Optimum Multi DG units Placement and Sizing Based on Voltage Stability Index and PSO

Transcription

1 Optmum Mult DG unts Placement and Szng Based on Voltage Stablty Index and PSO J. J. Jaman, M.M. Aman 2 jasrul@fe.utm.my, mohsnaman@sswa.um.edu.my M.W. Mustafa, G.B. Jasmon 2 wazr@fe.utm.my, ghauth@um.edu.my H. Mohls 2, A.H.A. Baar 2 hazl@um.edu.my, a.halm@um.edu.my M.N. Abdullah 3 mnoor@uthm.edu.my Faculty of Electrcal Engneerng, Unverst Tenolog Malaysa, 830 UTM Johor Bahru, Malaysa. 2 Faculty of Engneerng, Unversty of Malaya, Kuala Lumpur, Malaysa. Faculty of Electrcal Engneerng, Unverst Tun Hussen Onn, Johor Bahru, Malaysa. Abstract-Optmum DG placement and szng s one of the current topcs n restructured power system. Most of the authors have wored out on ther optmum placement base on the power losses reducton concept. However, the mprovement on power losses value n the networ wll not guarantee to the planner to have lower voltage stablty ndex (VSI for the system. Ths paper proposes a new approached for mult DG placement and szng for dstrbuton systems whch s based on a voltage stablty ndex. The most optmum DG sze wll be found out usng several types of PSO optmzaton algorthm. The output results wll also compared wth EPSO, REPSO, and IPSO. The proposed algorthm s tested on 2-bus, modfed 2-bus and 69- bus radal dstrbuton networs. Index Terms Mult-DG placement, PSO, Voltage Stablty Index, DG szng I. INTRODUCTION Dstrbuted Generaton placement and szng s one of the major ssues of power system, partcularly after restructurng of power system. Many companes are nvestng n small scale power generaton. In England and Wales, DG producton was only.2 GW durng However today ths fgure has ncreased substantally and reached up to over 2 GW []. Accordng to Energy Networ Assocaton (ENA report [2], the UK government s targetng to acheve 5% of electrcty from the renewable sources by 205. However there are several ssues concernng the ntegraton of DGs wth exstng power system networs that needs to be addressed. The ntegraton of DG changes the system from passve to actve networs, whch affects the relablty and safe operaton of a power system networ [3]. Furthermore, the non-optmal placement and szng of DG can result n an ncreased system losses and thus mang the voltage profle lower than the allowable voltage lmt [3-5]. The ncrement n actve power loss represents loss n savngs to the utlty as well as a reducton n feeder utlzaton. Studes have shown that 70% of power losses are due to dstrbuton system [6] and the losses resultng from Joule effect only account for 3% of the generated energy [7]. Ths non-neglgble amount of losses has a drect mpact on the fnancal results and the overall effcency of the system. The unbundlng of utlty has forced the dstrbuton companes to reduce the losses and operate at the hghest effcency for ther own economcal benefts. Snce utltes are already facng techncal and non-techncal ssues, they cannot tolerate such addtonal ssues. Hence an optmum placement of DG s needed n order to mnmze overall system losses and therefore mprove voltage profles. Dfferent approaches have been used to get the optmal placement of DG such as usng the maxmum power losses reducton ndcator [8], heurstc optmzaton technque [9-0] and others. However, n ths study, the allocaton of the DG wll be based on the wea node or bus that can cause the system near to collapse pont and the DG wll be placed on the weaest bus. Voltage stablty ndex wll be developed to dentfy the weaest bus and the optmzaton algorthm wll be appled to fnd the most optmum DG sze. The proposed algorthm wll be tested on 2-bus and 69-bus radal dstrbuton networs. II. DEVELOPMENT OF VOLTAGE STABILITY INDEX To dentfy the weaest bus n the system, the voltage stablty ndex s developed to gve the ndcaton for the stablty of the buses and also the effect of DG wll be ncorporated. For dervaton of the ndex, a smple two bus networ s consdered, shown n Fg.. (a (b Fg. : A Smple Two Bus Networ wth and wthout Actve Power Compensaton From Fg., we can wrte. * S L = PL + jql = Vr Ir ( V r = V s IrZ (2 Where, ( PL PG j( QL QG Ir = (3 * Vr

