Optimal DG Placement and Sizing in Distribution System for Loss and THD Reduction

Transcription

1 International Journal of Electronic and Electrical Engineering. ISSN Volume 5, Number 3 (2012), pp International Researc Publication House ttp:// Optimal Placement and Sizing in Distribution System for Loss and THD Reduction G.V.K Murty 1, S. Sivanagaraju 2 S. Satyanarayana 3 B. Hanumanta Rao 2 1 Department of EEE, SSNEC, Ongole, Andra Prades, India 2 Department of EEE, UCEK, JNTUK, Kakinada, Andra Prades, India 3 Department of EEE, VRSYRN College of engg. and tecnology, Cirala, Andra Prades, India 1 corresponding autor murty260@gmail.com Abstract Tis paper presents a metod for optimal sitting and sizing of Distributed Generation () in distribution systems for voltage profile improvement, loss reduction, and THD (Total Harmonic Distortion) reduction. Appropriate size and location of distributed generation plays a significant role in minimizing power losses in distribution systems. Among te benefits of distributed generation is te reduction in active power losses, wic can improve te system performance, reliability and power quality. Artificial Bee Colony (ABC) algoritm is proposed to determine te optimal -unit size and location by loss sensitivity index in order to minimize te real power loss, total armonic distortion (THD) and voltage sag index improvement. Simulation study is conducted on 33-bus radial test system to verify te efficacy of te proposed metod. Keywords: distributed generation, artificial bee colony algoritm, loss reduction, total armonic distortion, voltage sag, radial distribution network. 1. INTRODUCTION Te increasing use of armonic polluting loads causes armonic current and voltage distortion tat may propagate trougout te distribution system. It is well known tat bot voltage and current waveform distortion may adversely affect te equipment con-nected to te power system. In particular, armonic resonance penomena in power systems are known to cause severe distortion levels [1], and even failure of power system components. Several governments worldwide promote distributed power generation using renewable energy sources. Te market sare of distributed

2 228 G.V.K Murty et al power generation will steadily increase. As more and more small-scale distributed power generation applications will be installed, several interaction problems of tese inverters wit te distribution network may arise in te near future and may lead to excessive armonic current generation and even to an undesirable disconnection of te converters. Terefore, te reduction of armonic distortion is an important aspect of te power quality issue. Restricting te armonic voltage and current distortion to a minimum is a prerequisite for trouble-free exploitation of te electric power system and te connected loads. Te penetration of may impact te operation of a distribution network in bot beneficial and detrimental ways. Some of te positive impacts of are: voltage support, power loss reduction, support of ancillary services and improved reliability, wereas negative ones are protection coordination, dynamic stability and islanding. In order to maximize benefits and minimize problems, tecnical constraints concerning te interconnection of units and teir penetration levels are being adopted worldwide. Furtermore, te presence of in te deregulated market as raised new regulatory issues, concerning financial incentives, cost allocation metods, generation management tecniques, etc. Distribution networks are presently attracting increasing interest by all electrical market stakeolders. In te recent years in fact, due to economical, environmental and political reasons, te traditional power system, caracterized by centralized bulk power production and wide/long transmission networks, is increasingly supported also by energy-resources connected to te distribution grid, a tendency commonly denoted as distributed generation (). Determination of appropriate location and optimal size of wit respect to network configuration and load distribution in a radial distribution feeder is a main callenge in te canging regulatory and economic scenarios. Several researcers ave worked in tis area. s are placed at optimal locations to reduce losses. Some researcers presented some power flow algoritms to find te optimal size of at eac load bus [2, 3]. Wang and Nerir ave sown analytical approaces for optimal placement of in terms of loss [4]. Ciradeja as quantified te benefit of reduced line loss in radial distribution feeder wit concentrated load [5]. Furter, many researcers ave used evolutionary computational metods for finding te optimal placement. Mitulanantan as used GA for placement of to reduce te losses [6]. Celli and Giani ave used a multiobjective evolutionary algoritm for te sizing and placement of [7]. Nara et.al., ave used Tabu searc algoritm to find optimal placement of distributed generator [8]. T.N Sukla ave proposed genetic algoritm for optimal allocation of to reduce system losses. [9]. Tis paper is proposed to find te optimal location of by loss sensitivity index and size of by using an Artificial Bee Colony (ABC) metod to reduce te loss wit te consideration of armonic effects. 2. PROBLEM FORMULATION Te problem is formulated to determine te optimal size and location in a radial

