Economic Dispatch in 150 KV Sulselrabar Electrical System Using Ant Colony Optimization

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1 IOSR Journal of Electrcal and Electroncs Engneerng (IOSR-JEEE) e-issn: ,p-ISSN: , Volume 13, Issue 3 Ver. II (May. June. 2018), PP Economc Dspatch n 150 KV Sulselrabar Electrcal System Usng Ant Colony Optmzaton Tasrf 1, Suyono, Had 2, Hasanah, Rn Nur 3 1 (Department Of Electrcal Engneerng, Unversty Of Brawjaya, Indonesa) 2 (Department Of Electrcal Engneerng, Unversty Of Brawjaya, Indonesa) 3 (Department Of Electrcal Engneerng, Unversty Of Brawjaya, Indonesa) Correspondng Author: Tasrf Abstract: Ant Colony Optmzaton (ACO) method n ths research s used to solve the problem of Economc Dspatch on 150 kv electrcal system of South, Southeast, and West Sulawes (Sulselrabar). As a comparson, Lagrange method s also used. Case studes used nclude peak loads n both daytme and nghttme. The ACO algorthm looks for an economcal thermal generaton combnaton. In ths economc dspatch, whch serves, as an objectve functon s the cheapest cost of generaton. From the optmzaton results for the frst case of peak of daytme load, ACO generates a total generaton cost of IDR 94,670, per hour to generate power of 270,309 MW wth losses of 25,918 MW. Meanwhle, by usng the Lagrange method, t provdes a total generaton cost of IDR 117,121, per hour to generate power of MW wth losses of 25,016 MW. In real system, the total generaton cost s IDR 127,881, per hour to generate power of MW wth losses of 24,956 MW. The ACO s able to reduce the cost of generatng the system Sulselrabar IDR 33,211, per hour or 25.98% at peak of daytme load. Meanwhle, by usng Lagrange method t can reduce the cost of generatng Sulselrabar system of IDR 10,760,142.6 per hour or 8.41% at peak of daytme load. From the optmzaton results for the second case of the peak of nghttme load, ACO generates a total generaton cost of IDR 131,473, per hour to generate power of 400,812 MW wth losses of 26,219 MW. Whereas by usng the Lagrange method can be generated a total generaton cost of IDR 134,889, per hour to generate power of MW wth losses of 28,352 MW. In real system, the total generaton cost s IDR 140,806, per hour to generate power of MW wth losses of 26,299 MW. The ACO s able to reduce the cost of generatng the system Sulselrabar IDR 9,333,004.9, per hour or 6.62% at nght peak load. Whle usng the Lagrange method s able to reduce the cost of generatng the system Sulselrabar IDR 5,916,876.7 per hour or 4.2% at peak of nghttme load. Keywords: economc dspatch, ant colony optmzaton, cost, lagrange, losses Date of Submsson: Date of acceptance: I. Introducton The operaton of some generatng unts requres good management. Partcularly n loadng and the amount of power, a generatng unt must contrbute that or a generatng center nto the system must be well regulated. The economcal operaton of operatons, especally thermal generaton, can save the cost of power producton, especally the ncreasng fuel costs, whle the power system load s constantly changng over tme so that the load of thermal generatng unts also changes wth tme n servng the load of the power system. Ths resulted n the cost of fuel was also unty of tme n rupah per hour also took change accordng to tme. In the thermal generatng unt, the ncrease n load drves the ncrease of fuel amount per unt tme and ultmately ncreases the cost ncrement. The method of artfcal ntellgence s a method or algorthm that s nspred from a partcular behavor or event. Ths ntellgent algorthm s created and used for a soluton to complex computng problems and requres a hgh degree