Optimal Placement of Solar PV in Distribution System using Particle Swarm Optimization

Transcription

1 ISSN (Prnt) : ISSN (Onlne): Internatonal Journal of Advanced Research n Electrcal, Electroncs and Instrumentaton Engneerng (An ISO 3297: 2007 Certfed Organzaton) Vol. 2, Specal Issue 1, December 2013 Optmal Placement of Solar PV n Dstrbuton System usng Partcle Swarm Optmzaton Athra Jayavarma 1, Tbn Joseph 2 P.G Student, Dept. of EEE, Santgts College of Engneerng, Pathamuttom, Kerala Inda 1, Assstant Professor, Dept. of EEE, Santgts College of Engneerng, Pathamuttom, Kerala Inda 2 Abstract: Solar PhotoVoltacs (SPV) are among the fastest growng energy resources n the world. Most of the SPV had been nstalled n the dstrbuton systems as dstrbuted generaton. Now, a day s Dstrbuted generatons (DGs) play an mportant role n dstrbuton networs. Among many of ther merts, loss reducton and voltage profle mprovement can be the salent specfcatons of DG. Studes show that non-optmal locatons of DG unts may lead to losses ncrease, together wth bad effect on voltage profle. So, ths paper presents a new methodology usng Partcle Swarm Optmzaton(PSO) for the placement of Solar PV n the radal dstrbuton systems. The proposed algorthm wll dentfy the optmal locaton of Solar PV wth mnmum actve power losses.. The developed algorthm has been tested on modfed IEEE 14-bus test. The result shows a consderable reducton n the total power loss n the system and mproved voltage profles of the buses. Keywords- Dstrbuted Generators (DG); Fuel Cell ;Solar Photo Voltacs (SPV); Partcle Swarm Optmsaton(PSO); I. INTRODUCTION Dstrbuted generaton s any electrcty generatng technology nstalled by a customer or ndependent electrcty producer that s connected at the dstrbuton system level of the electrc grd [1]. It can be sad that DO s assocated wth the use of small generaton unts located close to or n the load centers. The effects of DO on voltage profle, lne losses, short crcut current and system relablty are to be evaluated separately before nstallng t n a dstrbuton networ. DG technologes can be categorzed nto renewable and non-renewable energy resources. The DG technologes that based on renewable are solar, wnd, small-hydro, bomass, geothermal etc. whereas the DG technologes that based on non-renewable are combuston turbnes, steam turbnes, mcro turbnes, recprocatng engnes etc. Fuel cells can be categorzed nto renewable (usng hydrogen) and non-renewable (usng natural gas or petrol) [2] [3]. The benefts of DG are numerous [4, 5] and the reasons for mplementng DGs are an energy effcency or ratonal use of energy, deregulaton or competton polcy, dversfcaton of energy sources, avalablty of modular generatng plant, ease of fndng stes for smaller generators, shorter constructon tmes and lower captal costs of smaller plants and proxmty of the generaton plant to heavy loads, whch reduces transmsson costs. Also t s accepted by many countres that the reducton n gaseous emssons (manly CO2) offered by DGs s major legal drver for DG mplementaton [6]. Photovoltacs(PV) s a method of generatng electrcal power by convertng solar radaton nto drect current electrcty usng semconductors that exhbt the photovoltac effect. Solar photovoltacs s now, after hydro and wnd turbne, the thrd most mportant renewable energy source n terms of globally nstalled capacty. Integratng PV n the dstrbuton system has postve mpacts. Some of them are, Solar energy s suppled by nature thus t s abundant, t can be made avalable almost anywhere there s sunlght, ease of operaton and neglgble operatng