×

A new multi objective optimization approach in distribution systems. (English) Zbl 1288.90085

Summary: This paper presents a multi-objective optimal location of AVRs in distribution systems at the presence of distributed generators based on a modified teaching-learning-based optimization (MTLBO) algorithm. In the proposed MTLBO algorithm, teacher and learner phases are modified. The proposed objective functions are energy generation costs, electrical energy losses and the voltage deviations. The proposed algorithm utilizes several teachers and considers the teachers as an external repository to save found Pareto optimal solutions during the search process. Since the objective functions are not the same, a fuzzy clustering method is used to control the size of the repository. The proposed technique allows the decision maker to select one of the Pareto optimal solutions (by trade-off) for different applications. The performance of the suggested algorithm on a 70-bus distribution network in comparison with other evolutionary methods such as GA, PSO and TLBO, is extraordinary.

MSC:

90C29 Multi-objective and goal programming
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Gu, Z., Rizy, D.T.: Neural networks for combined control of capacitor banks and voltage regulators in distribution systems. IEEE Trans. Power Deliv. 11, 1921–1928 (1996) · doi:10.1109/61.544277
[2] Milosevic, B., Begovic, M.: Capacitor placement for conservative voltage reduction on distribution feeders. IEEE Trans. Power Deliv. 19, 1360–1367 (2004) · doi:10.1109/TPWRD.2004.824400
[3] Vu, H., Pruvot, P., Launay, C., Harmand, Y.: An improved voltage control on large-scale power systems. IEEE Trans. Power Syst. 11, 1295–1303 (1996) · doi:10.1109/59.535670
[4] Chang, S.-K., Marks, G., Kato, K.: Optimal real time voltage control. IEEE Trans. Power Syst. 5, 750–758 (1990) · doi:10.1109/59.65902
[5] Niknam, T.: A new approach based on ant colony optimization for daily Volt/Var control in distribution networks considering distributed generators. Energy Convers. Manag. 49, 3417–3424 (2008) · doi:10.1016/j.enconman.2008.08.015
[6] Niknam, T., Bahmani Firouzi, B., Ostadi, A.: A new fuzzy adaptive particle swarm optimization for daily Volt/Var control in distribution networks considering distributed generators. Appl. Energy 87, 1919–1928 (2010) · doi:10.1016/j.apenergy.2010.01.003
[7] Olamaei, J., Niknam, T., Gharehpetian, G.: Application of particle swarm optimization for distribution feeder reconfiguration considering distributed generators. Appl. Math. Comput. 201, 575–586 (2008) · Zbl 1143.78370 · doi:10.1016/j.amc.2007.12.053
[8] Ng, H.N., Salama, M.M.A., Chikhani, A.Y.: Classification of capacitor allocation techniques. IEEE Trans. Power Deliv. 15, 387–392 (2000) · doi:10.1109/61.847278
[9] Masoum, M.A.S., Jafarian, A., Ladjevardi, M., Fuchs, E.F., Grady, W.N.: Fuzzy approach for optimal placement and sizing of capacitor banks in the presence of harmonic. IEEE Trans. Power Deliv. 16, 822–829 (2004) · doi:10.1109/TPWRD.2003.823187
[10] Alencar de Souza, B., do Nascimento Alves, H., Ferreira, H.A.: Micro genetic algorithms and fuzzy logic applied to the optimal placement of capacitor banks in distribution networks. IEEE Trans. Power Syst. 19, 942–947 (2004) · doi:10.1109/TPWRS.2004.825901
[11] Masoum, M.A.S., Ladjevardi, M., Jafarian, A., Fuchs, E.: Optimal placement, replacement and sizing of capacitor banks in distorted distribution networks by genetic algorithms. IEEE Trans. Power Deliv. 19, 1794–1801 (2004) · doi:10.1109/TPWRD.2004.835438
[12] Chiou, J., Chang, C., Su, C.: Ant direction hybrid differential evolution for solving large capacitor placement problems. IEEE Trans. Power Syst. 19, 1794–1800 (2004) · doi:10.1109/TPWRS.2004.835651
