 and 𝑄 𝐷𝑖 are load demand at a) the voltage at a particular bus is over the the node i respectively [14]. upper limit 𝛼 𝑖𝑖 𝑃 𝐷𝑖 + 𝑎𝑄 𝐷𝑖 − 𝑋 𝑖 − 𝑎𝑌𝑖 b) The total size of DG units is over the 𝑃 𝐷𝐺𝑖 = (2) 𝑎2 𝛼 𝑖𝑖 + 𝛼 𝑖𝑖 total plus loss 𝑄 𝐷𝐺𝑖 = ± (tan( cos−1 (𝑃𝐹 𝐷𝐺 ))) ∗ 𝑃 𝐷𝐺𝑖 (3) c) The maximum number of DG units is Where unavailable 𝑛 d) The new iteration loss is greater than the 𝑋𝑖 = 𝛼 𝑖𝑖 𝑃𝑗 − 𝛽𝑖𝑗 𝑄 𝑗 previous iteration loss. The previous 𝑗 =1 iteration loss is retained otherwise, repeat 𝑗 ≠𝑖 𝑛 steps 2 to6. 𝑌𝑖 = 𝛼 𝑖𝑖 𝑄 𝑗 + 𝛽 𝑖𝑗 𝑃𝑗 𝑗 =1 IV. HARMONY SEARCH ALGORITHM 𝑗 ≠𝑖 The harmony search algorithm (HSA) is a „+‟ sign for injecting Reactive power new meta-heuristic algorithm. The harmony search „- „sign for consuming Reactive power algorithm (HSA) is simple in concept, few in The exact loss formula is a function of loss parameters and easy in implementation. Harmony coefficients 𝛼 and 𝛽.These coefficients depends on search algorithm is concept from natural musical magnitude of voltage and voltage angle at each bus. performance processes [8]. In music improvisation, So for every DG placement at each bus the 𝛼 and 𝛽 each musician plays within possible pitches to make changes so for that every time requires load flow a harmony vector. If all the pitches create good calculation. But the results show that with and harmony, the musician saved them in memory and 1291 | P a g e
  3. 3. K.Srinivasa Rao, M.Nageswara Rao / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.1290-1294 with probabilit y HMCRincreases good or better harmony for next time.Similarly, in the field of engineering optimization, at xi  first each decision variable value is selected within with probabilit y (1 - HMCR)the possible range and formed a solution vector. If A PAR of 0.3 indicates that the algorithm willall decision variable values lead to a good solution, choose a neighboring value with 30% × HMCReach variable that has been experienced is saved in probasbility.memory and it increases the possibility of good or Step 4: Update the HMbetter solutions for next time. Both processes intend In this stage, if the New Harmony vector isto produce the best or optimum better than the worst harmony vector in the HM inStep 1: Initialize the optimization problem and terms of the objective function value, the existingalgorithm parameters worst harmony is replaced by the New Harmony.In this step the optimization problem is specified as Step 5: Repeat steps 3 and 4 until the terminationfollows: criterion is satisfied Minimize f(x) Termination criterion: Subject to xi ∈Xi, = 1, 2, ..., N The computations are terminated when thewhere f(x) is the objective function; x is a candidate termination criterion (maximum number ofsolutions consisting of N decision variables (xi); Xi improvisations) is satisfied. Otherwise, steps 3is the set of possible range of values for each (improvising New Harmony from the HM) and 4decision variable, that is, Lxi≤Xi≤Uxi for (updating the HM) are repeated [9].continuous decision variables where Lxi and Uxi arethe lower and upper bounds for each decision V. RESULTS AND ANALYSISvariable, respectively and N is the number of In this paper IA method and Harmonydecision variables. In addition, HS algorithm search algorithm are tested on 33-bus [10] and 69-parameters that are required to solve the desired bus [11] radial distribution system. Here Type 3 [1]optimization problem are specified in this step. DG is considered Step 2: Initialize the Harmony Memory (HM) A. Assumptions In this step, the Harmony Memory (HM The assumptions for this paper are as follows:matrix), is filled with