Vol. 7, No. 3, Oktober 2014 ISSN

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1 Vol. 7, No., Otober 014 ISSN ADVANCE OIMIZAION OF ECONOMIC EMISSION DISACH BY ARICLE SWARM OIMIZAION (SO USI CUBIC CRIERION FUNCIONS AND VARIOUS RICE ENALY FACORS a Joo tono, b Ad Soepryanto, c Maurd Hery urnomo a Department of Electrcal Engneerng, K/VEDC Malang b,c Department of Electrcal Engneerng, Sepulu Nopember Insttute of ecnology, Surabaya Emal: j_ptono@yaoo.com Abstract e classcal economc dspatc problem could be solved based on sngle objectve functon of power system operaton by mnmzng te fuel cost. However, te sngle objectve functon s not sustanable because te envronmental ssues arse from te emssons generated by fossl-fueled termal electrc power plants. Varous pollutants suc as sulfur doxde (SO, ntrogen oxdes (NOX and carbon doxde (CO affect envronmental ssues. e economy-envronment dspatc problem as been generally solved by consderng eac objectve separately or by applyng Wegted Sum Metod on bot objectves. s paper formulates te soluton of dspatc SO metod tat consders te mpact of varous pollutants and varous factors suc as te prce penalty Mn-Max, Max- Max, and Average n solvng mult-objectve problems usng cubc crteron functon for te cost of fuel and emsson values. Mult-objectve functons metod proposed n ts researc was valdated usng IEEE 0-bus systems wt sx generatng unts. e results of smulaton usng Mn-Max penalty factor ndcated less total fuel cost value compared to te smulaton usng Max-Max and Average penalty factor. In general, te comparson of Mn-Max type= 100%, Max-Max type= 66.9%, and Average type= 191.8%; Max-Max penalty factor provded less emsson value wt comparson to Mn-Max and Average penalty factors. In general, te comparson Max-Max type= 100%, Mn-Max type= 10%, and Average type= 100.% to ESO wle for ENO and ECO s not sgnfcantly dfferent; Average penalty factor provded less fuel cost value compared to Max-Max and Average penalty factor. In general, te comparson of Average type= 100%, Mn-Max type= 101.8%, and Max-Max type= 100.%. Keywords: Economc-Emsson Dspatc, Mult-Objectve, Cubc Crteron Functon, rce enalty Factors, artcle Swarm Optmzaton. 15

2 154 Jurnal Ilma KURSOR Vol. 7, No., Otober 014, lm INRODUCION e electrcal energy supply system faces ts man problems, namely generator effcency, transmsson effcency, dstrbuton system, or combnaton of tese tree problems. revous efforts to solve tese problems were concentrated on mnmzng operatonal cost of fuel consumpton wc as become te objectve functon and oter requrements as te constrants. ere were varous OF formulaton depended on ts objectve functons and certan constrants beng developed. revous researces were concentrated on OF problems solvng by consderng te system securty [1], []. Recent optmzatons tecnques ave been developed n a dfferent area of electrcal energy system were sngle objectve functon SO, multple objectve functons SO, and ybrd SO. Sng and Erlc ad attempted to estmate based on optmal bloc ncremental cost obtaned from te nstantaneous ncremental eat rate curve of generatng unt usng SO approac []. K. anusod as aceved a satsfyng result n applyng SO tecnque to solve Economc Dspatc usng a smoot and nonsmoot cost functons by consderng te effects of valve-pont loadng [4],[5]. Z.Al-Hamouz as successfully demonstrated SO algortm applcaton to solve Optmal Reactve ower lannng problems by reducng sort-term operatng costs and nvestment costs [6]. Anoter problem on electrcty today