Smart Control Techniques based Economic Power Generation Scheduling

Transcription

1 Internatonal Journal of Engneerng & Coputer Scence IJECS-IJENS Vol:12 No:06 5 Sart Control Technques based Econoc Power Generaton Schedulng Nadjauddn Harun *, Tajuddn Wars *, Syafaruddn *, Takash Hyaa ** Abstract-- The study as to gan the beneft of usng fuzzy logc syste for the econoc the schedulng of power generaton by eans the nu of the generaton cost. The nput pattern of the proposed fuzzy logc control s derved fro LaGrange ethod. The fuzzfcaton process ends up wth sngle nput and sngle output syste that are vald for all varables. The ntal condton of the case power syste s analyzed usng Newton-Raphson load flow ethod and the nput-output characterstc equatons deterned by the Least Square ethod. The target of generaton schedulng s all the theral power plants connected n the South-Sulawes nterconnected syste, Indonesa. The sulaton results showed ncreasng n the operatonal cost effcency fro low to peak loadng. In addton, the results related to operatonal cost and total cost effcency usng ths sart control technque are averagely of 4, Rp/MWh and 23.4 %, respectvely; that eans better than the ert loadng as the conventonal technque used n the regon. Index Ter-- Sart control, fuzzy logc syste, La Grange ethod, Newton-Raphson ethod, least-square ethod, econoc schedulng. I. INTRODUCTION For the econocal operaton of power syste, the unt generaton schedulng s one of the portant consderatons. It s due to the electrcal characterstcs of each generators connected n the syste are dfferent and the varaton n load profle s rando. In fact, the operaton of these generators should be n synchrons and the nu cost of total generaton ust be acheved. For ths reason, the control to schedule the aount of power generaton on each unt s requred. Ths knd of echans needs the excellent anageent of electrcal power syste operaton for the optzaton purpose (1). In case of theral power plant, the level of nu cost can be reached by only pleentng sart schedulng n the corrdor of allowable electrcal energy qualty. It eans that whle the load profle s dffcult to predct and the unt generator should run, the coordnated power output control s very portant to ensure the balance between the generaton and load for the sake of nonal frequency syste. Due to the varety of daly load, t s necessary to know whch generator should be start-up and shut-down n the econoc pont of vews and what s the order. The procedure to have proper decson n ths proble s * Departent of Electrcal Engneerng of Unverstas Hasanuddn, Taalanrea-Makassar, Indonesa ** Departent of Coputer Scence and Electrcal Engneerng of Kuaoto Unversty, Kuroka, Kuaoto , Japan coonly operated by the unt cotent controlled by dfferent ntellgent control technque approaches (2). Followng ths condton, the autoatc generaton control should be developed under steady state pont of vew. One of the reasonable solutons for the optzed schedulng s the sart control technque by eans of fuzzy logc control syste. Agan, that the fuzzy logc can be useful for the process of uncertan data nput, precson output and proven to be easy and cheap n the pleentaton level. The applcaton of fuzzy logc control for the econoc schedulng of power syste s contnuously receved ore attenton (3). One of the reasons of usng ths ethod s the capablty to reach the axu effcency operaton of power syste as a part of our an goal. The econoc schedulng s portant n all power syste to gan revenue of our early nvestent of the syste. The acheveent n axu effcency wll reduce the generaton cost n kwh n the custoer and utlty s sde and the cost and of course to reduce the total fuel consupton. In the econoc operaton of power syste, t can be dvded nto two an consderatons. The frst one s related to the nu cost of power producton and the next one s related to the nu loses n the transsson lne. The nu cost generaton s pretty close to the nu fuel cost consupton needed for the overall operaton of syste. For ths reason, the econoc schedulng focuses on the coordnated electrcty producton cost of all generators. Conversely to the nu losses durng power transsson, t