Optimal Feeder Reconfiguration of Distribution System with Distributed Generation Units using HC-ACO

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1 Internatonal Journal on Electrcal Engneerng and Informatcs Volume 6, Number 1, March 014 Optmal Feeder Reconfguraton of Dstrbuton System wth Dstrbuted Generaton Unts usng HC-ACO Manas Ranjan Nayak Deptt. of Electrcal Engg., I.T.E.R., Sksha o Anusandhan Unversty, Bhubaneswar, , Odsha, Inda, manasnk7@gmal.com Abstract: The objectve of optmal feeder reconfguraton of radal dstrbuton system problem s to obtan the best set of branches to be opened, one each from each loop, such that the resultng radal dstrbuton system has the desred performance. Ths paper presents a feeder reconfguraton problem n the presence of dstrbuted generators to mnmze the system power loss whle satsfyng operatng constrants usng Hyper Cube-Ant Colony Optmzaton (HC-ACO) algorthm. Loss Senstvty analyss s used to dentfy optmal locaton for nstallaton of DG unts. Smulatons are conducted on 33 bus radal dstrbuton system at four dfferent cases to verfy the effcacy of the proposed method wth other recent publshed approaches reported n the lterature. The result shows that the method proposed s fast and effectve. Keyword: Dstrbuton system, radal dstrbuton system, Dstrbuton feeder reconfguraton, Dstrbuted generator, Ant Colony Optmzaton (ACO) algorthm, Real power loss. Nomenclature P Real power flowng out of bus Q Reactve power flowng out of bus P j Real power flowng out of bus j Q j Reactve power flowng out of bus j P L Real power load connected at bus Q L Reactve power load connected at bus PLj Real power load connected at bus j Q Lj Reactve power load connected at bus j R j Resstance of lne secton between and j X j Reactance of lne secton between and j P Lj, eff Effectve real power load connected at bus j Q Lj, eff Effectve reactve power load connected at bus j Current n lne secton between buses and j I j I j max Maxmum current n lne secton between buses and j V nom, Nomnal voltage of bus V Voltage of buse Voltage of buse j V j V mn Mnmum value of bus voltage magntude Receved: December 1 nd, 013. Accepted: February 4 th,

2 Manas Ranjan Nayak Maxmum value of bus voltage magntude P Loss(, j) Real power loss of the lne secton between buses and j P TLoss, Total real power loss n Total number of buses A Bus ncdence matrx PSUB Real power njecton of substaton Q SUB Reactve power njecton of substaton P DG Real power generaton of the DG connected at bus Q DG Reactve power generaton of the DG connected at bus mn P DG, Lower lmt of actve power generaton of the DG connected at bus max P DG, Upper lmt of actve power generaton of the DG connected at bus mn Q DG, Lower lmt of reactve power generaton of the DG connected at bus max Q DG, Upper lmt of reactve power generaton of the DG connected at bus p. f Power factor of the DG connected at bus V max DG 1. Introducton Electrcal power dstrbuton system conssts of groups of nterconnected radal crcuts. They have swtches to confgure the networks va swtchng operatons to transfer loads among the feeders. There are two types of swtches used n the dstrbuton system, sectonalzng swtches (normally closed swtches) and te swtches (normally open swtches), whose states determne the confguraton of network. The confguraton of the dstrbuton system s changed by openng sectonalzng swtches and closng te swtches so that the radal structure of the network s mantaned and all of the loads are supported and reduced power losses, mprove voltage profle, mprove power qualty, ncrease system securty, releve overload n the network [1]. However, due to dynamc nature of loads, total system load s more than ts generaton capacty that makes relevng of load on the feeders not possble and hence voltage profle of the system wll not be mproved to the requred level. In order to meet requred level of load demand, DG unts are ntegrated n dstrbuton network to mprove voltage profle, to provde relable and unnterrupted power supply and also to acheve economc benefts such as mnmum power loss, energy effcency and load levelng. Network reconfguraton and DG placement n dstrbuton networks are consdered ndependently. But, n the proposed method, network reconfguraton and then DG nstallaton are done for mproved loss mnmzaton and voltage profle. Snce network reconfguraton s a complex combnatoral, non-dfferentable constraned optmzaton problem, many algorthms are proposed n the past. Merln and Back [], frst proposed network reconfguraton problem and they used a branch-and- bound-type optmzaton technque. The drawback wth ths technque s the soluton proved to be very tme consumng as the possble system confguratons are, where lne sectons equpped wth swtches s. Based on the method of Merln and Back [], a heurstc algorthm has been suggested by Shrmohammad and Hong [3]. The drawback wth ths algorthm s smultaneous swtchng of the feeder reconfguraton s not consdered. A heurstc algorthm [4] was suggested, where a smple formula was developed to determne change n power loss due to a branch exchange. The dsadvantage of ths method s only one par of swtchng operatons s consdered at a tme and reconfguraton of network depends on the ntal swtch status. An algorthm [5] was presented based on the heurstc rules and fuzzy mult-objectve approach for optmzng network confguraton. The dsadvantage n ths s crtera for selectng membershp 108

