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  • 1. Planning of Renewable DGs for Distribution Network Considering Load Model: A Multi-Objective Approach Tanushree Bhattacharjee Department of Electrical Engineering, Bengal Engineering and Science University, Shibpur, Howrah, India Partha Kayal Department of Electrical Engineering, Future Institute of Engineering and Management, Kolkata, India Chandan Kumar Chanda Department of Electrical Engineering, Bengal Engineering and Science University, Shibpur, Howrah, India 12/11/2013 1/25
  • 2. Introduction  Distributed generation(DG)promises to supply eco-friendly, reliable and cost effective electricity to the customer.  It includes renewable technologies (photovoltaic, biomass and wind power) and high efficiency non-renewable power (fuel cell, internal combustion engine, gas turbine and micro turbine).  MNRE report, Govt. of India has set a target to produce 9,000 MW wind, 1,780 MW biomass and 50 MW solar power with grid interaction in the 11th plan (2011-2017).  Indiscriminant application of individual DG may cause higher distribution power losses and poor voltage stability of the network.  So, appropriate location, type and size selection of renewable DGs in the distribution network is a very crucial aspect of energy system planning. This paper presents a multi-objective particle swarm optimization (MOPSO) based approach to allocate renewable DGs optimally in a 28-bus radial distribution network employing trade-off between multiple objectives. 12/11/2013 2/25
  • 3. Literature Survey • • • • • • • • Wang et al (2004) [3] have formulated an expression which only chooses proper location keeping size of DG constant. Acharya et al (2006) [4] have select appropriate location of DG in distribution network based on loss sensitivity of buses and appropriate size of DG was evaluated based on exact loss formula. Singh et al (2009) [8] have presented multi-objective genetic algorithm approach for DG sitting and sizing. In Zangeneh et al (2009) [10], multi-objective genetic algorithm is applied to produce optimal planning schemes by taking into account cost and grant functions as objectives. In Arya et al (2012) [6], voltage stability criterion has been used to find DG location and differential evaluation optimization technique is used to determine DG capacity considering objective of power loss minimization. In Guo et al (2012) [9], optimal generation dispatch by renewable energy has been calculated using weighted sum multi-objective particle swarm optimization technique. Kathod et al (2013) [5] has present evolutionary programming based optimization technique to place renewable DGs optimally . In Injeti et al (2013) [7] a simulated annealing optimization technique has been utilized for optimal placement and sizing of DGs. 12/11/2013 3/2 5
  • 4. OBJECTIVE FUNCTIONS The researchers choose objective functions from different technical, economical and environmental aspect and they are not empirical. While some authors have given concern on economy for DG selection [13, 14], others emphasized on power loss minimization [3, 4] or both economical benefit and power loss minimization [6]. Optimal placement and sizing of DG based on system security and reliability are also reported in the literature [15, 16]. Some authors have discussed about location and size selection of DG on view point of voltage stability improvement [7, 9]. This paper studied with objective of benefit to cost ratio maximization, voltage stability enhancement and network security improvement satisfying power and voltage constraint criteria. 12/11/2013 4/2 5
  • 5. Benefit to cost ratio  BCR indicates the economical benefit that can be realized with respect to cost for implementation of the project. is investment, operating and maintenance cost I Icij and OMCij are investment cost; and operating and maintenance cost of type-j renewable DG at bus-i ni is the number of DG unit that can be connected at bus-i. li is location variable at bus-i P is the power generated by type-j DG at bus-I N is the number of buses in the network 12/11/2013 5/2 5