2 Solvng Eqns. (-3, the followng relaton could be derved. 4r ( P P [ V Cos( θ δ ] j L G 2 Ths new ndex s based on actve power value (due to the DG that operatng n PQ mode s derved for fndng the most optmum ste of DG based on the most crtcal bus n the system that can lead to system nstablty. Ths proposed ndex s referred as real power system stablty ndex (PSI as defned n (5. 4rj ( PL PG PSI = 2 [ V Cos( θ δ ] (5 Under the stable operaton, the PSI should be less than unty and f the PSI value s closer to zero, the system at stable condton. III. OPTIMIZATION TECHNIQUES IN DG SIZING The optmzaton technque wll be mplemented n DG szng to obtan ts optmal value after the allocaton of DG has done. By havng the optmal sze of DG, t wll help the system to have the mnmum power losses compared to the ntal condton. Many optmzaton technques have been employed to solve dfferent DG unts problems. Ths ncludes Artfcal Bee Colony (ABC, Genetc Algorthm (GA, Smulated Annealng (SA, Evolutonary Programmng (EP and Partcle Swarm Optmzaton (PSO. [-3]. Some of these technques also have been modfed to enhance ther performance to overcome other lmtatons [4-5]. However, the PSO algorthm has more advantages as compare to others optmzaton methods. PSO algorthm s easer to be mplemented, less memory requred, has ablty to reach global optmum soluton and also can obtaned good soluton n a short computng tme [6]. Four dfferent types of PSO are used n ths study to obtan the optmal DG sze. Ths ncludes Tradtonal PSO, Evolutonary PSO (EPSO, Ran Evolutonary PSO (REPSO and Iteraton PSO (IPSO. The second and thrd PSO types are the hybrdzaton of PSO wth the Evolutonary Programmng technque whle the last PSO types s the modfcaton on PSO algorthm that n ntroduce by [7]. The objectve functon for all the optmzaton technques s to determne the mnmum total actve power losses as shown n Eqn. (6: Mn P Lt n = I = 2 R A. Tradtonal Partcle Swarm Optmzaton The tradtonal Partcle Swarm Optmzaton (PSO s mplemented from the behavor of populaton such as fsh, brds and others anmal to fnd ther food source. Ths (4 (6 populaton wll reman movng n the group based on the experence or nformaton that s acheved by ndvdual or group. Ths dea s used n the PSO algorthm [8]. From the random number (partcles that s generated by the computer. The Local Best (P best and Global Best (G best parameters are ntroduced to help these random partcles to move toward the optmal soluton (hghest densty of food source. The P best s defned as the best result (mnmum or maxmum that s acheved by ndvdual partcle untl the current teraton and G best s the best result that s obtaned from the whole populaton. Besdes that, there are 3 weght coeffcents n the PSO algorthm whch are w, c and c 2. All weghted coeffcents wll affect the performance of PSO n explorng or explotng the global results. Eqn. (7 and (8 show the updated velocty and poston of the partcles untl obtanng the optmal soluton. v+ = ω V + cr ( Pbest x + c2r2 ( Gbest x (7 + = v + x (8 x + B. Evolutonary Partcle Swarm Optmzaton The Evolutonary Partcle Swarm Optmzaton (EPSO s an mprovement method that s ntroduced by [9] for avodng trappng n local optmal value and mae the algorthm to have faster computng tme. The concept of Evolutonary Programmng (EP s hybrdzed nsde the PSO algorthm for mprovng ts performance. Snce the EP consst of combnaton, competton and selecton process, t mae only the wnnng (hgh potental partcle wll reman n the group whle the others wll be termnated. Fg. 2 llustrated the way of competton that s done for PSO partcles. The star s presented as the optmal soluton that needs to be acheved by the algorthm. The prevous populaton s combned wth the current populaton (updated ones and the competton s based on the percentage that s set by the user. Let the number of partcles (N are 6 and the competton rate are 25%, each partcles wll randomly competed wth others 2 partcles. When the partcles have better results (mnmum or maxmum based on objectve functon compared to ts compettor, the partcles wll obtaned pont. The process s repeated untl all partcles have done the competton. Fnally, only the hghest wnnng pont wll reman as survval partcle for next teraton. Intal Iteraton ( th Iteraton New Poston + Competton wth prevous teraton Fnal Poston ((+ th Iteraton Fg. 2: The llustraton of tournament n EPSO algorthm

