Economic/Emission dispatch including wind power using ABC-Weighted-Sum

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1 Internatonal Journal of Engneerng and Techncal Research (IJETR) ISSN: (O) (P), Volume-3, Issue-10, October 2015 Economc/Emsson dspatch ncludng wnd power usng ABC-Weghted-Sum Sebaa Hadd, Tarek Bouktr Abstract Ths paper presents a mean of renewable energy ntegraton by searchng the best compromse settng of mx generaton cost and thermal generaton gas emssons by usng one of best meta-heurstc based populaton technque ABC, followed by weghted sum technque. The study s done on a standard IEEE 30 bus test system. The total generaton cost as well as the total emsson of the entre system can be reduced clearly, by solvng the EED load dspatch problem ncludng, wnd farms plants wth the cost related to ther stochastc nature, so that both operatng cost of conventonal sources and wnd farms, and the emssons caused by the conventonal ones, are solved by the proposed method; usng ABCWS approach. Results for optmzaton of total cost as well as emsson are then nvestgated n ths paper. Index Terms EED problem, mult-objectve optmzaton, ABCWS Algorthm. I. INTRODUCTION The economc dspatch problem deals wth fndng the optmal allocaton of electrcal power output from avalable generators vector, by the computaton of the mnmum generaton cost. The bascally EED problem nvolves only the conventonal thermal energy power generators, whch use depletable sources of energy, as fossl fuels [1]. Due to the shortage of energy, the blackouts, and the envronmental concerns worldwde, there s a need to explot the alternatve energy resources so called renewable; and ther effectve ntellgent ntegraton n exstng power grds. One of the major dffcultes n optmzng the operaton of Smart Grd s the uncertanty assocated wth the weather profles, unpredcted weather varatons causes fluctuatons n the power outputs of renewable energy sources, such as wnd farms and solar panels, and such fluctuatons can cause serous problems to the system operator, thus there are operatonal challenges to mantan the generaton-load balance, especally n case of hgh level of renewable power penetraton. One of the renewable sources that mean nowadays more wdespread used, especally n the North Europe s the wnd power, whch s after the startng cost of land and captal prces, there s essentally no cost nvolved n the producton of power from these sources, the same thng s seeng about solar energy [2]. On the other hand the operaton of smart grds often requres Sebaa Hadd, Department of Electrcal Engneerng, Faculty of Technology Unversty, Of Sétf 1, Algera, Tarek Bouktr, Department of Electrcal Engneerng, Faculty of Technology Unversty, Of Sétf 1, Algera, optmzng conflctng objectves such as cost, rsk level, envronmental mpact and relablty, due to gas emssons, and customer preferences. Snce t s mpossble to optmze every sngle objectve ndependently, Pareto optmzaton can be used to fnd the optmal soluton [3]. Pareto fronts contan a wealth of nformaton, provdng a set of solutons of good qualty for the decson makers to choose accordng to ther preferences. Such approach s ntroduced n order to fgure out the optmal amounts of the generated powers from the thermal unts and wnd farms, by mnmzng the emsson level and cost of generaton smultaneously, whch s known as economc emsson dspatch (EED) problem, by searchng the best compromse soluton of the cost of generaton power outputs and related gas emsson, subject to operatonal equalty and nequalty constrants. [4] In ths work, the problem of EED consderng avalablty of wnd sources and seekng mnmzaton of gas emsson n a IEEE30 bus test system by usng ABCWS mult-optmzaton technque to fnd the best compromse soluton of the problem under study. For ths purpose, ths work s dvded as follows; followng the ntroducton, general problem formulaton