3 Optmal Feeder Reconfguraton of Dstrbuton System wth Dstrbuted functons for objectves are not provded. A soluton usng a genetc algorthm (GA) [6] was presented to look for the mnmum loss confguraton n dstrbuton system. A refned genetc algorthm (RGA) [7] was presented to reduce losses n the dstrbuton system. In RGA, the conventonal crossover and mutaton schemes are refned by a competton mechansm. Harmony Search Algorthm (HSA) [8] was proposed to solve the network reconfguraton problem to get optmal swtchng combnatons smultaneously n the network to mnmze real power losses n the dstrbuton network. Many methods are proposed for the best placement and szes of DG unts whch s also a complex combnatoral optmzaton problem. An analytcal method [9] was ntroduced to determne optmal locaton to place a DG n dstrbuton system for power loss mnmzaton. A Lagrangan based approach to determne optmal locatons for placng DG n dstrbuton systems consderng economc lmts and stablty lmts was presented by Rosehart and Nowck [10]. A mult-objectve algorthm usng GA [11] was presented for sttng and szng of DG n dstrbuton system. Placement and penetraton level of the DGs under the SMD framework was dscussed by Agalgaonkar [1]. Ths paper s to propose a 33- bus radal feeder reconfguraton technology based on the Hyper-Cube (HC) Framework Ant Colony Optmzaton (ACO) algorthm wth DG to mnmze system real power loss and bus voltage devaton n the dstrbuton network wthout volatng operaton constrants and mantanng the radal structure. The HC-ACO algorthm s a useful evolutonary algorthm wth strong global search ablty. The characterstcs of the HC- ACO algorthm nclude postve feedback, dstrbuted computaton and a constructve greedy heurstcs. Postve feedback makes sure of a rapd search for a global soluton; dstrbuted computaton avods premature convergence, and constructve greedy heurstcs help fnd acceptable soluton as soon as possble. These propertes are counterbalanced by the fact that, for some applcatons, the HC-ACO can outperform other heurstcs. The rest of the paper s organzed as follows: In secton II, modellng of power flow n radal dstrbuton network s dscussed. Modellng of DG unts are gven n secton III. Senstvty analyss for DG nstallaton s gven n secton IV. In secton V, the problem formulaton s descrbed. The Ant Colony Optmzaton s brefed n secton VI. In secton VII, applcaton of Ant Colony Optmzaton n the Hyper-Cube (HC) Framework to solve problem s descrbed. The test system, numercal results and dscusson are presented n secton VIII and Secton IX, concludes ths paper.. Modelng of power flow usng backward and forward sweep method In ths paper, network topology based backward and forward sweep method [13] s used to fnd out the load flow soluton for balanced radal dstrbuton system. Conventonal NR and Gauss Sedel (GS) methods may become neffcent n the analyss of dstrbuton systems, due to the specal features of dstrbuton networks,.e. radal structure, hgh R/X rato and unbalanced loads, etc. These features make the dstrbuton systems power flow computaton dfferent and somewhat dffcult to analyze as compared to the transmsson systems. Varous methods are avalable to carry out the analyss of balanced and unbalanced radal dstrbuton systems and can be dvded nto two categores. The frst type of methods s utlzed by proper modfcaton of exstng methods such as NR and GS methods. On the other hand, the second group of methods s based on backward and forward sweep processes usng Krchhoff s laws. Due to ts low memory requrements, computatonal effcency and robust convergence characterstc, backward and forward sweep based algorthms have ganed the most popularty for dstrbuton systems load flow analyss. The voltage magntude and phase angle of the source should to be specfed. Also the complex values of load demands at each node along the feeder should be gven. Startng from the end of the feeder, the backward sweep calculates the lne secton currents and node voltages (by KCL and KVL) back to the source. The calculated voltage at the source s compared wth ts orgnal specfed value. If the error s beyond the lmt the forward sweep s performed to update the node voltages along the feeder. In such a case, the specfed source voltage and the lne secton currents already calculated n the 109

4 Manas Ranjan Nayak prevous backward sweep are used. The process keeps gong back and forth untl the voltage error at the source becomes wthn the lmt. The shunt admttance at any bus to ground s not consdered. It s assumed that the three-phase radal dstrbuton network s balanced and can be represented by ther equvalent sngle-lne dagram. Fgure 1 represents the electrcal equvalent of a typcal branch of a dstrbuton system. Fgure 1. Dstrbuton system wth DG nstallaton at any locaton. A. Backward Sweep By startng from the endng buses and movng backward to the slack bus (substaton bus), the power flow through each branch s expressed by the followng set of recursve equatons: ' ' P j + Q j P = P j + P Lj + R j (1) V j ' ' P j + Q j Q = Q j + Q Lj + X j () V j Where ' ' P + V = Vj ( Rj Pj+ Xj Qj) + ( Rj+ Xj) V ' P = j P + j P Lj and ' Q = j Q + Q j Lj ' ' j Qj j (3) B. Forward Sweep: By startng from the slack bus (substaton bus) and movng forward to endng bus, the actve and reactve power flows at the recevng end of branch ( P j and Q j )and the voltage magntude at the recevng end ( V j ) are expressed by the followng set of recursve equatons: P Q j j = = P Q P Q Lj Lj R X j j P P P j + Q V j = V ( R j P + X j Q ) + ( R j+ X j) V j V V + + Q Q j (4) (5) (6) 110

