Published on March 14, 2014
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 2, February (2014), pp. 44-50 © IAEME 44 PERFORMANCE IMPROVEMENT OF DISTRIBUTION SYSTEM WITH MULTI DISTRIBUTED GENERATION USING PARTICLE SWARM OPTIMIZATION P. Sobha Rani1 , Dr. A. Lakshmi Devi2 1 Assoc.Professor, Dept.E.E.E. , N.B.K.R.I.S.T, Vidyanagar, Andhrapradesh 2 Professor, Dept. E.E.E., S.V.University College of Engineering, Tirupathi ABSTRACT Distributed generators are beneficial in reducing losses effectively in distribution systems as compared to other methods of loss reduction. Sizing and location of DG sources places an important role in reducing losses in distribution network. In this paper both genetic algorithm and particle swarm optimization methods are presented for optimal location and sizing of DG sources. The methodology is based on exact loss formula. The objective of this methodology is to calculate size and to identify the corresponding optimum location for DG placement for minimizing the total power losses and to improve voltage profile in primary distribution system. The proposed methodology is tested on IEEE-33 distribution system and the results are compared. Keywords: Distributed generation (DG), exact loss formula, Genetic algorithm (GA), Particle swarm optimization (PSO), Power losses. 1. INTRODUCTION Distribution system provides a final link between the high voltage transmission and consumers. Power loss in a distribution system is high because of low voltage and hence high current. One of modern important techniques in electrical distribution systems to reduce losses is to accommodate small scale decentralized generating units known as distributed generation (DG). The DGs are small scale power generation technologies of low voltage type that provide electrical power at a site closer to consumption centers. Developments of DGs will bring new chances to traditional distribution systems. Appropriate size and location of DG play a significant role in minimizing power losses in distribution systems. Examples of DG are diesel generators, small hydro, wind electric systems, solar electric systems, batteries, photo voltaic and fuel systems. INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY (IJEET) ISSN 0976 – 6545(Print) ISSN 0976 – 6553(Online) Volume 5, Issue 2, February (2014), pp. 44-50 © IAEME: www.iaeme.com/ijeet.asp Journal Impact Factor (2014): 2.9312 (Calculated by GISI) www.jifactor.com IJEET © I A E M E
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 2, February (2014), pp. 44-50 © IAEME 45 In the present vast load growing electrical system usage of DG have more advantages like reduction of transmission and distribution cost, electricity price, saving of fuel, line loss reduction, better voltage profile, power quality improvement. DG can be classified into four major types based on their terminal characteristics in terms of real and reactive power delivering capability as follows: 1. DG capable of injecting active power only. 2. DG capable of injecting reactive power only. 3. DG capable of injecting both active and reactive power. 4. DG capable of injecting active power but consuming reactive power. Photo voltaic, micro turbines, fuel cells which are integrated to main grid with the help of converters/ inverters are good examples of type1. Synchronous compensators such as gas turbines are examples for type2. DG units that are based on synchronous machine fall in type 3. Type 4 is mainly induction generators that are used in wind farms. This paper is organized as follows: Methodology is explained in section II, Problem formulation is discussed in section III, Simulation results are shown in section IV. 2. METHODOLOGY Technical advances and availability of renewable energy sources have resulted in a constantly increasing penetration of DG integrated with distribution networks. For the connection of new DG installations to the networks a variety of factors have to be taken into account to ensure that the DG doesn’t adversely affect the operation and power quality of networks. In a large distribution system network with high power loss, it is difficult to select a particular bus from many buses so as to place a DG unit for loss reduction. Power losses are present at every bus and identification of bus with highest Power loss is important because losses at that bus includes majority of total losses in the system. This can be partially accomplished by DG unit placement in the network. If DG size exceeds certain value of limit, power loss at that bus becomes negative. This situation must be avoided. The beneficial effects of DG mainly depend on its location and size. Selection of optimal location and size of DG is a necessary process to maintain reliability of existing system effectively before it is connected to grid. There are many approaches to determine the optimum sizing and sitting of DG units in distribution systems considering overall system efficiency, system reliability, voltage profile, load variation, network losses. In some research the optimum location and size of a single DG unit is determined and in some others optimum location and sizes of multiple DG units are determined. In this Paper exact loss formula is used to determine the size and location of Type-1 and Type-2 DG. 3. PROBLEM FORMULATION The objective of problem is to find the location of DGs and its size for type-1 and type-2 DG to minimize the real power losses and to improve the voltage profile. Power loss in the system can be calculated by equation (1), given the system operating condition, ∑== −++= n ji jijiijjijiijL QPPQQQPPP 1,1 )]()([ βα (1) Where )( ji ji ij ij Cos VV r δδα −= and )( ji ji ij ij Sin VV r δδβ −=
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 2, February (2014), pp. 44-50 © IAEME 46 The objective is to minimize PL subject to power balance constraints as per equation(2) Σ PDGi = Σ PDi + PL (2) Voltage constraints: Vimin ≤ Vi ≤ Vimax Where PL is real power loss in the system, PDGi is real power generation DG at bus i, PDi is power delivered at bus i. Type-1 DG For type-1 DG, power factor is unity. The optimal size of DG at each bus i for minimizing losses is given by equation (3). ∑≠= −+ −= n ijij ijjijjiii ii DiDGi QPQPP , )]([ 1 βαβ α (3) Type-2 DG For type-2 DG, the optimal size of DG at each bus i for minimizing losses is given by equation (4) ∑≠= +− += n ijij ijjijjiii ii DiDGi PQPQQ , )]([ 1 βαβ α (4) The optimal sizes at various locations have been calculated for different types of DG and the losses are calculated with optimal sizes for each case. The case with minimum losses is selected as optimal location for each type DG. This paper uses GA and PSO for solving problems of optimal sitting and sizing of DG. 3.1 Genetic algorithm Genetic algorithms (GA) are a part of evolutionary computing which is a rapidly growing area of artificial intelligence. GA begins with a set of solutions (represented by chromosomes) called population. Solution from one population are taken and used to form a new population. This is motivated by a hope that new population will be better than old one. Solutions that are then selected to form new solutions (offspring) according to their fitness- the more suitable they are the more chances to reproduce. This is repeated until some condition is satisfied. Simplicity of operation and power of effect are advantages of GA approach. Algorithm 1. Read the system data, perform load flow, and generate initial population size. 2. Start iteration count iter=1 3. Determine the sizes of DG units at each candidate node by decoding the population. 4. Place the DG unit at a candidate bus and run the load flow for each string of population and find the losses. With the obtained losses, calculate the fitness function. Fitness=1/total loss. 5. Arrange the elements of the fitness function in the descending order and hence find the maximum fit and average fit. 6. Find the error using Error=(maxfit) – (average fit) If this error is less than epsilon, go to step 9.
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 2, February (2014), pp. 44-50 © IAEME 47 7. Carry out cross over and mutation on the off springs and generate new population. Increment the iteration count. 8. If (iter<=itermax) go to step 3. 9. If the problem is converged in itermax iterations stop. 10. Print the DG unit rating and power losses after compensation. 3.2 Particle swarm optimization Particle swarm optimization is an algorithm capable of optimizing a nonlinear and multi dimensional problem which gives good solutions. The basic concept of algorithm is to create a swarm of particles which move in the space around them (the problem space) searching for their goal, the place which best suits their needs is given by a fitness function. Each particle keeps track of its coordinates in the solution space which are associated with best solution (fitness) that has achieved so far by that particle. This value is called personal best (pbest). Another best value is the best value obtained so far by any particle in the neighborhood of that particle. This value is called gbest. The basic concept of PSO lies in accelerating each particle toward its pbest and gbest locations with a random weighted acceleration. The modification of the particle’s position can be mathematically modeled according the following equation: Vi k +1 = wVi k +c1 rand1(…) x (pbesti-si k ) + c2 rand2(…) x (gbest-si k ) (5) where, vi k : velocity of agent i at iteration k, w: weighting function, cj: weighting factor, rand: uniformly distributed random number between 0 and 1, si k : current position of agent i at iteration k, pbesti: pbest of agent i, gbest: gbest of the group. The following weighting function is usually utilized in (6) w = wMax-[(wMax-wMin) x iter]/maxIter (6) where wMax = initial weight, wMin = final weight, maxIter = maximum iteration number, iter = current iteration number. si k+1 = si k + Vi k+1 (7) Algorithm 1. Read the system data, perform load flow 2. Randomly generate an initial population of particles with random positions and velocities on dimensions in solution space. 3. Initialize the swarm from the solution space. 4. Evaluate fitness of individual particles. 5. Modify gbest, pbest and velocity. 6. Move each particle to a new position. 7. Go to step 3, and repeat until convergence or a stopping condition is satisfied.
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 2, February (2014), pp. 44-50 © IAEME 48 4. SIMULATION The proposed algorithm is tested on IEEE-33 bus system. The original total real power loss and reactive power loss in the system are found to be 301.696 kW and 220.6 kvar respectively by conducting Backward-Forward load flow method. For GA parameters population size=100, crossover probability=0.7; the maximum number of DG is 3 for each type. One type-1 DG can reduce total real and reactive power loss by23.77 and 25.44 respectively. For three type-1 DGs they can further reduce the loss by 66.68 compared to 29.65 in type-2 DG. The results are shown following tables. Table 1. Summary of results for type-1 DG Number of DG Bus Number TPL(kw) DG size (kw) TQL(kvar) Voltage(p.u) Without DG With DG % loss reduction Without DG With DG %loss reduction Without DG With DG % voltage increment 1 DG G A 9 301.69 251.63 19.89 288.926 220.6 182.15 21.11 0.8866 0.9016 1.69 P S O 8 301.69 243.74 23.77 383.442 220.6 175.86 25.44 0.8971 0.9127 1.74 2DG G A 8 301.69 200.952 50.14 383.424 220.6 143.08 54.18 0.8971 0.9275 3.39 8 383.424 P S O 8 301.69 200.85 50.20 383.98 220.6 143.0 54.26 0.8971 0.9275 3.39 8 383.98 3DG G A 9 301.696 181.69 66.67 288.08 220.6 127.98 72.37 0.8866 0.9173 3.46 15 146.518 0.8613 0.8986 4.33 32 333.72 0.8868 0.9166 3.36 P S O 8 301.696 181.009 66.68 384.13 220.6 127.9 72.47 0.8971 0.9329 3.99 8 384.13 0.8971 0.9329 3.99 17 130.837 0.8565 0.9030 5.42 Table 2: Summary of results for type-2 DG Number of DG Bus Number TPL(kw) DG size (kvar) TQL(kvar) Voltage(p.u) Without DG With DG % loss reduction Without DG With DG %loss reduction Without DG With DG % voltage increment 1 DG G A 31 301.696 263.74 14.39 339.274 220.6 191.88 14.96 0.8882 0.9029 1.65 PS O 31 301.696 263.75 14.38 339.074 220.6 191.89 14.96 0.8882 0.9029 1.65 2 DG G A 27 301.696 247.43 21.93 350 220.6 182.03 21.18 0.9284 0.9378 1.012 32 279.471 0.8868 0.9076 2.345 PS O 8 301.696 246.19 22.54 206.013 220.6 178.65 23.48 0.8971 0.9107 1.49 31 339.076 0.8882 0.9070 2.116 3 DG G A 9 301.696 232.71 29.64 179.038 220.6 170.6 29.31 0.8866 0.9043 1.996 27 349.937 0.9284 0.9403 1.282 32 279.469 0.8868 0.9082 2.41 PS O 27 301.696 232.7 29.65 350 220.6 170.58 29.32 0.9284 0.9409 1.346 30 235.76 0.8966 0.9184 2.43 32 279.47 0.8868 0.9122 2.86
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 2, February (2014), pp. 44-50 © IAEME 49 Simulation results are shown in the following figures. Fig 1: Bus voltages for one Type-1 DG Fig 2: Line losses for one Type-1 DG Fig 3: Bus voltages for three Type-2 DGS 0 5 10 15 20 25 30 35 0.85 0.9 0.95 1 BUS NUMBER Voltageinp.u. without DG with DG GA with DG PSO 0 5 10 15 20 25 30 35 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Branch Number LineLosses without DG with DG GA with DG PSO 0 5 10 15 20 25 30 35 0.85 0.9 0.95 1 Bus Number Voltageinp.u without DG with DG GA with DG PSO
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 5, Issue 2, February (2014), pp. 44-50 © IAEME 50 Fig 4: Line losses for three Type-2 DGs 5. CONCLUSION In this paper both genetic algorithm and particle swarm optimization methods are used for optimal placement of multi DG sources. Exact loss formula is used to determine optimal size and the location for both type-1 and type-2 DGs. The two types of DGs effectively reduced the power loss and voltage profile is also improved. REFERENCES . M.H.Moradi, M.Abedini, A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution system, Electric power energy systems 34 (2012) 66-74. . P.Sobha Rani, Dr.A.Lakshmi Devi, Sizing and placement of multi DG using exact loss formula,, International journal of advanced research in electrical electronics and instrumentation engineering, vol.2, issue 11, Nov 2013. . Duong quoc Hung, Nadarajah Mithulananthan, Multiple distributed generations in primary distribution networks for loss reduction, IEEE transactions on industrial electronics, vol.60, No.4, April 13. . W. El-hattam, M.M.A. Salma, DG technologies definition and benefits, Electrical power system research, vol.71, pp 119-128, 2004. . A.M.El-Zonkoly, optimal placement of multi DG units including different load models using particle swarm optimization, Swarm and evolutionary computation, 1(2011) 50-59. . D. Zhu, R.P.Broad water, K.Tam, R. Seguin, H. Asgeirsson, Impact of DG placement on reliability and efficiency with time varying loads, IEEE transactions on power systems 21(1) 2006,419-427. . T.Gozel, M.H.Hocaoglu, An analytical method for the sizing and sitting of distributed generators in radial systems, International journal of electric power system research 79(2009) 912-918. . Dr.T.Ananthapadmanabha, Maruthi Prasanna.H.A., Veeresha.A.G. and Likith Kumar. M. V, “A New Simplified Approach for Optimum Allocation of a Distributed Generation Unit in the Distribution Network for Voltage Improvement and Loss Minimization”, International Journal of Electrical Engineering & Technology (IJEET), Volume 4, Issue 2, 2013, pp. 165 - 178, ISSN Print : 0976-6545, ISSN Online: 0976-6553. 0 5 10 15 20 25 30 35 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Branch Number Lossinp.u without DG with DG GA with DG PSO
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