for economc emsson dspatch problem ncludng wnd power s formulated n secton two. Then n secton three, a flowchart of ABCWS technque s gven. Secton four deals wth the applcaton of the proposed method on IEEE 30 bus test system wth dfferent scenaros; such as load varyng level, Pareto front for dfferent wnd rates and results dscusson, fnally n secton fve a concluson. II. PROBLEM FORMULATION A mult-objectve problem seeks to fnd the best compromse soluton between conflctng objectves and can be expressed by: Mnmze T f 1( P), f1( P),..., ft ( P) (1) ( P) 0 Subject to (2) ( P) 0 Where the overall objectve functon s the combned objectves functon, г and Ѱ are the equalty and nequalty constrants, respectvely. P s the vctor of decson varables. The soluton to the above problem s not unque, but a set of Pareto optmal set that consttute the non-domnated soluton 61

2 Economc /Emsson Dspatch ncludng Wnd Power usng ABC Weghted sum of the problem. The solutons that are non-domnated wthn the search space are denoted as Pareto optmal front [5]. A Pareto optmal soluton s the best soluton vector out of several numbers of soluton vectors that could be acheved wthout dsadvantagng other objectves. In EED problem, Pareto optmal soluton s the best generaton schedule out of several sets of generaton schedule that can be acheved wthout dsadvantagng both fuel cost mnmzaton objectve and emsson mnmzaton objectve. The above mult-objectve problem may be converted to a sngle objectve optmzaton problem by ntroducng the prce penalty factor, thus the total operatng cost of the system s the cost of generaton plus the mpled cost of emsson. In ths case the objectve functon s: [6] mnmze c f p f 1 fng ng ($/h) (3) By ths b-objectve functon a set of Pareto optmal solutons s acheved, and then non-domnated solutons can be generated usng weghtng factor α, as follows: mnmze c ( ) f (1 ) p f (4) 1 fng ng In ths secton, the economc emsson dspatch seeks a balance between the fuel cost and gas emssons amount, ths may be consdered as a mult-objectve problem, and may be formulated by: mnmze Cop ( Pg, P w ),E( Pg ) (5) Subject to; mn max P P P g g g (6) 0 P P w w,r (7) M N Pg P w P D 1 1 Where Cop Combned operatng cost of thermal unts and wnd farms. E Emssons of thermal unts Pg Output of th thermal generatng unt w Scheduled output of th wnd farm wr Rated output of th wnd farm PD System load ncludng losses M Number of thermal unts N Number of wnd farms As shown n [7], the combned operatng cost can be formulated as; M N N N C C ( P ) C ( P ) C ( W ) C ( W ) op g w w p, ue r,oe Where (8) (9) C Operatng cost for th thermal generatng unt Cw Operatng cost for th wnd farm Cp Penalty cost coeffcent for not usng all avalable power from th wnd farm due to under-generaton Cr Reserve cost coeffcent due to the reserve capactes used to compensate the over-estmated wnd power from the th wnd farm. These quanttes can be represented by; C a b P c P (10) C 2 w d P (11) w v v k k r, o,, ue ( wr, w, ) exp exp k k c c W p p k k pwr, v n, v r, v 1, pw, exp exp k k vr, v n, c c k k pwr, v n 1 v1, 1 vr, 1, 1, vr, v n, k c k c W v v k k n, o,,o e ( pw, ) 1 exp exp k k c c k k pwr, v n, v n, v 1, pw, exp exp k k vr, v n, c c (12) (13) k k pwr, v n 1 v1, 1 vn, 1, 1, vr, v n, k c k c Where W,ue W,, oe are expected value of wnd power under and over estmaton for th wnd turbne. A, b and c Cost Coeffcents of th thermal unt. d Cost coeffcent of th wnd farm. k and c Webull PDF parameters of the th wnd turbne. v, v r and v 0 Cut-n, rated and cut-out wnd speeds, are the wnd speed for whch the wnd turbne starts the generaton and for whch wnd turbne s dsconnected from network. For wnd farms, the operatng cost s consdered lnearly proportonal to the power output. The mbalance cost due to over-generaton or under-generaton of wnd farms s assumed to be lnearly proportonal to the dfference