Journal of Artificial Intelligence in Electrical Engineering, Vol. 2, No. 5, May 2013

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1 Journal of Artfcal Intellgence n Electrcal Engneerng, Vol. 2, No. 5, May 2013 Mult Objectve Optmzaton lacement of DG roblem for Dfferent Load Levels on Dstrbuton Systems wth urpose Reducton Loss, Cost and Improvng Voltage rofle Based on DASO Algorthm ABSTRACT: Mehd Sadegh Department of Electrcal Engneerng, Ahar Branch, Islamc Azad Unversty, Ahar, Iran Emal:m_sadegh_12@yahoo.com Along wth economc growth of countres whch leads to ther ncreased energy requrements, the problem of power qualty and relablty of the networs have been more consdered and n recent decades, we wtnessed a notceable growng trend of dstrbuted generaton sources (DG) n dstrbuton networs. Occurrence of DG n dstrbuton systems, n addton to changng the utlzaton of these systems, has provded the opportunty for these companes to be able to desgn systems wth lower costs. In ths paper, the problem of placement and capacty determnaton of DG were carred out usng multple methods. The man objectves of ssue were mprovng the voltage profle, losses reducton and reduce the cost of operaton that were carred out based on an economc functon. Usng the multple methods to mprove some purposes and utlzaton of weghtng coeffcents provded an approprate plan. DASO algorthm was used for optmzaton and varous experments carred out on real networ. KEYWORDS: DASO Algorthm, Dstrbuton Networ, Dstrbuted Generaton INTRODUCTION: In recent years, n order to provde the power of networ, the use of dstrbuted generaton sources ncreased. It s due to the characterstcs of DG such as small sze, low nvestment cost, proxmty to locaton of consumpton and no need to expand the networs of generaton and transmsson. They sgnfcantly reduce nvestment costs of power supplyng due to load growth. Due to the competton and restructurng of the power systems and ssues such as restrctons on the constructon of power transmsson lnes, hgh senstvty to the envronment, consumers greeted servces wth hgh relablty, etc. t s expected that DG systems have an mportant role n the future [1]. Due to the declnng DG resources prce, these sources are expected to play an mportant role n the future of dstrbuted systems. Besdes, the presence of DG n dstrbuton systems ncreases the complexty of the problem of desgnng and developng these systems and maes t neffcent the use of exstng methods for solvng that problem. So, wth the exstence of DG, the ssue of desgnng and development of dstrbuton systems needs to be revewed and t taes new models and methods for solvng t. Some studes have been done for DG placement. In a paper, DG placement based on dstrbuton system voltage stablty s studed [2]. In ths method, those buses whch are more senstve to the falure of

2 Mehd Sadegh: Mult Objectve Optmzaton lacement of DG roblem for Dfferent Load the voltage are determned based on current flow lnage analyss. Then, the dstrbuted generaton sources are placed at specfed bus locaton and power losses and voltage are measured. Another study offers an analytcal method for determnng the locaton and optmal sze of the sources of power loss reducton n power systems. In ths paper, the am of applyng DG s a power loss reducton n the dstrbuton system. Study n both constant and tme-varyng load s done. In ths case, DG appled by dstrbuton companes for provdng power due to load growth has been studed [4]. The purpose of applyng these sources n the dstrbuton systems s to mnmze the cost of nvestment and utlzaton of new equpment to provde load posts, transformers, feeders and DG. The optmzaton s performed by GAMS software. lacement of DG on power networs usng mult-objectve algorthm (MSO) or (mult objectve) to mnmze the economc cost of producton s dscussed [5]. In ths paper, two objectve functons are wrtten: frst functon for mnmzng the cost of producer and second one for mnmzng the envronmental emssons. MSO s used to smultaneously mnmze the cost and the thermal emsson by changng the locaton and sze of DG. In the other paper, the target s a mult-objectve optmzed locatng of dstrbuted productons usng Smulated Annealng [6]. The proposed method defnes the optmzed locaton and sze n a calculatng tme less than (GA) and (TS). Also, the man purpose of ths paper s to mnmze the real power losses and envronmental emssons. In a paper, a comprehensve methodology for DG placement wth consderaton of the lmtatons of utlzaton, upsde networ and communcaton Feeders s dscussed and the combned method s used for optmzaton [7]. Fnally, varous experments have been tested on the networ to show the effectveness of the method. In a paper, placement and determnaton of sze of DG by reducng losses and mprovng voltage profles usng ABC algorthm s conducted [8]. Restrctons on utlzaton and the electrcty n the upsde source are consdered. In ths paper, the problem of determnng a locaton and sutable capacty of DG, usng a multple method, n the dstrbuton networ was nvestgated. Due to the complexty of the selected target functon, usng powerful algorthms for solvng ths problem s essental. So, two algorthms (DASO, SO) are used for the optmzaton. Fnally, the results have to be compared. Voltage profle mprovement, loss reducton, the cost of nvestment reducton and utlzaton are the man objectves of the plan. To demonstrate the effcency of the algorthm and method, varous tests were performed on a real networ. 1- FORMULATION OF THE ROBLEM: Consderng the man objectves of the problem, formulaton to determne the locaton and optmal capacty of DG s done wth regard to the constrants and necessary restrctons. To provde a practcal and economcal desgn, multple methods of weghtng coeffcents are used.