cost, polluton free, they are totally slent, producng no nose at all, and have no mechancally movng parts [7]. Optmal placement and szng of PVS unts n dstrbuton systems s a complex combnatoral optmzaton problem [8]. Recently, metaheurstcs optmzaton methods are beng successfully appled to combnatoral optmzaton problems n power systems partcularly n DG allocaton and szng. In [8]-[12], the DG placement problem was presented usng genetc algorthm (GA) technque. The placement problem presented n [8] s evaluated based on the relaton of beneft obtaned by the nstallaton of DG and the nvestment and operatonal cost ncurred n ther nstallaton. The authors n [9] presented the steps of DG allocaton n two separate ways,.e. not contnuous, the optmal locaton s determned frst, and then the optmal sze of the DG s solve second. The wor presented n [13] Copyrght to IJAREEIE 329

2 ISSN (Prnt) : ISSN (Onlne): Internatonal Journal of Advanced Research n Electrcal, Electroncs and Instrumentaton Engneerng (An ISO 3297: 2007 Certfed Organzaton) Vol. 2, Specal Issue 1, December 2013 dscussed the combnaton of genetc algorthm (GA) and smulated annealng (SA) whle evolutonary programmng optmzaton technque was used n [14] to solve the DG allocaton problem. In [15] tabu search algorthm s presented. As n [8], the authors n [14]-[15] dscussed the placement of DG n two separate ways, the optmal locaton s determned frst, and then the optmal szng of the DG s second. One of the metaheurstcs optmzaton recently developed was Partcle Swarm Optmzaton (PSO). Comparng to another algorthms, Partcle Swarm Optmzaton [16] has the flexblty to control the balance n the search space and PSO overcomes the premature convergence problem and enhances the search capablty. Here the soluton qualty doesn t rely on the ntal populaton. In ths paper, an algorthm s developed to fnd the optmal locaton of Solar PV n the dstrbuton system. The problem s formulated as a sngle objectve functon of mnmzng the system actve power losses consderng the constrants on actve power generaton and voltage lmts. Ths optmzaton problem s solved usng Partcle Swarm Optmzaton (PSO) algorthm. At each step, Solar PV s placed at a bus and the power flow analyss s carred out by Newton-Raphson method to evaluate the varaton n power losses of the system consderng the constrants. Ths paper s organzed as follows: Proposed methodology and modellng of the power system and Solar PV are descrbed n secton II. Problem formulaton for the optmal placement of Fuel Cell DG and Solar PV and the PSO algorthm are presented n secton III. The results and dscussons are descrbed n secton IV. Fnally a bref concluson s deduced n secton V. II. PROPOSED METHODOLOGY & MODELING The proposed methodology conssted of fndng the best sutable bus for connectng the Solar PV as shown n Fgure 1. The development of the algorthm requred problem formulaton wth modelng of Solar PV and the dynamc model of IEEE 14-bus system. Fg. 1 Proposed Methodology A. Modelng of Power System Components IEEE 14-bus system wth Solar PV has been modeled n ths paper for the analyss. The dynamc model of IEEE 14- bus system has been analyzed and the power flow results are verfed wth the standard values. The Solar PV model has been explaned n the next secton. B. Modelng of Solar PV PV s the most versatle, smplest to nstall and cheapest to mantan, and provdes a hghly valued product electrcty- generally at or close to the pont of use, avodng the cost and rs of falure of nfrastructure[19].a storage system s n general absent n large grd-connected SPVG nstallatons, except for small crtcal loads of the plant such as start-up controls. However, there are some nstances n whch consderable storage has been ntegrated nto large scale SPVGs [18]. Fg.2 SPVG Model 1 Copyrght to IJAREEIE 330