was caused by pollutants resulted by fuel consumpton process. Energy source dversfcaton ad been done. One of ts mplementaton was te usage of coal as power plant fuel wc was effectve n reducng energy costs. However, te use of coal as fuel resulted carbon doxde (CO, sulfur doxde (SO and oxdes of ntrogen (NO X wc polluted ar. ese pollutants caused acd ran wc contrbuted on forest and plantaton damages. ese pollutants gnted greenouse effect wc ncreased global temperature and caused oter sde effects. o antcpate te pollutant problem, te SO proposed algortm contanng multobjectve functons,.e. economc objectve functon (fuel cost and transmsson losses and emsson objectve functon. ECONOMIC - EMISSION ROBLEM FORMULAION. OF problem s non-lnear optmzaton problem wt objectve functon and nonlnear constrants. It was used to calculate te generaton system and dstrbuton of electrc power n order to obtan te best and most proftable results. Metods of problem solvng n te conventonal OF, namely te Newton metod, Gradent and Interor ont, ad been used extensvely. OF problem solvng requred non-lnear equatons, te descrpton of optmzaton, securty and operaton of power systems. Accordng to te desgnaton, te optmzaton problem can be matematcally expressed by Equaton (1 to Equaton (. were, Mnmze F ( x, u (1 Subject to g ( x, u 0 ( ( x, u 0 ( x V u G L V G t Q SH Equaton (1 defnes te general objectve functon, wle equalty constrants represented n Equatons ( and ( were te nequalty constrants of vector arguments x and u. x s te state varables and u s te vector of control varables. e state varables are angle ( and voltage ( V of L load buses. e control varables are generator actve power (, bus voltage ( G V G, transformers tap-settng ( t, and sunt capactors/reactors ( Q. Objectve Functon SH e economc-emsson dspatc for all-termal power generaton systems was for-mulated as a

3 Joo tono d., Measurng User Experence In 155 mult-objectve optmzaton problem. As a result, te economc-emsson dspatc problem consders four conflctng and noncommensurable objec-tves. Besdes te fuel cost, tese objec-tves were sulpur-doxde emsson SO, ntrogenoxde emsson NO X and carbon-doxde CO emssons. Matematcally, tese objectve functons are expressed as follows : Economc Objectve Functon Operatng termal plants total costs ncludes labor and mantenance costs n addton to te costs of fuel and oter supples. In general, te economc dspatc process consders te cost of te fuel burnt n te fossl unts. Rater tan beng neglected, te oter costs are commonly assumed as fxed percentage of te ncomng fuel costs. e nput to te termal plant s generally measured n MBtu/ nown as eat-rate curve and te output power s n MW. e eat-rate curve s converted to te fuel cost curve representng te relatonsp of te operatng cost of a fossl-fred termal unt and ts output power. s cost s approxmated as a cubc functon model of te real power generaton. e frst objectve F BB s te fuel cost functon of te termal generatng unts as expressed n Equatons (4 F BB F ( 1 (4 G ( a b c d $/r 1 1 G 1 were, G s te real power output of an t generator; s te number of termal generatng unts; a, b, c and d, are te fuel cost curve coeffcents of an t generator, respectvely. G Emsson Objectve Functon e objectve for mnmzaton of emsson quantty mnmzaton s formulated 1 G 1 by ncludng te reducton of emsson as an objectve by followng equaton : e second objectve s total sulpur doxde emsson (E SO referrng to te amount of SO emsson modeled as a cubc functon of te output