s hghly dependng on how the power flow controlled n the syste. Both the econoc schedulng and nu losses probles can be solved conventonally usng optal power flow progra, where the paraeters can be autoatcally controlled to eet the allowable argn of syste whle nzng the specfc objectve functon. The classcal approach of the econoc schedulng s to dstrbute the capacty of the ost econoc generaton unt. In ths case the transsson losses are represented by the output functon and the varety of generaton unts. In coparson, the nu cost and power delvery are obtaned by schedulng all generator unts usng optal power flow based sart control technque (4). Nowadays, the nterconnected operaton of power systes s nevtable for the reasons of stablty, contnuty n power supply wthn econoc consderaton. The econoc schedulng wthn nterconnected syste usng sart control technque provde the autoatc generaton control to operate and to stop generator unts based on desgn progra and algorth stored n the an coputer unt (5). Ths progra s bascally desgned to share the generator output to the overall load followng the axu effcency of systes. One of odel for load dstrbuton n the nterconnected systes s the ert

2 Internatonal Journal of Engneerng & Coputer Scence IJECS-IJENS Vol:12 No:06 6 Fg. 1. Sngle lne dagra of our case study syste loadng functons as unt cotent to deterne whch generator should run and how uch the capacty should be generated based on the prorty table. Ths table s ade accordng to the operaton cost of each generator per energy generaton. The cheapest one s the ost prorty optons. However, wth the coplexty of power syste ncreases wth the exstence of ndependent power producers, the ert loadng s not sutable anyore. One of the an constrants n the nterconnected syste s the hghly fluctuated load that ust be followed by optal power dspatch. Consequently, the optal power flow changes to nse the MW output and needs reschedulng. There so any optzaton technques deal wth optal power flow proble; for nstance based dynac prograng, lnearzaton as well non-lnear approaches and ntellgent control. In ths work, the fuzzy logc control syste s utlzed for sart schedulng of generator systes. The reason for takng the fuzzy logc control technque s to reduce devaton between load and generaton under uncertanty condton of power syste operaton. If the proposed ethod s properly worked out, then load dstrbuton by unt generatons can be exactly balanced and consequently affected the nonal frequency syste stablty, reduced fuel consupton and power losses of syste n broad ter. II. CASE STUDY AND PROPOSED SMART CONTROL SYSTEMS Now, we have the good background to understand the econocal schedulng that needs the load dstrbuton between generators. For exaple, the total output of two generators should balance wth load share so that the ncreental fuel cost of one generator s hgher than the others. Consequently the load s transferred fro the hgher cost generator to the lower one. Reducng load fro one unt of generaton wth hgh ncreental cost wll reduce sgnfcant operatonal cost of syste. The load transfer process s contnuously worked untl the ncreental cost of both generators s equal (6). Ths approach s also sutable for the syste wth ore than two generators. The pont s the econoc load sharng between generators ust follow crtera that all unts of generator should be operated n the equal ncreental fuel cost. When the ncreental fuel cost and all unts of generator operate alost lnear wth assupton of output power n the allowable range, the calculaton can be uch easer (7). For ths reason, there are two consderatons for econocal schedulng for each unt of generatons,.e the assupton that the total output of generator s vared and calculaton of the ncreental fuel cost ncludng the characterstc analyss of each generaton s consdered. Ths conventonal technque requres ore graphcal analyss, therefore agan we need spler ethod wth hgh accuracy perforance such as ntellgent technques approach.