as many randomly generated 1. The maximum number of DG units issolution vectors as HMS and sorted by the values of three, with the size each from 250KW tothe objective function. the total load plus loss.Step 3: Improvise a new harmony from the HM 2. The maximum voltage at each bus is 1.0 A New Harmony vector is generated from p.u.the HM based on memory considerations, pitch B. 33-Bus systemadjustments, and randomization. For instance, the The simulation results of the optimalvalue of the first decision variable for the new location and optimal sizing of DG shown in Table-I.vector can be chosen from any value in the specified The real power loss of 33-bus system is 211kWHM range Values of the other decision variables can without DG. In single DG placement by IA methodbe chosen in the same manner. There is a possibility the DG size is 2.6 MW and in case of Harmonythat the new value can be chosen using the HMCR search algorithm 2.5MW, the real power loss is 111parameter, which varies between 0 and 1 as follows: kW. In case of 2 DG‟s placement the DG size by IA   1 2  xi  xi ,xi ,........xi xi   HMS  with probabilit y HMCR method 1.9 MW, 0.6 MW and Harmony search algorithm 1.6 MW, 0.7 MW, the real power loss is  xi  X i with probabilit y (1  HMCR) 91.55 kW. In case of 3 DG‟s placement the DG size  by method 1.3 MW, 0.6 MW, 0.6 MW and by The HMCR sets the rate of choosing one Harmony search algorithm 1.5 MW, 0.5 MW, 0.3value from the historic values stored in the HM and MW, the real power loss is 79.69 kW.(1-HMCR) sets the rate of randomly choosing onefeasible value not limited to those stored in the HM. TABLE-IFor example, a HMCR of 0.9 indicates that the HS COMPARISON OF DIFFERENT TECHNIQUESalgorithm will choose the decision variable value ON 33-BUS SYSTEMfrom historically stored values in the HM with the90% probability or from the entire possible rangewith the 10% probability. Each component of theNew Harmony vector is examined to determinewhether it should be pitch adjusted. This procedureuses the PAR parameter that sets the rate ofadjustment for the pitch chosen from the HM asfollows:Pitch adjusting decision for 1292 | P a g e
  4. 4. K.Srinivasa Rao, M.Nageswara Rao / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.1290-1294 Cases DG With With DG VI. CONCLUSION schedule out 1 DG 2 3 In this paper harmony search algorithm is DG DG‟s DG‟s proposed for multiple DG placement. The DG location is finding by IA expressions and the Optimum ----- 6 6 6 optimum DG size is finding by IA method and HSA Bus 15 15 algorithm. The results are compared with IA 33 method. Results shows that HSA algorithm gives DG size ----- 2.6 1.9 1.3 same real power loss and voltage with less DG size (MW) 0.6 0.6 occurred in IA method.IA 0.6 REFERENCESmethod Loss 211 111 91.55 79.69 [1]. Duong Quoc Hung, Nadarajah (kW) Mithulananthan “Multiple Distributed Generator Placement in Primary DG size ----- 2.5 1.6 1.5 Distribution Networks for LossHS (MW) 0.7 0.5 Reduction,” Industrial Electronics, IEEEAlgorith 0.3 Transactions on, Feb.2011.m [2]. D. Singh and R. K. Misra, “Effect of load Loss 211 111 91.54 79.69 models in distributed generation planning,” (kW) IEEE Trans. Power Syst., vol. 22, no. 4, pp. 2204-2212, Nov. 2007.C. 69-Bus system: [3]. M.N. Marwali, J.W. Jung, and A. Keyhani, The simulation results of the optimal “Stability analysis of load sharing controllocation and optimal sizing of DG shown in Table-II for distributed generation systems”, IEEE.The real power loss of 69-bus system is 224 kW Trans. Energy Convers., vol. 22, no. 3, pp.without DG. In single DG placement by IA