Özlem Coşgun Fatih University, Department of Industrial Engineering, Istanbul, 34500, Turkey

Transcription

1 Internatonal Journal of Computatonal Intellgence Systems, Vol. 7, No. 4 (August 204), Data Envelopment Analyss Applcaton n Tursh Energy Maret Özlem Coşgun Fath Unversty, Department of Industral Engneerng, Istanbul, 34500, Turey Gamze Ogcu Kaya Fath Unversty, Department of Industral Engneerng, Istanbul, 34500, Turey E-mal: gamzeogcu@st.fath.edu.tr Receved 6 September 203 Accepted June 204 Abstract In ths study, we consder data envelopment analyss for comparson between thermal energy plants whch are natural gas based and coal based power plants. Results of ths study wll gve a roadmap to decson maers n selectng type of thermal plant they wll construct. Also publc and prvate energy supply sources are compared based on ther performances. We not only mae a solely comparson but also mae a ranng of the power plants consdered by usng goal programmng technque. Keywords: Data Envelopment Analyss, Goal Programmng, Energy, Power Plants. Introducton Hghly growng Tursh Economy n the last decade leads to substantal ncrease n energy consumpton. In order to respond to ths ncrease n demand, new power plants are beng constructed by both prvate and publc enterprses at the supply sde. Ths stuaton resulted n decson maers who are constructng new power plants to face wth a crucal decson on whch energy source to nvest. Effcences of power plants n Turey are nvestgated and compared n ths study. Turey s a fast developng country whose electrc power supply and demand have shown rapd ncreases n the last decade and ths trend s expected to contnue n the future. Accordngly, new power plant nvestment decson and operaton of exstng power plants s a crucal ssue. In ths regard, type of source, effcency and ownershp of power plants are ey characterstcs to be nvestgated, n answerng the queston Whch one s better wth respect to others and why?. Many studes nvolvng comparsons among types of power plants have been made n order to obtan the best answer to ths queston. Ths study nvolves a mult-nput mult-output performance evaluaton and comparson of a set of power plants n Turey based on real data. We analyse and compare effcences of publc versus prvate power plants and coal based power plants versus natural gas based power plants. Ths ssue s handled wthn the framewor of an approprate mathematcal tool, named data envelopment analyss (DEA), whch was ntroduced by Charnes, Cooper and Rhodes 2. DEA (Data envelopment analyss) s an effcent tool for evaluatng relatve effcency of enttes whch are called as DMU (Decson mang unt). It s a wdely used technque that can be appled to decson mang unts of any nd, DMUs can be selected from producton sector and servce sector. DEA can be appled to evaluate effcences of homogenous multple nput and output organzatons, such as ban branches, schools, arports, tax offces, and hosptals 3,4. Effcency score of a DMU can be between 0 and or can be expressed as 0% and 00%. The effcency score or 00% of a decson mang unt (DMU) pont out that DMU s relatvely effcent to other unts n the data sample. There are plenty of studes on DEA applcatons n health sector 5,6,7,8. Snce DEA methodology has a wdespread applcaton area, t s also used n farmng 9,0,,2. DEA can also be used as a benchmarng tool that recognzes neffcent DMUs by comparng them wth smlar DMUs Özlem Coşgun, ozlem_nce@hotmal.com, Fath Unversty, Department of Industral Engneerng, Istanbul, Turey 636