power of te generatng unts wc s expressed n Equaton (5 : E SO ( a SO 1 SO SO G b SO g/r (5 SO SO G c SO G d SO a, b, c, d are sulpur-doxde emss-on coeffcents of generator unt e trd objectve s total ntrogen oxde (E NO emsson referrng to te amount of NO X emsson as expressed n Equaton (6: E NO ( a NO 1 NO NO G b g/r (6 NO NO NO G c d NO G NO a, b,c,d are ntrogen-oxde emss-on coeffcents of generator unt e fourt objectve s total carbon doxde emsson (E CO referrng to te amount of CO emsson as expressed n Equaton (7: E CO CO 1 CO ( a CO G b CO g/r (7 CO CO G c CO G d a, b, c, d are carbon-doxde emss-on coeffcents of generator unt ese objectve functons are subject to varous equalty and nequalty constrants as seen n Equaton (8. CO G D L 1 (8

4 156 Jurnal Ilma KURSOR Vol. 7, No., Otober 014, lm were D s te total load demand and L s te transmsson power losses as a functon of te real power generaton. Generaton capacty lmts can be seen n Equaton (9. mn G G max G (9 mn max were G and G are te mnmum and maxmum generaton lmt of te t generatng unt. Formulaton of Mult-Objectve Functon Fuel cost and emsson are te two objectves to be mnmzed smultaneously n a b-objectve problem. ree types of prce penalty factors are appled to convert ts mult-objectve optmzaton to a sngle objectve optmzaton problem for te varous emssons. e next problem s related to te mpact of all tree emssons s solved for all emssons smultaneously at te same power demand. B-Objectve Optmzaton of Cost and Emsson. ree problems are separately formulated for every emsson, as expressed by Equatons (10, (11, and (1. ( a b c d G G G F SO 1 ( a b c SO SO G SO G SO G d SO $/r (10 ( a b c d G G G F NO 1 ( a b c d NO NO G NO G NO G NO $/r (11 ( a b c d G G G F CO 1 ( a b c CO CO G CO G CO G d CO Optmzaton of Four Objectves. otal fuel cost for SO, NO X, and CO emssons s gven by Equaton (1. F OAL 1 (a b c d G G G (a b c d SO SO G SO G SO G (a b c d NO NO G NO G NO G (a b c d CO CO G CO G CO G SO CO NO $/r (1 Formulaton of rce enalty Factors e prce penalty factor for te combned economc-emsson dspatc problem s te rato of fuel cost to emsson value. e role of all penalty factors s to transfer te pyscal meanng of emsson crteron from wegt of te emsson to te fuel cost for emsson. e use of tree types of prce penalty factor n problem solvng optmzaton SO metod developed n ts study provdes an alternatve opton optmzaton results OF problem, weter focused on te cost of fuel or emssons produced as a man objectves functon. e prce penalty factor Mn-Max s expressed n Equaton (1, (14, and (15. e prce penalty Factor Max-Max can be seen n Equaton (16, (17, and (18. Meanwle te average s sown n Equaton (19, (0, and (1. rce enalty Factor Mn-Max MnSO MnNO MnCO Were (a (a (a G mn (a SO G max (a NO Gmax b b Gmn b Gmn (a CO Gmax b G mn SO Gmax Gmn b NO Gmax Gmn b CO Gmax c c c SO c c NO c CO G mn Gmn Gmn Gmax d d d Gmax Gmax - Mn-Max of SO Emsson MnSO - Mn-Max of NO X Emsson MnNO - Mn-Max of CO Emsson MnCO d d d SO NO CO (1 (14 (15 $/r (1