between the actual and scheduled wnd powers. In case of under-estmaton penalty, f the avalable wnd output s more than what was specfed, that power wll be wasted, and the system operator must pay a cost to the wnd power producer for ths wasted capacty, so the penalty cost for not usng all the avalable wnd power wll be lnearly related to the dfference between the avalable and actual wnd power used. If a certan amount of wnd power s assumed and that power s not avalable at the assumed tme, power must be purchased from another alternate source or load must shed, thus the reserve cost coeffcent for the not avalablty of the 62

3 assumed wnd power s calculated. [8] Internatonal Journal of Engneerng and Techncal Research (IJETR) ISSN: (O) (P), Volume-3, Issue-10, October 2015 The ncomplete gamma functon s used for smplfyng calculatons of both (12) and (13). The envronmental emssons from thermal unts can be expressed as n [9], by; M E P P 2 g g exp( Pg ) 1 (t/h) (14) Where; α, β, γ, ξ, and λ are coeffcents of the th generator s emsson characterstc. III. USED ALGORITHM Artfcal Bee colony (ABC) Is one of the most recently defned algorthms by Drv.Karaboga n 2005, motvated by the ntellgent behavor of honey bees. ABC as an optmzaton tool provdes a populaton based search procedure n whch ndvduals called food postons are modfed by the artfcal bees wth tme and the bee s am s to dscover the places of food sources wth hgh nectar amount and fnally the one wth the hghest nectar. [10] A. ABC Algorthm The ABC algorthm follows the flow chart shown s based on the followng bees movements. [11] a) Movement of employed Bees; V X Xj X j kj j kj (15) Where x ( = 1, 2... N); s represented by a D-dmensonal vector, where D s the number of parameters to be optmzed. Vj s the new poston of the employed bee k є {1, 2.., n}, and j є {1, 2.., D} are randomly chosen ndexes. Øj s a random number between [0 1]. b) move of onlooker bees for selected stes and evaluaton of ftness based on the probablty functon as; P ft s ft n n1 (16) Where; P defned the probablty of the food source wth respect to ts ftness. c) move of scout bees; d) The followng equaton corresponds to ther movement: X X rand mn (0,1) * X max X j j j mn (17) Where X j and j Є {1, 2 D} new food source, X jmax and X jmn are the mnmum and maxmum lmts of the parameter to be optmzed. B. MO-ABCWS technque: multobjectve ABC Weghted Sum optmzaton [12] The followng fgure.1, presents a flowchart for the proposed approach, used n ths study. Fg. 1. Flowchart for ABCWS IV. APPLICATION ON IEEE30 BUS SYSTEM A mathematcal equvalent model of the system under applcaton s ndcated n fgure. 7. The parameters of generators cost and lmts are ndcated n table I, and n table II, wnd generaton ones are depcted. TABLE I. IEEE30 BUS COST COEFFICIENTS AND POWER GENERATION LIMITS N a b c.10-4 P mn (MW) P max (MW) P g P g P g P g P g P g In table I and II, are depcted the cost parameters and power lmts of conventonal sources and renewable sources rated characterstcs, used n ths study, as well as the power lmts of generators, but the rated powers of the wnd turbnes are changeable between 4 and 6.5MW. TABLE II. USED WIND FARMS PARAMETERS N Wnd 1 Wnd 2 Drect cost d1=1.0 $/h d 2 =1.1$/h V (m/s) 5 5 Vr (m/s)

4 emsson (t/h) Economc /Emsson Dspatch ncludng Wnd Power usng ABC Weghted sum N Wnd 1 Wnd 2 Drect cost d1=1.0 $/h d 2 =1.1$/h V (m/s) 5 5 V 0 (m/s) Shape factor k 2 2 Scale factor c Penalty factor k p 2 2 Reserve factor k r front de Pareto for step=0.02 best compromse soluton V. RESULTS AND DISCUSSIONS In ths secton, smulatons where carred out usng MATLAB software have been conducted on IEEE 30-bus power system shown n Fg.8. In 30-bus test system, bus 1 s consdered as slack bus, whle bus 2, 3, 5,8,11 and 13 are taken as generator buses and other buses are load buses. Dfferent scenaros of renewable energy