of accuracy based on the objectve functon used. Ant Colony Optmzaton s a computatonal technque that solves an optmzaton problem based on the behavor of a group of ants n fndng the shortest path from the nest to a food source. The applcaton of ACO as an economc dspatch optmzaton method has already been done and shown good results [1-9]. The applcaton of ntellgent methods to the Sulselrabar electrcty system has been done [10-20] and showed good result, and optmum use of ACO method n economc dspatch n some electrcal system study, hence n ths research wll be proposed as Economc Dspatch Optmzaton at 150 kv Sulselrabar electrcal system usng Ant Colony Optmzaton (ACO). DOI: / Page

2 Economc Dspatch In 150 KV Sulselrabar Electrcal System Usng Ant Colony Optmzaton II. Economc Dspatch Economc Dspatch s the dvson of load on each generatng unt so that the operatonal cost of each generatng unt s economcal by usng equalty and nequalty constrans. The cost functon of each generator can be formulated mathematcally as an objectve functon as gven n the equaton [21]: n C T = (1) =1 C P C T = The amount of total cost on the generator C (P ) = The nput-output cost functon of the generator n = Number of generator unts The nput-output characterstc s a characterstc that descrbes the relatonshp between the fuel nput (lter / hour) and the output generated by the generator (MW). Generally, the nput output characterstc of the generator s approxmated by a second order polynomal functon e[22]: 2 C = α + β. P + γ. P (2) = Generator fuel nput (rupah / hour). C P = Generator output(mw). α β γ = The nput-output constant of the -th power plant. Parameter determnatonα, β, and γ requres data obtaned from expermental results related to C fuel nputs (amercan dollar / hour) and P generatng output (MW). The nput characterstcs of the generatng unt output can be expressed as follows: The nput of the generator s expressed n H = Mbtu / hour (heat energy requred), or C = IDR / hour (total fuel cost). Whle the output of the generator s expressed n P = MW (power). The output of each generator unt has the mnmum and maxmum lmt of nequalty constrants,.e.[22]: P mn P P max (3) P mn, P max s the mnmum and maxmum power output of generator. At equlbrum power, Equalty constrant must be met e the total power generated by each generatng unt must be equal to the total load requrement n the system. Equalty constrant equlbrum power s [22]: n g P =1 = P D (4) P = output of each generator (MW). P D = total load requrement on the system (MW). To calculate Economc Dspatch s done by usng equalty and nequalty lmtaton. The equalty boundary reflects a balance between the total powers generated by the total load power of the system. The nequalty lmt reflects the mnmum and maxmum lmts of generaton to be met to obtan the optmum total fuel cost. III. Implementaton Ant Colony Optmzaton for Economc Dspatch Determnaton of Inter-Cty Dstance The cty referred to here s the magntude of the generaton value of each plant. Pror to the trp, the dstance between the value of the generaton of one power plant and the other s calculated frst (ntalzed). After ntalzaton, the ants are placed n the frst cty at random. Then the ants wll contnue ther journey from one cty to another randomly to the fnal destnaton, the last cty. Once the journey s over, the locaton of the ctes that the ant has passed through wll be used to calculate the soluton resultng from the trp. Ant Tour Ants choose a path to be traversed from pont r to pont s n a journey wth probablty: (r,s) k p(r,s) s,l Nr (r,l) t The matrx γ (r, s) represents the amount of pheromone ntensty between r and sponts. Then the pheromones wll be updated through the followng equaton: k (r,s) (r,s) y (r,s) (6) (5) DOI: / Page