5 Joo tono d., Measurng User Experence In 157 rce enalty Factor Max-Max MaxSO MaxNO MaxCO Were (a (a (a (a SO Gmax Gmax (a NO Gmax b b Gmax (a CO Gmax b Gmax b G max SO G max Gmax b NO Gmax Gmax b CO Gmax c c c SO c c NO c Gmax G max Gmax Gmax Gmax CO Gmax d d d d d - Max-Max of SO Emsson MaxSO d - Max-Max of NO X Emsson MaxNO - Max-Max of CO Emsson MaxCO rce enalty Factor Average AveSO AveNO AveCO Were SO NO CO F BB E SO F BB E SO SO Gmn Gmax SO Gmax Gmax F BB E NO F BB E NO NO Gmn Gmax NO Gmax Gmax F BB E CO F BB E CO CO Gmn Gmax CO Gmax Gmax - Average of SO Emsson AveSO - Average of NO X Emsson AveNO - Average of CO Emsson AveCO (16 (17 (18 (19 (0 (1 ARICLE SWARM OIMIZAION ALGORIHM SO algortm s based on partcles nsde a populaton tat wor togeter to solve te exstng problems regardless of ts pyscal postons. SO algortm combnes local searc metod and global searc metod to balance exploraton and explotaton. SO as several smlartes wt GA. e system s started by a populaton formed by random solutons, and system wll see for optmzaton troug random generaton canges [7],[8]. Eac partcle stores te poston traces n te searc space s defned as te best soluton as been aceved. ersonal best (pbest s te best te value of te partcle, wle te global best (gbest s te best value wc taes nto account all te partcles n te populaton. Eac partcle n every teraton s gven nformaton about te latest gbest value tat becomes nformaton sarng mecansm n one drecton to mae te process of fndng te best soluton wt rapd convergence movement. SO algortm conssts of tree steps, namely determnng te partcle's poston and velocty, updatng velocty, and updatng poston. e poston x and velocty v of partcles are randomly ntalzed usng te value of te gest and lowest varable accordng to te desgn, wle te rand (r s a random value between 0 and 1. Eac partcle tres to update ts poston usng suc nformaton, current poston, current velocty, dstance between te current poston of te pbest and te current poston of gbest. Matematcally, partcle velocty update ( v s expressed by Equaton (. v v c r ( p x c r ( p x g ( Acevng te results obtaned from te new velocty calculaton for eac partcle based on te dstance from pbest owned and dstance from te gbest poston. artcle poston update ( x (. 1 s formulated on Equaton x x v ( 1 1 able 1. Actve power lmt of eac plant Generator Bus mn (MW max (MW 1 50,00 00,00 0,00 80, ,00 50, ,00 50, ,00 50,00 1 1,00 40,00

6 158 Jurnal Ilma KURSOR Vol. 7, No., Otober 014, lm SIMULAION RESULS OF MULI- OBJECIVE DISACH ROBLEM Optmzaton studes usng te IEEE-0 Bus est System as 6 unts of termal power plant at bus 1 ( 1, bus (, bus 5 ( 5, bus 11 ( 11, and bus 1 ( 1. Optmzaton problem s formulated n four conflctng objectve functons, namely fuel costs objectve functon (F BB as Equaton (4, SO emsson objectve functon (E SO as Equaton (5, NO X emsson (E NO objectve functon as Equaton (6, and CO emsson objectve functon (E CO as Equaton (7. Eac generator as a generator lmts, fuel cost coeffcents, SO emsson coeffcents, NO X emsson coeffcents, and CO emsson coeffcents n te form of a cubc equaton. ypes of prce penalty factors used by a generator are Mn-Max, Max-Max, and Average. able 1. sows te actve power lmt of eac plant. e coeffcent of fuel cost and emsson coeffcents of eac plant are sown n able. able. e coeffcent of fuel cost and emsson coeffcents of eac plant Objectve Fuel Cost $/r Emsson SO g/r Emsson NO g/r Emsson CO g/r Coeffcents Generator Bus a 0,0010 0,0004 0,0006 0,000 0,001 0,0004 b 0,090 0, ,1000 0,100 0,0840 c 14,50,00,00 1,50 11,50 1,50 d -16,00 -,50-81,00-14,50-9,75 75,60 a SO 0,0005 0,0014 0,0010 0,000 0,001 0,001 b SO 0,150 0,055 0,05 0,070 0,10 0,080 c SO 17,00 1,00 10,00,50 1,50,50 d SO -90,00-0,50-80,00-4,50-19,75 5,60 a NO 0,00 1 0,0004 0,0016 0,001 0,000 0,0014 b NO 0,050 0,0450 0,0500 0,0700 0,0400 0,040 c NO 18,50 1,00 1,00 17,50 8,50 15,50 d NO -6,00-5,00-15,00-74,00-89,00-75,00 a CO 0,0015 0,0014 0,0016 0,001 0,00 0,0014 b CO 0,090 0,050 0,0550 0,0100 0,0400 0,0800 c CO 14,0 1,5 1,5 1,5 1,0,0 d CO -16,0-9,5-85,0-4,5-59,0-70,0