5 Optmal Feeder Reconfguraton of Dstrbuton System wth Dstrbuted Hence, f the V o, P o, Q o at the frst bus of the network are known, then the same quanttes at the other nodes can be calculated by applyng the above branch equatons. By applyng the backward and forward update methods, we can get a power flow soluton. The real power loss of the lne secton connectng between buses and j s calculated as P Loss P + Q (, j) = R j (7) V The total real power loss of the all lnes sectons n n bus system ( PT, addng up the losses of all lne sectons of the feeder, whch s descrbed as n 1 T, Loss = P loss (, j) = 1 Loss ) s calculated by P (8) 3. Mathematcal model of Dstrbuted Generaton Unts A dstrbuted generaton (DG) unt can be modeled as ether a voltage-controlled bus (PV bus) or as a complex power njecton (PQ bus) n the dstrbuton system. If DGs have control over the voltage by regulatng the exctaton voltage (synchronous generator DGs) or f the control crcut of the converter s used to control P and V ndependently, then the DG unt may be modeled as a PV type. Other DGs, lke nducton generator based unts or converters used to control P and Q ndependently, are modeled as PQ types. The most commonly used DG model s the PQ model. In ths work, the PQ-DG unts are represented as a negatve PQ load model delverng actve and reactve power to a dstrbuton system. Ths gves flexblty n modelng varous types of DG. DG can be classfed nto four major types[14] based on ther termnal characterstcs n terms of actve and reactve power delverng / consumng capablty as follows: Type 1: DG capable of njectng real power ( P DG ) only (Photovoltac, 1 p. f DG = ) Type : DG capable of njectng both real power ( P ) and reactve power ( DG Q DG ) (Mcro Turbne, 0 < p. f DG < 1) Type 3: DG capable of njectng real power ( P DG ) but consumng reactve power ( ) QDG (Wnd Turbne, 0 < p. f DG < 1) Type 4: DG capable of njectng reactve power ( QDG ) only (Synchronous condenser, p. f DG = 0 ) The present studes were consdered wth Type 1 DGs only. By consderng the propertes of these resources and n order to modelng them n the mentoned optmzaton problem, the njected actve powers at bus are modeled as follows: Type 1: P = P P and Q = Q (9) DG L L 4. Senstvty analyss for DG nstallaton j R j + j X j P + jq Lj, eff Lj, eff 111

6 Manas Ranjan Nayak Consder a lne secton consstng an mpedance of R j + j X j and a load of connected P + jq between and j buses as gven above. Lj, eff Lj, eff The real power loss of the lne secton connectng between buses and j s gven by ( ) j P + Lj, eff Q R Lj, eff P loss = V j (10) The loss senstvty factor (LSF) can be defned wth the equaton P loss * P L j, eff * R j = P Lj, eff V j (11) Usng (11), LSFs are calculated from load flows and values are arranged n descendng order for all buses of the gven system. The LSFs decde the sequence n whch buses are to be consdered for DG unt nstallaton. The sze of DG unt at canddate bus s calculated usng HC-ACO. 5. Problem Formulaton The objectve functon s a constraned optmzaton problem to fnd an optmal arrangement of feeder for the dstrbuton system and DG placement n whch the value of functon f ( x ) s mnmzed. A. Objectve Functon The objectve functon f ( x) conssts of goals: reducng the real power losses and mprovng the voltage profle n a gven radal dstrbuton system whle satsfyng all constrants for a fxed number of DGs and specfc total capacty of the DGs. A.1. Mnmzaton of the real power losses ( f 1 ) : The real power loss of the lne secton connectng buses and j can be computed as P Loss (, j) = R j P + Q V The total real power losses of the all lnes sectons s descrbed as n 1 f 1 = P T, Loss = P Loss(, j) = 1 (1) (13) A.. Mnmzaton of Voltage devaton or Improvement the voltage profle ( f ) : The objectve functon for mnmzaton of voltage devaton s defned as f n V V, nom (14) = 1 = VD = 11

