Distributed Generation Optimal Placement and Sizing to Enhance Power Distribution Network Performance Using MTLBO


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Abstract


Integrating distributed generation (DG) into an electric radial distribution system has an overall positive impact on the system. The power injections from renewable DG units located close to the load centers provide an opportunity for system power loss reduction, cost reduction, voltage profile improvement, voltage stability improvement, environmental friendliness, postponement system upgrading and increasing reliability. This impact can be enhanced via optimal DG placement and sizing. The optimal DG placement and sizing problem is formulated as a mixed integer nonlinear optimization problem subject to highly nonlinear equality and inequality constraints. Evolutionary methods are used to solve this problem because of their independence from type of the objective function and constraints. In this paper, a Modified Teaching Learning Based Optimization Algorithm (MTLBO) for placement and sizing of multi-DGs in a radial distribution system is proposed.  The objective function is considered to minimize the network active power losses, to improve the voltage profile and to improve the voltage stability index within the frame work of system operation and security constraints. A detailed performance analysis is carried out on 33 and 69 bus radial distribution system to demonstrate the effectiveness of the proposed methodology. Besides this, it has also been carried out a comparison using several results available in other published articles.
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Keywords


Distributed Generation (DG); Radial Distribution System (RDS); Modified Teaching Learning Based Optimization Algorithm (MTLBO); Active Power Loss; Voltage Deviation; Voltage Stability Index

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References


T. Ackermann, G. Andersson, L. Soder, Distributed generation: a definition, Electr. Power Syst. Res. vol.3,2001, pp.195 – 204.

Popovic, D.H., Greatbanks, J.A., Begovic, M., Pregelj, A, ‘Placement of distributed generator and reclosers for distribution network security and reliability’, Int. J. Electr. Power Energy Syst., 27, (5-6), 2005, pp. 398-408.

Khan, H., Mohammad, A.C. ‘Implementation of distributed generation algorithm for performance enhancement of distribution feeder under extreme load growth’, International Journal of Electrical Power and Energy Systems. 32 (9), 2010, pp. 985-997.

Borges, C.L.T., Falcao, M.D., ‘Optimal distributed generation allocation for reliability, losses and voltage improvement’, Int. J. Electr. Power Energy Syst., vol.28, 2006, pp.413-20.

Ziari, I., Ledwich, G., Ghosh, A., ‘Integrated distribution systems planning to improve reliability under load growth’ , IEEE Trans. Power Del., 27, (2), 2012, pp. 757- 765.

Kamel, R.M. and Karmanshahi, B., ‘Optimal size and location of DGs for minimizing power losses in a primary distribution network’, Transaction on Computer Science and Electrical and Electronics Engineering, 16 (2), 2009, pp. 137-144.

Singh, D., Singh, D. and Verma, K.S., ‘Multi-objective optimization for DG planning with load models’. IEEE Transactions on Power Systems. 24 (1), 2009, pp. 427-436.

EI-Zonkoly, A.M., ‘Optimal placement of multi-distributed generation units including different load models using particle swarm optimization’, Swarm Evol. Comp., 2011, 1, (1), pp. 50-59.

Ganguly, S., Sahoo, N.H., Das D., ‘Multi-objective particle swarm optimization based on fuzzy-pareto-dominance for possibilistic planning of electrical distribution systems incorporating distributed generation’, Fuzzy sets syst., 213, 2013, pp. 47-73.

Moradi, M.H. , Abedini , M. ‘A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems’, Int. J. Electr. Power Energy Syst., 34 (1) , 2012, pp. 66-74.

Nayak, M.R., Dash, S.K., Rout, P.K., ‘Optimal placement and sizing of distributed generation in radial distribution system using differential evolution algorithm’, Swarm, Evolutionary, and Memetic Computing, Lecture Notes in Computer Science, 7677 2012, pp 133-142.

Acharya, N., Mahat, P., Mithulananthan, N., ‘An analytical approach for DG allocation in primary distribution network’, Int. J. Electr. Power Energy Syst., 28, (10), 2006, pp. 669-678.

Abu-Mouti, F.S., El-Hawary, M.E., ‘Optimal distributed generation allocation and sizing in distribution systems via artificial bee colony algorithm’, IEEE Trans. Power Del., 26, (4), 2011, pp. 2090-2101.

Sookananta, Utaton, B., Utaton, P., Khongsila, R., ‘Determination of the optimal location and sizing of distributed generation using ant colony search’, Int. Conf. Electrical Engineering/Electronics Computer Tele. Information Tech., ECTI-CON-2010, pp. 814 – 817, 19th -21st May 2010.

Wang, L.F., Singh, C., ‘Reliability-constrained optimum placement of reclosers and distributed generators in distribution networks using an ant colony system algorithm’, IEEE Trans. Syst. Man Cybernetics Part C: Applications and Reviews, 38, (6), 2008, pp. 757 – 764.

Kumar, I.S., Kumar, N.P. ‘A novel approach to identify optimal access point and capacity of multiple DGs in a small, medium and large scale radial distribution systems’, Int. J. Elect. Power Energy Syst., 2013, 45, (1), pp. 142-151.

Mojarrad, H.D., Gharehpetian, G.B., Rastegar, H., Olamaei, J.: ‘Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithm’, Energy, Available online 18 February 2013.

Mohammadi, M., Nafar, M. ‘Optimal placement of multitypes DG as independent private sector under pool/hybrid power market using GA-based Tabu Search method’, Int. J. Elect. Power Energy Syst., 2013, 51, pp. 43–53.

Haque, M.H.. ‘Efficient load flow method for distribution systems with radial or mesh configuration’. IEE Proc. On Generation, Transmission and Distribution. 1996, 143 (1): 33-38.

Charkravorty M., Das D., ‘Voltage stability analysis of radial distribution networks’, International Journal of Electrical Power and Energy Systems. 23(2), 2001, pp. 129-135.

Vovos PN, Bialek jw, ‘Direct incorporation of fault level constraints in optimal power flow as a tool for network capacity analysis ’, IEEE Trans Power Syst, 2005, 20 (4), pp.2125-34.

Rao, R.V., Savsani, J.V., Balic, J., ‘Teaching-learning based optimization algorithm for unconstrained and constrained real-parameter optimization problems’, Engg. Opt., 2012, 44, (12), pp.1447-62.

Nayak, M.R., Nayak, C.K., Rout, P.K., ‘Application of Multi – Objective Teaching Learning Based Optimization Algorithm to Optimal Power Flow Problem ’, Procedia Technology, Elsevier, vol.6, 2012, pp. 255 – 264.

Shashank, T.R., Rajesh, N.B., Analysis of Fast Voltage Stability Index on long transmission line using Power World Simulator, (2013) International Review on Modelling and Simulations (IREMOS), 6 (3), pp. 888-892.

Bagheri, A., Noroozian, R., Jalilvand, A., Jalilzadeh, S., Voltage and reactive power control in distribution systems in the presence of distributed generation, (2012) International Review on Modelling and Simulations (IREMOS), 5 (2), pp. 528-536.


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