2 Ö.Coşgun, G. Ogcu Kaya consdered as effcent. In contrast wth other benchmarng methods that depend mostly on the vew of managers, DEA s capable of establshng best examples that are very hard to be precsely found by remarably 3. DEA has receved sgnfcant attenton n recent years due to ts advantages over tradtonal methods for performance evaluaton purposes 4,5,6,7,8. DEA has also many applcatons on energy sector. Onut and Soner generated a DEA model for the evaluaton of energy use and effcences of medum szed enterprses n Turey 9. Energy cost s ncluded n the operatonal costs of a company and a decrease of t wll add to the proftablty of the company. In order to obtan energy effcency, a company can ether use less energy or ncrease rate of producton. For enterprses, energy effcency objectve can be mantaned by usng hgher effcency equpments, mprovng energy use n operaton and mantenance practces durng producton or usng systems for montorng or controllng energy use. For the DEA model used n the study, annual total sales and annual total proft are outputs where annual electrcty consumpton, annual natural gas consumpton, annual ol consumpton and annual LPG consumpton are taen as nputs. Martínez consdered energy effcency performances of non-energy ntensve sectors n Germany and Colomba 20. Authors nvestgated energy effcency development from 998 to Olatub, Dsmues studed cost effcences of coal fred power plants and ponted out effects of fuel type, technology, vntage and sze on operatng effcency 2. Applcaton of DEA on energy effcency s not restrcted to thermal energy sources; José Ramón San Crstóbal studed the effcency of the renewable energy technologes 22. Lee appled data envelopment analyss on the energy effcency of government buldngs by nvestgatng envronmental and management factors 23. Lee and Lee also consdered buldng energy performance wth the help of DEA methodology 24. Sadjad and Omran generated data envelopment analyss model for performance assessment of electrcty dstrbuton companes where there s uncertanty n parameters 25. Ertur and Türüt Aşı analyzed the performance of natural gas dstrbuton companes by usng data envelopment analyss on Tursh natural gas dstrbuton companes and revealed sgnfcant factors affectng effcency 26. Fallah, Ebrahm, Ghader used DEA for evaluatng techncal effcences n electrc generaton management companes 27. Sözen, Alp and Klnc consdered effcency evaluaton of renewable energy power plants, partcularly hydroelectrc power plants n Turey and used two effcency ndexes based on producton and energy unt cost performance 28. Another research on power plant effcency by usng DEA s the wor by Lu, Ln and Lews where the authors evaluated thermal power plant effcences 29. Golany et al. also consdered power plant effcency by usng DEA methodology where the authors selected nstalled capacty, fuel consumpton, manpower as nputs and generated power, operatonal avalablty, devaton from operatonal parameters and SO 2 emssons as output factors. These factors were selected from a bgger lst after examnng the correlaton between factors and elmnatng redundant ones. The authors emphaszed ar polluton n ther model by usng the factor SO 2 emssons as the categorcal varable whch has three levels as good, medum and bad emssons 33. Another study on applcaton of DEA n power plant effcency s the wor by Chtara n whch coal based generatng unts belongng to Natonal Thermal Power Corporaton of Inda are analyzed. Generaton per unt of coal consumed, generaton per unt of ol consumed and generaton per unt of auxlary power consumpton are used as performance ndcators 34. Behera and Dash also analyzed effcency of coal-based power plants n Inda by usng Data Envelopment Analyss and concluded that average techncal effcency of the plants was found to be 83.2% 35. DEA approach s also used by Sueyosh and Goto for evaluatng Japanese fossl fuel power generaton. DEA approach proposed n the study ncludes the output separaton as desrable and undesrable ones and also the nput separaton as energy and non-energy ones 36. In ths study, DEA methodology s used for evaluatng performance of thermal power plants consstng of coal fred and natural gas fred power plants. For the analyss, 20 plants are selected half of whch belong to prvate sector companes and the other half belong to publc sector. Evaluaton s done based on two perspectves whch are operatonal and nvestment effcences for both publc sector plants and prvate sector plants separately. Operatonal effcency s for evaluatng performance of a power plant from operatonal perspectve whch ncludes fnancal, envronmental and productvty measures. On the other hand, nvestment effcency s for evaluatng performance of a power plant consderng ts nvestment phase whch contans cost, tme and capacty perspectves. Also an overall comparson s made for all power 637