3 Internatonal Journal of Engneerng & Coputer Scence IJECS-IJENS Vol:12 No:06 7 TABLE I cheapest to the ost expensve one. Ths proble tres to be T HERMAL UNIT AVAILABLE IN THE CASE STUDY SYSTEM solved usng sart control technque by eans fuzzy logc controller. For ths work, the coplete nforaton about the No. Output power Sector electrcal power systes n the regon, such as sngle lne Generaton Unt of /branch Installed Delvered dagra ncludng data transsson syste, data of theral unt power plant and power flow analyss under several loadng Tello, Stea theral condton s odelled n the Matlab sulaton envronent. 30 kv Gas theral WESTCAN To deterne the ntal condton of the syste or noral Gas theral ALSTHOM operaton, the power flow progra based on Desel MIRRLES Newton-Raphson ethod s run under several scenaros of Tello, Desel MITSUBISHI loadng condtons. The results of ths sulaton as the 150 kv Desel SWD ntal condton s the voltage, power loses and the phase Gas theral GE angle. Based on ths nforaton, then we deterne the Gas theral GE nput-output characterstc equaton for every generator unt The proposed of sart controlled syste s pleented based on the operatonal cost of generator usng Least n electrcal power syste n South Sulawes, Indonesa. Square ethod. The operatonal cost conssts bascally of The syste conssts of 7 centres of power plants wth 23 fxed and varable costs. The varable cost s the functon of generators, 20 load centres and 27 power transsson lnes the generator output as shown n Table II. The varable costs to connect between the power plant and load centres as can be calculated usng nput-output characterstcs of shown n Fg. 1. The focus of schedulng s on the generator usng second order polynoal equaton. non-prvate theral power plants, whch ther capactes The unt cotent of generaton can be expressed as shown n Table I. The syste s recognzed large n the regon but the operaton procedure stll follows the conventonal one;.e by the order of cost of generaton fro TABLE II DATA INPUT-OUTPUT OF THERMAL UNIT PLANT IN SOUTH-SULAWESI, INDONESIA Generaton Unt Stea theral-2 (MFO) Input NHR Ltre/hour Rp/hour Gas theral WESTCAN (HSD) Gas theral ALSTHOM (HSD) Gas theral GE-1 (HSD) Gas theral GE-2 (HSD) Desel MITSUBISHI (MFO) Desel SWD (MFO) Desel MIRRLES (MFO) , ,200 1, ,725,000 1,646,250 2,555,000 2,756,250 3,390,000 1,703,250 2,340,000 2,983,500 3,843,000 2,895,750 4,158,000 5,1,150 6,573,600 2,767,050 4,019,400 5,301,450 6,514, , , ,752 1,113, , , ,652 1,108,800 52,650 87, , ,800 Output the optal decson related to the schedulng of start-up and shut-down of the theral unt n order to reduce the generaton cost as long as the reserved argn s enough to

4 Internatonal Journal of Engneerng & Coputer Scence IJECS-IJENS Vol:12 No:06 8 support the syste. The proble s the wde devaton where s the order of plant th ; a, b and c are the constant between the fluctuated load and the generaton unt cong characterstc of each generator, P s the total output of generator to the syste. Therefore, we need to take soe assuptons, and the F s the varable cost. such as the load s constant n one perod of te as results In addton, the calculaton of Increental Fuel Rate (IFR), Net fro load estaton, transsson losses s gnored and theral Heat Rate (NHR) and effcency for the syste can be solved usng power reserve has been scheduled. Followng these assuptons, the followng equatons: the nzaton of objectve functon of unt cotent n ters of fuel cost and start-up cost can be forulated as: dh IFR ( ltre/ MWh) (8) Cost N H 1 1 H 1, 2,..., N J ( F cos t { G ( H)} SCost ), where Cost s the total cost n one perod of te, FCost s the cost of generaton of unt G n hour te H, SCost s the cost durng start-up generaton of unt G, whle N and J s the te nterval (perod). Wth constrant crtera, the balance between generaton and load s obtaned as follows. J G 1 (1) ( H) L( H) ; H 1,2,..., N (2) where G (H) s total power generaton of unt G n hour te H and L(H) s the load power consupton durng H perod. The argn of generaton capacty s defned as: P PH P ; H 1,2,..., N n ax (3) where P n s the nu generaton capacty of unt, P ax s the axu generaton capacty of unt and P H s the power generated of unt n hour te H. Meanwhle, the argn of spnnng reserve s defned as follows: J Pax SH L H) R( H) ; H 1,2,..., N 1 ( (4) where S H s the status of unt (ether