  • 6. Cumulative Present Value (CPV) of future cost includes interest rate, inflation rate, and economic life of equipment .It can be determined as follows:PV is the present value of cost. IF and IR are the percentage of inflation rate and interest rate Nyr is the number of year in planning horizon N BenefitrenwDG {( j type PrenwDG ,ij * ni * li ) PlossrenwDG }* Chr *8760* CPV i 2 is the power loss reduction due to allocation of renewable DGs Chr is the cost of electricity 12/11/2013 6/2 5
  • 7. Voltage stability index VSI is a very fast and effective tool and widely used by the power engineers for off-line measurement of voltage stability condition of buses Where ri and xi are resistance and reactance of line connected between bus-i and bus-i+1 PD,i +1 and QD,i +1 are active and reactive load at bus-i+1 respectively Vi is the voltage magnitude at bus-i Voltage stability condition of the whole network can be realized as 12/11/2013 7/2 5
  • 8. Network security index To get idea about network security index we need to know line loading of the network. Line Loading (LL) is the MVA flow through the line with respect to maximum MVA capacity of that line network security is represented as 12/11/2013 8/2 5
  • 9. LOAD MODEL Consideration of voltage dependent load model is more relevant than constant power load model for analysis of a system as loads of the buses in distribution network depend on their respective bus voltages.So practical voltage dependent load model i.e. residential, industrial and commercial used have been adopted for investigation. PD ,i QD ,i P0,i * Vi V0,i Vi Q0,i * V0,i PD,i, , QD,i and Vi are active load, reactive load and voltage magnitude at bus-i; P0.i , Q0,i and V0,i are nominal active load, reactive load and voltage magnitude at bus-i; α and are active and reactive power exponents 12/11/2013 9/2 5
  • 10. Active and reactive power exponents are different for different types of load [15, 18] and shown in Table 1 Table 1: Values of exponents used for different types of load Load type (1) Constant power 0 0 (2) Residential 0.92 4.04 (3) Commercial 1.51 3.40 (4) Industrial 0.18 6.0 12/11/2013 10/2 5
  • 11. PROBLEM FORMULATION The problem of this study is to generate potential solution with BCRrenwDG NSI and maximization of minimization of VSI renwDGrenwDG minimization of Overall objective can be formulated as Equality constraint: N N PGSS j type P renwDG ,ij * ni * li i 2 PD ,i PL 0 i 2 N QGSS QD ,i QL 0 i 2 12/11/2013 11/2 5
  • 12. Inequality constraints: 1. Generation limit constraint at bus-i PrenwDG ,ij * ni ,min PrenwDG ,ij * ni PrenwDG ,ij * ni ,max 2. Bus voltage tolerance constraint at Bus-i Vi ,min Vi Vi ,max 3. Line capacity constraint of line connecting bus-i and bus-j Sij max Sij 12/11/2013 12/2 5
  • 13. Multi-objective Particle Swarm Optimization (MOPSO) Particle swarm optimization (PSO): PSO is basically a population-based search procedure in which individual particle adjusts its position according to its own experience, and the experience of fittest neighboring particle. • The first one is best solution it has achieved so far which is called pbest.. • Second one is the best solution in the whole swarm and it is called gbest. • In each flight the particles modify their position and velocity and try to converge in more promising region of solution. Non-dominated sorting and crowding distance concepts are incorporated into PSO and extended it to handle multi-objective optimization problems. 12/11/2013 13/2 5
  • 14. Multi-objective Particle Swarm Optimization (MOPSO) In MOPSO, the whole population is sorted into various non-domination front levels based on non-domination criteria. A solution is said to be non-dominated if it is impossible to improve one component of the solution without worsening the value of at least one other component of the solution. After complete of set iteration, MOPSO generate a set of nondominated solutions. Fuzzy rule have the capability to select the optimal non-dominated solution in better way. 12/11/2013 14/2 5
  • 15. max min fi fi A linear fuzzy membership function is chosen with and corresponds to unacceptable and satisfactory value of objective f Objective function in MOPSO can be defined as: 0 if fi max i fi max if fi min fi 1 i fi fi max if fi fi max fi fi min fi min is the membership value ofth i objective Overall membership value of any non-dominated solution k is N obj N obj is the number of objective functions k i k M i 1 N obj k i M is number of non-dominated solution k 1 i 1 12/11/2013 15/25