7 Optmal Feeder Reconfguraton of Dstrbuton System wth Dstrbuted B. System Constrants The objectve functon s subjected to the followng constrants: B.1. Power balance constrants n n SUB + DG, = L + T, Loss = 1 = 1 n SUB = L + T, Loss = 1 P P P P (15) Q Q Q (16) B.. Bus Voltage lmt mn max V V V (17) B.3. Thermal Lmts max I j I (18) j B.4. Radal structure of the network det (A) = 1 or -1 (radal system) (19) det (A) = 0 (not radal) (0) B.5. Power lmts of DG : (1) P mn DG, P DG, P max DG, 6. General descrpton of ant colony optmzaton algorthm Ant Colony Optmzaton (ACO) s a recently proposed metaheurstc approach for solvng hard combnatoral optmzaton problems. The nsprng source of ACO s the pheromone tral layng and followng behavor of real ants whch use pheromone as a communcaton medum. In analogy to the bologcal example, ACO s based on the ndrect communcaton of a colony of sample agents, called artfcal ants, medated by artfcal pheromone trals. The pheromone trals n ACO serve as dstrbuted, numercal nformaton whch the ants use to probablstcally construct solutons to the problem beng solved and whch the ants adapt durng the algorthm s executon to reflect ther search experence [15, 16 &17]. Artfcal ants used n ACO are stochastc soluton constructon procedures that probablstcally buld a soluton by teratvely addng soluton components to partal soluton by takng nto account () heurstc nformaton on the problem nstance beng solved and () artfcal pheromone trals whch change dynamcally at run tme to reflect the agents acqured search experence [15, 16 &17]. The concept of ACO s clear but the algorthm s not unque. The model of selecton of a proper algorthm depends on the applcaton. The proposed ACO algorthm that s ntroduced here s shown n the flow chart of fgure. The followng steps gve explanatons to the flow chart of fgure. 1) Close all the te and sectonalzng swtches n the network to construct meshed loops. The number of meshed loops equal the number of te swtches. ) Generate the number of artfcal ants arbtrary. 3) Intalze the parameters, heurstc parameter ( β ), pheromone parameter ( α ), evaporaton parameter ( ρ ) for local updatng rule, evaporaton factor ( μ ) for global updatng rule, and ntal pheromone values for each swtch. 113

8 Manas Ranjan Nayak 4) State transton rule: Ants select ther next state (swtch) accordng to ths rule gven by () S 1 f q q0 S k(, j) = () S otherwse α β S1 = argmax [ τ(, j)].[ η(, j) ] (3) Where S k(, j) s the state (swtch) that an t k chooses n ts next move ; k s the ant ; and j are the current and next state respectvely ; S 1 and S are random varables represent the state (swtch) that ant k selects accordng to transton state transton rule ; τ (, j) s the pheromone deposted by ants durng move; q s a random number unformly dstrbuted n [0,1] ; q 0 s a parameter between 0 and 1 (0 q 1) 0 accordng to equaton (5) ; η(, j) s the heurstc nformaton of the problem ;α s a parameter represents the mportance of pheromone ; β s a parameter represents the mportance of heurstc. S s selected accordng to a pseudo random rule or a pseudo random proportonal rule gven by (4) Start Read System Data Form the messed loops and make all the swtches closed Iteraton N=0 Solve load flow for the system and compute the objectve functon (13) to determne the ntal power loss. Generate the no of ants and ntalze ts parameters Select state (Swtch) accordng to the state transton rule () & (3) N=N+1 Estmate the objectve functon (13) for dfferent cases for each ant Update local pheromone usng update rule (6) Update global pheromone usng update rule (7) N < N max End Fgure. Flow chart of ACO algorthm 114

9 Optmal Feeder Reconfguraton of Dstrbuton System wth Dstrbuted S α β [ τ(, j)].[ η(, j) = α [ τ(, j)].[ η(, j) l N () k β f jε N c f 0 cycle n q = f 0 cycle 0 c n c f 0 cycle n max k () (4) (5) Where N K() the set of states s (swtches) that selected by ant k that s called tabu lst ; n 0 and n 1 are teraton frequency ; n max s maxmum teraton number; c o s a parameter ts value between 0 and 0.4; c 1 s a parameter ts value between 0.8 and 1. 5) Objectve functon calculaton: After ants fnsh from selectng the swtches (states), the objectve functon s calculated for dfferent cases of real power loss. 6) Local updatng pheromone rule: Whle constructng a soluton each ant modfes the pheromone by ths rule gven by (6). τ(, j) = (1 ρ). τ(, j) + ρτ. (6) 0 τ Where s the ntal value of pheromone; ρ s a heurstcally defned parameter. The 0 local updatng rule shuffles the search process. 7) Global updatng pheromone rule: When all ants complete ther tour (an teraton), ths rule s appled to the states (swtches) belongng to the best soluton. Ths rule provdes a great amount of pheromone to best soluton and s gven by (7) m τ(, j) = (1 μ). τ(, j) + μ Δτ(, j) (7) k= 1 Q f an t k selects the edge or state j Δ τ (, j) = L K 0 otherwse (, ) (8) Where Δτ (, j) s the change n the pheromone; μ s the pheromone evaporaton factor; Q s a constant between 1 and 10,000; L K s the best objectve functon solved by ant k ; m s the number of states. 8) Repeat step 4 to step 6 contnuously untl satsfyng the condton of abort teraton. 9) Up to abort teraton, the soluton of mnmum objectve functon n all local optmum soluton s global optmum soluton. 7. Applcaton of ACO n the Hyper-Cube (HC) framework to solve the problem The (HC) framework s a recently developed framework for the general ACO [18 & 19]. It s based on changng the pheromone update rules used n ACO algorthms so that the range of pheromone varaton s lmted to the nterval [0-1], thus provdng automatc scalng of the auxlary ftness functon used n the search process and resultng n a more robust and easer to 115