3 Data Envelopment Analyss Applcaton plants. Moreover, we also compare the effcences of natural gas based power plants and coal based power plants to gve nformaton about the type of thermal plant that wll be constructed. The study s mportant for contrbutng energy sector by mang DEA applcaton whch gves nsghts for the maret players. Also as mentoned n the lterature secton, Sözen, Alp and Klnc 28 consdered effcency evaluaton of renewable energy power plants n Turey and Lu, Ln and Lews 29 evaluated thermal power plant effcences n Tawan. So we mae a study le a combnaton of these two studes. We studed power plant effcences n Turey. In addton to evaluaton of plants, the ranng between the plants s done by usng Goal Programmng snce DEA doesn t provde ranng. 2. DEA Methodology DEA s a (lnear) programmng based technque and the basc model only requres nformaton on nputs and outputs. Method s a specal lnear programmng model for evaluatng comparatve effcency of multple-nput multple-output decson mang unts (DMUs). DEA uses mathematcal programmng methods, t focuses on ndvdual observaton and emphasze revealed best practce fronter. The strength of DEA comes from ts ablty to smultaneously utlze multple outputs and multple nputs wth each beng stated n dfferent unts of measurement. The technque also has no restrcton on the functonal form of the nput output relatonshp (Charnes et al., 994). Performance of DMU s drectly compared wth the most effcent of a peer or a combnaton of the most effcent peers. In ths study, we used CCR model proposed by Charnes, Cooper and Rhodes (978) 30. CCR Model developed by the authors can be grouped nto two as nput orented and output orented models. Both nput orented and output orented methods am ncreasng effcency of a DEA neffcent DMU and mae t effcent. For ncreasng effcency, an nput orented model provdes nformaton on the requred proportonal reducton of nputs whle eepng the current level of output. On the contrary, an output orented model provdes nformaton on how much ncrease of output s requred but at the same tme mantanng the current level of nputs. Output orented CCR model s used n ths study snce ncrease n outputs for both operatonal and nvestment effcency evaluatons are desred. General form of output orented CCR model ntroduced by Charnes, Cooper and Rhodes (978) s as follows: Mn Z = Subject to; m j= n b y a j 0 n a 0 0 = x j o = m b x o y 0 forh =,2,..., 0 h j0 jh = j= a, b 0 j0 0 for =,..., n and j =,..., m where Z 0 s the effcency score of 0 th organzaton, x h s the observed value of nput for the organzaton h, y j h s the observed value of output j for the organzaton h, a 0, bj 0 are the weghts assgned to nput and output j of organzaton 0, s the number of organzatons, m s the number of outputs and n s the number of nputs. 3. Goal Programmng Model Goal Programmng (GP) s an mportant technque for decson-maers (DMs) to consder smultaneously several objectves n fndng a set of acceptable solutons. It can be sad that GP has been, and stll s, the most wdely used technque for solvng mult-crtera and mult-objectve decson- mang problems. The DMs for ther goals set some acceptable aspraton levels, g ( =, 2,..., n), for these goals, and try to acheve a set of goals as closely as possble (Tamz, et al., 998) 3. The purpose of GP s to mnmze the devatons between the achevement of goals, f ( X ), and these acceptable aspraton levels, g ( =, 2,..., n). A mathematcal formulaton of GP s gven below. (GP) n Mnmze f ( X) g = subject to X F, ( Fs a feasble set) where f ( X ) s the lnear functon of the th goal, and g s the aspraton level of the th goal. The majorty of GP applcatons n the lterature have been mplemented usng varous methods such as lexcographc GP (preemptve GP), weghted GP (Archmedean GP), and MINMAX GP (Chebyshev GP) (Romero, 200) 32. In order to solve GP, we let + the functon f( X) = d d + g, then GP can be formulated as the followng achevement functon. 638