  • 16. Simulation algorithm of multi-objective problem : 1. Initialize number of particle in MOPSO and dimension of each particle. Select dime nsion of particle with location variable, type variable and size variable (number of insta lled renewable DG units at load bus). Generate initial position and velocity of each par ticle randomly within specified range. 2. Run power flow algorithm and calculate the values of objective functions. 3. Do non-dominated sorting based on domination of objectives for different particles. 4. Sort particles in ascending order according to their occupancy of front levels and ca lculate crowding distance of each particle. 5. Select Pbest of particle as the best position it has occupied so far. 6. Eliminate solutions that are not satisfying constrain criteria. 7. Choose Gbest randomly from top 5% of non-dominated list. 8. Select maximum size of archive equal to number of particle. 9. For iteration number one, store all particles in an archive. Otherwise compare the fr ont level and crowding distance of current particles with previous particle stored in arc hive. Particles with better front level and crowding distances are stored by replacing pr evious particles. 10. Update position and velocity of each particle. 11. Repeat step 2 to 10 up to maximum iteration. 12. Select particle with highest fuzzy membership value from archive as optimal non-d ominated solution. 13. Display value of each objective function and variables of particle for optimal non-d ominated solution. Show the values of location variable, size variable and type variabl 16/2 e correspond to best non-dominated solution. 12/11/2013 5
  • 17. Simulation results  Simulation is carried out on 11kV, 28-bus rural Indian distribution network.  Simulation is done in MATLAB 7.10 software and Newton-Raphson power flow algorithm is used.  Base values are taken for the network as 1MVA and 11 kV .  For planning horizon of 10 years, IF, IR and Chr are taken 5%, 3% and Rs 5.8/kW-hr .  Required system data and maximum capacity of lines are collected from Investme Operating and mai [13, 20]. 20 Wind turbine 125 Biomass 200 15,330 20 (3) 11,826 25 (2) Single line diagram of 28-bus Indian distribution network Solar photovoltai c (PV) 4,818 100,000 (1) ntenance cost (Rs/k W-year) 75,000 Plant f actor ( %) nt cost ( Rs/kW) 350,000 DG technology Comme rcial siz e (kW) 60 Technical and economical data of wind , biomass and solar based renewable DGs 12/11/2013 17/2 5
  • 18. Case of constant power load Solution generated by MOPSO compromising three conflicting objectives Location , type and size of renewable DGs in the network for best trade-off between objective functions 12/11/2013 18/25
  • 19. Comparative graphical representation of voltage magnitudes for different buses with and without renewable DGs in the network Comparison of substation (S/S) power consumption, total power loss, VSI and NSI of DG installed network with before DG installation case 12/11/2013 19/2 5
  • 20. Case of voltage dependent load Solution generated by MOPSO by trade-off between three conflicting objectives Location, type and size of renewable DGs in the network for best trade-off between objective functions 12/11/2013 20/2 5
  • 21. Comparative graphical representation of voltage magnitudes for different buses with and without renewable DGs in the network Comparison of substation (S/S) power consumption, total power loss, VSF and NSI of DG installed network with before DG installation case 12/11/2013 21/2 5
  • 22. Conclusion  The article presents a novel multi-objective formulation of renewable DG planning for distribution network incorporating load model.  A set of non-dominated solution is generated by trade-off between three objective functions viz. benefit to cost ratio, voltage stability index and ne twork security index.  In this study satisfactory economical benefit, voltage stability improveme nt and network security enhancement is obtained for best non-dominated solution.  This investigations show the importance of load models on choice of DG s.  It has shown that proper allocation of renewable based DGs have potenti al to improve voltage profile at distribution buses.  Considerable reductions in consumption of grid power and network wer losses are also obtained.  The method can be used to allocate renewable DGs in any radial stribution system and find automatically the best solution. 12/11/2013 po di 22/2 5