3 C. Ran Evolutonary Partcle Swarm Optmzaton The Ran Evolutonary Partcle Swarm Optmzaton (REPSO s an mprovement technque for the EPSO methods. The competton concept that s used n EPSO analyss mght cause the lucy partcle stll survvng n the next teraton. From the Fg. 2, the *-partcle s deleted due to the lower mar even though t has better ftness value compared to other survval partcles. Thus, n the REPSO, the competton concept s replaced wth the ranng concept. After the combnaton between prevous and current teraton, all partcles wll be ran based on ts ftness value and the top N from the total populaton wll be selected as survval partcle whle the others wll be deleted. Ths process wll mae the only best result remaned n the populaton and acheved the optmal soluton n shorter tme. The others process of REPSO s same as n EPSO algorthm. Table shows the example of REPSO process n determnng the survval partcles. Let all the values n the table as the ftness value of each partcle n Fg. 2 and the objectve functon s to fnd maxmum value of a functon. Column 2 presents the prevous ftness values that are acheved by the partcles and the column 3 presents the current teraton ftness values. After the ranng process (column 4, only 6 number of partcles remaned for the next teraton and the others are deleted. Therefore, the ranng process wll sort the potental partcles at the top ranng for them to survve n the next teraton. poston of the partcles. Otherwse, the prevous teraton I best wll be used n the calculaton. The process of P best and G best n the IPSO s same as tradtonal PSO. Furthermore, the dfferent approached on acceleraton coeffcent for the I best s used where the authors used dynamc acceleraton technques rather than a constant value. Ths c 3 value wll change base on c parameter value and the number of teraton as stated n (9. c. 3.( c = c e (9 Where: = number of teraton Therefore, the new velocty formula for the IPSO algorthm wll have addtonal coeffcents compared tradtonal PSO as shown n (0. v + = ω v + c r ( P + c.( e best c. x ( I best + c r ( G x 2 2 best x IV. PROPOSED ALGORITHM FOR OPTIMAL ALLOCATION AND SIZING OF DG USING PSI AND VARIES PSO METHODS Fg. 3 shows the process to allocate and sze ether a sngle or multple DG unts n the dstrbuton networ usng PSI and vares PSO technque. (9 Partcle Table : The example of REPSO algorthm process n fndng the maxmum ftness functon -th teraton +-th teraton Combnaton Ranng Selecton No No No No No No D. Iteraton Partcle Swarm Optmzaton The Iteratve Partcle Swarm Optmzaton (IPSO s an mprovement method on soluton qualty and computng tme whch s proposed by [20]. Compared to tradtonal PSO, the IPSO conssts of 3 best values whch are G best, P best and I best. The I best s defned as any P best value that s selected randomly among exstng partcles n that present teraton. After the selecton process, the current I best value wll be compared wth prevous I best. If the current I best s better than prevous result, the value of I best wll be remaned and be used to update the Fg.3: Proposed Algorthm Flow Chart

4 The left sde of the fgure s explanng the process to allocate the DG usng PSI technque and the zoom n part of the fgure shows the procedure to determne the optmal DG sze usng optmzaton technque. After the allocaton of the DG has done wth the optmzaton results, the whole system s assumed as the base case for the 2 nd allocaton of the DG and contnues untl specfc number of DG has allocated. However, n ths study, only two DG unts wll be allocated for 2 bus and 69 bus test systems. V. SIMULATION AND RESULTS A. Test System The proposed algorthm s tested usng 2-Bus [2] and 69- Bus [22] radal dstrbuton networs shown n Fgs. 4 and 5. Fg. 4. Sngle Lne Dagram of 8-Bus Radal Networ Fg. 7. Sngle Lne Dagram of 69 Bus Radal Networ From Fgs. 6 and 7, t could be vsualzed that the DG should be placed at the end of lne 8 (9 th bus and lne 60 (6 th bus n 2-bus and 69-bus test systems respectvely. C. System Voltage Profle In order to show the mprovement n voltage buses, voltage profle for 2-bus and 69-bus dstrbuton networ s plotted n Fgs 8 and 9, respectvely. Fg. 5. Sngle Lne Dagram of 69-Bus Radal Networ B. Calculaton of PSI The PSI values for each lne are found out usng Eqn. (5. The calculated values for each lne of each case are shown n Fgs. 6 and 7. Fg. 8. Effect of DG on System Voltage Profle of 2-Bus system Fg. 9. Effect of DG on System Voltage Profle of 69-Bus system Fg. 6. Sngle Lne Dagram of 69-Bus Radal Networ From Fgs. 8 and 9, t could be vsualze that the voltage profle has mproved as compared to wthout DG.