4 Ö.Coşgun, G. Ogcu Kaya n + Mnmze ( d + d ) = + subject to f ( X) g = d d, =,2,..., n X F, ( Fs a feasble set) + d, d 0, =,2,..., n where d + = max(0, f( X) g) and d = max(0, g f( X)) are, respectvely, overand under-achevement of the th goal; other varables are defned as n GP. We use goal programmng to mae a ranng for effcent power plants snce DEA cannot mae a ranng for effcent DMUs. It gves only the effcent unts. 4. Case study In ths study, effcences of thermal power plants publc and prvate- n Turey are compared. We consder power plants based on only natural gas and coal fred. For the effcency analyss of thermal power plants, we generated two effcency perspectves whch are operatonal and nvestment based comparsons. The DEA model proposed for the operatonal performance evaluaton of thermal power plants conssts of sx parameters. These parameters are based on expert opnons whch are determned wth respect to ntervews made wth frm representatves. The parameters nclude electrcty producton, operatng cost, avalablty percentage, carbon monoxde (CO) producton, thermal effcency and envronmental cost. Among these parameters, selected nputs are: carbon monoxde producton, operatng cost and envronmental cost. Outputs are: electrcty producton, thermal effcency and avalablty percentage. These parameters are selected as outputs snce ncrease n ther levels are desred. Carbon monoxde producton s the annual CO emsson of the plant. The parameter s a sgnfcant ndcator of the plants envronmental mpact and snce decrease of the emsson s a desred stuaton, the parameter s selected as nput. Operatng cost s the total annual cost of the nput fuel and labor cost used n the plant. It s selected as nput snce a decrease of operatng cost s a desred change. Envronmental cost s the annual emsson of envronmentally hazardous partcles. Snce decrease n envronmental cost s wanted, the parameter s selected as nput. Electrcty producton s the annual amount of electrcty produced by the power plant and ts unt s Wh. The reason that ths parameter s selected as output s that an ncrease n electrcty producton s a demanded change. Thermal effcency s the average annual effcency of converson to electrc energy from the dsspated heat. 639 Avalablty percentage s the average annual percentage reflectng the tme that the power plant s actually avalable for producng power. Ths parameter s an mportant ndcator reflectng the relablty of the plant and taen as output snce an ncrease n avalablty percentage s a postve change. For the long term nvestment based effcency, the DEA has four parameters whch are nvestment cost, constructon tme, avalablty and power. These parameters are chosen to reflect the overall effectveness of a electrcty generatng faclty, durng ts full economc lfe. The frst two are selected as nputs and the remanng are outputs. Investment cost s the total captal used for the constructon of the power plant for all the processes up to the start of operatons. Snce decrease n nvestment cost s wanted, the parameter s selected as nput. Constructon tme s the tme passes from the start of the power plant constructon to start of electrcty producton. It s selected as nput snce a decrease of constructon tme s a desred change. Avalablty s the actual avalable tme of the plant for electrcty producton n a 5 years perod. Ths perod s selected as 5 years rather than one year for reflectng long term relablty of the power plant. Avalablty s taen as output snce an ncrease n avalablty s a postve change. Power s parameter reflectng the electrcty producton capablty of a power plant at a unt of tme and ts unt s MWh. Power s selected output snce an ncrease n power s a postve change. The data on the nvestgated power plants are combned under two sets: Frst s operatonal performance data set and the second s nvestment performance data set of thermal power plants. These two sets are dsplayed n Table. The effcences of the power plants are compared for dfferent cases: Case. Publc and prvate power plants are analyzed for operatonal performances ndvdually and compared. Case 2. Publc and prvate power plants are analyzed for nvestment performances ndvdually and compared. Case 3. All power plants are compared for operatonal and nvestment performances ndvdualy. Case 4. All power plants are compared for operatonal and nvestment performances together. After ther relatve effcences are calculated by DEA model, the ranngs of DMUs are determned. For mang comparson of all power plants based on operatonal and nvestment performances together, t s seen that there s hgh correlaton between varables CO (tone) and Producton (MWh) as shown n Table 2 whch s a handcap for DEA analyss. Ths stuaton drected us to remove

5 Data Envelopment Analyss Applcaton CO (tone) varable from the analyss and also snce varable Envronmental cost (000 $) contans CO emsson, t s unproblematc for our analyss. After ths removal of nput varable CO (tone), effcences of two DMUs change whch are: Prvate as 0.89 and Prvate 6 as.00 as ndcated n Table 3. Accordng to overall effcency results t can be seen that there are totally 5 neffcent DMUs. For mang these neffcent DMUs effcent, we have used the reference set of each neffcent unt. In order to mae an neffcent unt to be effcent, the gap between the neffcent unt and effcent unts that are n ts reference set must be closed. Target values for nput values of neffcent unts are calculated based on the correspondng nput values of effcent unts n the reference set. Table 4 presents reference sets for neffcent DMUs and ther lambda values. Input values of an neffcent unt whch must be acheved to be effcent are calculated based on weghted average of ts reference set nput values as follows: where, n = λ o j j j R R = reference set of DMU n = new value of parameter of neffcent DMU λ = weght of reference unt j R j o j =orgnal value of parameter of reference unt j R In order to clarfy the calculatons, the new value for nvestment cost of Publc 2 s calculated as follows: [(Prvate0 weght * Prvate0 nvestment cost) + (Prvate4 weght * Prvate4 nvestment cost) + (Publc 7 weght * Publc 7 nvestment cost)] whch yelds: [(0.225*$9,988,60) + (0.038*$9,533,559) + (.037*$632,578,70)] = $658,593,833 So, n order to mae neffcent Publc2 as effcent, ts nvestment cost should be ncreased from $625,057,330 to $658,593,833. Other target values for remanng nputs are calculated wth the same manner. Target nput values for neffcent unts are gven n Table

