Optimal Placement and Sizing of DGs in Radial ... - IEEE Xplore

2015 International Conference on Circuit, Power and Computing Technologies [ICCPCT]

Optimal Placement and Sizing of DGs in Radial Distribution System (RDS) using Bat Algorithm Snigdha Rani Behera1

Soumya Prakash Dash1

B. K. Panigrahi2

Electrical Engineering Department Indian Institute of Technology Delhi India



Abstract — In deregulated power system, the implementation of DGs becomes relevant due to environmental and economical benefits. The presented paper has solved the problem of optimization of both position and size of DG to be placed in the RDS for the objectives of real power loss reduction and bus voltage profile improvement through Bat algorithm. The effect of optimal DG placement on different operational parameters has been evaluated. The proposed approach has been implemented for IEEE 69 bus RDS with constant power load per hour and variation of loads on 24 hours basis. Hourly variable load case has been analyzed for energy (KWh) saving analysis. The simulation results are discussed and compared with the previous results of different algorithms. Index Terms - Distributed Generation (DG), Teng's power flow method, reactive power, Bat algorithm, hourly varying loads.

I. NOMENCLATURE

II. INTRODUCTION he requirement of reliable power supply has been increased with the growing demand of energy from both the consumers and utilities side. In Distribution system (DS), the power is supplied to the consumers through the passive network from the substation. Along with the increasing loads, the high resistance to reactance ratio of the distribution lines leads to the voltage drop at nodes and real power loss in the system. Therefore the existing power systems fail to supply the required load at rated voltage to the consumers.

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Distributed generators [1] are of small capacity generation supplying the load locally in DS. Implementation of DGs converts the DS from passive to active network [2]. The existing power system is affected economically and technically with the placement of DGs. It is necessary to predetermine the optimal position and sizing of DGs to enhance different operational conditions such as bus voltages, real power loss reduction, reliability and efficiency of the distribution network. The primary objective of the presented paper is to minimize real power loss with bus voltage improvement for RDS. The effects of DGs implementation on reactive power loss, voltage stability and maximum load ability of the system have been evaluated. Previously, researchers have determined the optimal position and sizing of DGs by using different techniques. The DG placement problem has been solved by the combination of GA and simulated annealing in [3] with very low penetration of DG capacity. Investment Cost minimization is the objective of [4, 5] with the application of GA and Tabu Search Method is used in [6] for power loss minimization. The heuristic approaches are used in [7] and [8]. DG allocation with the technique of fuzzy-GA based [13, 14] has been carried out for loss reduction considering DG size and number as constraints. The DG placement bases on selection of buses those are sensitive towards the voltagecollapse has been carried out in [15]. Optimal DGs penetration level based on annual energy loss has been analyzed in [16]. Optimal penetration level of DG based on minimum real power loss has been evaluated in [20]. 978-1-4799-7075-9/15/$31.00 ©2015 IEEE

2015 International Conference on Circuit, Power and Computing Technologies [ICCPCT] In this paper the optimization problem has been solved to find the optimal positioning and sizing of the DG(s) for IEEE 69 bus system for constant power load using a meta-heuristic algorithm known as Bat Algorithm.

inequality constrains in this work. The kVA loading of the DG is given by and this should not exceed the pre-specified value. 3

The Teng's power flow [17] method has been used for power flow analysis of RDS. There are various power flow methods used for distribution system. Some of the conventional methods are based on topology like transmission system or required new data formats and in some cases computation is complicated. In Teng's power flow method, the inputs are conventional bus - branch orientation data. Here the mathematical formulation has been taken the advantages of network topology and characteristics of DS. Through the formulation, two matrices BIBC (Bus Injection to Branch Current) and BCBV (Branch Current to Bus Voltage) are developed. Then simple matrix multiplications are used to find out the power flow solution. The implementation LU decomposition or forward-backward substitution of jacobian matrix is not required in this method. This method is very simple, efficient and robust as compare to other conventional methods. The different modeling approaches of DGs are described in [18]. The ‘constant power factor model' is used in this paper to include DGs in power flow calculations. These DGs are able to supply reactive power depending on their real powers and power factor. This paper has been organized in five sections. First section describes formation of mathematical modeling of the objective where as the applications of bat algorithm (BA) is explained in next section. The analysis of process is described in the third part. The simulation results, comparisons and analysis are represented in the end part. A special section discussed about the analysis with hourly varying load. III. MATHEMATICAL MODELING OF OBJECTIVE The mathematical representation of the DG optimization problem may vary. Here the objective function has been considered as the minimization of the power loss. A. Objective Function The objective is the minimization of the real power losses in a RDS and the improvement of bus voltage. The mathematical representation of the objective is given by (1). | |