ON or OFF), R(H) s the allowable power reserve n hour te H and L(H) s the load n hour te H. Accordng to the unt cotent objectve functon n (1), t s necessary to deterne the fuel cost F cost (H,J) n all operatng condtons as representaton of the total cost. Ths fuel cost s deterned by econoc operaton of on-lne unts at J condton n te hour H. The objectve functon to nze ths fuel cost s expressed as: Cost J ( H, J) F ( G ) (5) 1 where F (G ) s the fuel cost of unt and usually expressed n quadratc equaton as: 2 F ( G ) a ( G ) b G c (6) wth the constrants are shown n (2) and (3). In ths respect, the Lagrange ethod s enough to gve soluton for the probles entoned above. For schedulng purpose wth order of generaton unt, Eq. (6) s the objectve functon that needs to be nsed n ths proposed work and the equaton s odfed nto: F a P 2 b P c (7) dp In order to deterne the heat rate and the fuel consupton for theral power plant, we need to use the equaton of Net Heat Rate (NHR) as follows: Input ltre / hour NHR (9) Output( MW ) It can be seen fro eq. (9) that NHR s bascally slar to the effcency calculaton shown n (10): Output Effcency x100% (10) Input Conventonally, the heat rate rato s nversely proportonal to the fuel effcency consupton. For ths reason, the lower heat rate represents the hgher effcency of fuel consupton (8). The axu effcency can be reached at the total output generator n our syste around MW, whch requres fuel effcency consupton for about 31%. The deand of fuel for specfc generaton output can be easly converted nto Rupah/MWh (note: Rupah= Indonesan currency). In ths respect, the load dstrbuton aongst generator s equvalent to the ncrease or decrease n the total cost. Therefore, the ncreental fuel cost can be obtaned fro the slope nput-output curve between two unt generators. It eans that the ncrease n fuel cost consupton s the dfferental functon of fuel cost at certan generator to the total output (9). The ncrease n fuel cost consupton at defnte output power can be consdered as the addtonal cost n Rp/hour for every 1 MW. The coplete data nput-output of theral power plant n the case study syste s avalable n Table II. Ipleentng the Eq. (7) nto the case study syste s very portant. Based on the characterstc of each unt of generator, t s obtaned the nput-output equatons for 8 dfferent types of theral generaton unt exst n the syste, ncludng the fuel cost and ncreental fuel cost. These equatons are tabulated n Table III. Then, the generaton unts can be grouped based on the voltage level connecton and the theral type. Therefore, the cost equvalent of nput-output related to the fuel cost and ncreental fuel cost followng Table III above s suarzed n Table IV. The next step s the optzaton of load durng nterval load peak te of theral power plants usng Lagrange ethod. The daly load pattern and losses calculaton durng peak load te n n South Sulawes nterconnected syste, the average load s MW where the axu and nu load of theral generator are MW and 90.3 MW, respectvely. The optzaton results usng Lagrange ethod about the schedulng of theral unt durng peak load can be seen n Table V. It can be seen that Gas theral GE-2, Desel MITSUBISHI and Desel

5 Internatonal Journal of Engneerng & Coputer Scence IJECS-IJENS Vol:12 No:06 9 TABLE III INPUT-OUTPUT EQUATIONS INCLUDING FUEL AND INCREMENTAL FUEL COSTS OF THERMAL UNITS Generaton Unt Input-output equaton (H ) n Ltre/hour Fuel cost equaton (F) n (Rp/hour) Increental fuel cost (IFC) (Rp/MWh) Stea theral P P P P P Gas theral WESTCAN Gas theral ALSTHOM P-11.5P P+6.6P P -6900P P+3960P P P Desel MIRRLES P+41.P P+16532P P Desel MITSUBISHI Desel SWD P P P P P P P P P P Gas theral GE-1 Gas theral GE P-0.1P P-0.2P P-60P P-120P P P TABLE IV EQUIVALENT EQUATIONS OF FUEL COST AND INCREMENTAL FUEL COST Theral type and locaton Fuel cost equaton (F) n (Rp/hour) Increental fuel cost (IFC) (Rp/MWh) Tello 150kV, Gas theral Tello 150kV, Desel Tello 30kV P-40P P+40P P P P P P Load TABLE V ECONOMIC DISPATCH OF THERMAL UNITS USING LAGRANGE METHOD Gas theral, 150 kv Desel, 150kV Tello, 30kV GE-1 GE-2 Mtsubsh SWD Stea Gas theral Gas theral theral-2 WESTCAN ALSTHOM Desel MIRRLES