6 Ö.Coşgun, G. Ogcu Kaya 64

7 642 Data Envelopment Analyss Applcaton

8 Ö.Coşgun, G. Ogcu Kaya Table 3. New Effcency Scores Power plant Overall CCR Effcency Publc.00 Publc Publc3.00 Publc4.00 Publc5.00 Publc6.00 Publc7.00 Publc8.00 Publc9.00 Publc0.00 Prvate 0.89 Prvate Prvate3.00 Prvate4.00 Prvate Prvate6.00 Prvate Prvate8.00 Prvate9.00 Prvate0.00 Table 4. Reference sets of Ineffcent Power Plants Power Plant References Set (Lambda) Publc2 Prvate Prvate Publc7.037 Prvate Prvate Publc Table 5. Target Input Values for Ineffcent Power Plants Power Plant Input Target Value Publc2 Investment Cost($) 658,593, Constructon tme (month) Operatng cost (mllon TL) 4,209,892, CO (Tone) 5.0 Envronmental cost (000 $) Prvate Investment Cost($) 97,044, Constructon tme (month) 4.39 Operatng cost (mllon TL) 546,292, CO (Tone) 6.27 Envronmental cost (000 $) 38, Prvate2 Investment Cost($) 4,755, Constructon tme (month) 2.69 Operatng cost (mllon TL) 8,983, CO (Tone) Envronmental cost (000 $) 3,023.4 Prvate5 Investment Cost($) 45,885, Constructon tme (month) 55.2 Operatng cost (mllon TL),735,923, CO (Tone) 5.87 Envronmental cost (000 $) 6,99.0 Prvate7 Investment Cost($) 2,503, Prvate2 Prvate0.295 Publc Constructon tme (month) Operatng cost (mllon TL) ,36, Prvate5 Publc CO (Tone) 6.99 Prvate Envronmental cost (000 $) Publc Prvate7 Prvate0.037 Prvate

9 Data Envelopment Analyss Applcaton The effcency measure obtaned by DEA can be used for ranng DMUs, but ths ranng cannot be appled to effcent unts. Therefore we use goal programmng whch s one of the methods that can be used to ran the effcent unts. Whle ranng effcent DMUs, some goals must be satsfed. The aspraton levels of these goals are gven n Table 6. These aspraton levels are determned by mang ntervews wth frm representatveness and these levels correspond to goals of the frms. For example, average operatng cost of the thermal plants cannot be greater than 94,229,000 mllon TL and operatonal avalablty must be at least 92%, etc. The general mathematcal model of goal programmng s as follows. Parameters of the model are; = ndex of power plant j = crteron ndex, j =, 2,, 6 for operatonal crtera and j = 7,8,, J = 0 for nvestment related crtera, where; : operatng cost 2: operatonal avalablty, 3: producton, 4: envronmental cost, 5: CO, 6: thermal effcency, 7: nvestment cost, 8: constructon tme, 9: power, 0: avalablty Table 6. Aspraton levels of goals Crtera name Operatng cost Goal values (aspraton levels) 94,229,000 mllon TL 0.20 Oper avalablty Producton 7,60,700 MWh 0.25 Envronmental cost 00,900 (000 $) 0.2 CO 00,900 (000 $) 0.4 Thermal Effcency Investment cost 384,28,700 $ 0.7 Constructon tme 43 months 0.8 Power 833 MW 0.3 Normalzed values = crtera that goal s set on, K E = set of effcent DMUs (power plant) obtaned from DEA model a j = the jth value of publc power plant b j = the jth value of prvate power plant c j = the rght hand sde value for the functonal constrant related wth crteron j=5,6 g = the aspraton level for the goal constrant related wth crteron. Decson varables of the model are; x = bnary varable whch s, f the publc power plant s selected ( =,2,,0 ) whch s 0, otherwse y = bnary varable whch s, f the prvate power plant s selected ( =,2,,0 ) whch s 0, otherwse d = under achevement (negatve defcency) from objectve d + = over achevement (postve defcency) from objectve The mathematcal model: mn ( dm + dm ) () m K s.t. 0 ( a x + b y ) + d d = g, (2) = 0 ( a5x + b5y) c5, (3) = 0 = (4) ( a x + b y ) c, Avalablty 833 MW 0.28 E 644 ( x + y) = (5)