3 ISSN (Prnt) : ISSN (Onlne): Internatonal Journal of Advanced Research n Electrcal, Electroncs and Instrumentaton Engneerng (An ISO 3297: 2007 Certfed Organzaton) Vol. 2, Specal Issue 1, December 2013 Fg.3 SPVG Model 2 In the current paper, the followng models are consdered for the SPVG : Model 1: Constant P and constant Q control. Model 2: Constant P and constant V control. A. Objectve Functon & Constrants III. PROBLEM FORMULATION A general constraned sngle-objectve optmzaton problem consderng actve power loss of all the transmsson lnes n the system has been formulated to fnd the optmal locaton of the Solar PV. Accordngly, the objectve functon has been formulated for any tme (t) as: Mnmze, F ntl P LK 1 Subjected to the followng equalty constrants P P G Q Q P G D Q D Nb j 1 VV Y Cos(δ δ θ ) Nb j 1 And the followng nequalty constrants j j VV Y Sn(δ δ θ ) j j j j j j (1) (2) Q V Q Q Gmn G G max V V mn max =1,, N G =1,, N b (3) P P ; j 1,... N j max j l (4) Where F s the objectve functon. P LK s the actve power loss n the K th lne. ntl s the number of lnes n the system N b s the set of buses ndces N G s the set of generaton bus ndces Y j and θ j are the magntude and phase angle of element n admttance matrx P g and Q g are the actve and reactve power generaton at bus P d and Q d are the actve and reactve power load at bus Copyrght to IJAREEIE 331

4 ISSN (Prnt) : ISSN (Onlne): Internatonal Journal of Advanced Research n Electrcal, Electroncs and Instrumentaton Engneerng V s the voltage magntude at bus. (An ISO 3297: 2007 Certfed Organzaton) Vol. 2, Specal Issue 1, December 2013 B. Partcle Swarm Optmzaton (PSO) PSO s a robust stochastc optmzaton technque based on the movement and ntellgence of swarms. PSO apples the concept of socal nteracton to problem solvng. It was developed n 1995 by James Kennedy (socal-psychologst) and Russell Eberhart (electrcal engneer). It uses a number of agents (partcles) that consttute a swarm movng around n the search space loong for the best soluton. Each partcle treated as a pont n a N-dmensonal space whch adjusts ts flyng accordng to ts own flyng experence as well as the flyng experence of other partcles. Each partcle eeps trac of ts coordnates n the soluton space whch are assocated wth the best soluton (ftness) that has acheved so far by that partcle. Ths value s called personal best, P best. Another best value that s traced by the PSO s the best value obtaned so far by any partcle n the neghbourhood of that partcle. Ths value s called G best. The basc concept of PSO les n acceleratng each partcle toward ts P best and the G best locatons, wth a random weghted acceleraton at each tme step. Each partcle tres to modfy ts poston usng the followng nformaton: the current postons, the current veloctes, the dstance between the current poston and P best, the dstance between the current poston and the G best. The modfcaton of the partcle s poston can be mathematcally modeled accordng the followng equaton: V 1 ω V a rand 1 1 *(P best X ) a rand 2 2 *(G best X ) (5) X 1 X V 1 (6) In the updatng, a new velocty for each partcle based on ts prevous velocty s V determned. The partcle s P locaton at whch the best ftness ( best G ) and the best partcle among the neghbours ( best ) have been acheved. The learnng factors, a1 and a2, are the acceleraton constants whch change the velocty of a partcle towards