SWD are always operated durng peak load; therefore they have ebershp degree of 1 n fuzzy logc control, eans not the target for the scheatc control developent. Based on the optal schedulng results n Table V, the load range can be deterned and consdered as the nput varable for the set of developed fuzzy syste. However, the optzed result cannot stll guarantee the econoc generaton n overall operaton condtons. Therefore, the optzaton results usng Lagrange ethod s just soe knd of pattern for the sart control of fuzzy logc syste. The developed fuzzy logc control syste has structure wth sngle nput and sngle output (SISO). The scheatc dagra can be seen n Fgure 2. The nput varable s the theral load nforaton suppled load by 5 unts of theral generator; Gas theral GE-1, Stea theral-2, Gas theral WESTCAN, Gas theral ALSTHOM and Desel MIRRLES, whle the output varable s the econoc dspatched load suppled by these unts. The peak load nforaton fro the load center s obtaned and processed by plcaton rules. Only 5 unts of theral generator s optzed n ths respect due to the other 3 unts s constantly supply durng peak load. Fg. 2. Scheatc dagra of developed fuzzy logc control syste

6 Internatonal Journal of Engneerng & Coputer Scence IJECS-IJENS Vol:12 No:06 10 It s well known that a fuzzy logc controller s a closed crcle syste; eans there s not operator n any parts of controlled crcle syste as shown n Fg. 3. Bascally, the output varable s feedback to the nput sgnal through process n the sensor syste and then to be copared wth reference value. If the error s hgh, the nput varables of syste (E) should be apped nto fuzzy sngleton n the lngustc parts contans fuzzfcaton nterface (FI). Furtherore, ths process s contnued to decson akng logc (DML) that should produce fuzzy concluson as the control acton. Ths acton s bascally obtaned by Fg. 4. Mebershp functons of nput varable evaluatng nuber of fuzzy rules (knowledge base) for each fuzzfed nput. In the output part, defuzzfcaton nterface (DFI) s a part of the last step to draw the fuzzy concluson. TABLE VI Ths part ncludes gvng heavy and cobnaton of several fuzzy set that gvng crsp fuzzy sngleton fro each output. VARIABLE LINGUISTIC VARIABLES AND MEMBERSHIP FUNCTION FOR INPUT DATA No Lngustc varables LOW MED HIGH 1 N P Fg. 3. Sart control technque of fuzzy logc syste (SCT FL) The detal explanaton about the fuzzy desgn syste s explaned as follows. The fuzzy control syste desgn follows three portant stages,.e fuzzfcaton, fuzzy nference, defuzzfcaton stages. In the begnnng n the fuzzfcaton stage (ntal strategy), the ebershp functon of nput and output varable are ade overlap. Ths s to ensure that all crsp values are ncluded n the exstng sets and to ake possble that ore than one rule nvolved n deternng output. In ths case, we cobne the trangle shape and half-trapezu to represent the nput varable. There are fve fuzzy varables,.e N,, and P as lngustc varable for nput-output data. The nput ebershp degree s utlzed as functonal defnton where a trapezu shape s consdered for the 1 st and n th ebershp functon and a trangle shape s for the 2 nd to (n-1) th ebershp functon. For nstance, the n th ebershp functon s a trangle functon that conssts of a, b and c values, represents as LOW,, and HIGH values. The LOW value fro the (n-1) ebershp functon ust take the value fro the (n-2) ebershp functon, as well as the HIGH value ust take value fro n ebershp functon. In ths respect, the value s HIGH value fro ebershp functon (n-2) and could also be a LOW value fro n ebershp value. Ths approach can be seen n Fg. 4. It s necessary to brng our approach to our study syste n the case of controllng the output of theral generator. In ths case, the objectve functon that needs to be optzed s the cost generaton functon wthn constrant functon of electrcal load balancng suppled fro all theral generator TABLE VII LINGUISTIC VARIABLES AND MEMBERSHIP FUNCTION FOR OUTPUT Theral Unts Gas theral GE-1 Stea theral-2 Gas theral WESTCAN Gas theral ALSTHOM Gas theral MIRRLES DATA VARIABLE OF THERMAL UNITS Lngustc varables N P N P N P N P N P LOW MED HIGH unts. Followng the assupton n Fg. 4, the lngustc varable for nput ebershp functon s defned fro the nuercal varable of estated load profle as the predefned condton of fuzzfcaton stage as shown n Table VI. Meanwhle,