10 Manas Ranjan Nayak mplement verson of the ACO procedure. The dstrbuton system s represented as an undrected graph G (B, L) composed of set B of nodes and a set L of arcs ndcatng the buses and ther connectng branches (swtches) respectvely. Artfcal ants move through adjacent buses, selectng swtches that reman closed to mnmze the system power losses. The soluton terates over three steps: A. Intalzaton: The Soluton starts wth encodng parameters by defnng (). System parameters such as; set of supply substatons S; set of buses N B ; set of branches N R where each branch has possble states ether 0 for an opened te swtch or 1 for a closed sectonalzng swtch); load data P load, Q ; branch data load Rm, X m base confguraton of the system ( 0) C defned by the system s te swtches, ( 0) ntal power losses of the system f C by solvng the power flow for ( 0) C and evaluatng the ftness functon f. ( ) (). Algorthm parameters such as; number of artfcal ants n each teraton N; ntal pheromone quantty τ 0 assgned to each swtch; evaporaton factor of pheromone trals ρ ; the parameters α and β that determne the relatve mportance of the lne `s pheromone versus ts vsblty; a counter h for the number of teratons, a counter x that s updated at the end of the teraton wth no mprovement n the objectve functon; maxmum number of teratons H max, and maxmum number of teratons X max wth no mprovement n the objectve functon respectvely. The base confguraton s then set as an ntal reference confguraton and as the best ( 0) ( 0) confguraton found so far such thatcbest = Cbest = C. B. Ants Reconfguraton and Pheromone Updatng: In each teraton h, a reference confguraton s set as the best confguraton of the prevous ( h 1) ( h) teraton such that Cbest = Cref. N Ants are ntally located on N randomly chosen open th swtches and are sent n parallel n such a way that each ant n n the h teraton ntroduces a ( h) new radal confguraton C n by applyng the state transton rule. Once all ants fnsh ther tour, the confguraton correspondng to each ant s evaluated by computng the objectve functon ( h f ) ( C n ). The best confguraton of the h th ( h) teraton C best s dentfed whch s the confguraton corresponds to the mnmum evaluated objectve functon of all ants (mnmum power loss). The best confguraton of the h th teraton ( ) C h best ( ) f ( Cbest ) s compared to the best confguraton so far Cbest such that f ( h) f Cbest <, the overall best confguraton s ( h) updated such C best = C best. Fnally, the pheromone updatng rules are appled such that for all swtches that belong to the best confguraton, the pheromone values are updated usng (9). Otherwse, the pheromone s updated usng (30). τ ( h) ( h 1) = (1 ρτ ) + ρσ ( h) ( 1 ) τ = (1 ρτ ) h (9) (30) 116

11 Optmal Feeder Reconfguraton of Dstrbuton System wth Dstrbuted Where, ( h) τ s the new pheromone value after the h th teraton, ( h 1 ) τ s the old value of th pheromone after the ( h 1 ) teraton, ρ s arbtrarly chosen from the nterval [0-1] and σ s a heurstcally defned parameter whch was chosen to be equal ( h) f ( C best) / f C snce best ( h) ( ) best f C cannot be lower that ( best) ( ( )) f C the pheromone assgned to any swtch cannot fall outsde the range [0-1] so that the pheromone update mechansm s fully consstent wth the requrements of the (HC) framework. C. Termnaton Crtera: The soluton process contnues untl maxmum number of teratons s reached h = H max, or untl no mprovement of the objectve functon has been detected after specfed number of teratons x = X max. 8. Test system descrpton, smulaton results and analyss A. Test system descrpton: The test system s a hypothetcal three phase balanced 1.66KV 33-bus radal dstrbuton system. The data for the dfferent loops wthn the 33-bus radal dstrbuton system s gven n appendx Table A.II. The load data and lne data are gven n appendx Table 1. Fgure 3. The base confguraton of the 33- bus radal dstrbuton system The base confguraton of the system s shown n Fgure 3 can be defned by the system te swtches [8 1(33), 9 15(34),1 (35), 18 33(36), 5 9(37) ].The system consstng of one supply pont, 33 buses, 3 laterals, 37 branches, 5 loops or Te swtches (Swtches no.33-37) normally open swtches whch are shown by dotted lnes and 3 sectonalzng swtches (Swtches no. 1-3) normally closed swtches whch are ndcated by sold lnes. The total number of swtchable lnes for each of these loops s 6,4,6,5 and 5. The total real and reactve power for the whole system loads of the ntal confguraton are 3.715MW and.300mvar, respectvely. The base real power loss s 0.67 KW. B. Assumptons and constrants: (1). Power flow calculaton s performed usng S base = 100MVA and V base =1.66KV. (). Three small dstrbuted generators that operated at unty power factors.e. nject (3). only pure real power are nstalled n to the system n case 3 & 4.. (4). The bus at whch load s connected s consdered as the locaton for DG. (5). The source bus s not consdered as the locaton for DG placement. (6). The lmts of DG unt szes for nstallaton at system bus locatons are assumed to be (7). 0 to MW. (8). Voltage at the prmary bus of a substaton s 1.0 p.u. (9). The upper and lower lmts of voltage for each bus are 1.05p.u. and 0.9 p.u., (10). respectvely. (11). The maxmum allowable number of the parallel DG s one, n each bus. 117