P best and G best. The random numbers, rand 1 and rand 2, are unformly dstrbuted numbers n range [0, 1]. Fnally, each partcle s poston X s updated by (6). C. PSO Algorthm Step 1: Input lne data, bus data, PV data, voltage lmts, lne lmts and PSO settngs. Step 2: Identfy the best locaton for Solar PV placement by the calculaton of total actve power loss of the system and connect the Solar PV to that partcular bus. Step 3: Calculate the base case power flow wth Solar PV connected at the dentfed bus. Step 4: The populaton of N partcles s ntalzed wth random postons, x and the velocty, v of each partcle s set to zero. Each partcle can have d number of varables. Step 5: The objectve functon s evaluated wth all partcles n order to fnd the objectve value. If the value of a partcle and the objectve value obtaned from that partcle are wthn the lmt,, that partcle wll be accepted. Otherwse, new partcle wll be generated and ths step wll be repeated. Then P best s set as the current poston and G best s set as the best ntal partcle. Step 6: The new velocty,v+1 and the new poston, x+1, s calculated usng equatons (5) and (6) and the values of the current Gbest and Pbest. Step 7: Evaluate the objectve values of all partcles usng the new poston. Step 8: The objectve value of each partcle s compared wth ts prevous objectve value. If the new value s better than the prevous value, then update the Pbest and ts objectve value wth the new poston and objectve value. If not, mantan the prevous values. Step 9: Determne the best partcle of the whole updated populaton wth the Gbest. If the objectve value s better than the objectve value of Gbest, then update Gbest and ts objectve value wth the poston and objectve value of the new best partcle. If not, mantan the prevous Gbest. Step 10: If the stoppng crteron s met, then output Gbest and ts objectve value; otherwse, repeat step sx. Step 11: Dsplay the optmal soluton to the target problem. The best poston gves the locaton for Solar PV resultng n mnmum total actve power loss for the system. Copyrght to IJAREEIE 332

5 ISSN (Prnt) : ISSN (Onlne): Internatonal Journal of Advanced Research n Electrcal, Electroncs and Instrumentaton Engneerng (An ISO 3297: 2007 Certfed Organzaton) Vol. 2, Specal Issue 1, December 2013 Fgure 4 gves the flowchart of the Proposed algorthm. Fg. 4 Flow Chart of Proposed Algorthm IV. RESULT & DISCUSSION A. Specfcaton of Test system The proposed soluton method was tested on an IEEE 14 bus test system, shown n Fgure.5. The networ conssts of 6 generators, of whch one s slac and there are 20 lnes. The results consst of two steps. The frst step s to access the best locaton of Solar PV and the second s the calculaton of mnmum actve power loss. The proposed methodology has been tested on IEEE14-bus system as shown n fgure 4. Bus-2, 13 are PV buses and 3, 6 and 8 are synchronous compensator buses. Solar PV have been connected to any of the bus (other than slac bus and buses connected to transformers), voltage and angle settngs of slac bus and Solar PV ratngs are consdered for mnmsng the actve power loss. Loads were modeled as constant power loads (PQ load) and were solved by usng Newton Raphson Power flow Routne. The program was coded n MATLAB Copyrght to IJAREEIE 333