source are consdered n order to perform such computaton; as gven by the followng cases; Frst Scenaro: system wthout consderng of wnd generaton. Second Scenaro: system consderng of wnd generaton. A. Fgures and Tables The obtaned results are depcted n the followng tables; and fgures. ABC-WS s used to dentfy the Pareto front and the assocated power outputs of thermal and wnd unts; the smulaton results are depcted n the followng tables and fgures cost ($/h) Fg. 2. Pareto Front wthout Wnd power Fg. 3. Generaton output profle wth and wthout wnd farm TABLE III POWER GENERATION, COST,POWERS PLoad=2.834 (p.u) Sngle cost wthout wnd ($/h) Sngle emsson wthout wnd (t/h) Pg1 (MW) Pg2(MW) Pg3(MW) Pg4(MW) Pg5(MW) Pg6(MW) Total gen. (MW) Fuel cost ($/h) Real power loss (MW) Total emssons(t/h) Pwnd (MW) - - Table III shows the results for the extreme cases when α=1; and α=0 where the mult-objectve equaton (4), becomes a sngle objectve equaton. Fg. 4. Total emsson levelswth and wthout Wnd By nvestgaton of fgures 2 and 5 we see clearly that the nserton of renewable source reduce sgnfcantly the amount of power generated by conventonal sources, as well as total real loss of the entre system fgure 4, table VI, shows that, the best tradeoff soluton s obtaned, n presence of wnd sources, n the rated capacty for each wnd farm s taken as 6.5MW. 64

5 emsson (t/h) emsson (t/h) emsson (t/h) TABLE IV PLoad=2.834 p.u POWER GENERATION, COST, AND OTHER BENEFITS POWER Case 1:best soluton wthout wnd Internatonal Journal of Engneerng and Techncal Research (IJETR) ISSN: (O) (P), Volume-3, Issue-10, October 2015 Case 2 : wth 02 wnd farms at bus 10 and bus 24 Pg Pg Pg Pg Pg Pg Total gen Fuel cost ($/h) Real power loss (MW) Total emssons Pwf1 (MW) - Pw1 = 6.5 Pwf2 (MW) - Pw2 = wnd rate var Pareto front usnf ABCWS Pwr=4.5 Pwr=6.5 Pwr= cost 2 Pareto front for 2Wnd Fg. 7. Pareto Front for dfferent wnf power rates Best pont cost ($/h) As seen n fg. 6 by the ncrease of load the emsson of gazes as well as generaton cost ncrease, wth the nserton of the wnd farm source, t can be observed from table IV that the total generaton cost as well as the total actve loss of the power system, are reduced comparng wth the standard case; wthout any renewable source, then by keepng the load demand at certan level and ncreasng the capacty of wnd farms gradually n order to study the mpact of wnd power nstalled capacty on the emssons and the total generaton cost, we get the curve of Pareto fronts shown n fgure 7, By nvestgatng fg. 7, we can see that the ncrease of wnd rated power or the wnd capacty, can sgnfcantly enhance the generaton cost and decrease the amount of gas emssons. Fg. 5. Pareto Front n presence of wnd Farms and for base Load Table IV also presents the best soluton vector wthout and wth the penetraton of wnd power sources, and by the ntegraton of more renewable source the total conventonal outputs of generaton power can be changed; the total cost as well as the total loss of the power system are reduced; n the case of two wnd farms can see that the amount of the related emsson n lower than the case wthout wnd Load var Pareto front usnf ABCWS lambda=1 lambda=1.2 lambda=1.3 5 Fg. 8. IEEE 30 Bus test System cost Fg. 6. Pareto Fronts for dfferent demand levels VI. CONCLUSION In ths paper, the fuel cost objectve functon of the IEEE30 bus system s optmzed consderng dfferent operatng condtons of the power system under study; n frst tme we consder the system wthout any renewable source; then the penetraton of wnd farms n the IEEE-30 bus can reduce 65