12 Manas Ranjan Nayak (1). The load model whch s used n the smulatons s unform wth constant power. C. Smulaton Results and Dscusson: The proposed method has been mplemented by usng Mat-lab programs and run on a personal computer wth Pentum dual core processor havng 1.86GHZ speed and 1GB RAM. To valdate the effectveness of the proposed algorthm, the HC - ACO algorthm has been mplemented to 33-bus radal dstrbuton system and smulaton results are compared wth other technques as reported n the lterature for real power loss objectve functon havng four cases. Case-1: The system s wthout dstrbuted generators and feeder reconfguraton (Base case). Case-: The same as case-1 except that the feeder can be reconfgured by the avalable sectonalzng swtches and the te swtches. Case-3: The same as case-1 except that there are 3 numbers of DG unts nstalled who can provde only frm actve power to the system. Case-4: Reconfguraton and nstallng DG unts smultaneously n the base confguraton. In ths paper the man parameters used n HC-ACO are N = 10, α = 0.1, β = 0.9, ρ = 0.04, τ = 1, 0 H max = 100, and W max = 10. To fnd the mnmum ftness (objectve functon value), the HC-ACO s run for 10 ndependent runs. The magntude of bus voltage at each bus for dfferent cases s shown n Table I. The bus voltage devaton, mnmum bus voltage wth bus number, bus voltage mprovement and ncrement n mnmum bus voltage are gven n Table III & IV. The voltage profle of the system for dfferent cases are shown n Fgure 4, 5, 6 and 7. The mnmum bus voltage (MBV) of the ntal confguraton of the system for case-1 s that of bus 18 and s equal to p.u. In case-, most of the bus voltage are mproved after reconfguraton as shown n Fgure 5 such that the MBV value s that of bus 3 equals to p.u. achevng.781% mprovement than case-1(base case). In case-3, addng three number DG unts wth optmal placement and szng to the base confguraton of the system, all bus voltages are mproved as shown n Fgure 6 and the MBV value s mproved to p.u. achevng % mprovement than case-1(base case). After reconfguraton addng three number DG unts wth optmal placement and szng (case-4), most of the bus voltages are mproved and the MBV value s mproved by % wth respect to case-1(base case) as shown n Fgure 7. 1 Bus Voltage(p.u) Bus Number Fgure 4. Voltage profles at varous buses for case

13 Optmal Feeder Reconfguraton of Dstrbuton System wth Dstrbuted 1 Bus Voltage(p.u) Bus Number Fgure 5. Voltage profles at varous buses for case-. Table 1. Bus voltage magntudes of 33- Bus System Bus No. Case -1 Case - HSA[8] HC-ACO Case -3 Case

14 Manas Ranjan Nayak Table. Bus Voltage Angles of 33- Bus System Bus No. Case -1 Case - HSA[8] HC-ACO Case -3 Case

15 Optmal Feeder Reconfguraton of Dstrbuton System wth Dstrbuted 1 Bus Voltage(p.u) Bus Number Fgure 6. Voltage profles at varous buses for case-3. 1 Bus Voltage(p.u) Bus Number Fgure 7. Voltage profles at varous buses for case Bus Angle(Degree) Bus Number Fgure 8. Voltage angles at varous buses for case-1. 11

16 Manas Ranjan Nayak 0.0 Bus Angle(Degree) Bus Number Fgure 9. Voltage angles at varous buses for case-. Bus Angle(Degree) Bus Number Fgure 10. Voltage angles at varous buses for case Bus Angle(Degree) Bus Number Fgure 11. Voltage angles at varous buses for case-4. 1

17 Optmal Feeder Reconfguraton of Dstrbuton System wth Dstrbuted Table 3. Comparson of Results usng HC- ACO Item Case- 1 Case - Case- 3 Case-4 Real power loss (KW) Bus voltage devaton V mn (p.u.) / Bus No / / / 31 & / 18 Te swtches 33, 34, 35,3 6,37 7, 14,9,3,37 33,34,35,36,37 7,14,9,17, 37 Locaton of DG , 17, 3 33, 3, 31 Sze of DG (MW) , , , 0.178, Table 4. Comparson of Results n % wth respect to Case 1 usng HC- ACO Item Case - Case -3 Case -4 Real power loss reducton (%) Bus voltage mprovement (%) Increment n mnmum bus voltage (% ) No. of te swtches changed The value of bus voltage angles at each bus for dfferent cases s shown n Table II. The angle of the voltages at varous buses n the system for dfferent cases s shown n Fgure 8, 9, 10 and 11. The mprovement n angles of voltages n the system for dfferent cases ( Fgure 9, 10 and 11) s an ndcaton of relevng of overload on feeders of the system. From Fgure8, 9, 10 and 11, It s observed that the feeders n mddle of the system are relved of hgh load than those at ends. Objectve Functon(P loss n KW) Iteraton Fgure 1. Convergence characterstcs for case- 13