7 Internatonal Journal of Engneerng & Coputer Scence IJECS-IJENS Vol:12 No:06 11 have total of 8 unts theral power plants, specfed by ther fuel types,.e Gas theral GE-1 (HSD), Gas theral GE-2 (HSD), Desel MITSUBISHI (MFO), Desel SW D (MFO), Stea theral-2 (MFO), Gas theral WESTCAN (HSD), Gas theral ALSTHOM (HSD), Desel MIRRLES (MFO). The coplete data can be seen n Table II prevously. The TABLE VIII prce for dfferent type of fuels n the runnng year found that HSD (Hgh Speed Desel) s Rp /lter and MFO (Marne Fuel Ol) s Rp /lter. The other THEN Theral Power Plant characterstcs of the systes are the theral unts of GE-2, MITSUBISHI and SWD ust generate power at the N axu rate durng peak load te. For ths reason, these three unts are not usng our proposed sart control systes. The typcal peak load perod analysed n ths study fro 6 p.. to 8 p.. wthn the range of load for theral generaton s between 90.3MW and 120.3MW. P The generaton schedulng usng proposed sart control at 6 p s shown n Fg. 5. The total heavy load whch ust be suppled by all theral power plants are 90.3 MW, the lngustc varable for output ebershp functon defned by fve theral unts, such as Gas theral GE-1, Stea theral-2, Gas theral W ESTCAN, Gas theral ALSTHOM and Gas theral MIRRLES s shown Table VII. In the fuzzy nference stage or coordnaton strategy, t s IF-THEN RULES FOR THERMAL GENERATION OPERATION CONTROL IF Load VERY LOW LOW HIGH VERY HIGH portant to calculate the fre strength that akes t possble for the case study syste by eans to provde and fulfll deterned rules. The rules obtaned for the theral generaton operaton control shown n Table VIII. Fnally, n the the defuzzfcaton stage, t needs to convert the lngustc varables to nuercal varables (crsp value) of the output varables. In ths case, the ethod of Center of Area (COA) or centre of gravty s defned for the defuzzfcaton stage. The general equaton for the defuzzfcaton stage usng COA ethod s stated n (11) as follows: coprses of GE-1 s 15.2 MW, GE-2 s MW, MITSUBISHI s MW, whle MW for SW D; 5.44 MW for Stea theral- 2; 9.58 MW for WESTCAN; 4.58 MW for ALSTHOM and 2.46 MW for MIRRLES. In ths fgure also, we can also obtan the load dstrbuton of each generator usng the ert loadng approach. In general, the fuzzy logc control syste can provde axu load dstrbuton better than usng the conventonal ethod. The arrangeent of generaton schedulng uses sart z M ( z R k 1 M k 1 R k )* z ( z ) k k (11) where R (z k ) s the centre of gravty of each output ebershp functon, z k s the respectve output varable and z s the output sgnal. The results for these approaches ake the output generaton can be optzed wthn nu cost durng load change perod. III. SIMULATION RESULTS AND DISCUSSION Practcally n our case study syste lke other power systes, the load profle always fluctuates. In fact, t s dffcult to predct the aount of load changes due to soe unknown factors. In order to antan the frequency stablty, t s necessary to have coordnated output power control to balance between the load and generaton. In ths pont, econoc generaton schedulng needs to be perfored usng fuzzy logc systes. The econoc schedulng applcaton tself nvolves the analyss procedure based on the electrcty data nput and the generaton characterstcs (10). It has been entoned n the prevous secton that our study s focused on the non-prvate theral power plants nterconnected n the South Sulawes Syste. Currently, we Fg. 5. Sart control technque of fuzzy logc syste (SCT FL) Descrptons: 1 = GE-1, 2 = GE-2, 3 = MITSUBISHI, 4 = SWD, 5 = Stea theral-2, 6 = WESTCAN, 7 = ALSHTOM, 8 = MIRRLES control technque of fuzzy logc syste s ore effcent copared wth conventonal ethod appled durng the te. The ncreasng of effcency due to ths soft-coputng ethod s obtaned durng schedulng process; whch has hgher response capacty and autoatc toward load changes that occur randoly. More than these results, agan the fuzzy logc control syste has shown ts capablty to solve the optzaton probles n the pleentaton level.