6 Economc /Emsson Dspatch ncludng Wnd Power usng ABC Weghted sum effcently the total actve loss, as well as the total generaton cost of the power system. By the ntegraton of more wnd farms n addton to conventonal power sources these dfferent performances are enhanced enough. ABC technque s employed among other métha-heurstc methods for calculaton purpose because of ts sure and fast characterstcs, less computatonal tme n combnaton weghted sum method n order to acheve the best compromse soluton of the problem, and gves good performances, for the optmal ntegraton of renewable sources as wnd farms regardng both gas emsson and cost reducton n stochastc envronment. REFERENCES Sebaa Hadd: was born n An Lahdjar Sétf Algera, follows hs study n the Unversty of Ferhat Abbes Sétf 1, has got hs B.S degree n Electrcal Engneerng Power system from Sétf Unversty (Algera) n 1997, and hs MSc degree n 2009 n the feld of electrcal network, now he prepares for the Doctorate degree n the Department of Electrcal Engneerng, of the unversty of Sétf, hs area of nterest s the optmzaton n power system, optmal ntegraton of renewable sources, Facts devce Electrc Vehcles etc. Tarek Bouktr: Prof. Tarek Bouktr was born n Ras El-Oued, Algera n He receved the B.S degree n Electrcal Engneerng Power system from Sétf Unversty (Algera) n 1994, hs MSc degree from Annaba Unversty n 1998, hs PhD degree n power system from Batna Unversty (Algera) n Hs areas of nterest are the applcaton of the meta-heurstc methods n optmal power flow, FACTS control and mprovement n electrc power systems, Mult-Objectve Optmzaton for power systems, and Voltage Stablty and Securty Analyss. He s the Edtor-In-Chef of Journal of Electrcal Systems (Algera), the Co-Edtor of Journal of Automaton & Systems Engneerng (Algera). [1] Jhon.Hetzer, Davd C.Yu, Kalu Battara, An Economc Dspatch Model Incorporatng Wnd Power, IEEE transacton on energy converton, vol 23, June [2] A.T. Al-Awam, E.Sortomme, M.A.El-sharkaou Optmzaton of Economc/Envronmental Dspatch wth Wnd and thermal Unts, IEEE paper, [3] Robert T.F.Ah kng, Harry C.S.Rughooputh, Kalyanmoy Deb, Stochastc Evolutnary Multobjectve Envronmental/Economc Dspatch, IEEE Congress on Evolutopnary computaton Canada, July [4] Soumra Mondal, A.Bhattacharya, Sunta Hnee Dey, Mult-objectve economc emsson load dspatch soluton usng gravtatonal search algorthm and consedrng wnd power penetraton, Electrcal Power and Energy Systems Vol:44 pp: , [5] M.A. Abdo, Envrmental/economc power dspatch usng multobjectve evolutonary algorthms, IEEE trans. Power., Vol.18, pp , Nov [6] T. Bouktr, R. Labdan, L. Slman, Economc power dspatch of power system wth polluton control usng multobjectve partcle swarm optmzaton, Journal of Pure and Appled Scences, Vol. 4, pp , [7] J. Hetzer, D. C. Yu, K. Bhattara, "An Economc Dspatch Model Incorporatng Wnd Power," IEEE Trans. Energy Converson, vol. 23, no. 2, pp , June 2008 [8] J. Kennedy and RC. Eberhart, Partcle swarm optmzaton," n Proceedngs of IEEE nternatonal conference on neural networks, vol. 4, 1995, pp [9] D.Karaboga and B. Basturk Artfcal Bee Colony (ABC) optmzatonalgorthm for SolvngConstranedOptmzatonproblems, IFSA 2007, LNAI 4529, pp [10] N.M.Ramya, M.Ramesh Badu, T.D.Sudhakar, Soluton of Stochastc Economc Dspatch Problem usng Modfed PSO Algorthm, Internaton conference on Intellgent Instumentaton, Optmzaton And Sgnal Processng, [11] Sebaa Hadd, Amroune Mohamed, Tarek Bouktr, Artfcal Bee Colony Algorthm For Optmal Power Flow usng FACTS Devces, 3thrd nternatonal conference on system and nformaton processng, ICSIP 13 Guelma, [12] E.D.Manteaw, Dr.N.A.Odero, Mult-Objectve Envronmental/Economc Dspatch Soluton Usng ABC_PSO Hybrd Algorthm, Internatonal Journal of Scentfc and Research Publcatons, Dec [13] Mohamed A.Abuella, Study of Partcule Swarm For Optmal Power Flow n IEEE Benchmark Systems Includng Wnd Power Generators, [14] N. Srnvas and Kalyanmoy Deb, Multobjectve Optmzaton Usng Nondomnated Sortng n Genetc Algorthms, Evolutonary Computaton 2 (1994), no. 3, [15] Hossen Ghas, Damano Pasn, A non-domnated hybrd algorthm for mult-objectve optmzaton of engneerng problems, Engneerng Optmzaton, vol, 43, January