18 Manas Ranjan Nayak 97.8 Objectve Functon(P loss n KW) Iteraton Fgure 13. Convergence characterstcs for case-3 Objectve Functon(P loss n KW) teraton Fgure 14. Convergence characterstcs for case-4 The results obtaned through dfferent cases takng power loss nto account as objectve functon are shown n Table 3 and 4. As shown, reconfguraton of the system before the DG unts are added (case-) resulted n the fnal confguraton [7, 14, 9, 3, 37] wth total power loss of KW. Ths amount to 3.74% reducton n losses than case-1. In case-3, ncluson of DG unts n the base confguraton of the system reduced the total power losses of case-1 by 5.46%. Reconfguraton of the network wth the exstence of DG unts as n case-4 reduced power losses by 53.89% wth respect to base case (case-1).in Fgure 1, 13 and 14, the convergence characterstcs of the aforementoned evolutonary algorthm for cases-, 3 and 4 are depcted. From Table 3 and 4, t s observed that mprovement n power loss reducton and voltage profle for case-4 are hgher when compared to other cases (1, and 3). Ths ndcates that DG nstallaton after reconfguratons gves better results. Canddate bus locaton by usng senstvty analyss and sze of three numbers of DG unts for case 3 and 4 are gven n table 3. 14

19 Optmal Feeder Reconfguraton of Dstrbuton System wth Dstrbuted Now, the proposed HC-ACO s compared wth other methods such as HSA [8], GA [8], RGA [8]. The results are shown n Table V. As can be seen from ths table proposed method gves better results compared to other. Table 5. Comparson of Smulaton Results of 33-Bus System Method Item Case- Case-3 Case -4 HC- ACO HSA[8] GA[8] RGA[8] DG placement , 17, 3 33,3,31 DG Sze Open Swtches 7, 14, 9, 3, 37 33, 34, 35, 36, 37 7, 14, 9, 17, 37 Real Power loss(kw) % Real Power loss V mn (p.u.) DG placement ,17,33 3,31,30 DG Sze Open Swtches 7, 14, 9, 3, 37 33, 34, 35, 36, 37 7, 14, 9, 3, 37 Real Power loss (KW) % Real Power loss V mn (p.u.) DG placement DG Sze Open Swtches 33, 9, 34, 8, 36 33, 34, 35, 36, 37 33, 9, 34, 8, 36 Real Power loss(kw) % Real Power loss V mn (p.u.) DG placement DG Sze Open Swtches 7, 14, 9,, 37 33, 34, 35, 36, 37 7, 14,9, 3, 37 Real Power loss (KW) % Real Power loss V mn (p.u.) Concluson In ths paper, the HC-ACO algorthm s proposed to reduce dstrbuton system losses. The valdty of the results and effectveness of HC-ACO s nvestgated by a 33 buses, 1.66KV radal dstrbuton system. The merts of the HC-ACO s that t reached the optmum soluton n a fewer teratons than the HSA snce the HC-ACO s a constructve and greedy search approach that make use of postve feedback such as the gradent nformaton of the objectve functon as well as pheromone trals and heurstc nformaton that gude the search and lead to rapd dscovery of good solutons. That s why requres less practce to reach the optmum soluton whle HSA s a random search that does not requre any pror nformaton to generate a soluton vector and so needs a lot of practce to dentfy the soluton space and to reach the optmum soluton n a reasonable tme. The Convergence characterstcs of general ACO algorthm, the optmum soluton s reached at hgher teraton compared to only a fewer teratons for the HC-ACO algorthm.it s clear that mplementng ACO algorthm n the Hyper-Cube (HC) framework comes wth the beneft of scalng objectve functon value allowng rapd dscovery of good solutons and fast optmum convergence. The computatonal results of the 33-bus system show that the HC-ACO method s better than the HSA one. From the results of ths paper, t can be seen that DG has the mprovement effects on loss reducton 15