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The maximum limit on real-power generation of the DG has been specified by the thermal consideration and minimum limit was due to instability of boiler. Similarly, the maximum limit on reactive power generation was due to over-heating of rotor and minimum limit was by stability limit of machine. 6 7

The total power supplied by the DGs should be less than that of substation (S/S). Along with DG, the voltage magnitude at each bus of the distribution system should be maintained within desired limits (8). Here the maximum voltage is set to be the S/S voltage irrespective of DGs placement. The S/S voltage is assumed to be 1p.u. | |

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C. MW Loss Index (MWLI) The MWLI indicates the improvement in real power loss reduction with the implementation of DGs. This index is calculated in percentage to represent the improvement clearly. /

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B. Inequality Constraints The constrains concerns with DGs are considered as

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D. MVar Loss Index (BVSI) The MVLI indicates the improvement in reactive power loss reduction with the implementation of DGs. This index is calculated in percentage to represent the improvement clearly. /

Subjected to

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E. Bus Voltage Stability Index (BVSI) Each branch of RDS is fed by a single node (sending node/bus) and fed to a single node (receiving node/bus). So the branch current depends on voltage of two concerned buses and the impedance of itself.

2015 International Conference on Circuit, Power and Computing Technologies [ICCPCT] The power at any bus is the function of the voltage at that bus and current injected at that bus [22]. Considering both concepts the BVSI is evaluated. This Index reflects the weakest bus of the system that is prone to instability. BVSI is calculated at each node. The sensitive bus will have minimum BVSI value. The BVSI→1 represents the more stable system.

Step1. Bat Population The number of DG’s to be placed in the distribution system is initialized according to the requirement within the limits of operation of the DGs. The population of Bats is initialized randomly. The pulse frequency (p) for each of the solutions can be defined as follows; 15

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F. Maximum Loadability Index (MLI) The excessive loading of the system leads to voltage collapse. The MLI represents the factor up to which the system withstands the increase in load demand without facing the voltage collapse. The effect of optimum DG on MLI is evaluated.

‘β’ has the random value in the range of [0, 1] which is generated uniformly. Initial pulse rates Ri and loudness Li are also defined [10, 11]. Step2. Velocities, positions and solutions The adjusted frequencies will generate the new solutions. The updations of velocities and positions of the bats have been followed as (16, 17). 16

This Index is concerned about the voltage collapse irrespective of voltage limits. Here the real and reactive load demand is considered in same proportion. MLI and bus voltage are inversely proportional to each other.

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x* = the current global_best solution at iteration time t. The 12

G. Modeling of DGs The DGs are modeled as ‘constant power factor (pf)’ model. Two cases are considered here. 13

Case1: DG with power factor 1 is restricted to supply real power to the RDS. Case2: With power factor 0.85[21], the DG is able to supply both active and reactive power to the RDS. IV. BAT ALGORITHM Bat Algorithm (BA) is a gradient-free meta-heuristic optimization algorithm [10]. The basic concept is the echolocation characteristics of micro-bats. Frequency tuning and the control of loudness and pulse emission rates are the basic features of BA. In different directions, Bats are flying randomly with their adjusted velocities and frequencies following the position of their food/prey. The steps of the algorithm can be summarized as follows.

velocities and positions have been updated for the next iteration. Step3. Local-solution by random walk A random number has been generated and compared with the pulse rate in that iteration. Accordingly, a solution has been chosen among the best solutions randomly and a local search has carried out around that solution (18). Here є is selected in random manner from [-1, 1] and Lt is the avr_loudness of all the bats in the present iteration. 18

Step4. The pulse rate and loudness The loudness value has been compared with generated random number and the fitness values. Accordingly the best solutions were accepted for the nest iteration. The pulse rate and loudness of the bats have been adjusted as follows. 19

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exp

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‘α’ and ‘γ’ are constant with the numerical value equals to 0.9 for the presented work.

