Multi-Objective Fuzzy Model in Optimal Sitingand Sizing of DG for Loss Reduction
This paper presents a possibilistic (fuzzy) model in optimal siting and sizing of Distributed Generation (DG) for loss reduction and improve voltage profile in power distribution system. Multi-objective problem is developed in two phases. In the first one, the set of non-dominated planning solutions is obtained (with respect to the objective functions of fuzzy economic cost, and exposure) using genetic algorithm. In the second phase, one solution of the set of non-dominated solutions is selected as optimal solution, using a suitable max-min approach. This method can be determined operation-mode (PV or PQ) of DG. Because of considering load uncertainty in this paper, it can be obtained realistic results. The whole process of this method has been implemented in the MATLAB7 environment with technical and economic consideration for loss reduction and voltage profile improvement. Through numerical example the validity of the proposed method is verified.
[1] B. Mohammadi., A.R. Seifi, Fuzzy load flow in radial distribution
systems with considering load uncertainty, Proc. of the 21th
International power system conference, Tehran, Iran, 2006, Article No:
98-F-PDS-773.
[2] K. Kauhaniemi, Fuzzy models and techniques for the calculation of
radial distribution networks, Proc. Of the IEEE/NTUA Athens Power
Technical Conference, Athens, Greece, Sept. 1993, pp. 423-428.
[3] Carmen L.T. Borges, Djalma M. Falcao, Optimal distributed generation
allocation for reliability, losses, and voltage improvement, Electrical
Power and Energy Systems, Vol. 28, No. 6, 2006, pp. 413-420.
[4] G. Celli, F. Pilo, Optimal distribution generation allocation in MV
distribution networks, Proc. of the 22nd IEEE International Conference
on Power Industry Computer Applications, Power Engineering Society,
2001, pp. 81-86.
[5] N. Mithulananthan, T. Oo, L.V. Phu, Distributed generation placement
in power distribution system using genetic algorithm to reduce loss,
Thammasa International Journal of Science and Technology, Vol. 9,
No. 3, 2004, pp. 55-62.
[6] T. Niknam, A.M. Ranjbar A.R. Shirani, Impact of distributed generation
on Volt/Var control in distribution networks, Proc. of the IEEE Bologna
Power Technology Conference, Bologna, Italy, Vol. 3, 2003, p. 7.
[7] C. Pathomthat , R. Ramakumar, An approach to quantify the technical
benefits of distributed generation, IEEE Trans. on Energy Conversion,
Vol. 19, No. 4, 2004, pp. 764-773.
[8] G. P. Harrison, A. Piccolo, P. Siano A. R. Wallace, Distributed
generation capacity evaluation using combined genetic algorithm and
OPF, International Journal of Emerging Electric Power Systems, Vol. 8,
No. 2, 2007.
[9] V. H. M. Quezada, J. R. Abbad, T. G. S. Román, Assessment of energy
distribution losses for increasing penetration of distributed generation,
IEEE Trans. on power systems, Vol. 21, No. 2, 2006
[10] E. Haesen, M. Espinoza, B. Pluymers, I. Goethals, V. V. Thong, J.
Driesen, R. Belmans, B. D. Moor, Optimal placement and sizing of
distributed generator units using genetic optimization algorithm,
Electrical Power Quality and Utilisation, Journal, Vol. 11, No. 1, 2005,
pp. 97-104.
[11] F. R. Davalos, M. R. Irving, An Approach on the Strength Pareto
evolutionary algorithm 2 for power distribution Planning,
www.lania.mx/~ccoello/rivas05.pdf.gz.
[12] G. Celli, E. Ghiani, S. Mocci, F. Pilo, A multiobjective evolutionary
algorithm for the sizing and siting of distributed generation, IEEE Trans.
on Power Systems, Vol. 20, No. 2, 2005, pp. 750 - 757.
[13] I. J. R. Rosado, and J. A. D. Navarro, New multi-objective Tabu search
algorithm for fuzzy optimal planning of power distribution systems,
IEEE Trans. on Power Systems, Vol. 21, No. 1, 2006, pp. 224- 233.
[14] J. T. Saraiva, V. Miranda, L.M.V.G. Pinto, Impact on some planning
decision from a fuzzy modeling of power system, IEEE Trans. on Power
Systems, Vol. 9, No. 2, 1994, pp. 819 - 825.
[15] W. El-Khattam, K. Bhattacharya, Y. Hegazy, M. M. A. Salama,
Optimal investment planning for distributed generation in a competitive
electricity market, IEEE Trans. on power systems, Vol. 19, No. 3, 2004,
pp. 1674 - 1684.
[16] B. Venkatesh, R. Ranjan, H. B. Gooi, Optimal reconfiguration of radial
distribution systems to maximize loadability, IEEE Power Engineering
Society General Meeting, Vol. 1, 2004, p. 409.
[1] B. Mohammadi., A.R. Seifi, Fuzzy load flow in radial distribution
systems with considering load uncertainty, Proc. of the 21th
International power system conference, Tehran, Iran, 2006, Article No:
98-F-PDS-773.