3 Optimal Placement and Sizing in Distribution System 229 distribution system by minimize te real power loss, THD and voltage sag. To find te optimal location of by loss sensitivity index and size of te is using an Artificial Bee Colony (ABC) algoritm. 2.1 Loss minimization Te losses depend on te line resistance and currents are usually referred to as termal losses. Terefore loss of any distribution system can be calculated as n-1 2 P L = min R I k=1 k k (1) P L is total system loss, R k is resistance of k t line, I is absolute of k t line current. Te inequality constraints are tose associated wit te bus voltages and to be installed. Te bus voltage magnitudes are to be kept witin acceptable operating limits trougout te optimization process. Tat is 5% of te nominal voltage value. sys sys sys Vmax V i V min Te inequality constraint is te -unit size Smax S i S min Were S max = 80% of S load S min = 10% of S load 2.2 Harmonic power flow To solve te placement problem wit armonic distortion consideration, it is necessary to perform armonic power flow calculations under different armonic orders suc tat armonic rms voltages and THD of bus voltages can be obtained as follows Step 1: Calculate V for fundamental case from general load flow Step 2: Calculate te armonic load impedance Z L Were Z L R L jx L for t order armonic Step 3: calculate te armonic injection current I Li I Li = % of armonic injection I Li

4 230 G.V.K Murty et al Were i =1, 2 n Step 4: calculate te line impedance Z Z R jx Step 5: Calculate te Z bus matrix Step 6: Calculate te armonic voltages V V Z I bus Step 7: Calculate te Total Harmonic Distortion (THD) 100 n 2 THD % = V 1 i (2) V =2 i Were 1 V i =fundamental voltage V i = t order of armonic voltage 2.3 Voltage sag calculation Voltage sag is a decrease in RMS voltage at te power frequency for durations from 0.5 cycles to 1 minute, reported as te remaining voltage. Te duration of voltage sag is not a function of system topology, and is usually related to oter parameters. Te magnitudes of voltage dips are strongly dependent on te pat from wic te fault current is supplied and te equivalent impedances among te node under study, te source and te faulted points. As te result, only te magnitude of voltage sags as been considered in tis paper. In radial distribution networks te following simplified equation can be applied to calculate te sagged voltage at bus i (Point of Common Coupling (PCC)) caused by a fault at node j [10]: sag Z ij +Z t V ij = (3) Z s + Z ij + Zt sag Were V ij is te sagged voltage at PCC during te fault at node j, Z s is te source impedance at PCC, Z ij is te impedance between PCC and fault location j and Z t is te fault impedance. Tis equation is derived by assuming te pre fault voltages equal to 1 p.u. To study te worst case, Z t is considered zero. Te voltage sag index can be calculated as

5 Optimal Placement and Sizing in Distribution System 231 NPCC n 2 Sag Voltage sag index = V i=1 j=1 ij (4) Were NPCC is number of Point of Common Coupling. According to equation (4), iger value of voltage sag index means te bus voltages remains iger during te faults and ence sows te improvement in power quality. 3. ARTIFICIAL BEE COLONY ALGORITHM (ABC) Artificial Bee Colony (ABC) is one of te most recently defined algoritms by Dervis Karaboga in 2005, motivated by te intelligent beavior of oneybees. ABC as an optimization tool provides a population based searc procedure in wic individuals called food positions are modified by te artificial bees wit time and te bee s aim is to discover te places of food sources wit ig nectar amount and finally te one wit te igest nectar. In tis algoritm [11, 12], te colony of artificial bees consists of tree groups of bees: employed bees, onlookers and scouts. First alf of te colony consists of te employed artificial bees and te second alf includes te onlookers. For every food source, tere is only one employed bee. In oter words, te number of employed bees is equal to te number of food sources around te ive. Te employed bee wose food source as been abandoned becomes a scout [13]. Tus, ABC system combines local searc carried out by employed and onlooker bees, and global searc managed by onlookers and scouts, attempting to balance exploration and exploitation process [14]. Te ABC algoritm creates a randomly distributed initial population of solutions (f = 1,2,..,E b ), were f signifies te size of population and E b is te number of employed bees. Eac solution x f is a D-dimensional vector, were D is te number of parameters to be optimized. Te position of a food-source, in te ABC algoritm, represents a possible solution to te optimization problem, and te nectar amount of a food source corresponds to te quality (fitness value) of te associated solution. After initialization, te population of te positions (solutions) is subjected to repeated cycles of te searc processes for te employed, onlooker, and scout bees (cycle = 1, 2,, MCN), were MCN is te maximum cycle number of te searc process. Ten, an employed bee modifies te position (solution) in er memory depending on te local information (visual information) and tests te nectar amount (fitness value) of te new position (modified solution). If te nectar amount of te new one is iger tan tat of te previous one, te bee memorizes te new position and forgets te old one. Oterwise, se keeps te position of te previous one in er memory. After all employed bees ave completed te searc process, tey sare te nectar information of te food sources and teir position information wit te onlooker bees waiting in te dance area. An onlooker bee evaluates te nectar information taken from all employed bees and cooses a food source wit a probability related to its nectar amount. Te same procedure of position modification and selection criterion used by te employed bees is applied to onlooker bees. Te greedy-selection process is