2015 International Conference on Circuit, Power and Computing Technologies [ICCPCT] Step5. Best Solution The bats are ranked according to their fitness values and the best solution has been recorded per iteration. V. FLOWCHART AND DESCRIPTION

Step4: The minimum and maximum limit for DGs capacity in MW has been set. Step5: Then the load flow analysis has been done through the optimization algorithm (BA) with MW loss as optimization function. Step6: In optimization program, the positions of DGs are chosen randomly from the all buses except the substation. The random values for the size of DGs are chosen simultaneous along with position. Step7: In the each iteration a number of DGs positions and sizes are chosen and corresponding total MW loss of the system is calculated. Out of these loss values, the minimum is selected along with the concerned DGs position and sizes for that iteration. Step8: Next the minimum value of MW loss among the iterations has been selected as the best and the concerned DGs size and positions are selected to be optimum. Step9: This best (minimum) MW loss has been compared with the base case MW loss and percentage of reduction has been calculated. The corresponding voltage profile of the system has been compared with that of base-case. VII. RESULTS AND ANALYSIS FOR 1HOUR LOADING CONDITION

Fig. 1. Flow Chart for the selection of Optimal position and Sizing of DGs

VI. DESCRIPTION OF PROCEDURES FOR SELECTION OF OPTIMAL POSITION AND SIZING OF DGS The basic concept is to find out the optimal positioning and sizing of the DG/DGs which should results in the reduction of the real power loss of the system and improvement of system voltage profile at different buses. Step1: The Teng’s method has been used for load flow analysis of RDS. From the base case load flow, the actual MW loss and voltage profile of the system have been calculated. Step2: The numbers of DGs need to be installed has been selected. Step3: The power factor for the DG has chosen. This is required for the calculation of reactive power support given by the DGs for the system. Here the DGs are modeled as 'constant pf' model [27]. These DGs supplies both real and reactive power to the distribution system.

It has been observed that if the penetration level of DG is 100% of the total load of the system, i.e. 3.8MW, the optimal size of DG(s) is found to be 1.9MW [20] only for MW loss reduction with the of improvement of voltage profile up to1p.u. Therefore, here the work has been confined to penetration level of the DG to 50%. The DG penetration level has been kept independent of number of DG. The power factor (pf) is known as the ratio of real power (kW) to apparent power (kVA) delivered to an AC circuit. If the power-factor < 1 (low) then the current higher than desired is drawn from the generating unit. This results with more I2R losses and inefficiency. A. IEEE-69 Radial Distribution System The IEEE 69 Radial distribution system has the total real and reactive power loads of 3.8MW and 2.69 MVAR respectively. Here the power factors are considered to be 1 and 0.85[21] with base MVA as100MVA and base kV as 12.6kV. In this case DGs number has been varied from 1 to 3. The DGs size is restricted by the total real power demand of the system. The considered penetration of DG is 50% of MW load demand of the system.


TABLE I MW loss, DG positions and sizes for constant load with 50% DG penetration (pf 1and 0.85) in IEEE 69 Bus RDS

TABLE II BVSI and MLI with 50% DG penetration (pf 1) in IEEE 69 Bus (RDS)

TABLE III Comparisons of results for DGs Placement in IEEE 69 Bus RDS

The result and graphical analysis are represented in table I, II and Fig. 2 and 3 respectively. In IEEE 69 bus system, bus no 27, 61 and 65 are week buses of the system fig.3. With optimal DGs placement the voltage profile of all buses are improved up to 1p.u.

Fig. 3. Improved voltage profile in IEEE 69 Bus Radial Distribution System with DGs

Fig. 2. Variation of kW loss w.r.t. number of iterations in IEEE 69 Bus Radial Distribution System

B. Comparisons of results. The results obtained from the Bat algorithm have been compared with other published papers and summarized in Table III, IV and V. The presented results are compared with the results obtained by using optimization techniques PSO [8, 12], GA [8], SGA [12] and HPSO [21].