7 Joo tono d., Measurng User Experence In 159 able. optmzaton of te total fuel costs Output Generator ower Demand 50 MW ower Demand 00 MW Mn-Max Max-Max Average Mn-Max Max-Max Average 1 MW 57,644 50, ,014 70, ,956 5,1467 MW 41, ,48 48,604 59, , ,059 5 MW 0,9845 6,671,791 5,9990 4, ,564 8 MW 50, , , , , , MW 49,09 4,89 4,406 50, , , MW 5, , , , , ,0000 ower Losses MW 4,418 4,1846 4,56 6,840 6,7147 6,7091 F BB $/r 5181, 5081,1 5079,7 677,8 6667,5 6649,9 E SO g/r 65,7 614,0 6181,1 7878,4 7665,7 777,7 E NO g/r 445,8 476,4 479,6 5771,1 5557,7 5485,7 E CO g/r 517,9 507, 5041,4 6797,4 6591,4 6605,5 F OAL $/r 7487,1 198, ,0 998,5 655, ,0 able 4. e otal Fuel Cost $/r ower Demand 5 MW ower Demand 00 MW Mn-Max Max-Max Average Mn-Max Max-Max Average F BB 4488, , , , , ,900 F SO 647, ,100 71, , ,00 415,000 F NO 674,19 49,000 7, , , ,00 F CO 609,114 9,00 7, , , ,000 F OAL 6418, , , , , ,000 Objectve Functons Optmzaton,otal Fuel Cost, Fuel Cost of SO emsson, Fuel Cost of NO X emsson, and Fuel Cost of CO emsson. e smulaton s done by combnng F BB, Fuel Cost of SO emsson, Fuel Cost of NO X emsson, and Fuel Cost of CO emsson smultaneously to obtan te optmal total fuel costs n electrc power system tang nto account te constrants tat ave been specfed. able. sows te best results of te optmzaton of te total fuel costs F OAL wtn tree types of prce penalty factors for consderaton n operatng te termal electrc power system. able 4. Sows tat te total Fuel Cost F OAL s determned by te cost of emssons F SO, F NO, and F CO. e emsson costs on Mn-Max type s far below te value of F BB wt unsgnfcant dfference, wle te emsson costs on Max-Max type s almost smlar to te value of F BB wt unsgnfcant dfference. Fgure 1. sows te results of four cost optmzaton objectve functons are obtaned from te total Fuel Cost on power demand 5 to 00 MW. F OAL s te result of a combnaton of F BB, F SO, F NO, and F CO. Costs of F SO, F NO, and F CO ndcates unsgnfcant dfferences on any type of prce penalty factors n te same power demand.

8 160 Jurnal Ilma KURSOR Vol. 7, No., Otober 014, lm otal Fuel Cost F OAL Cost $/r FCO $/r FNO $/r FSO $/r FBB $/r Mn-Max Max-Max Average Mn-Max Max-Max Average 5 MW 00 MW Fgure 1. Result of Four Cost Optmzaton Objectve Fucntons able 5. e results of optmzaton FBB and ESO for Max-Max type between SO metod and Lagrange s algortm rce enalty Factor ower Demand MW F BB $/r E SO g/r SO LAG [9] SO LAG [9] ,00 79,49 19, ,648 Max-Max 175 6,00 475, , , , ,0 4670, , ,00 510,54 546, ,498 able 6. Results of E NO optmzaton for Max-Max type rce enalty Factor Max-Max ower E NO g/r E CO g/r Demand MW SO LAG [9] SO LAG [9] , ,18 607,100 57, , , ,000 61, ,00 10, , , , ,8 440, ,5

9 Joo tono d., Measurng User Experence In 161 OIMIZAION SUDY OF SO MEHOD AND LAGRAE S ALGO-RIM (LAG FOR MULI-OBJECIVE FUNCIONS able 5 sows te results of optmzaton FBB and ESO for Max-Max type between SO metod and Lagrange s algortm on te power demand 150 to 5 MW. FBB and ESO of SO metod s always better tan te Langrange's algortm and tere are sgnfcant dfferences. Fgure. suggests tat results of optmzazton F BB for Max-Max type, SO metod s better tan te Langrange s algortm on te power demand 175 to 5 MW, wle on te power demand 150 MW was not a sgnfcant dfference. e results of SO metod n te form of a stragt lne (lnear and te greater power demand yeld greater fuel cost. Cost $/r Fuel Cost - F BB ower Demand