[13] Bridenbaugh, C.J., DiMascio, D.A., D’Aquila, R.: Voltage control improvement through capacitor and transformer tap optimization. IEEE Trans. Power Syst. 7, 222–226 (1992) · doi:10.1109/59.141707
[14] Roytelman, Ganesan, V.: Modeling of local controllers in distribution network application. IEEE Trans. Power Deliv. 15, 1232–1237 (2000) · doi:10.1109/61.891508
[15] Augugliaro, Dusonchet, L., Favazza, S., Riva, E.: Voltage regulation and power losses minimization in automated distribution networks by an evolutionary multi objective approach. IEEE Trans. Power Syst. 19, 1516–11527 (2004) · doi:10.1109/TPWRS.2004.825916
[16] Civanlar, S., Grainger, J.J.: Volt/Var control on distribution systems with lateral branches using shunt capacitors and voltage regulators, part I the overall problems. IEEE Trans. Power App. Syst. PAS 104, 3278–3283 (1985)
[17] Civanlar, S., Grainger, J.J.: Volt/Var control on distribution systems with lateral branches using shunt capacitors and voltage regulators, part II the solution method. IEEE Trans. Power App. Syst. PAS 104, 3284–3290 (1985) · doi:10.1109/TPAS.1985.318843
[18] Civanlar, S., Grainger, J.J.: Volt/Var control on distribution systems with lateral branches using shunt capacitors and voltage regulators, part III the numerical results. IEEE Trans. Power App. Syst. PAS 104, 3291–3297 (1985) · doi:10.1109/TPAS.1985.318844
[19] Safigianni, Salis, G.: Optimum voltage regulator placement in radial power distribution network. IEEE Trans. Power Syst. 15, 879–886 (2000) · doi:10.1109/59.867188
[20] Mendoza, J., Morales, D., Lopez, R., Lopez, M., Lopez, E., Vannier, J.C.: Optimal location of voltage regulators in radial distribution networks using genetic algorithms. In: Computation Conference, Belgium (2005)
[21] Niknam, T., Zeinoddini Meymand, H., Nayeripour, M.: A practical algorithm for optimal operation management of distribution network including fuel cell power plants. Renew. Energy 35, 1696–1714 (2010) · doi:10.1016/j.renene.2009.12.019
[22] Niknam, T.: A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem. Appl. Energy 87, 327–339 (2010) · doi:10.1016/j.apenergy.2009.05.016
[23] Tan, C.H., Goh, C.K., Tan, K.C., Tay, A.: A cooperative coevolutionary algorithm for multi-objective particle swarm optimization. In: IEEE Congress on Evolutionary Computation, pp. 3180–3186 (2007).
[24] Niknam, T., Ranjbar, A.M., Shirani, A.R.: An approach to Volt/Var control in distribution networks with distributed generation. Sci. Iran. 12, 34–42 (2005)
[25] Niknam, T., Ranjbar, A.M., Shirani, A.R.: Volt/Var control in distribution networks with distributed generation. In: IFAC conference, Korea, pp. 1105–1110 (2003).
[26] Niknam, T., Ranjbar, A.M., Shirani, A.R.: A new approach based on ant algorithm for Volt/Var control in distribution network considering distributed generation. Iran. J. Sci. Technol. Trans. 29, 1–15 (2005)
[27] Niknam, T., Ranjbar, A.M., Shirani, A.R.: An approach for Volt/Var control in distribution network with distributed generation. Sci. Iran. 12, 34–42 (2005)
[28] Niknam, T.: An efficient hybrid evolutionary algorithm based on PSO and HBMO algorithms for multi-objective distribution feeder reconfiguration. Energy Convers. Manag. 50, 2074–2082 (2009) · doi:10.1016/j.enconman.2009.03.029
[29] Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput. Aid. Des. 43, 303–315 (2011) · doi:10.1016/j.cad.2010.12.015
[30] Debaprya, Das: A fuzzy multi-objective approach for network reconfiguration of distribution systems. IEEE Trans. Power Deliv. 21, 202–209 (2006)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.