3 Journal of Artfcal Intellgence n Electrcal Engneerng, Vol. 2, No. 5, May 2013 MnK T {. Knpf 2. K. Ls 3. Kn 1 cs 2.1- VOLTAGE ROBLEM: Ths functon represents the voltage dfference between dfferent buses of the networ and ts reference value. In fact, t states the networ voltage drop whch s an mportant element n the qualty of the networ. The objectve s to mnmze the voltage drop n the networ. } ls : The loss of functon n the presence of DG bls losses functon wthout DG K n.ls : normalzed losses functon (, j) mpedance of the lne between buses, j I(,j) the amount of current flowng between, j Mn T. f V V 1 ypf ref 2.3- RESECTIVE COST ROBLEM: K npf pf npf Kpf: base voltage drop after nstallng DG K bpf base voltage drop before nstallng DG K npf normalzed amount of voltage profle The purpose s mnmzaton of costs. These costs nclude the fxed costs of the ntal nvestment costs and varable costs nclude the cost of mantenance and utlzaton of DG. The followng equaton can be used to express the total cost: Mn K cs { ng 1 SC( ( j)) nt t 1 ( ng t pw) t 1 [ MC( ( j)) OC ( ( j))] 8760 nt t 1 V ref reference voltage V voltage of th bus W ss nlp W ( ) nf l 1 j 1 W ( j) ls ndg 1 DG ( ( ) 2.2- NETWORK LOSSES ROBLEM: The am of ths secton s reducton and mnmzaton of networ losses. For ths purpose, the followng equaton can be used: m K nls ls. ndf df ls nls nb nb 1 1 j 1 (, j) (, j) I 2 (, j, d) (, j) varable ndcatng whether or not the relatonshp s between buses pw 1 1 Infr Intr ( j ) represents the capacty of DG Zero ndcates that DG s not nstalled n the desred locaton. ng: number of canddate locatons for nstallng DG SC the cost of nstallng DG n canddate place that s determned accordng to the canddate. Nt plannng perod (years) n b number of buses