[2] K. Kauhaniemi, Fuzzy models and techniques for the calculation of
radial distribution networks, Proc. Of the IEEE/NTUA Athens Power
Technical Conference, Athens, Greece, Sept. 1993, pp. 423-428.
[3] Carmen L.T. Borges, Djalma M. Falcao, Optimal distributed generation
allocation for reliability, losses, and voltage improvement, Electrical
Power and Energy Systems, Vol. 28, No. 6, 2006, pp. 413-420.
[4] G. Celli, F. Pilo, Optimal distribution generation allocation in MV
distribution networks, Proc. of the 22nd IEEE International Conference
on Power Industry Computer Applications, Power Engineering Society,
2001, pp. 81-86.
[5] N. Mithulananthan, T. Oo, L.V. Phu, Distributed generation placement
in power distribution system using genetic algorithm to reduce loss,
Thammasa International Journal of Science and Technology, Vol. 9,
No. 3, 2004, pp. 55-62.
[6] T. Niknam, A.M. Ranjbar A.R. Shirani, Impact of distributed generation
on Volt/Var control in distribution networks, Proc. of the IEEE Bologna
Power Technology Conference, Bologna, Italy, Vol. 3, 2003, p. 7.
[7] C. Pathomthat , R. Ramakumar, An approach to quantify the technical
benefits of distributed generation, IEEE Trans. on Energy Conversion,
Vol. 19, No. 4, 2004, pp. 764-773.
[8] G. P. Harrison, A. Piccolo, P. Siano A. R. Wallace, Distributed
generation capacity evaluation using combined genetic algorithm and
OPF, International Journal of Emerging Electric Power Systems, Vol. 8,
No. 2, 2007.
[9] V. H. M. Quezada, J. R. Abbad, T. G. S. Román, Assessment of energy
distribution losses for increasing penetration of distributed generation,
IEEE Trans. on power systems, Vol. 21, No. 2, 2006
[10] E. Haesen, M. Espinoza, B. Pluymers, I. Goethals, V. V. Thong, J.
Driesen, R. Belmans, B. D. Moor, Optimal placement and sizing of
distributed generator units using genetic optimization algorithm,
Electrical Power Quality and Utilisation, Journal, Vol. 11, No. 1, 2005,
pp. 97-104.
[11] F. R. Davalos, M. R. Irving, An Approach on the Strength Pareto
evolutionary algorithm 2 for power distribution Planning,
www.lania.mx/~ccoello/rivas05.pdf.gz.
[12] G. Celli, E. Ghiani, S. Mocci, F. Pilo, A multiobjective evolutionary
algorithm for the sizing and siting of distributed generation, IEEE Trans.
on Power Systems, Vol. 20, No. 2, 2005, pp. 750 - 757.
[13] I. J. R. Rosado, and J. A. D. Navarro, New multi-objective Tabu search
algorithm for fuzzy optimal planning of power distribution systems,
IEEE Trans. on Power Systems, Vol. 21, No. 1, 2006, pp. 224- 233.
[14] J. T. Saraiva, V. Miranda, L.M.V.G. Pinto, Impact on some planning
decision from a fuzzy modeling of power system, IEEE Trans. on Power
Systems, Vol. 9, No. 2, 1994, pp. 819 - 825.
[15] W. El-Khattam, K. Bhattacharya, Y. Hegazy, M. M. A. Salama,
Optimal investment planning for distributed generation in a competitive
electricity market, IEEE Trans. on power systems, Vol. 19, No. 3, 2004,
pp. 1674 - 1684.
[16] B. Venkatesh, R. Ranjan, H. B. Gooi, Optimal reconfiguration of radial
distribution systems to maximize loadability, IEEE Power Engineering
Society General Meeting, Vol. 1, 2004, p. 409.
@article{"International Journal of Electrical, Electronic and Communication Sciences:61559", author = "H. Shayeghi and B. Mohamadi", title = "Multi-Objective Fuzzy Model in Optimal Sitingand Sizing of DG for Loss Reduction", abstract = "This paper presents a possibilistic (fuzzy) model in optimal siting and sizing of Distributed Generation (DG) for loss reduction and improve voltage profile in power distribution system. Multi-objective problem is developed in two phases. In the first one, the set of non-dominated planning solutions is obtained (with respect to the objective functions of fuzzy economic cost, and exposure) using genetic algorithm. In the second phase, one solution of the set of non-dominated solutions is selected as optimal solution, using a suitable max-min approach. This method can be determined operation-mode (PV or PQ) of DG. Because of considering load uncertainty in this paper, it can be obtained realistic results. The whole process of this method has been implemented in the MATLAB7 environment with technical and economic consideration for loss reduction and voltage profile improvement. Through numerical example the validity of the proposed method is verified.
", keywords = "Fuzzy Power Flow, DG siting and sizing, LoadUncertainty, Multi-objective Possibilistic Model.", volume = "3", number = "4", pages = "949-6", }