10 Ö.Coşgun, G. Ogcu Kaya x, y (0,), (6) d, d + 0, (7) The objectve functon () ams to mnmze the devatons from the goal constrants and the constrant (2) satsfes the goal constrants. Constrants (3) and (4) are the functonal constrants whch are wrtten for crteron CO and thermal effcency. Any goal related wth these attrbutes sn t defned. Moreover, constrant (5) s the DEA effcency constrant that provdes ranng the effcent power plants whch s the am of ths model. In fact, ths s the more general model (for case 4). When t s for only publc or prvate power plants, or terms are dscarded or when t s for operatonal or nvestment performances, then the crtera s taen only from to 6 or 6 to 0 respectvely. When ths model s solved, only one power plant s selected that satsfes these constrants. It means that t s the most effcent plant. Then the selected plant s dscarded and the model s run for the remanng plants agan. Then the second most effcent power plant s selected and goes on. 4.. Effcency comparsons of publc versus prvate power plants At frst, comparsons are solely made between publc and prvate power plants. In case and 2 operatonal and nvestment performances are observed ndvdually to decde on whch plants are more effcent. In terms of nvestment performances the number of effcent prvate power plants s more than the number of effcent publc plants. Because on average, nvestment cost of publc sector plants s hgher than that of the prvate sector plants (ndcatng the more expensve projects of the publc sector). Furthermore, constructon tme of the publc sector plants s also hgher (ths ponts to longer project completon tmes and longer lead tmes); moreover, publc sector owns larger sze plants. Another nterestng pont s that avalablty of the publc sector plants s less than that of the prvate sector. On the other hand, n terms of operatonal performances the number of effcent plants s smlar n both of power plants. Accordng to the data, average productons of the two subsets are smlar (whch actually enables a more relable comparson regardng the other attrbutes). Avalablty values show the prvate sector to be more advantageous. An nterestng pont s envronmental cost ncurred by the publc sector plants: The publc plants have relatvely hgher envronmental cost. Then we observe all plants together n terms of operatonal and nvestment performances ndvdually n case 3. Accordng to the operatonal effcency results, agan we see that the number of effcent publc and prvate plants s smlar. Therefore we construct the goal programmng model and get the ranng of plants as seen n Table 7. Prvate power plants are more effcent than the publc power plants. So, set of effcent power plants s determned by DEA methodology wthout mang a ranng between them and ther ranng s revealed by GP methodology. In Table 8, nvestment effcent results are gven. Smlar results are obtaned as n case 2. Number of effcent publc power plants s very low wth respect to the prvate plants. Moreover, prvate power plants are raned at the frst orders agan. Fnally, we observe the effcences of all plants accordng to the all crtera nvolved operatonal and nvestment data together. But n ths case, after we obtan the DEA results we construct a weghted goal programmng. We thn together wth the frm representatves that nvestment related goals are more mportant than the operatonal goals snce huge nvestments are made n the constructon of such plants and ths affects the decson of constructng plants. Therefore we assgn hgh weghts to the nvestment related goals by talng wth the experts of ths subject. Table 7. Ranng of power plants accordng to the operatonal effcency Effcent plants (DEA) Publc3 Publc4 Publc6 Publc8 Publc0 Prvate Prvate3 Prvate5 Prvate6 Prvate7 Prvate8 Raned effcent plants (GP) Prvate8 Prvate3 Prvate Publc0 Prvate5 Prvate7 Prvate6 Publc4 Publc6 Publc8 Publc3 When weghts are ncluded, the objectve functon of the model s changed as follows. We have eght goals and the last four are nvestment related. 645

11 Data Envelopment Analyss Applcaton mn ω ( d + d + d + d ) + ω ( d + d + d + d ) where ω denotes the weght of operatonal related goals and ω2 denotes the nvestment related goals and ω2 > ω. Table 8. Ranng of power plants accordng to the nvestment effcency Effcent plants (DEA) Publc4 Publc7 Raned effcent plants (GP) Prvate6 Publc7 Furthermore, nvestment cost and constructon tme values of coal plants are hgher than natural gas ones; avalablty, power, effcency are also hgh for natural gas as seen n Fgure. Moreover when we ran the effcent power plants, natural gas has the frst orders. Table 9. Ranng of power plants accordng to the operatonal and nvestment effcency Effcent plants (DEA) Publc Publc3 Publc4 Raned effcent plants (GP) Publc7 Prvate9 Prvate0 Prvate Prvate2 Publc5 Prvate3 Prvate2 Prvate Publc6 Prvate8 Prvate3 Prvate0 Publc7 Publc8 Prvate6 Publc4 Publc8 Prvate4 Prvate0 Prvate3 Accordng to case 4, the followng results are obtaned as gven n Table 9. In ths case, the number of effcent publc power plants s hgher than the prvate one but agan the prvate plants are more effcent than the publc plants except the publc plant 7 accordng to ranng results. It s mportant to eep n mnd that prevous operatonal and nvestment based comparsons are performed on prvate and publc power plants separately whereas comparson wth respect to both operatonal and nvestment effcences are carred out on overall bass. As a result, when we compare the effcences of publc and prvate power plants, prvate plants are more effcent than the publc plants. The result s not surprsng snce publc power plants of Turey are older than prvate ones whch lead to older technology usage n publc power plants. Furthermore, nvestment effcency performances are hgher for prvate plants and operatonal effcency performances are smlar for two types of plants. Publc9 Publc0 Prvate3 Prvate4 Prvate8 Prvate9 Prvate0 Normalzed value 0,25 0,20 0,5 0,0 0,05 - Publc9 Publc5 Publc4 Publc0 Publc6 Publc Publc3 coal natural gas 4.2. Effcency comparsons of natural gas based versus coal based power plants For addtonal nsght, the set of thermal power plants s decomposed accordng to nput source (coal and natural gas). The comparson of these two subsets s made. The frst fve of publc and prvate power plants are coal based and the remanng are natural gas based. Accordng to Table 9nvolvng operatonal and nvestment analyss together, coal plants seem to be at a clear dsadvantage wth respect to natural gas plants. Averagely, natural gas has hgher effcency scores relatve to coal. 646 Fg.. Comparson of coal-based and natural gas-based power plants Another comparson s made between publc versus prvate coal based and natural gas based power plants as gven n Fgure 2 and Fgure 3. An