8 Internatonal Journal of Engneerng & Coputer Scence IJECS-IJENS Vol:12 No:06 12 The strength pont of the sart control load dstrbuton another perod between 6.30p and 8p. The load results s cong fro the nput pattern created by dstrbuton for econoc schedulng of each generaton LaGrange ethod. Ths ethod s agan helpng us to select unts s shown n Fg. 6. Durng the observed te perod, autoatcally the theral plant unts wth certan heavy load the power generaton s ostly supply at 7p (except, the that ust be gvng prorty. LaGrange functon s necessary stea theral unt 2 produce hgh at 6.30p for about 9). to establsh the requred condton for an extree value of The gas theral GE-1 s MW, W ESTCAN s 12.5MW, the objectve functon, to add the constrant functon related ALSTHOM s 10MW and MIRRLES s 3MW. The cause to the objectve functon after constrant functon has been for these condtons s the hghest theral load cae to the ultpled by an undeterned ultpler. If the total syste at 7p; therefore ost theral unts produce power theral load reaches the set pont between 90.3MW and on ther axu capactes wth output control coes 120.3MW, the optal schedulng control for all exstng fro set pattern. Agan, the proposed ethod (SCT-FL) can theral unts operate. As results, at 6p, the gas theral show uch better proveent n ters of axu power unt GE-1, whch connected to 150 kv and t has capacty of generaton acheved durng certan perods of te MW contrbute power supply to the syste of 15.2MW. copared wth the ert loadng ethods. The axu aount of power supply for control actons In ters of optzaton and operatonal cost related to s desgned based on the nput pattern derved fro the effcency usng sng SCT-FL Technques shown n LaGrange ethod. The ethod gves ltaton to the Table IX. It can be seen that our proposed ethod results s hghest generaton level of MW for load capacty not far fro the results usng ert loadng approach. between MW and 124MW, whle the lowest of Meanwhle, there s sgnfcant proveent n ters of 13.9MW for load capacty of 90MW. The stea theral-2 operatonal cost effcency usng fuzzy logc syste based of 30 kv, 11.5MW s able to supply power of 9.58, whch econoc schedulng control. Ths cost effcency s very close to the axu ratng output. In ths case, the obtaned fro the fuel cost equaton n Rp/hour for each unt, ltaton of control for load range s set axally at where ntally forng nput-output equaton n Lter/hour, MW n the load capablty of 124 MW and nally at then processed usng the Least Squared ethod. The 5.44MW for load capacty of 90MW. The sae approach for equaton resulted fro ths approach s then ultpled wth gas theral WESTCAN, 30 kv s able to supply power fuel prce for each unt. Fnally, there s ncreasng generaton of 9.58MW. The ltaton of ths unt s effcency between 16 and 28 percent durng the perod of 12.5MW for the hghest load capablty of 124 MW and peak loadng n the syste for the lowest for load capacty of 90 MW. In case of Theral plant schedulng conssts of 5 unts, two unts are gas theral unt ALSTHOM, 30 kv supples 4.58 MW wth usng MFO fuel (Stea theral unt 2 and MIRRLES), capacty 10 MW. Control range for ths unt s 4.58MW and whle others consue HSD fuel (GE-1, W ESTCAN and 10MW for load capacty of 90MW and 124MW, ALSTHOM). In the lowest load theral (90.3MW), alost respectvely. The desel unt of MIRRLES, 30 kv supples each theral unt operated far fro ther capacty, such as 2.46 MW. Ths unt s the lowest capacty (3 MW) n the GE-1 supples 15.2MW fro ts capacty of MW; the syste, but t can contrbute power generaton at peak theral unt-2 supples 5.44MW fro ts capacty of load wth output control ltaton fro 2.43MW to 3 MW. 11.5MW. On the other hand, for power generaton unts The next scenaro s the result of schedulng control for whch use MFO fuel (stea theral unt 2 and MIRRLES) Fg. 6. Load dstrbuton on theral plant unts te between 6.30p and 8p for every 30 nutes usng sart control fuzzy logc syste (SCT-FL) and ert loadng ethods Descrptons: 1 = GE-1, 2 = GE-2, 3 = MITSUBISHI, 4 = SWD, 5 = Stea theral-2, 6 = WESTCAN, 7 = ALSHTOM, 8 = MIRRLES