3 Economc Dspatch In 150 KV Sulselrabar Electrcal System Usng Ant Colony Optmzaton α s the endurance of a pheromone wth nterval 0 <α <1, then (1- α) represents the evaporaton occurrng n pheromones and Δγk (r, s) s the number of pheromones that ants drop on lne (r, s). Power Flow Analyss The method used n conductng the power flow analyss here s the Newton Raphson method. The steps used are the same as the power flow analyss used n the Lagrange method. The power generated by the bus and slack bus generators s obtaned from the travels made by the ant colony. The equatons used to obtan the generated power of each plant are as follows. P (P (P P ). Pgn ) (7) mn max P = power generated by -th generator P m n = mnmum power lmt generated to the -th generator P m ax = maxmum power lmt generated to the -th generator P gn = value of the ant trp for the -th generator mn Update Local Pheromones The pheromone trace (r, s) for the best trp the ant has made (the ant that generates the smallest generaton cost) wll be updated usng the followng equaton. Q k ( r,s) (r,s) r,s Jbest (8) fbest Q s a very large postve constant. Update Global Pheromones To avod stagnaton (a stuaton where ants wll follow the same path, whch wll produce the same soluton), the pheromone trace strength s lmted to the followng ntervals: f (r,s) mn mn (r,s) max f (r,s) max (9) The upper and lower lmts are as follows: 1 max fbest (10) max mn 2 M (11) M s the number of ants that travel. Ants Plot Travel The soluton of the ant colony's journey n mnmzng the cost of generaton s plotted nto a graph up to the maxmum teraton lmt. Best Tour Plot Travel wth the best soluton of the ant colony (mnmum generaton cost) for each teraton s plotted to the maxmum teraton threshold. Parameters ofant Colony Optmzaton Some parameters used n Ant Colony Optmzaton method n ths thess are as follows: Number of ants = 10 Maxmum teraton = 100 Pheromone endurance (alpha) = 0.9 DOI: / Page

4 Economc Dspatch In 150 KV Sulselrabar Electrcal System Usng Ant Colony Optmzaton Intalzaton of Pheromones (Tau Matrx) The tau matrx has a sze of n x m, wth the number of buses n the system, whereas the number of generator unts generated on a scale of 0 to 1 havng a 0.01 nterval. The value of ths matrx wll be updated every tme the ant colony travels. IV. Results and Analyss In ths research, Ant Colony Optmzaton (ACO) method wll be used to solve the problem of Economc Dspatch on 150 kv electrcal system of South Sulawes, Southeast, and West (Sulselrabar). As a comparson, the method used conventonal Lagrange optmzaton. The case studes used are peak day load and nght peak load. In ths research Matlab program s used, to make ACO algorthm, calculate cost functon (Cost Functon) of fuel, by prevously countng nput-output characterstc of each unt of thermal generator of Sulselrabar system. The ACO algorthm wll look for optmal / economcal thermal generaton combnaton. All of four hydro generator unts wll be maxmzed because t s the cheapest generaton. In ths economc dspatch, the objectve functon s the cheapest generaton cost. The functon of the total generaton cost of the plants connected to the system s as follows. C n g t 1 P P 2 The output power of the generator should not exceed or less than the ratng of each generator to obtan a stable generator operaton. Therefore, generator generated power should be lmted to the maxmum and mnmum. Restrctons or constrants are gven by the followng two equatons, equalty constrant s gven by equaton 13, and ts nequalty constrant s gven by equaton 14. n g 1 P P P D P P (14) mn max Wth P mn and P max and s the mnmum and maxmum lmts of the power generated by the -th generator. Input-Output Characterstcs of Thermal Generators To calculate the cost functon of each thermal generator, frst calculatng the nput output characterstcs of each thermal generator. The nput-output equaton can actually be obtaned wth the help of MatLab, and s dsplayed n the followng nput-output characterstc table. Table 1. Characterstcs of Input-Output of Sulselrabar thermal plant No Unts Input-Output Equaton (Lter / Hour) 1 PLTD Pare-Pare P P 2 2 PLTD Suppa P + 0.4P 2 3 PLTU Barru P P 2 4 PLTU Tello P P 2 5 PLTD Agrekko/T.Lama P P 2 6 PLTD Sgmnsa P P 2 7 PLTD Arena/Jeneponto P P 2 8 PLTD Matekko/Bulukumba P P 2 9 PLTD Pajelasang/Soppeng P P 2 10 PLTGU Sengkang P P 2 11 PLTD Malea/Makale P P 2 12 PLTD Palopo P + 50P 2 Cost Functon Thermal Generator The fuel cost equaton of each plant s obtaned by multplyng the generator's nput-output equaton at ts fuel prce. Then the full fuel cost equaton s shown n the followng table. Table 2. Equal Fuel Cost of Sulselrabar thermal plant No Unts Input-Output Equaton (Lter / Hour) 1 PLTD Pare-Pare P P 2 2 PLTD Suppa P P 2 3 PLTU Barru P P 2 4 PLTU Tello P P 2 5 PLTD Agrekko/T.Lama P P 2 6 PLTD Sgmnsa P P 2 7 PLTD Arena/Jeneponto P P 2 DOI: / Page (12) (13)

5 Economc Dspatch In 150 KV Sulselrabar Electrcal System Usng Ant Colony Optmzaton 8 PLTD Matekko/Bulukumba P P 2 9 PLTD Pajelasang/Soppeng P P 2 10 PLTGU Sengkang P P 2 11 PLTD Malea/Makale P P 2 12 PLTD Palopo P P 2 Peak Nght Load Table 6 shows the real generaton and cost for the Sulselrabar thermal system at nght's peak load before beng optmzed. The smulaton results performed usng Ant Colony Optmzaton (ACO) method and Lagrange method are shown n table 3 below. The graph of optmzaton of the cost of generaton and global best tour can be seen n fgure 1 and 2. Table 3. Real Thermal Generaton Expenses at nght peak loads No Unts Real System P (MW) Cost (IDR/hour) Losses (MW) 1 PLTD Pare-Pare PLTD Suppa PLTU Barru PLTU Tello PLTD Agrekko/T.Lama PLTD Sgmnsa PLTD Arena/Jeneponto PLTD Matekko/Bulukumba PLTD Pajelasang/Soppeng PLTGU Sengkang PLTD Malea/Makale PLTD Palopo Sum From the analyss result for the second case that s the peak of nghttme load condton before optmzed, the generatng load charged to the thermal unt s MW, wth total generaton cost of IDR 140,806, Losses generated before optmzaton amounted to 26,299 MW. The total system load s MW. Fourhydro power plants bear respectvely: PLTA Bakaru 126 MW, PLTM TeppoPnrang 0.3 MW, PLTA TangkaManpSnja 3.5 MW, PLTMBl-Bl 7.1 MW. Furthermore, by usng the proposed method s by usng the ntellgent method based on Ant Colony Optmzaton (ACO) obtaned a more optmal generaton results. As a comparson, thetechnque n ths study used Lagrange method. More s shown n Table 4 below. Table 4. Results of thermal power plant optmzaton for nght peak load Lagrange Method ACO Method No Unts Losses Losses P (MW) Cost (IDR/hour) P (MW) Cost (IDR/hour) (MW) (MW) 1 PLTD Pare-Pare PLTD Suppa PLTU Barru PLTU Tello PLTD Agrekko PLTD Sgmnsa PLTD Arena PLTD Matekko PLTD Pajelasang PLTGU Sengkang PLTD Malea/Makale PLTD Palopo Sum DOI: / Page

6 Economc Dspatch In 150 KV Sulselrabar Electrcal System Usng Ant Colony Optmzaton Fgure 1. Graph of cost optmzaton of Sulselrabar system generaton at nght peak load by usng Ant Colony Optmzaton (ACO) Fgure 2. The global chart of the Sulselrabar system tour at nght peak loads usng Ant Colony Optmzaton (ACO) Analyss The ACO optmzaton graph shows the decreasng cost of generaton on each teraton although on some teratons there s a slght oscllaton. Fgure 1 shows how ACO performance n reducng generaton costs. On the graph, the oscllatons occur untl about the 34 th teraton. Ths shows from the begnnng of teraton to the 34 th teraton, ant colones n each teraton produce optmal journey (optmal soluton) whch s dfferent. Whle fgure 2 shows the global best tour that ant colony s done. The global best-tour graph shows the best objectve functon value that can be derved from the ntal teraton algorthm to maxmum teraton (100). Fgure 3 shows the comparson of generaton costs at nght peak loads for each method. The optmzaton result usng ACO method resulted n total generaton cost of IDR 131,473, per hour to generate power of 400,812 MW wth losses of 26,219 MW, whereas usng the Lagrange method, generatng a total generaton cost of IDR 134,889, per hour to generate power of MW wth losses of 28,352 MW. In real system, the total generaton cost s IDR 140,806, per hour to generate power of MW wth losses of 26,299 MW. From the results of ths smulaton can be concluded that Ant Colony Optmzaton (ACO) s able to reduce the cost of generatng the system Sulselrabarto IDR 9,333,004.9 per hour or 6.62% at nght peak load. Whle usng the Lagrange method s able to reduce the cost of generatng the system Sulselrabar IDR 5,916,876.7 per hour or 4.2% at nght peak load. All four unts of each hydro generator DOI: / Page

7 Economc Dspatch In 150 KV Sulselrabar Electrcal System Usng Ant Colony Optmzaton are maxmzed because those are the cheapest generaton. The Sengkang PLTGU power plant unt acts as a slack bus n ths system, whch generates the most expensve thermal generaton cost of IDR 71,509, per hour, wth generated power of 166,102 MW. Whle the cheapest thermal generator unt at MaleaMakale power plant s IDR 1,460, per hour, wth a rased power of 3,520 MW. Fgure 3. Comparson of the cost of rasng the peak of nghttme load V. Concluson Comparng to the current system, mplementaton of ACO for power generaton provdes a cost reducton of 25.98% n the peak of daytme load, and 6.62% n the peak of nghttme load. On the other sde, mplementaton of Lagrange optmzaton method for power generaton provdes a cost reducton of 8.41% n the peak of daytme load, and 4.2% n the peak of nghttme load, comparng to the real system. It verfes that ACO mplementaton for optmzaton has a better effectveness than Lagrange optmzaton method. References [1]. S. Pothya, I. Ngamroo, and W. Kongprawechnon, "Ant colony optmsaton for economc dspatch problem wth non-smooth cost functons," Internatonal Journal of Electrcal Power & Energy Systems, vol. 32, pp , [2]. I. Musrn, N. H. F. Ismal, M. R. Kall, M. K. Idrs, T. K. A. Rahman, and M. R. Adzman, "Ant colony optmzaton (aco) technque n economc power dspatch problems," n Trends n Communcaton Technologes and Engneerng Scence, ed: Sprnger, 2009, pp [3]. I. B. K. P, "ECONOMIC DISPATCH MENGGUNAKAN ANT COLONY OPTIMIZATION PADA SISTEM TRANSMISI 500 KV JAWA BALI," Undergraduate Theses, Electrcal Engneerng, ITS Surabaya, [4]. H. T. CAHYANINGSIH, "IMPLEMENTASI METODE CHAOTIC ANT SWARMOPTIMIZATION (CASO) UNTUK ECONOMIC DISPATCHPADA SISTEM PEMBANGKIT 500kV JAWA - BALI," Undergraduate Theses, JURUSAN TEKNIK ELEKTRO, UNIVERSITAS JEMBER, [5]. R. Effatnejad, H. Alyar, H. Tadayyon, and A. Abdollahshraz, "Novel optmzaton based on the ant colony for economc dspatch," [6]. N. M. Afrooz, K. Isapour, M. Hakmzadeh, and A. Davod, "Soluton Economc Power Dspatch Problems by an Ant Colony Optmzaton Approach," World Academy of Scence, Engneerng and Technology, Internatonal Journal of