MW SO LAG Fgure. Results of Optmzaton F BB for Max-Max type Fgure. sows te results of E SO optmzaton for Max-Max type, SO metod s better tan te Langrange s algortm on te power demand 175 to 5 MW. e largest dfference occurs n te results of E SO optmzaton on te power demand 5 MW s g / r. As results of E NO optmzaton for Mn- Max type, able 6 sows te results of E NO optmzaton for Max-Max type. Lagrange's algortm s better tan te SO metod, but te results of E SO optmzaton, SO metod s better tan Lagrange's algortm. Fgure 4. sows te results of E NO optmzaton Lagrange's algortm for Max- Max type as a dfferent sape to results of E NO optmzaton Lagrange's algortm for Mn-max type. E NO of SO metod produces te same form for bot types. Fgure. Results of E SO optmzaton for Max-Max type Emsson g/r Emsson g/r Emsson SO - E SO ower Demand MW Emsson NO X - E NO ower Demand MW SO SO LAG LAG Fgure 4. e results of E NO optmzaton Lagrange's algortm for Max-Max type Based on te results of E NO optmzaton, SO metod as better consstency, accuracy, and stablty n all objectve functons compared to Lagrange s algortm. Fgure 5 sows te detaled results of E CO optmzaton between SO metod and Lagrange s algortm wc suggests tat te results of SO metod s always better, especally on te power demand of

10 16 Jurnal Ilma KURSOR Vol. 7, No., Otober 014, lm , 00, and 5 MW. e largest dfference occurs on te power demand of 5 MW s g / r, wle te power demand of 175 MW s g / r Emsson g/r Fgure 5. e detaled results of E CO optmzaton between SO metod and Lagrange s algortm able 7. sows te results of te objectve functon total fuel cost F OAL optmzaton and networ power losses between te SO metod and Lagrange s algortm for Max- Max type. ere are very sgnfcant dfferences between te results of te objectve functon total fuel cost F OAL optmzaton of SO metod and Lagrange s algortm, especally on te power demand 175 to 5 MW. e results of te objectve functon total fuel cost F OAL optmzaton, SO metod s always better on every power demand. able 7. Results of te objectve functon total fuel cost FOAL optmzaton and networ power for te Max-Max rce enalty Factor ower Demand MW Emsson CO - E CO ower Demand MW F OAL $/r SO LAG [9] SO LAG ower Losses MW , ,566 1, , ,517, , ,409, , ,8,46 Networ power losses on eac power demand s obtaned from SO metod, because te Lagrange s algortm does not consder or gnore networ power losses. Fgure 6. sows te dfferent results of te objectve functon total fuel cost F OAL optmzaton between SO metod and Lagrange s algortm. Real dfference n te results of optmzaton occurs on te power demand of 5 MW s $ 56.8 / r, wle on te power demand of 175 MW s $ 86 / r. Cost $/r Fgure 6. e dfferent results of te objectve functon total fuel cost FOAL optmzaton between SO metod and Lagrange s algortm Fgure 7 s a networ power losses of SO metod for Max-Max type on te power demand 150 to 5 MW. ower Losses MW otal Fuel Cost - F OAL ower Demand MW ower Losses SO LAG ower Demand MW Fgure 7. A networ power losses of SO metod for Max-Max type on te power demand 150 to 5 MW ere s no sgnfcant dfference between networ power losses for Mn-Max and Max- Max type of te sape and percentage. Networ power losses s not lnear-saped,