6 232 G.V.K Murty et al suitable for unconstrained optimization problems. Te probability of selecting a foodsource p f by onlooker bees is calculated as follows: fitness P = f (5) E b fitness f =1 f Were fitness f is te fitness value of a solution f, and E b is te total number of food-source positions (solutions) or, in oter words, alf of te colony size. Clearly, resulting from using (5), a good food source (solution) will attract more onlooker bees tan a bad one. Subsequent to onlookers selecting teir preferred food-source, tey produce a neigbor food-source position f+1 to te selected one f, and compare te nectar amount (fitness value) of tat neigbor f+1 position wit te old position. Te same selection criterion used by te employed bees is applied to onlooker bees as well. Tis sequence is repeated until all onlookers are distributed. Furtermore, if a solution f does not improve for a specified number of times (limit), te employed bee associated wit tis solution abandons it, and se becomes a scout and searces for a new random food-source position. Once te new position is determined, anoter ABC algoritm (MCN) cycle starts. Te same procedures are repeated until te stopping criteria are met. In order to determine a neigboring food-source position (solution) to te old one in memory, te ABC algoritm alters one randomly cosen parameter and keeps te remaining parameters uncanged. In oter words, by adding to te current cosen parameter value te product of te uniform variant [-1, 1] and te difference between te cosen parameter value and oter random solution parameter value, te neigbor food-source position is created. Te following expression verifies tat: x new = x old +u x old - x fg fg fg mg (6) Were m f and bot are 1,2,...,E b. Te multiplier u is a random number g 1,2,...,D. In oter words, x fg is te g t parameter of a between [-1, 1] and solution x f tat was selected to be modified. Wen te food-source position as been abandoned, te employed bee associated wit it becomes a scout. Te scout produces a completely new food source position as follows: new x = min x +u max x - min x (7) fg fg fg fg Were (7) applies to all g parameters and u is a random number between [-1, 1]. If a parameter value produced using (6) and/or (7) exceeds its predetermined limit, te parameter can be set to an acceptable value. In tis paper, te value of te parameter exceeding its limit is forced to te nearest (discrete) boundary limit value associated wit it. Furtermore, te random multiplier number u is set to be between [0, 1] instead of [-1, 1].

7 Optimal Placement and Sizing in Distribution System 233 Tus, te ABC algoritm as te following control parameters: 1) te colony size CS, tat consists of employed bees E b plus onlooker bees E b ; 2) te limit value, wic is te number of trials for a food-source position (solution) to be abandoned; and 3) te maximum cycle number MCN. Te proposed ABC algoritm for finding size of at selected location to minimize te real power loss is as follows: Step-1: Initialize te food-source positions x f (solutions population), were f = 1,2,..,E b. Te x f solution form is as follows. Step-2: Calculate te nectar amount of te population by means of teir fitness values using 1 Fitness = 1+ powerloss (8) Step-3: Produce neigbor solutions for te employed bees by using (6) and evaluate tem as indicated by Step 2. Step-4: Apply te greedy selection process. Step-5: If all onlooker bees are distributed, go to Step 9. Oterwise, go to te next step. Step-6: Calculate te probability values P f for te solutions x f using (5) Step-7: Produce neigbor solutions for te selected onlooker bee, depending on te value, using (8) and evaluate tem as Step 2 indicates. Step-8: Follow Step 4. Step-9: Determine te abandoned solution for te scout bees, if it exists, and replace it wit a completely new solution using (7) and evaluate tem as indicated in Step 2. Step-10: Memorize te best solution attained so far. Step-11: If cycle = MCN, stop and print result. Oterwise follow Step RESULTS AND ANALYSIS To ceck te validity of te proposed ABC algoritm, 33 -bus radial distribution system was considered. Te Control parameters of ABC metod are colony size (Cs) is 30 and MCN is 20. Te radial distribution feeder as 33- buses wit rated voltage of 12.6 kv. Te line and load data of tis system are considered from [15]. Te original total power loss in te system is kw and te minimum voltage is p.u. Te typical armonic spectrum of tese nonlinear loads is provided as Table 1. Initially, a load flow was run for te case study in bot fundamental frequency and armonics frequencies witout installation of. Being te most sensitive node, bus 6 [9] is selected as te first candidate location for placement in te 33-bus system. Four points of common coupling ave been considered (Busses 18, 24 and 27). Te results are summarized in Table 2.

8 234 G.V.K Murty et al Table 1: Load composition in terms of armonic sources Bus number Harmonic injection current Order of injected armonic 9 91% % % 3 Results of voltage profile, armonic voltages and THD of 33-bus system witout and wit are given in Table 3. According to tis table, te minimum voltage occurs in bus 18 ( p.u) and te maximum THD takes places in bus 33 ( %) witout. te minimum voltage is improved in bus 18 ( p.u) and te maximum THD reduces in bus 33 ( %) wit. Table 2: Summary of test results Description Witout Wit location 6 size (kw) Minimum voltage (p.u) Total loss (kw) Loss reduction (%) Total THD (%) Voltage sag index Te results from Table 2, installing size of kw at bus 6 will reduce te total real power losses from kw to kw. Tis amounts to a reduction of 47.33% in total power loss. Te minimum voltage of te system is improved from p.u to p.u after placement. It can also be seen tat te total THD is decreased from % to % and te voltage sag index is improved from to Results of voltages, armonic voltages and THD of 33-bus system witout and wit are sown Tables 3. Branc losses of te system are sown Fig.1. Table 3: Results of voltage, armonic voltage and THD of 69-bus system witout Bus number Witout Wit Witout Wit Witout Wit Voltage (p.u) Angle (rad) Voltage (p.u) Angle (rad) Harmonic voltage (p.u) THD (%)

9 Optimal Placement and Sizing in Distribution System

10 236 G.V.K Murty et al Fig 1: Branc losses of te 33-bus system CONCLUSION In tis paper a armonic penetration study and voltage sag calculation as been performed for 33-bus radial distribution network in a power quality oriented. Artificial Bee Colony (ABC) algoritm is proposed to find te optimal size of for maximum loss reduction of a radial distribution system. By introducing in te system, voltage profile can be improved because can provide a portion of te real power to te load locally. Te results obtained by te proposed metod sow tat te presence of at appropriate location reduces real power loss, total armonic distortion (THD), voltage sag and improve te voltage profile. REFERENCES [1] J.H.R. Enslin, P.J.M. Heskes., Harmonic interaction between a large number of distributed power inverters and te distribution network, IEEE transaction on power electronics, Vol. 19, pp , [2] Rau NS and Wan YH. Optimum location of resources in distributed planning, IEEE Transaction on Power System, pp.14 20, [3] Kim JO, Nam SW, Park SK and Sing C. Dispersed generation planning using improved Hereford ranc algoritm, Electrical Power System Researc, 1998.

11 Optimal Placement and Sizing in Distribution System 237 [4] Wang C and Nerir M Hasem. Analytical approaces for optimal placement of distributed generation sources in power systems. IEEE Transaction on Power System, pp , [5] Ciradeja P. Benefit of distributed generation: a line loss reduction analysis, IEEE/PES transmission and distribution conference & exibition: Asia and Pacific, Cina Dalian; pp. 1 5, [6] Mitulanantan N, Oo Tan and Pu Le Van. Distributed generator placement in power distribution system using genetic algoritm to reduce losses, Tammasat Int J Science Tecnology, pp , [7] Celli G, Giani E, Mocci S and Pilo F. A multiobjective evolutionary algoritm for te sizing and sitting of distributed generation, IEEE Transaction Power System, pp , [8] Nara K, Hayasi Y, Ikeda K and Asizawa T. Application of tabu searc to optimal placement of distributed generators, IEEE PES Winter Meet, pp , [9] Sukla T.N, Sing S.P, Srinivasarao and Naik. K. B. Optimal sizing of distributed generation placed on radial distribution systems, Electric power components and systems, Vol. 38, pp , [10] Heine P, Letonen M, Voltage sag distributions caused by power system faults, IEEE Transactions on Power Systems, Vol.18, pp , [11] Dervis Karaboga and Bariye Basturk, Artificial Bee Colony (ABC) Optimization Algoritm for Solving Constrained Optimization Problems, Springer-Verlag, IFSA 2007, LNAI 4529, pp , [12] Karaboga, D. and Basturk, B., On te performance of artificial bee colony (ABC) algoritm, Elsevier Applied Soft Computing, Vol. 8, pp , [13] Hemamalini S and Sisaj P Simon., Economic load dispatc wit valve-point effect using artificial bee colony algoritm, xxxii national systems conference, NSC 2008, pp.17-19, [14] Faad S. Abu-Mouti and M. E. El-Hawary Optimal Distributed Generation Allocation and Sizing in Distribution Systems via artificial bee Colony algoritm, IEEE transactions on power delivery, Vol. 26, No. 4, [15] M.A Kasem, V. Ganapaty, G.B Jasmon, M.I Buari., A novel metod for loss minimization in distribution network, IEEE International Conference on Electric Utility Deregulation and Restructuring and Power Tecnologies, pp , 2000.

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