6 ISSN (Prnt) : ISSN (Onlne): Internatonal Journal of Advanced Research n Electrcal, Electroncs and Instrumentaton Engneerng (An ISO 3297: 2007 Certfed Organzaton) Vol. 2, Specal Issue 1, December 2013 Bus 13 Bus 12 Bus 10 Bus 14 Bus 11 Bus 09 Bus 07 Bus 06 Bus 04 Bus 08 Bus 01 Bus 05 Bus 02 Bus 03 Fg. 5 IEEE- 14 Bus system wth Solar PV The base case wthout Solar PV bus voltage level s compared aganst the base case wth Solar PV voltage lmt n Fgure 6. The fgure shows that optmal placement of Solar PV adjusted the voltages of PV buses and slac bus for mnmsng the losses. The fgure clearly states that all the bus voltages are wthn the set lmts at mnmum actve power loss wth Solar PV at optmum locaton Bus Voltage[p.u] Base Model Wth Solar PV Bus 7 no: Fg. 6 Typcal voltage levels wth and wthout Solar PV Copyrght to IJAREEIE 334

7 ISSN (Prnt) : ISSN (Onlne): Internatonal Journal of Advanced Research n Electrcal, Electroncs and Instrumentaton Engneerng (An ISO 3297: 2007 Certfed Organzaton) Vol. 2, Specal Issue 1, December 2013 Fg.7 Real Power Generaton Wth and Wthout Solar PV Fgure 7 shows the bus generatons at mnmum actve power loss usng SPV at optmum locaton. Fg. 8 Reactve Power Generaton Wth and Wthout Solar PV Fgure 8 shows the bus reactve power generatons at mnmum actve power loss usng SPV at optmum locaton. Fg.9 Reactve Power Generaton Wth and Wthout Solar PV The actve power flows n varous lnes are gven n Fgure 9. Except for lne 7 and 13, the power carred through all other transmsson lnes s reduced whch n turn reduces the losses. Copyrght to IJAREEIE 335

8 ISSN (Prnt) : ISSN (Onlne): Internatonal Journal of Advanced Research n Electrcal, Electroncs and Instrumentaton Engneerng (An ISO 3297: 2007 Certfed Organzaton) Vol. 2, Specal Issue 1, December 2013 TABLE I. ACTIVE POWER LOSS REDUCTION Actve Power loss[p.u] Base model wthout Solar PV Base model wthout Solar PV From ths table, t s clear that the total actve power loss of the system s reduced by the optmal allocaton of Solar PV. V. CONCLUSION The new methodology proposed to optmally place the Solar PV so as to mnmze the actve power loss of the system usng PSO has dscussed n ths paper.partcle Swarm Optmzaton algorthm, s easy to mplement and the tme taen for the teraton s less compared to other conventonal methods and t s accurate.the results shows that the optmal allocaton of Solar PV wll mnmze the real power loss and t s tested on IEEE 14 bus system. ACKNOWLEDGMENT The authors gratefully acnowledge Dr Federco Mlano, for hs excellent smulaton software PSAT. REFERENCES [1] Thomas Acermann, Goran Andersson, Lenart Soder, "Dstrbuted generaton: a defnton," Electrc Power Systems Research, Volume 57, Issue 3, Pages ,20 Aprl [2] G. Pepermans, J. Dresen, D. Haeseldoncx, R. Belmans, and W. D'Haeseleer, "Dstrbuted generaton: defnton, benefts and ssues", Energy Polcy, vol. 33, pp , [3] A. A. B. Rujula, J. M. Amada, J. L. Bernal-Agustn, J. M. Y. Loyo, Navarro, "Defntons for Dstrbuted Generaton: A Revson", Internatonal Conference on Renewable Energy and Power Qualty, March [4] P.A. Daly, J. Morrson, Understandng the potental benefts of dstrbuted generaton on power delvery systems, Rural Electr Power Conference, 29 Aprl 1 May 2001, pp. A211 A213. [5] P. Chradeja, R. Ramaumar, An approach to quantfy the techncal benefts of dstrbuted generaton IEEE Trans Energy Converson, vol. 19, no. 4, pp , [6] Kyoto Protocol to the Unted Natons Framewor Conventon on clmate change, [7] Gudmetla.B, Katrrae.F Aquero.J.R and Ensln.J.H.R, Integraton of Mcro-Scale Photovoltac Dstrbuted Generaton on Power Dstrbuton Systems: Dynamc Analyss,IEEE Transmsson and Dstrbuton Conference and Exposton,7 may [8] Carmen L.T. Borges, Djalma M. Falcão, Optmal dstrbuted generaton allocaton for relablty, losses, and voltage mprovement, Internatonal Journal of Electrcal Power & Energy Systems, Volume 28, Issue 6, July 2006, Pages [9] B Kur, M A Redfern and F L, Optmsaton of Ratng and Postonng of Dspersed Generaton wth Mnmum Networ Dsrupton, Power Engneerng Socety General Meetng, Denver, Colorado, USA, June 6-10, 2004 [10] Amed R. Abdelazz and Wald M. Al, Dspersed Generaton Plannng Usng A New Evolutonary Approach, Proceedngs of Power Tech Conference, Bologna, Italy, June, [11] Jen-Hao Teng, Tan-Syh Luor and Y-Hwa Lu, Strategc Dstrbuted Generator Placement for Servce Relablty Improvements, Power Engneerng Socety Summer Meetng, Chcago, IL, USA, 25 July, [12] G. Cell and F. Plo, Optmal Dstrbuted Generaton Allocaton n MV Dstrbuton Networs, Power Engneerng Socety Internatonal Conference on Power Industry Computer Applcatons, Sydney, NSW, Australa, May, [13] M. Gandomar, M. Valan and M. Ehsan, A Combnaton of Genetc Algorthm and Smulated Annealng for Optmal DG Allocaton n Dstrbuton Networs, Proceedngs of Canadan Conference on Electrcal and Computer Engneerng, Sasatchewan, Canada, 1-4 May, [14] T. K. A. Rahman, S. R. A. Rahm and I. Musrn, Optmal Allocaton and Szng of Embedded Generators, Proceedngs of Natonal Power and Energy Conference, November, [15] Koch Nara, Yasuhro Hayash, Kazushge Ieda, and Tomoo Ashzawa, Applcaton of Tabu Search to Optmal Placement of Dstrbuted Generators, Power Engneerng Socety Wnter Meetng, Columbus, OH, USA, 28 January - 01 February, [16] W. Krueasu and W. Ongsaul, "Optmal Placement of Dstrbuted Generaton Usng Partcle Swarm Optmzaton", Australan Unverstes Power Engneerng Conference, Dec [17] Modelng new forms of generaton and storage, CIGRE techncal brochure, Nov Copyrght to IJAREEIE 336

9 ISSN (Prnt) : ISSN (Onlne): Internatonal Journal of Advanced Research n Electrcal, Electroncs and Instrumentaton Engneerng (An ISO 3297: 2007 Certfed Organzaton) Vol. 2, Specal Issue 1, December 2013 [18] Y. Ueda, S. Suzu, and T. Ito, Grd stablzaton by use of an energy storage system for a large-scale PV generaton plant, ECS Transactons, Vol. 16, No. 34, pp , Oct [19] Behnam Tamm, Claudo, Kanar Bhattacharya, Modelng and Performance Analyss of Large Solar Photo-Voltac Generaton on Voltage Stablty and Inter-area Oscllatons, IEEE PES conference meetng. [20] Dolata, Ryan, Song, Hwachang, Optmal Allocaton of PV Systems n Dstrbuton Systems usng Partcle Swarm Optmzaton, The Internatonal Conference on Electrcal Engneerng [21] A. Yazdan and P. P. Dash, A control methodology and characterzaton of dynamcs for a photovoltac system nterfaced wth a dstrbuton system, IEEE Trans. Power Delvery, pp , Jul [22] D. Rama Prabha, R.Mageshvaran, Endla Raghunath, Guda Raghuram, Determnng the Optmal Locaton and Szng of Dstrbute Generaton Unt usng Partcle Swarm Optmzaton Algorthm, Internatonal Conference on Computer Communcaton and Informatcs [23] F. Mlano, An Open Source Power System Analyss Toolbox, IEEE Transactons on Power Systems, vol. 20, no. 3, pp , Aug [24] G. Cell, E. Ghaan, S. Mocc, F. Plo, "A Multobjectve Evolutonary Algorthm for the szng and sttng of Dstrbuted Generaton," IEEE Trans. on Power Systems, vol.20, BIOGRAPHY Athra Jayavarma obtaned her B. Tech n Electrcal & Electroncs from Mahatma Gandh Unversty n 2011 and she s currently worng towards the Masters n Power Systems at Mahatma Gandh Unversty, Kerala. Tbn Joseph receved hs B. Tech n Electrcal & Electroncs and M.Tech n Power Electroncs & Power Systems from Mahatma Gandh Unversty, Kerala n 2008 and 2012 respectvely. He s currently worng as an Assstant Professor. Hs current nterests are n FACTS devces n power Grd and grd ntegraton of Renewable Sources, optmzaton technques. Copyrght to IJAREEIE 337