4 Mehd Sadegh: Mult Objectve Optmzaton lacement of DG roblem for Dfferent Load pw economc factor for converson costs that are spent durng the perod to ther current value. I ntr the annual nterest rate I nfr annual nflaton mantenance costs utlzng costs of DG (), whch s afflated wth the DG. ROVISIONS GOVERNING THE ISSUE: - rovsons relatng to the actve and reactve power: Q mn DG mn DG Q DG DG Q max DG max DG - Losses provsons: loss ( wthdg ) ( wthoutdg ) loss THE FINAL OBJECTIVE FUNCTION By consderng above formulatons and governng provsons, the fnal objectve functon can be expressed as: K K C. H I 0 t ( G G lc b ) / b H b otherwse v v 2- DASO ALGORITHM: Communty algorthm or partcles congeston s an optmzaton algorthm based on the collectve ntellgence, whch was ntroduced frst by Kennedy and Eberhart. Ths algorthm s nspred by the movng mass of brds and fsh, and due to ts ablty to respond n a short tme wth hgh qualty, s consdered by researchers. By developng research, they dscovered that the socal behavor model for members of ths class wth some terms could also serve as a powerful optmzaton method. A prelmnary verson of ths method was assgned only for solvng contnuous nonlnear optmzaton problems. Wth the development of new algorthms, DASO algorthm was proposed whch s the mproved model of SO algorthm. In SO algorthm, each partcle s followng two values: a proper response that the partcle has earned and a proper response that other partcles have receved. After commencement of searchng, searchng speed becomes zero and causes them to be stuc n local optmums. Inerta weght factor n the proposed algorthm s modfed as follows to stop ths process that s a functon of other parameters: c1r 1( Xbest _ X ) c2r2( XGbest 1 K K X ) The second part of the penalty functon assocated wth each member, shows the model regardng the provsons cross., represents amount of nactvty of partcle and Cp s penalty coeffcent (a huge number). lc H 1 K 1 X X v, 1,2,... n In ths algorthm, the nerta weght factor s nfluenced by the developmental state and wth the evolutonary speed coeffcent and the coeffcent of partcle, aggregaton s presented as follows:

5 Journal of Artfcal Intellgence n Electrcal Engneerng, Vol. 2, No. 5, May 2013 h mn( F( pbest max( F( pbest 1 ), F( pbest )) 1 ), F( pbest )) hstory of each partcle. It means that to have more speed, ''h'' should be smaller. Where F(pbest ) s pbest proprety level. Ths parameter taes nto account the s mn( F max( F pbest pbest, F), F) Start Input data Calculaton of profle, losses and cost by proposed load dstrbuton method and objectve functon wthout DG Intalzng the partcles Calculaton of total profle, losses, cost, the normalzed value of each one and total objectve functon Regstraton of best and G best Update the postons and veloctes of partcles Stop condton rnt locaton, type and sze of DG End Fgure.1. Flowchart of SO algorthm to solve the problem 3- CASE STUDIES: To show the effectveness of the proposed algorthm, the problem for a real networ (Tehran Networ) s shown n Fgure 2. Data networs are shown n tables. Fgure.2.Tested Networ 4.1- FIRST EXERIMENT The effect of weghtng coeffcents (a1 =.2, a2 =.5 and a3 =.3) As the results show, the presence of DG could mprove the desred goals, consderably. Voltages, losses and expenses wth DG were sgnfcantly mproved compared wth the case wthout DG. It should also be noted that consderng the mportance coeffcent chosen for the purposes, t wll have a greater mpact on the fnal objectve functon. Hence, the algorthm s tryng to mprove the objectve more. In ths experment, because the mportance coeffcent of losses functon s further, the greatest mpact occurs on losses. Comparson of varous algorthms s also gven n the table. It should be noted that the load of networ s varable. Due to lac of facltes for moment estmaton of load n order to hgh performance, the load problem s consdered at three levels (hgh, medum and low).

6 Mehd Sadegh: Mult Objectve Optmzaton lacement of DG roblem for Dfferent Load Table1. Results of the frst experment arameters Functon of normalzed voltage profles (F n. roof ) Before nstallng DG After nstallng DG (usng algorthms) SO DASO The actual value of the voltage profle n terms of per unt (F prof ) Normalzed loss functon (F n.loss ) The actual amount of the loss functon w (F loss ) Normalzed cost functon (F n.cost ) The actual value of the cost functon $ (F cost ) Normalzed value of the objectve functon (F ft ) DG nstallaton locatons DG capacty Fgure.3.Comparson of the voltage profle before and after nstallaton of DG for SO algorthm

7 Journal of Artfcal Intellgence n Electrcal Engneerng, Vol. 2, No. 5, May 2013 Fgure.4.Comparson of the voltage profle before and after nstallaton of DG for DASO algorthm Fgure.5.Comparson of the lne losses before and after nstallaton of DG for algorthm DASO