12 nterestng pont s that avalablty of publc gas plants s lower than ther prvately owned counterparts. Hgher nvestment cost of publc sector comes from the larger sze plants they operate; accordngly, constructon tme of publc plants s also somewhat hgher. Ths decomposton and analyss show that the man dsadvantage of the publc sector may be due to the coal based plants t owns. Better management and adaptaton of new technologes to these coal based plants can mprove the stuaton. Envronmental cost s expected to be hgher for coal-based plants snce CO level s hgher more than the natural gas. Ths s proved when publc plants are compared wth prvate plants n Fgure 2 and 3. Publc power plants have also hgher nvestment cost, lower avalablty and effcency for both coal and natural gas based plants. Normalzed value As a result, prvate coal-based and gas-based power plants have more advantageous than the publc one because of these reasons. 0,40 0,30 0,20 0,0 publc coal prvate coal Fg. 2. Comparson of coal-based publc and prvate power plants Normalzed value Ö.Coşgun, G. Ogcu Kaya 0,30 0,25 0,20 0,5 0,0 0,05 - publc gas prvate gas Fg. 3. Comparson of natural gas-based publc and prvate power plants 5. Concluson Energy s a hghly attractve sector that contnues to grow by new nvestments wth response to growng demand n Turey. New utltes are beng constructed by both publc and prvate sector companes and ths stuaton leads to the crtcal ssue to determne on whch energy source to nvest. Thermal energy sources consttute the major part of energy supply and due to ths reason, we focus on thermal energy power plants whch are natural gas based and coal based ones. By usng data envelopment analyss technque, effcences of thermal power plants are compared. After fndng the most effcent ones, goal programmng methodology s used to mae a ranng between effcent power plants. Our study s an mportant research for provdng gudance to energy sector actors that are eager to mae nvestments n ths sector. For the study, 20 power plants are selected where half of them are prvately owned and the remanng are publc enterprses. All power plants belong to the group of thermal power plants snce 0 of them are natural gas based and the other 0 are coal based plants. Operatonal and nvestment based crtera are the most mportant evaluaton aspects for a power plant, due to ths reason we compare nvestment and operatonal based effcences. For both publc and prvately owned power plants, nvestment and operatonal effcences are compared separately. Moreover, nvestment and also operatonal effcency evaluaton s done for the set of all power plants. Another evaluaton s done for overall performance combnng nvestment and operatonal effcences and all power plants are raned accordng to ther effcences. Fnally, coal based and natural gas based power plants are compared wth respect to ther overall effcences. Also coal based prvate and coal based publc plants are compared, n addton natural gas based prvate 647