9 Internatonal Journal of Engneerng & Coputer Scence IJECS-IJENS Vol:12 No:06 13 produce less power. These factors cause low operatonal cost effcency to about 16%. Meanwhle, when the load theral reaches 116.6MW, the overall syste yelds the hghest operatonal cost effcency of 28.4%. The hghest effcency acheveent s due to the contrbuton of each generatng unts operate at ther axu capacty, especally for generaton unt based MFO fuel. In ths case, the bg load ust be suppled autoatcally fro each unt followng the output schedulng control. In addton, Table IX provdes the nforaton about the nu cost generaton after pleentng the sart control technque. Agan, our proposed ethod can offer a eanngful TABLE IX OPTIMIZATION RESULTS AND OPERATIONAL COST EFFICIENCY USING Load FUZZY LOGIC SYSTEM SMART CONTROL TECHNIQUE Operatonal Cost (Rp./MWh) Schedulng (ML) Optzaton (SCT-FL) (Rp/MWh) Cost Effcency Percentage , , , , , , , , , , , , , , , effcency n ters of operatonal cost of theral generator unts the case study syste. IV. CONCLUSION Ths paper has nvestgated the portant feature of fuzzy logc control syste for econoc schedulng of power syste n South Sulawes, Indonesa under noral loadng condtons. The sulaton results showed ncreasng n the operatonal cost effcency fro low to peak loadng. In addton, the results related to operatonal cost and total cost effcency usng ths sart control technque are averagely of 4, Rp/MWh and 23.4 %, respectvely; that eans better than the ert loadng as the conventonal technque used n the regon. In the end, we can see that the fuzzy logc control syste s spler, ore accurate and can be autoatcally scheduled to acheve econocal schedulng, copared to that of conventonal ethod based graphcal analyss and analytcs, such as ert loadng technque. REFERENCES (1) C.A. Roa-Sepulveda, M. Herrera, B. Pavez-Lazo, U.G. Knght, A.H. Coonck, Econoc dspatch usng fuzzy decson trees, Electrc Power Systes Research, 66(2), 2003, pp (2) C. Srnvasa Rao, S. Sva Nagaraju, P. Sangaeswara Raju, Autoatc generaton control of TCPS based hydrotheral syste under open arket scenaro: A fuzzy logc approach, Internatonal Journal of Electrcal Power & Energy Systes, 31(7 8), 2009, pp (3) İlhan Kocaarslan, Ertuğrul Ça, Fuzzy logc controller n nterconnected electrcal power systes for load-frequency control, Internatonal Journal of Electrcal Power & Energy Systes, 27 (8), 2005, pp (%) (4) M.K. El-Sherbny, G. El-Saady, Al M. Youse, Effcent fuzzy logc load frequency controller, Energy Converson and Manageent, 43(14), 2002, pp (5) A. Deroren, E. Yesl, Autoatc generaton control wth fuzzy logc controllers n the power syste ncludng SMES unts, Internatonal Journal of Electrcal Power & Energy Systes, 26(4), 2004, pp (6) Dusanta Kuar Mohanta, Pradp Kuar Sadhu, R. Chakrabart, Fuzzy relablty evaluaton of captve power plant antenance schedulng ncorporatng uncertan forced outage rate and load representaton, Electrc Power Systes Research, 72 (1), 2004, pp (7) F. Czesla, G. Tsatarons, Iteratve exergoeconoc evaluaton and proveent of theral power plants usng fuzzy nference systes, Energy Converson and Manageent, 43 (9-12), 2002, pp (8) A. Sanchez-Lopez, G. Arroyo-Fqueroa, A. Vllavcenco-Rarez, Advanced Control algorths for stea teperature regulaton of theral power plants, Internatonal Journal of Electrcal Power & Energy Systes, 26(10), 2004, pp (9) Ilhan Kocaarslan, Ertugrul Ca, Hasan Tryak, A fuzzy logc controller applcaton for theral power plants, Energy Converson and Manageent, 47 (4), 2006, pp (10) A.L. Elshafe, K.A. El-Metwally, A.A. Shaltou, A varable-structure adaptve fuzzy-logc stablzer for sngle and ult-achne power systes, Control Engneerng Practce, 13(4), 2005, pp Nadjauddn Harun s a professor n Departent of Electrcal Engneerng of Unverstas Hasanuddn, Indonesa. Hs current research nterests anly n econoc dspatchng, econoc schedulng, stea theral power generaton and applcaton of fuzzy logc controller. Tajuddn Wars s lecturer and researcher n power syste area n Departent of Electrcal Engneerng, Unverstas Hasanuddn, Indonesa Syafaruddn receved hs B.Eng degree n Electrcal Engneerng fro Unverstas Hasanuddn, Indonesa, n 1996, M.Eng degree n Electrcal Engneerng fro Unversty of Queensland, Australa, n 2004 and D.Eng degree fro Kuaoto Unversty, Japan n He was workng n Kuaoto Unversty as a vstng assstant professor for Graduate School of Scence and Technology. Hs research nterests nclude dstrbuted generaton plannng, axu power pont trackng control of photovoltac syste, power syste real-te sulaton and neuro-fuzzy logc control applcaton n power syste. Takash Hyaa receved hs B.E., M.S., and Ph.D degrees all n Electrcal Engneerng fro Kyoto Unversty Japan n 1969, 1971, and 1980, respectvely. He s recently a professor eertus at Kuaoto Unversty, Japan and also workng as a Presdent for Kuaoto Prefectural College of Technology, Japan. Hs current nterests nclude ntellgent syste applcatons to electrc power systes and the applcatons of renewable energy power sources to power dstrbuton systes operaton, control and anageent. He s a Senor Meber of IEEE, a eber of IEE of Japan and Japan Solar Energy Socety.