5 D. Szng of DG (Optmzaton Technques Once the optmum DG locaton s found out, optmum DG sze s calculated usng PSO, EPSO, REPSO and IPSO algorthms. The computed results are shown n Table I-IV. The optmum DG sze and the effect on power losses are shown n Table I and III. However performances of optmzaton theorems are gven n Table II and IV. st DG Placement: TABLE I DG SIZING AND LOSSES AFTER ALLOCATION OF ST DG PLACEMENT No Test Systems DG DG Sze (MW Power Losses (KW Locaton PSO EPSO REPSO IPSO PSO EPSO REPSO IPSO 69-Bus Bus TABLE II NO. OF ITERATIONS AND COMPUTATION TIME IN ST DG PLACEMENT No Test Systems No of Iteraton Computaton Tme (s PSO EPSO REPSO IPSO PSO EPSO REPSO IPSO 69-Bus Bus nd DG Placement: TABLE III DG SIZING AND LOSSES AFTER ALLOCATION OF 2 ND DG PLACEMENT No Test Systems DG Locaton DG Sze (MW Power Losses(KW PSO EPSO REPSO IPSO PSO EPSO REPSO IPSO 69- Bus Bus TABLE VI NO. OF ITERATIONS AND COMPUTATION TIME IN 2 ND DG PLACEMENT No Test Systems DG No of Iteraton Computaton Tme locaton PSO EPSO REPSO IPSO PSO EPSO REPSO IPSO 69-Bus Bus E. Dscusson: From Tables I-IV, t can be sad that all PSO methods wll gve smlar results n DG szng and the power losses value up to the 4th decmal places. However, the number of teraton and the computng between them are dfferent. Thus, the new PSI value for the system wll be observed after the optmal DG value s obtaned before the 2 nd DG placement s dong. The results show that the 2 nd placement of DG wll gve lower power losses to the networ wth better VSI value for the whole system. It can be conclude that, the allocaton of DG base on PSI value wll gve postve mpact on power losses reducton as well as VSI value of the system. VI. CONCLUSION Ths paper has presented the mult DG allocaton based on Power Stablty Index (PSI value. Once the DG ste has been allocated, the optmum DG sze s calculated usng dfferent optmzaton technques such as PSO, IPSO, EPSO and REPSO based on mnmzaton of losses. The performances among all optmzaton technques were also compared and the REPSO was found superor based on computng tme and the number of teraton requred. In term of DG szng, almost all the optmzaton methods gave nearly smlar results. The advantage of the proposed method n mult DG allocaton usng PSI ndcator s lyng n smplcty. Furthermore, the results conclude that as compared to wthout DG placement, the losses have reduced consderably and voltage profle has mproved.