20 Manas Ranjan Nayak and can decrease the system bus voltage devaton. It can be observed that more loss reducton can be acheved by the HC-ACO comparng wth the other methods when reconfguraton of the feeder s done wth nstallng DG unts. Test results show that the proposed algorthm s very fast and gves optmal soluton. References [1] Baran, M.E. and Wu, F.F. Network reconfguraton n dstrbuton systems for loss reducton and load balancng, IEEE Trans. Power Delv., Vol. 4, No., pp ,1989. [] A.Merln and H. Back. Search for a mnmal-loss operatng spannng tree confguraton n an urban Power dstrbuton system, n Proc. 5 th Power System Computaton Conf. (PSCC), Cambrdge, U.K.pp.118, [3] D. Shrmohammad and H. W. Hong, Reconfguraton of electrc dstrbuton networks for resstve lne losses reducton, IEEE Trans. Power Del., vol. 4, no., pp ,1989. [4] S. Cvanlar, J. Granger, H. Yn, and S. Lee, Dstrbuton feeder reconfguraton for loss reducton, IEEE Trans. Power Del., vol. 3, no.3, pp , [5] D. Das, A fuzzy mult-objectve approach for network reconfguraton of dstrbuton systems, IEEE Trans. Power Del., vol. 1, no. 1, pp. 0 09, 006. [6] K. Nara, A. Shose, M. Ktagawoa, and T. Ishhara, Implementaton of genetc algorthm for dstrbuton systems loss mnmum reconfguraton, IEEE Trans. Power Syst., vol. 7, no. 3, pp , Aug.199. [7] J. Z. Zhu, Optmal reconfguraton of electrcal dstrbuton network usng the refned genetc algorthm, Elect. Power Syst. Res., vol. 6, pp. 37 4, 00. [8] R. Srnvasa Rao, K.Ravndra K.Satsh and S. V. L. Narasmham, Power Loss Mnmzaton n Dstrbuton System Usng Network Reconfguraton n the presence of Dstrbuted Generaton, IEEE Trans. Power Syst., vol. 8, no. 1, pp ,013. [9] C. Wang and M. H. Nehrr Analytcal approaches for optmal placement of dstrbuted generaton sources n power systems, IEEE Trans. Power Syst., vol. 19, no. 4, pp ,004. [10] W. Rosehart and E. Nowck, Optmal placement of dstrbuted generaton, n Proc. 14th Power Systems Computaton Conf., Sevllla, pp. 1 5, Secton 11, paper, 00. [11] G. Cell, E. Ghan, S. Mocc, and F. Plo, A mult-objectve evolutonary algorthm for the szng and the sttng of dstrbuted generaton, IEEE Trans. Power Syst., vol. 0, no., pp , 005. [1] P. Agalgaonkar, S. V. Kulkarn, S. A. Khaparde, and S. A. Soman, Placement and penetraton of dstrbuted generaton under standard market desgn, Int.J. Emerg. Elect. Power Syst., vol.1, no.1,004 [13] Haque, M.H.. Effcent load flow method for dstrbuton systems wth radal or mesh confguraton. IEE Proc. On Generaton, Transmsson and Dstrbuton. 1996, 143 (1): [14] Kumar, I.S., Kumar, N.P. A novel approach to dentfy optmal access pont and capacty of multple DGs n a small, medum and large scale radal dstrbuton systems, Int. J. Elect. Power Energy Syst., 013, 45, (1), pp [15] C. Blum, Ant colony optmzaton: ntroducton and recent trends, Physcs of Lfe Revew, ELSERVIER, pp , 005. [16] M. Dorgo and T.Stulzle, The ant colony optmzaton metaheurstcs: algorthms, applcatons and advances,in Handbook of Metaheurstcs, F.Glover and G. Kochenberger,EDS, Norwell, MA: Kluwer,,Internatonal Seres n Operaton Research and Management Scence, Vol.57,pp.51-85,000. [17] S. Alonso, O. Cordon, F.de Vana and F.Herrera, Integratng Evolutonary computaton components n Ant colony optmzaton Recent Developments n Bologcally Inspred Computng, Ideal Group Publshng, pp ,

21 Optmal Feeder Reconfguraton of Dstrbuton System wth Dstrbuted [18] E. Carpaneto and G. Chcco, Dstrbuton system mnmum loss reconfguraton n the Hyper-Cube Ant Colony Optmzaton framework, Electrc Power Systems Research, vol.78, pp , 008. [19] C. Blum and M. Dorgo, The hyper cube framework for ant colony optmzaton, IEEE Trans. System Man and Cybernatcs, vol.34, pp , 004. Lne No. From Bus To Bus Appendx Table A. I Data for 33-bus test system R(Ω) X(Ω) Recevng End Bus P Load (KW) Q Load (KVAR) 1 Man SS * * * * *

22 Manas Ranjan Nayak Loop Loop-1 Loop- Loop-3 Loop-4 Loop-5 Table A.III Loop detals for 33-bus radal system Lne Numbers Manas Ranjan Nayak was born n 197, nda. He receved hs B.E. degreee n Electrcal Engneerng from I.G.I.T.Sarang (Utkal Unversty, Inda) and M.E. degree n Electrcal Engneerng from U.C.E., Burla (Sambalpur Unversty, Inda) n 1994 and 1995 respectvely. For , he was wth Orssa Hydro Power Corporaton Ltd. (A Govt. Orssa PSU) as Asst. Manager (Electrcal) and snce 008 he has been wth Electrcal Engneerngg Deptt., ITER, Sksha O Anusandhan Unversty, Bhubaneswar, Odsha, Inda-75103nclude power system operaton and plannng, Dstrbuton Network, Dstrbuted Generaton, FACTS and Applcaton of Soft computng technques to power system optmzaton. Prof. Nayak has membershp n professonal socetes.e. IET ( ) and ISTE (LM-7107) and contnung as an Assocate Professor. Hs research nterests 18