2015 International Confeerence on Circuit, Power and Computing Technologiess [ICCPCT] The comparisons have shown that the proposed p algorithm and optimization technique is better than thee previously results in the literatures. TABLE IV Comparisons of results for 2 DGs in IEEE 69 6 Bus RDS

Basically the load demandedd is maximum between 9AM to 7PM and minimum during nighht hours from 7PM to morning 9AM. The total MW loss off the system for 24 hours is 2.95MW. o DGs, the reduced real power With the optimal placement of loss of the given system is founnd to be 0.74MW per day. The real power loss is reduced by 64% with 1DG where as with 2DGs it is found to be 75% of thhe base case real power loss. Again, In this case also it is observed o that the effect of 2DGs and 3DGs placement is almost similar for real power reduction of the system.

TABLE V Comparisons of results for 3 DGs in IEEE E 69 Bus RDS

Fig. 5. Variable loads for 24 hoours of a day for IEEE 69 Bus RDS TABL LE VI MW loss, DG positions and sizes for fo 24Hrs load variation with 50% DG penetration in IE EEE 69 bus RDS

The 24 hours variable load case c has been carried out in this paper to analyze the energy savving per day through real power loss reduction in RDS. The 2DGs 2 placement with optimum location and size for the givenn system, the energy saving per day is found to be 75% of the prrevious loss of energy. IX. CON NCLUSION

Fig. 4. Comparison of votage profile for best b solution for IEEE 69 Bus RDS

VIII. MW LOSS OPTIMIZATION FOR HOURLY Y VARYING LOADS The 24 hours load pattern fig. 5 has been generated g for IEEE 69 bus RDS to analyze the effect of DGs on o the reduction of real power loss of the system. The load pattern is based on the variation of load demanded for different hourrs.

In the presented paper Bat algorithm a has been used for the selection of optimal position annd size of the DGs to be placed for the minimization of the reall power loss in RDS. The IEEE 69-bus RDS with constant pow wer load and hourly varying load of a day (24 hours) has been annalyzed. The maximum number of DGs used is 3 and the total generations g of DGs are restricted to 50% of maximum load of thee system. The maximum number of DG Gs that are found to be effective in the case of reduction of reaal power loss is 2. The reduced real power loss with 2DGs (66.26%) is more than that of with s The effect of number of 1DG (63.02%) in IEEE 69bus system. DGs is found to be more efffective in the case of 24 hour variable loads in IEEE 69 bus RDS. R Here the real power loss is

2015 International Conference on Circuit, Power and Computing Technologies [ICCPCT] reduced to 74.91% with 2DGs and 64.31% with 1DG. The analyzed results has shown that the optimized positioning and sizing of DGs able to reduce the real power loss more than 50% of actual loss of the system. The 2 DGs and 3DGs placement had almost similar effect on the test system fig 2 and 3. It has been observed that positioning of the DGs are biased by the load demanded at the buses i.e. DGs are placed near or at the bus with maximum demand of load. In some cases the selectivity of buses to place the DGs is based on the distance between the load and substation. Along with the minimization of real power loss, DGs are found to be useful in enhancing the bus voltage profile up to 1p.u. Here the DGs are modeled to supply both real and reactive power to the bus it is connected. This supply of reactive power helps in the improvement of voltage profile of the whole system fig 3. In the case of analysis for different load patron for different hours of a day, it is found that the real power loss is reduced by 75% of its base case value with 2DGs placed in the system (IEEE 69 BUS). This implies the 75% energy saving with this optimum DG is possible w.r.t the previous energy loss. The used Teng's Power Flow method is found to be simple, accurate and efficient for radial distribution system. The optimization using Bat algorithm is more efficient as compared to the other optimization technique such as GA, PSO for real power loss minimization problem. X. REFERENCES [1]