8 Mehd Sadegh: Mult Objectve Optmzaton lacement of DG roblem for Dfferent Load Fgure.6.Comparson varous parts of the objectve functon before and after nstallaton of DG for 1) DASO 2) SO algorthms ** (Column 1: normalzed voltage profle, column 2: Losses, column 3: Cost functon, column 4: the total value of the normalzed objectve functon) A 3 =.3, A 2 =.2, A 1 =.5 : 4.2- SECOND EXERIMENT: In ths test, to demonstrate the effcency of algorthm, mportant factor of objectves has been changed. As the results show, due to the further mportance of voltage, the algorthm tres to mprove the voltage profle n order to further mprovement of overall functon. The results of these experments are presented n the followng fgures:

9 Journal of Artfcal Intellgence n Electrcal Engneerng, Vol. 2, No. 5, May 2013 Table2. Results of the second experment arameters Functon of normalzed voltage profles (F n. roof ) The actual value of the voltage profle n terms of per unt (F prof ) Normalzed loss functon (F n.loss ) The actual amount of the loss functon w (F loss ) Normalzed cost functon (F n.cost ) The actual value of the cost functon $ (F cost ) Normalzed value of the objectve functon (F ft ) DG nstallaton locatons Type of DG nstalled at each locaton. Before nstallng DG After nstallng DG Fgure.7.Comparson of the voltage profle before and after nstallaton of DG for DASO algorthm Fgure.8.Comparson of the lne losses before and after nstallaton of DG for algorthm DASO

10 Mehd Sadegh: Mult Objectve Optmzaton lacement of DG roblem for Dfferent Load 4- CONCLUSION: Due to the competton and restructurng of the power systems and ssues such as restrctons on the constructon of power transmsson lnes, hgh senstvty to the envronment, consumers greeted servces wth hgh relablty, etc. t s expected that DG systems have an mportant role n the future. In ths paper, the optmal placement of dstrbuted generaton sources n the mult - objectve form by usng of weghtng coeffcents was taen. Frst, the problem s optmzed by two nds of algorthms and then, results are compared wth each other. Obtaned results can be stated as: Dfferent levels of load regardng the realtes of the load whch are varable and useful for the most practcal soluton. Voltage profle wth DG, losses and expenses have been sgnfcantly mproved that are well dsplayed n charts and graphs. Weghtng coeffcents used n the experments showed that consderng the coeffcent selected for each of the three targets (voltage, losses, and cost), the most mportant objectve wll be mproved. Because t has the greatest mpact on the objectve functon. Comparng SO and DASO algorthms, DASO algorthm gves better results than SO algorthm due to ts nature. Crcumstances ndcate the sutablty of the algorthm for the Changes. For example, by changng the weghtng coeffcents, the algorthm also has changed tself well for offerng better results. [1] Y.G. Hegazy, M.M.A. Salama, and A.Y. IEEE Transactons on ower system, vol.18, no.1, 2003, pp Approaches for Optmal lacement of Dstrbuted trans. on ower Sys., Vol. 19, No. 4, november [3] H. Hedayat, S. A. Nabavna, and A. DG IEEE trans. on ower Delvery, Vol. 23, No. 3, July [4] W. El-Khattam, Y. G. Hegazy and M. M. A. Optmzaton Model for Dstrbuton System IEEE trans. On ower Sys., Vol. 20, No. 2, may [5]. raomcha honrattanasa Department of Electrcal Engneerng North Eastern Unversty Khonaen, Thaland 40000" Optmal lacement of DG Usng Multobjectve partcle Swarm Optmzaton ".IEEE, Mechancal and Electrcal Technology (ICMET), 2nd nternatonal conference, 2010, pp [6]. T. Sutthbun and. Bhasaputra Department of Electrcal and Computer Engneerng Faculty of Engneerng Thammasat Unversty, atumthan 12120, Thaland " Mult-Objectve Optmal Dstrbuted Generaton lacement Usng Smulated Annealng". Electrcal Engneerng / Electroncs Computer Telecommuncatons and Informaton Technology (ECTI CON), nternatonal conference, 2010, genetc algorthm and partcle swarm optmzaton for optmal DG locaton and szng n dstrbuton and Energy Systems 34 (2012) [8] Fahad S. Abu-Mout, M. E. El-Hawary Szng n Dstrbuton Systems va NO. 4 OCTOBER 2011, pp Artfcal Bee 5- REFERENCES: