Available Transfer Capability (ATC) Under Deregulated Power Systems

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1 Volume-4, Issue-2, Aprl-2, IN : Internatonal Journal of Engneerng and Management Research Avalable at: Page Number: 3-8 Avalable Transfer Capablty (ATC) Under Deregulated Power ystems M. vasatyanarayana, L. Raasekhar oud 2, J. Amarnath 3,2.P.R.Engneerng College(A), Kurnool, A.P, INDIA Jawaharlal Nehru Technologcal Unversty, Kukatpally, Hyderabad, A.P, INDIA 3 ABTRACT The am of ths paper s to develop an approach to analyze the electrcty transfer capablty among dfferent electrcty markets usng repeated power flow technque. Instead of mnmzng the total cost n the conventonal problem, the transfer capablty between two markets or two electrcty supples or generaton areas s maxmzed. The optmzaton shall be subected to the operatonal constrants, however as the tme taken by these tradtonal optmzaton methods are qute sgnfcant, but these methods may not be sutable for onlne applcaton. To reduce the tme requred to compute transfer capabltes and also n order to take advantage of the superor speed of Artfcal Neural Network (ANN) over conventonal methods, the Radal Bass Functon Network (RBFN) based approach has been proposed to mplement the transfer capablty calculatons. The results have been smulated by usng MATLAB. And the results have been presented and compared wth the conventonal methods. It has been observed that the results obtaned are a good agreement wth the publshed work. Index Terms Contngency, Deregulaton, Neural networks, Radal Bass functon, Transfer capablty. I. INTRODUCTION Electrc utltes around the world are confronted wth restructurng, deregulaton and prvatzaton. In the envronment of open transmsson access [, 2], transmsson networks tend to be more heavly loaded and transmsson servce becomes one of the most crtcal elements. Power system transfer capablty ndcates how much nter area power transfers can be ncreased wthout compromsng system securty [3]. For both plannng and operaton of the bulk power market, accurate dentfcaton of ths capablty provdes vtal nformaton. It s mportant for planners to know the system bottlenecks and t s also mportant for system operators to see that transfers are not to exceed the calculated transfer capablty. Estmates of transfer capabltes must be updated regularly as to avod the combned effect of power transfers from causng an undue rsk of system overloads, equpment damage, or blackouts. Due to deregulaton, power transfers are ncreasng both n amount and n varety [4]. However, ths s necessary as the market for electrc power becomes more compettve. Improvng accuracy and effectveness of transfer capablty computatons for all areas of power systems would prove a very strong economc ncentve. There are a number of methods and algorthms [5]-[7] for computng total transfer capablty (TTC). The repeated power flow () method has been used n ths work to calculate the transfer capabltes because of ease of calculaton and mplementaton. To reduce the tme requred to compute transfer capabltes and also n order to take advantage of the superor speed of artfcal neural network (ANN) over conventonal methods, the radal bass functon network (RBFN) has been proposed for ths work. II. DEREULATED POWER YTEM Electrc Deregulaton s the process of changng laws and regulatons that control the electrc ndustry to allow for competton and to provde customers for ther choce of electrcty supplers. The consumers are free to purchase electrcty from any retaler or trader. Deregulaton of the electrc power ndustry offers the potental for mprovng economy, effcency of the producton and use of electrcty. The prmary promse of deregulaton n power sector s to promote greater economc effcency n electrcty generaton, transmsson, and dstrbuton. There are many numbers of methods and algorthms are avalable for computng TTC. Only three of them are practcally sutable for large realstc applcatons. These are: ) contnuaton power flow (CPF) method [3], 2) repeated power flow () method, and 3) securty constraned optmal power flow (COPF) method. Among these methods the method s used to calculate TTC n ths work for ease of mplementaton. A Crtcal Contngences Durng transfer capablty studes, many generaton and transmsson system contngences throughout the network are evaluated to determne what type of outage s most restrctve to the transfer beng analyzed. The types of contngences evaluated are consstent based on ndvdual 3

2 system, (power pool or sub-regonal) and Regonal plannng crtera or gudelne. The evaluaton process should nclude a varety of system operatng condtons that are varable and the most crtcal system contngences and ther lmtatons could also vary. III. MODELLIN OF THE YTEM Fg., shows a smple nterconnected power system can be dvded nto three knds of areas: recevng area, sendng areas and external areas. Area can be defned n an arbtrary fashon. It may be an ndvdual electrc system, power pool, control area, sub-regons, etc whch consst of a set of buses. The transfer between two areas s the sum of the real powers flowng on all the lnes whch drectly connect wth one area to the other area and then base case transfer (exstng transmsson commtments) s determned. o the transfer s then gradually ncreased startng at the base case transfer untl the frst securty volaton s encountered. Therefore the real power transfer at the frst securty volaton s the total transfer capablty. E Fg.. R recevng area; sendng area; E external area;. transfer path A smple nterconnected power system The mathematcal modelng can be expressed n the form of power flow equatons, operatonal constrants and obectve functon to be optmzed [8]. The equatons are as follows: ubect to Power Flow Equatons: n P = V V Y cos ( θ δ + δ ) = n = V = V Y and Operatonal constrants sn ( θ δ + δ ) R E () (2) P V g mn g mn mn P max V g g V P the obectve functon to be optmzed s P r = P km m R, k R g max max g max IV. RADIAL BIA FUNCTION NETWORK The RBFN s a specal class of mult layer feed forward neural networks. The RBF network model n ts most basc form conssts of three layers: the nput layer, hdden layer and output layer [9-]. The nodes wthn each layer are fully connected to the prevous layer. The nput varables are assgned to each node n the nput layer and are passed drectly to the hdden layer wthout weghts. The hdden nodes (unts) contan the radal bass functons, and are analogous to the sgmod functon commonly used n the Back Propagaton Feed forward Neural Network (BPFN). The output layer supples the response of the network to the actvaton patterns appled to the nput layer. The transformaton from the nput space to the hdden unt space s non-lnear, where as the transformaton from the hdden unt space to the output space s lnear. Durng tranng, all of the nput varables are fed to hdden layer drectly wthout any weght and only the weghts between hdden and output layers have to be modfed usng error sgnal. Thus, t requres less tranng tme n comparson to BPFN model. The most common transfer functon n an RBFN s the aussan actvaton functon. The functon s gven by the expresson ( x w = exp 2σ 2 h 2 Where x s the th varable of nput; w, the center of the th RBF unt for nput varable ; and σ 2 s the wdth of the RBF unt. ) (3) (4) V. TET YTEM AND IMULATION REULT A - bus test system s used n ths work to demonstrate the proposed methods for the transfer capablty computatons. The sngle lne dagram of the bus system s shown n Fg.2. 4

3 2 5 7 Area 28 transfer capablty between two areas, and the voltage magntudes and voltage angles n those areas. In ths paper, t s generated a number of nput-output patterns at dfferent loadng condtons usng MATLAB [2]. Fg 3 shows the convergence performance of the RBFN method Area 2 Area 3 Fg 3: Convergence of the RBFN to the performance goal of e -6 Fg. 2. Three Area - ystem The voltage magntude and the voltage angles at dfferent buses for the areas under consderaton also have been calculated by performng the proposed transfer capablty program. The procedure s repeated for dfferent load operatng condtons. TABLE 2 Transfer capablty from area 2 to area 3 Comparson of method and RBFN method Load condton Transfer capablty(mw) Method RBF Method TABLE Transfer Capabltes for a base operatng Condton Areas Transfer capablty (MW) 85% 92.5% 97.5% 5% % % % From area to area 2 From area to area 3 From area 2 to area 3 From area 2 to area From area 3 to area From area 3 to area A. RBFN Based TTC Computatons In ths paper, the radal bass functon network (RBFN) model s utlzed for calculatng the total transfer capabltes between the dfferent system areas. The two areas consdered here are area 2 and area 3. Transfer capablty from area 2 to area 3 has been computed usng the proposed approach. The nput-output patterns for tranng the proposed ANN are generated from the proposed repeated power flow algorthm. The nputs to the proposed RBFN are the real and reactve power demands of the system. The outputs are the TABLE 3 Area 2 Voltage Magntudes: Comparson of method and RBFN method (85%Base operatng condton) Voltage Magntudes n p.u

4 TABLE 4 Area 3 Voltage Magntudes: Comparson of method and RBFN method (85%Base operatng condton) 22 Voltage Magntudes n p.u TABLE 5 Area 2 Voltage Angles: Comparson of method and RBFN method (85%Base operatng condton) 22 Voltage angles (degrees) TABLE 6 Area 3 Voltage Angles Comparson of method and RBFN method (85%Base operatng condton) TABLE 7 Area 2 Voltage Angles: Comparson of method and RBFN method (%Base operatng condton) Voltage angles (degrees). Method RBF Method TABLE 8 Area 3 Voltage Angles: Comparson of method and RBFN method (%Base operatng condton) Voltage angles (degrees) Voltage angles (degrees) Transfer Capablty (MW) % 9 % % 2% % Base operatng condton R BF N Fg. 4: Transfer capablty from area 2 to area 3: Comparson of method and RBFN method 6

5 Voltage angle (degree) RBFN - Voltage -2 Angle (degree) RBF N -2-3 Number -6-7 Number Fg. 5: Area 2 Voltage Angles Comparson of method and RBFN method (85%Base operatng condton) Fg.8: Area 3 Voltage Angles Comparson of method and RBFN method (%Base operatng condton) 5 4 Voltage 3 Angle (degree) Numbers Fg.6: Area 3 Voltage Angles Comparson of method and RBFN method (85%Base operatng condton) Voltage - Angle (degree) Number Fg.7: Area 2 Voltage Angles Comparson of method and RBFN method (%Base operatng condton) RBFN RBFN VI. CONCLUION The results have been presented by usng Repeated Power Flow () and Radal Bass Functon Network (RBFN) algorthms for the computaton of transfer capabltes between system areas. It has been observed that the RBFN s fast for the calculaton of total transfer capabltes. The results obtaned wth RBFN based approach are almost matchng wth those obtaned wth the conventonal method. Further RBFN method wll gve a sgnfcant reducton n computatonal tme, thus makng t a potental canddate for onlne applcaton. The work proposed n ths paper can also be used to calculate avalable transfer capabltes (ATC) under the open access envronment, by ncorporatng the FACT devces so that t may be possble to ncrease the transfer capabltes. REFERENCE [] Wllam W. Hogan, and John F.Kennedy, Electrcty Market Restructurng: Reforms after Reforms, 2 th Annual Conference Center for Research n Regulated Industres, May. [2] T. K. Abdel-all, E.F.E-aadany, and M.M.A.alama (), Effect of New Deregulaton Polcy on Power ualty Montorng and Mtgaton Technques, IEEE Transmsson and Dstrbuton Conference (TD ), 28 October-2 November, Atlanta, UA. [3] Ian Dobson, cott reene, Raesh Raaraman, Chrstopher L. Demarco Fernando L. Alvarado, Mevludn lavc, Janfeng Zhang, Ray Zmmerman, Electrc Power Transfer Capablty: Concepts, Applcatons, enstvty and Uncertanty, PERC Publcaton -34 November. [4] M.haaban, Y.X.N, and F.F.Wu, Transfer Capablty Computatons n Deregulated Power ystems, Proc. of the 33 rd Hawa Internatonal Conference on ystem cences

6 [5].C.Eebe, J.Tong, J..Waght, J..Frame,X.Wang, W.F.Tnney, Avalable Transfer Capablty Calculatons, IEEE Trans. Power ystems, Vol., 4, Nov.98, pp.-. [6] P. W. auer, Techncal Challenges of Computng Avalable Transfer Capablty (ATC) n Electrc Power ystems, Proc. of th Hawa Internatonal Conference on ystem cences, Vol.5, 97. [7] A.J.Wood and B.F.Woolenberg, Power eneraton, Operaton, and Control, John Wley & sons, 2 nd edton, 96. [8]..Rao, Engneerng Optmzaton: Theory and Practce, John & sons, 3 rd edton, 96. [9] mon Haykn, NEURAL NETWORK, Prentce Hall Internatonal, 2 nd edton, 99. [] X.Luo, A.D.Patton, and C.ngh, Real Power transfer capablty calculatons usng mult-layer feedforward neural networks," IEEE Trans. Power ystems, Vol., 2, May 2, pp [] T.Jan, L.rvastava, and.n.ngh, Fast Voltage Contngency creenng Usng Radal Bass Neural Network, IEEE Trans. Power ystems, Vol.8, 4, November, pp [2] MATLAB Verson 5.3, Mathwork Corporaton, 97. Copyrght -. Vandana Publcatons. All Rghts Reserved. 8

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