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improvement,” International Journal of Electrical Power & Energy Systems, Volume 33, Issue 2, February 2011, pp. 288-295. [10] Xin-She Yang, "A new meta-heuristic bat-inspired algorithm, in: Nature Inspired Cooperative Strategies for Optimization", (NISCSO 2010), Studies in Computational Intelligence, Vol. 284, pp. 65-74 (2010). [11] Xin-She Yang,"Bat algorithm for multi-objective optimization", Int. J. Bio-Inspired Computation, Vol. 3, No. 5, pp. 267-274 (2011). [12] Tan, W.S.; Hassan, M.Y.; Majid, M.S.; Rahman, H.A., "Allocation and sizing of DG using Cuckoo Search algorithm," Power and Energy (PECon), 2012 IEEE International Conference on , vol., no., pp.133,138, 2-5 Dec. 2012. [13] Kyu-Ho Kim; Yu-Jeong Lee; Sang-Bong Rhee; Sang-Kuen Lee; SeokKu You, "Dispersed generator placement using fuzzy-GA in distribution systems," Power Engineering Society Summer Meeting, 2002 IEEE , vol.3, no., pp.1148,1153 vol.3, 25-25 July 2002. [14] Vinothkumar, K., Selvan, M. P., “Fuzzy Embedded Genetic Algorithm Method for Distributed Generation Planning,” Electric Power Components and Systems, 2011, 39, pp. 346-366. [15] Hedayati, H.; Nabaviniaki, S. A; Akbarimajd, A, "A Method for Placement of DG Units in Distribution Networks," Power Delivery, IEEE Transactions on, vol.23, no.3, pp.1620, 1628, July 2008. [16] Quezada, V.H.M.; Abbad, J.R.; Román, T.G.S., "Assessment of energy distribution losses for increasing penetration of distributed generation," Power Systems, IEEE Transactions on, vol.21, no.2, pp.533, 540, May 2006. [17] Teng, J. –H., "A Direct Approach for Distribution System Load Flow Solutions", Power Delivery, IEEE Transactions on , vol.18, no.3, pp.882-887, July 2003. [18] Teng, J. –H., "Modeling distributed generations in three-phase distribution load flow," Generation, Transmission & Distribution, IET, vol.2, no.3, pp.330, 340, May 2008. [19] Gallego, L.A.; Carreno, E.; Padilha-Feltrin, A., "Distributed generation modelling for unbalanced three-phase power flow calculations in smart grids," Transmission and Distribution Conference and Exposition: Latin America (T&D-LA), 2010 IEEE/PES , vol., no., pp.323,328, 8-10 Nov. 2010. [20] Mohapatra, A.; Behera, S.; Nayak, S.; Panigrahi, B.K., "A study on DG and capacitor placement in radial distribution system," Power Electronics, Drives and Energy Systems (PEDES), 2012 IEEE International Conference on, vol., no., pp.1,6, 16-19 Dec. 2012. [21] Aman M.M., Jasmon G.B., Bakar A.H.A., Mokhlis H., “A new approach for optimum simultaneous multi-DG distributed generation Units placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm,” Energy, Volume 66, 1 March 2014, Pages 202-215. [22] Chakravorty M., Das D., “Voltage stability analysis of radial distribution networks,” International Journal of Electrical Power & Energy Systems, Volume 23, Issue 2, 1 February 2001, Pages 129-135.

XI. BIOGRAPHIES Snigdha Rani Behera has completed M.E.(2005) in Electrical Engineering with Power Systems as specialization from B.E.S.U., Shibpur, WB and B.E. (2001) from N.I.S.T, Berhampur, Odisha in Electrical and Electronics Engineering. She has more than 7 years of experiences in academic. Presently she is research scholar in Electrical Engg. Department, I.I.T. Delhi, India. Her area of research is Power System.


Soumya Prakash Dash has completed B.Tech in Electrical and Electronics Dept., I.I.T. Bhubaneswar in 2014. Presently, he is pursuing Ph.D in Electrical Engg. Department, I.I.T. Delhi. His research interests include signal processing, and soft and evolutionary computing.

Dr. B.K. Panigrahi is currently holding the position of Associate Professor in Electrical Engineering Department, I.I.T. Delhi. He has completed Ph.D from Sambalpur University. His areas of research are Power Quality, FACTS Devices, Power System Protection and AI Application to Power System.