13 Data Envelopment Analyss Applcaton and natural gas based publc plants are also compared. Based on the results of the study, t s shown that based on ther nvestment effcences, prvately owned utltes are more effcent whereas they seem to be equal on operatonal performance. In comparson of energy sources, natural gas based power plants result wth hgher effcency than coal based ones and natural gas power plants have more avalablty and effcency values. Based on the comparson between natural gas based prvately owned and publc power plants, t s shown that publc ones have less avalablty values and hgher constructon tmes. At the coal based power plant comparson, publc power plants seem to have hgher nvestment cost and lower effcency values. Data envelopment analyss results gve effcent and non-effcent power plants but t does not provde a ranng of effcent unts. However t s very mportant to decde on the most effcent plant and have the plants ordered accordng to ther effcences. Ths requrement s fulflled wth the help of goal programmng technque. After data envelopment analyss yelds the effcent power plants, we use goal programmng by consderng only these effcent plants and obtan a ranng between them. Goal programmng approach s used for ranng effcent utltes wth respect to ther operatonal effcences, nvestment effcences and overall effcences. Based on the goal programmng results, t s shown that prvate power plants are more effcent than publc power plants. Ths study s valuable for gvng nsghts not only on effcences of publc and prvately owned power plants but also effcences of coal based and natural gas based power plants. Data envelopment analyss and goal programmng approaches are used for the evaluaton. Future studes can be made by choosng dfferent nput and output parameters for the evaluaton and also the study can be extended by tang other types of power plants nto account le hydro and wnd power plants. References. K. Sarıca and İ. Or, Effcency Assesment of Tursh power plants usng data envelopment analyss, Energy 32 (2005) A. Charnes, W. W. Cooper and E. Rhodes Measurng the effcency of decson mang unts, European Journal of Operatonal Research 2 (978) W. D. Coo and L. M. Seford, Data Envelopment Analyss (DEA), Thrty Years On. European Journal of Operatonal Research 92 (2009) E. Thanassouls, Settng achevement targets for school chldren, Educaton Economcs 7(2) (999) D.T. Barnum, S.M. Walton, K.L. Shelds and G.T. Schumoc, Measurng Hosptal Effcency 648 wth Data Envelopment Analyss: Nonsubsttutable vs. Substtutable Inputs and Outputs, Journal of Medcal Systems 35 (20) S. Mrmran, Health Care Effcency In Transton Economes: An Applcaton Of Data Envelopment Analyss, Internatonal Busness and Economcs Research Journal (2008) 7. R. Corredora, J.A. Chlngeran and J.R. Kmberly, Analyzng performance n addcton treatment: An applcaton of data envelopment analyss to the state of Maryland system, Journal of Substance Abuse Treatment 4 (20) S. Suzu and P. Njamp, A stepwseprojecton data envelopment analyss for publc transport operatons n Japan, Letters n Spatal and Resource Scences 4 (20) I. Fraser and D. Cordna, An applcaton of data envelopment analyss to rrgated dary farms n Northern Vctora, Australa, Agrcultural Systems 59 (999) N. Banaean and M. Zangeneh, Study on energy effcency n corn producton of Iran, Energy, 36 (20) S.M. Nassr and S. Sngh, Study On Energy Use Effcency For Paddy Crop Usng Data Envelopment Analyss (DEA) Technque, Appled Energy 86 (2009) S.H. Mousav-Avval, S. Rafee, A. Jafar and A. Mohammad, Improvng Energy Use Effcency Of Canola Producton Usng Data Envelopment Analyss (DEA) Approach, Energy 36 (20) H.D. Sherman and G. Ladno, Managng ban productvty usng data envelopment analyss (DEA), Interfaces 25(2) (995) J. Johnes and L. Yu, Measurng The Research Performance Of Chnese Hgher Educaton Insttutons Usng Data Envelopment Analyss, Chna Economc Revew 9 (2008) A. Kamaura Wagner, T. Ratchford Bran and A. Jagds, Measurng maret effcency and welfare loss, J Consum Res (988) J. Parsons Leonard, Assessng salesforce performance wth data envelopment analyss, TIMS Maretng Scence Conference (990). 7. S. Boles James, N. Donthu and L. Rtu, Salesperson evaluaton usng relatve performance effcency: the applcaton of data envelopment analyss, J Pers Sell Sales Management 5(3) (995) Y. Lao and L. Lu, Performance evaluaton of bus lnes wth data envelopment analyss and geographc nformaton systems, Computers, Envronment and Urban Systems 33 (2009) S. Onut and S. Soner, Analyss of energy use and effcency n Tursh manufacturng sector SMEs, Energy Converson and Management, 48 (2007) C. Martínez, Energy effcency development n German and Colomban non-energy-ntensve sectors: a non-parametrc analyss, Energy Effcency 4 (20) W.O. Olatub and D.E. Dsmues, A data envelopment analyss of the levels and

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