method 737-745, Sep. 2007.the DG size is 1810 KW and in case of Harmony [4]. I. El-Samahy and E. El-Saadany, “Thesearch algorithm 1.7 MW, the real power loss is effect of DG on power quality in a86.97 kW. In case of 2 DG‟s placement the DG size deregulated environment,” in Proc. IEEEby IA method 1.7 MW, 0.5 MW and Harmony Power Eng. Soc. Gen. Meet., 2005, vol. 3,search algorithm 1.6 MW, 556kW, the real power pp. 2969-2976.loss is 75.03 kW. In case of 3 DG‟s placement the [5]. H.B. Puttgen, P.R. MacGregor, and F.C.DG size by method 0.3 MW, 0.5 MW, 1.5 MW and Lambert, “Distributed generation: Semanticby Harmony search algorithm 0.2 MW, 0.5 MW, hype or the dawn of a new era?”, IEEE1.4 MW; the real power loss is 71.58 kW. Power Energy Mag., vol. 1, no. 1, pp. 22- 29, Jan./Feb. 2003. TABLE-II [6]. Soma Biswas , Swapan Kumar GoswamiCOMPARISON OF DIFFERENT TECHNIQUES ,and Amitava Chatterjee “OptimumON 69-BUS SYSTEM distributed generation placement with voltage sag effect minimization” EnergyCases DG With With DG Conversion and Management 53 (2012) schedule out 163–174ss DG 1 DG 2 3 [7]. Satish Kansal1, B.B.R. Sai, Barjeev Tyagi, DG‟s DG‟s and Vishal Kumar “Optimal placement of Optimum ----- 63 63 63 distributed generation in distribution Bus 18 18 networks,” International Journal of 61 Engineering, Science and Technology Vol. DG size ----- 1.8 1.7 0.3 3, No. 3, 2011, pp. 47-55. (MW) 0.5 0.5 [8]. M. Damodar Reddy, N. V. Vijaya KumarIA 1.5 “Optimal capacitor placement for lossmethod Loss 224 86.97 75.03 71.59 reduction in distribution systems using (kW) fuzzy and harmony search algorithm,” ARPN Journal of Engineering and Applied DG size ----- 1.7 1.6 0.29 Sciences, vol. 7, no. 1, january 2012. (MW) 0.5 0.52 [9]. Hamed Piarehzadeh, Amir KhanjanzadehHS 1.45 and Reza Pejmanfer “Comparison ofAlgorit Harmony Search Algorithm and Particlehm Loss 224 86.97 75.03 71.58 Swarm Optimization for Distributed (kW) Generation Allocation to Improve Steady State Voltage Stability of Distribution 1293 | P a g e
  5. 5. K.Srinivasa Rao, M.Nageswara Rao / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 2, Issue 5, September- October 2012, pp.1290-1294 Networks,” Res. J. Appl. Sci. Eng. Technol., 4(15): 2310-2315, 2012. [10]. M. A. Kashem, V. Ganapathy, G. B. Jasmon, and M. I. Buhari, “A novel method for loss minimization in distribution networks,” in Proc. IEEE Int. Conf. Elect. Utility Deregulation Restruct. Power Technol., 2000, pp. 251-256. [11]. M. E. Baran and F. F. Wu, “Optimum sizing of capacitor placed on radial distribution systems,” IEEE Trans. Power Del., vol. 4, no. 1, pp. 735-743, Jan. 1989. [12]. W.El-Khattam, M.M.A.Salama, “Distribution system planning using distributed generation,” IEEE CCECE 2003, vol.1, pp. 579 – 582. [13]. D.P. Kothari and J.S. Dhillon, Power System Optimization. New Delhi: Prentice- Hall of India Pvt. Ltd., 2006. [14]. N. Acharya, P. Mahat, and N. Mithulananthan, “An analytical approach for DG allocation in primary distribution network,” Int. J. Elect. Power Energy Syst., vol. 28, no. 10, pp. 669-678, Dec. 2006. BIOGRAPHYK.srinivasa Rao is pursuing M.Tech in Departmentof Electrical Engineering, Jawaharlal NehruTechnological University, Kakinada, India. His areasof interest include electrical power systems andRenewable energy resources.M.Nageswara Rao is Assistant Professor in theDepartment of Electrical Engineering, JawaharlalNehru Technological University, Kakinada, India.His areas of interest include electric powerdistribution systems and AI Techniques applied topower systems. 1294 | P a g e

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