11 Joo tono d., Measurng User Experence In 16 but tere s a sarp rse on te power demand above 00 MW. Fgure 8 sows te dfference voltages at bus 1 to bus 0 on te power demand 150 to 00 MW for Mn-Max type. e voltage at te V buses and slac bus as not canged or equal to te value of te ntal voltage on te start, but te Q bus voltage s canged. Great power demand on te Q bus wll decrease te voltage. However, t sould not exceed 0.95 pu. e voltage at bus 6 and bus 0 as a large dfference voltage between te power demand 150 and 00 MW, compared to oter Q buses. Voltage pu ower Demand 150 and 00 MW Fgure 8. e dfference voltages at bus 1 to bus 0 on te power demand 150 to 00 MW for Mn-Max type CONCLUSION 1. e results of SO metod optmzaton ad been proved better tan te REFERENCES Bus 1 to 0 [1] Y.H, Song, Modern Optmsaton ecnques n ower System, Neterlands: Kluwer Academc ublser, [] R.V. Amarnat, N.V.Ramana, "State of Art n Optmal ower Flowsoluton Metodologes," JAI, vol.0, no., pp [] R.C. Bansal, "Optmzaton Metods for Electrc ower Systems: An Overvew," Internatonal Journal of Lagrange's algortm. Innovaton n form of recent mprovement by usng te fuel cost functon models of cubc form and tree prce penalty factors namely Mn- Max, Max-Max, and Average wle consderng te networ power losses was be able to solve te OF problem smultaneously.. rce penalty factor of Mn-Max, Max- Max, Average, and fuel cost functon model of te cubc form to solve OF usng SO metod was able to provde an optmzaton alternatve. 1 e type of Mn-Max produced te best at mnmum cost of F SO, F NO, F CO, and F OAL compared to Max- Max and Average types. In general, te comparson of Mn-Max type= 100%, Max-Max type= 66.9%, and Average type = 191.8%. Average type produced te best F BB separately or n combnaton as compared to Mn-Max and Max-Max types. In general, te comparson of Average type= 100%, Mn-Max type= 101.8%, and Max-Max type= 100.%. Max-Max type produced te lowest E SO emssons, E NO, and E CO separately or n combnaton compared to Mn-Max and average types. In general, te comparson te type of Max-Max = 100%, Mn-Max type = 10%, and Average type = 100.% for E SO. Meanwle, tere was no sgnfcant dfference between ENO and ECO. Emergng Electrc ower Systems, vol., pp [4] A. ccolo, A. Vaccaro, "Fuzzy Logc Based Optmal ower Flow Management n arallel Hybrd Electrc Vecles," Iranan Journal of Electrcal and Computer Engneerng, vol.4, no., pp [5] M.A. Abdo, "Optmal ower Flow Usng abu Searc Algortm," Electrc ower Components and Systems, pp , 00. [6] V.N. Deu, W. Ongsaul, "Augmented Lagrange Hopfeld Networ for Large Scale Economc Dspatc," n

12 164 Jurnal Ilma KURSOR Vol. 7, No., Otober 014, lm roceedngs of Internatonal Symposum on Electrcal & Electroncs Engneerng, HCM Cty, Vetnam Oct 4, 5, 007. [7] M. Younes, M. Ralga, "GA Based Optmal ower Flow Solutons," Electrcal and Instrumentaton Engneerng, Department apar Unversty, 008. [8] M. Sudaaran,.G. alanvelu, "GA and SO culled ybrd tecnque for economc dspatc problem wt probted operatng zones," Journal of Zejang Unversty, pp , 007. [9] S. Krsnamurty, R. zoneva, "Impact of te enalty Factors on te Soluton of te Combned Economc Emsson Dspatc roblem usng Cubc Crteron Functons," n roceedngs of ower and Energy Socety General Meetng IEEE, Calforna, 01. [10] S. Krsnamurty, R. zoneva, "Comparatve Analyses of Mn-Max and Max-Max rce enalty Factor Approaces for Mult Crtera ower System Dspatc roblem wt Valve ont Effect Loadng Usng lagrange s Metod," n roceedngs of ower and Energy Systems (ICS, 011 Internatonal Conference on IEEE, 011. [11] S. Krsnamurty, R. zoneva, "Applcaton of e artcle Swarm Optmzaton Algotrtm to A Combned Economc Emsson Dspatc roblem Usng New enalty Factor," n roceedngs of IEEE ES Conference and Exbton, 01.