Mathematcal, Computatonal, Physcal, Electrcal and Computer Engneerng, vol. 8, pp , [7]. T. SIDDIQI, "ANALISIS ALIRAN DAYA OPTIMAL MENGGUNAKAN ANT COLONY OPTIMIZATION (ACO) DAN MEMPERTIMBANGKAN BIAYA PEMBANGKITAN PADA SISTEM TRANSMISI 500 KV JAWA-BALI," Undergraduate Theses, TeknkElektro, UnverstasMuhammadyah Malang, [8]. K. Swarup, "Ant colony optmzaton for economc generator schedulng and load dspatch," [9]. D. C. Secu, "A method based on the ant colony optmzaton algorthm for dynamc economc dspatch wth valve pont effects," Internatonal Transactons on Electrcal Energy Systems, vol. 25, pp , [10]. M. R. Djalal, A. Imran, and I. Roband, "Optmal placement and tunng power system stablzer usng Partcpaton Factor and Imperalst Compettve Algorthm n 150 kv South of Sulawes system," n Intellgent Technology and Its Applcatons (ISITIA), 2015 Internatonal Semnar on, 2015, pp [11]. S. HUMENA, "OPTIMIZATION ECONOMIC POWER GENERATION USING MODIFIED IMPROVED PSO ALGORITHM METHODS," Journal of Theoretcal and Appled Informaton Technology, vol. 93, pp , [12]. M. R. Djalal, H. Nawr, H. Setad, and A. Imran, "An Approach Transent Stablty Analyss Usng Equvalent Impedance Modfed n 150 kv South of Sulawes System," Journal of Electrcal and Electroncs Engneerng UMSIDA, vol. 1, pp. 1-7, [13]. M. Y. Yunus, M. R. Djalal, and Marhatang, "Optmal Desgn Power System Stablzer Usng Frefly Algorthm n Interconnected 150 kv Sulselrabar System, Indonesa," Internatonal Revew of Electrcal Engneerng (IREE), vol. 12, pp , [14]. M. R. Djalal, M. Y. Yunus, H. Nawr, and A. Imran, "Optmal Desgn of Power System Stablzer In Bakaru Power Plant Usng Bat Algorthm," 2017, vol. 1, p. 6, DOI: / Page

8 Economc Dspatch In 150 KV Sulselrabar Electrcal System Usng Ant Colony Optmzaton [15]. M. R. Djalal, M. Y. Yunus, H. Nawr, and A. Imran, "Applcaton of Smart Bats Algorthm for Optmal Desgn of Power Stablzer System at Sengkang Power Plant," Internatonal Journal of Artfcal Intellgence Research, vol. 1, [16]. M. Hdayat, Y. S. Akl, I. Gunadn, and M. R. Djalal, "Short-Term Electrcty Demand Forecastng usng Fuzzy Logc-Flower Pollnaton Algorthm (FL-FPA)," presented at the The 2nd Internatonal Conference on Educaton, Scence, and Technology (ICEST) 2017, Four Ponts by Sheraton, [17]. I. Gunadn, Y. S. Akl, Srajuddn, and M. R. Djalal, "Applcaton Fuzzy Logc-Cuckoo Search Algorthm for Load Forecastng n 150 kv Sulselrabar Electrc Power System," presented at the The 2nd Internatonal Conference on Educaton, Scence, and Technology (ICEST) 2017, Four Ponts by Sheraton, [18]. M. R. Djalal and F. Fasal, "Intellgent Fuzzy Logc - Cuckoo Search Algorthm Method for Short-Term Electrc Load Forecastng n 150 kv Sulselrabar System," LontarKomputer :JurnalIlmahTeknologInformas, pp %@ , [19]. M. R. Djalal and Fasal, "Intellgent Fuzzy Logc-Cuckoo Search Algorthm Method for Short-Term Electrc Load Forecastng n 150 kv Sulselrabar System," LontarKomputer: JurnalIlmahTeknologInformas, vol. 8, pp , [20]. M. R. Djalal, H. Setad, D. Lastomo, and M. Y. Yunus, "Modal Analyss and Stablty Enhancement of 150 kv Sulselrabar Electrcal System usng PSS and RFB based on Cuckoo Search Algorthm," Internatonal Journal on Electrcal Engneerng and Informatcs, vol. 9, pp , [21]. H. Saadat, Power System Analyss: McGraw-Hll, [22]. A. J. Wood, Power Generaton, Operaton, and Control Thrd Edton: John Wley, IOSR Journal of Electrcal and Electroncs Engneerng (IOSR-JEEE) s UGC approved Journal wth Sl. No. 4198, Journal no Tasrf "Economc Dspatch n 150 KV Sulselrabar Electrcal System Usng Ant Colony Optmzaton." IOSR Journal of Electrcal and Electroncs Engneerng (IOSR-JEEE) 13.3 (2018): DOI: / Page