  • 23. References [1] Ackermann, T., Andersson, G., Soder, L. (2001) Distributed generation: a definition, Electric Power Syst Res, 57(2001), pp. 195–204. [2] Ministry of New and Renewable Energy report. (2011) Strategic plan for new and renewable energy s ector for the period 2011-17, MNRE, Govt. of India 2011, pp. 1-85. [3] Wang, C. and Nehrir, M.H., (2004) Analytical approaches for optimal placement of distributed generati on sources in power systems, IEEE Trans. on Power Syst, 19(4), pp. 2068-2076. [4] Acharya, N., Mahat, P., Mithulananthan, N. (2006) An analytical approach for DG allocation in primary distribution network, Int. J. of Electr. Power Energy Syst, 28(2006), pp. 669-678. [5] Hedayati, H., Nabaviniaki, S.A., Akbarimajd, A. (2008) A method for placement of DG units in distributi on networks, IEEE Trans. on Power Deliv, 23(3), pp. 1620-1628. [6] Ghosh, S., Ghoshal, S.P., Ghosh, S. (2010) Optimal sizing and placement of distributed generation in a network system, Int. J. of Electr. Power Energy Syst, 32(2011), pp. 849-856. [7] Abri, R.S.A., El-Saddany, E.F., Atwa, Y.M., (2013) Optimal placement and sizing method to improve th e voltage stability margin in a distribution system using distributed generation, IEEE Trans. on Power Syst, 28(1), pp. 326-334. [8] Kathod, D.K., Pant. V., Sharma, J. (2013) Evolutionary programming based optimal placement of rene wable distributed generators, IEEE Trans. on Power Syst, 28(2), pp. 683-695. [9] Arya, L.D., Koshti, A., Choube, S.C. (2012) Distributed generation planning using differential evolution accounting voltage stability consideration, Int. J. of Electr. Power Energy Syst, 42(2012), pp. 196-20 7. [10] Injeti, S.K., Kumar, N.P. (2013) A novel approach to identify optimal access point and capacity of mul tiple DGs in a small, medium and large scale radial distribution systems, Int. J. of Electr. Power Ener gy Syst, 45(2013), pp. 142-151. [11] Singh, D., Singh, D., Verma, K.S. (2009) Multiple optimizations for DG planning with load models, IE EE Trans. on Power Syst, 24(1), pp. 427-436. 12/11/2013 23/25
  • 24. References(continued) [12] Guo, C.X., Bai. Y.H., Zheng, X., Zhan, J.P., Wu, Q.H. (2012) Optimal generation dispatch with renewable energy embedded using multiple objectives, Int. J. of Electr. Power Energy Syst, 42 (2012), pp. 440-447. [13] Zangeneh, A., Jadid, S., Rahimi-Kian, A. (2009) Promotion strategy of clean technologies in di stributed generation expansion planning, Renewable Energy 34(2009), pp. 2765-2773. [14] El-Khattam, W., Bhattacharya, K., Hegazy, Y., Salama, M.M.A. (2004) Optimal invest planning for distributed generation in a competitive electricity market, IEEE Trans. on Power Syst, 19(3) , pp. 1674-1684. [15] Singh, R.K., Goswami, S.K. (2011) Multi-objective optimization of distributed generation planni ng using impact indices and trade-off technique, Electr. Power Comp. and Systems, 39(11), pp . 1175-1190. [16] Teng, J.H., Liu, Y.H., Chen, C.Y., Chen, C.F. (2007) Value-based distributed generator placem ents for service quality improvements, Int. J. of Electr. Power Energy Syst, 29(2007), pp. 268-2 74. [17] Kayal, P., Chanda, C.K. (2013) A simple and fast approach for allocation and size evaluation o f distributed generation, Int. J. of Energy and Environmental Engineering, 4(7), pp. 1-9. [18] IEEE Task Force on Load Representation for Dynamic Performance. (1995) Bibliography on lo ad models for power flow and dynamic performance simulation, IEEE Trans. on Power Syst, 10 (1), pp. 523-538. [19] Raquel, C.R., Naval, P.C. (2005) An effective use of crowding distance in multi objective particl e swarm optimization, Proceedings of Genetic and Evolutionary Computation Conference, US A, pp. 257-264. [20] Hazra, J. and Sinha, A.K. (2011) A multi-objective optimal power flow using particle swarm opti mization. Euro, Trans. Electr. Power, 21(2010), pp. 1028-1045. [21] Das, D., Nagi, H.S., Kothari, D.P. (1994) Novel method for solving radial distribution network. I EE Proc. Gener. Transm. Distribution, 141(4) pp. 291-298. [22] Balamurugan, P., Ashok, S., Joshe, T.L. (2009) Optimal operation of biomass/wind/PV hybrid e nergy system for rural areas, Int. J. of Green Energy, 6(1), pp. 104-116. 12/11/2013 24/25
  • 25. THANK YOU FOR YOUR KIND ATTENTION 12/11/2013 25/25