6 REFERENCES [] Jenns N, Eanayae J, Strbac G. Dstrbuted Generaton - IET Factfles Avalable at: [2] Energy Networs Assocaton (UK. Avalable at: [accessed on ]. [3] Hadjsad N, Canard JF, Dumas F. Dspersed generaton mpact on dstrbuton networs. IEEE Comput Appl Pow. 999;2:22-8. [4] Tutemwong K, Premrudeepreechacharn S. Expert system for protecton coordnaton of dstrbuton system wth dstrbuted generators. Internatonal Journal of Electrcal Power & Energy Systems. 20;33: [5] J.J. Jaman, H. Musa, M.W. Mustaf, H. Mohls, S.S. Adamu, Combned Voltage Stablty Index for Chargng Staton Effect on Dstrbuton Networ, Internatonal Revew of Electrcal Engneerng- IREE, vol. 6, no. 7, December 20, pp [6] Lund T. Analyss of dstrbuton systems wth a hgh penetraton of dstrbuted generaton: Techncal Unversty of Denmar; [7] Khodr HM, Olsna FG, Jesus PMDO-D, Yusta JM. Maxmum savngs approach for locaton and szng of capactors n dstrbuton systems. Electrc Power Systems Research. 2008;78: [8] S. M. Moghaddas-Tafresh, Elahe Mashhour, Dstrbuted generaton modelng for power flow studes and a three-phase unbalanced power flow soluton for radal dstrbuton systems consderng dstrbuted generaton, Electrc Power Systems Research, Volume 79, Issue 4, Aprl 2009, Pages [9] A. Soroud and M. Ehsan, Effcent mmune-ga method for DNOs n szng and placement of dstrbuted generaton unts, European Transactons on Electrcal Power, vol. 2, no. 3, Aprl 20, pp [0] S. Ghosh, S. P. Ghoshal, and S. Ghosh, Optmal szng and placement of dstrbuted generaton n a networ system, Internatonal Journal of Electrcal Power & Energy Systems, vol. 32, no. 8, Oct 200, pp [] Ghosh S, Ghoshal SP, Ghosh Sa. Optmal szng and placement of dstrbuted generaton n a networ system. Int J Elec Power. 200;32(8: The 0th MEPCON Internatonal Mddle East Power Systems Conference, December 9-2, 200. [3] F. S. Abu-Mout and M. E. El-Hawary, "Optmal Dstrbuted Generaton Allocaton and Szng n Dstrbuton Systems va Artfcal Bee Colony Algorthm," Ieee Transactons on Power Delvery, vol. 26, no. 4, pp , Oct.20. [4] G. Isazadeh, R. A. Hooshmand, and A. Khodabahshan, "Modelng and optmzaton of an adaptve dynamc load sheddng usng the ANFIS- PSO algorthm," Smulaton-Transactons of the Socety for Modelng and Smulaton Internatonal, vol. 88, no. 2, pp. 8-96, Feb.202. [5] J.J. Jaman, M.W. Mustafa, H. Mohls, J. Usman, Dstrbuted Generator Szng: An Iteraton Partcle Swarm Optmzaton Approach, Proceedngs of the IASTED Internatonal Conference Power and Energy Systems, AsaPES 202, Aprl 2-4, 202, [6] D. Nu, Z. Gu, M. Xng, Research on Neural Networ Based on Culture Partcle Swarm Optmzaton and ts Applcaton n Power Load Forescastng, Thrd Internatonal Conference on Natural Computaton [7] T. Y. Lee and C. L. Chen, Unt commtment wth probablstc reserve: An IPSO, Energy Converson and Management, vol. 5, no. 2, Dec.200, pp [8] J. Kennedy, R. C. Eberhart, Partcle Swarm Optmzaton, IEEE Internatonal Conference on Neural Networs IV, Pscataway, NJ, Vol.4, 995, pp [9] Angelne, P.J., Usng selecton to mprove partcle swarm optmzaton, Evolutonary Computaton Proceedngs, 998. IEEE World Congress on Computatonal Intellgence., The 998 IEEE Internatonal Conference on, 4-9, May 998, vol., no., pp [20] T. Y. Lee and C. L. Chen, Unt commtment wth probablstc reserve: An IPSO, Energy Converson and Management, vol. 5, no. 2, Dec.200, pp [2] Das D, Nag HS, Kothar DP. Novel method for solvng radal dstrbuton networs. In: IEE Proceedngs-Generaton, Transmsson and Dstrbuton. 994; 4(4; [22] Baran M, Wu FF. Optmal szng of capactors placed on a radal dstrbuton system. IEEE Transactons on Power Delvery. 989;4: [2] M.F.Kotb, K.M.Sheb, M. El Khazenda, A. El Hussen, Genetc Algorthm for Optmum Stng and Szng of Dstrbuted Generaton,