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A biased random key genetic algorithm applied to the electric distribution network reconfiguration problem

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Abstract

This work presents a biased random-key genetic algorithm (BRKGA) to solve the electric distribution network reconfiguration problem (DNR). The DNR is one of the most studied combinatorial optimization problems in power system analysis. Given a set of switches of an electric network that can be opened or closed, the objective is to select the best configuration of the switches to optimize a given network objective while at the same time satisfying a set of operational constraints. The good performance of BRKGAs on many combinatorial optimization problems and the fact that it has never been applied to solve DNR problems are the main motivation for this research. A BRKGA is a variant of random-key genetic algorithms, where one of the parents used for mating is biased to be of higher fitness than the other parent. Solutions are encoded by using random keys, which are represented as vectors of real numbers in the interval (0,1), thus enabling an indirect search of the solution inside a proprietary search space. The genetic operators do not need to be modified to generate only feasible solutions, which is an exclusive task of the decoder of the problem. Tests were performed on standard distribution systems used in DNR studies found in the technical literature and the performance and robustness of the BRKGA were compared with other GA implementations.

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References

  • Aiex, R.M., Resende, M., Ribeiro, C.C.: TTTPLOTS: a perl program to create time-to-target plots. Optim. Lett. 1, 355–366 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  • Baran, M.E., Wu, F.F.: Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Trans. Power Deliv. 2(4), 1401–1407 (1989)

    Article  Google Scholar 

  • Bean, J.C.: Genetic algorithms and random keys for sequencing and optimization. ORSA J. Comput. 6, 154–160 (1994)

    Article  MATH  Google Scholar 

  • Braz, H.D.M., Souza, B.A.: Distribution network reconfiguration using genetic algorithms with sequential encoding: subtractive and additive approaches. IEEE Trans. Power Syst. 2(26), 582–593 (2011)

    Article  Google Scholar 

  • Carreno, E.M., Romero, R., Padilha-Feltrin, A.: An efficient codification to solve distribution network reconfiguration for loss reduction problem. IEEE Trans. Power Syst. 4(23), 1542–1551 (2008)

    Article  Google Scholar 

  • Carreño, E.M., Romero, R., Padilha-Feltrin, A.: An efficient codification to solve distribution network reconfiguration for loss reduction problem. IEEE Trans. Power Syst. 4(23), 1542–1551 (2008)

    Article  Google Scholar 

  • Civanlar, S., Grainger, J., Lee, S.: Distribution feeder reconfiguration for loss reduction. IEEE Trans. Power Deliv. 3(3), 1217–1223 (1988)

    Article  Google Scholar 

  • Delbem, A.C.B., Carvalho, A.C.P.L.F.D., Policastro, C.A., Pinto, A.K.O., Honda, K., Garcia, A.C.: Node-depth encoding for evolutionary algorithms applied to network design. Proc. GECCO 1, 678–687 (2004)

    Google Scholar 

  • Enacheanu, B., Raison, B., Caire, R., Devaux, O., Bienia, W., Hadjsaid, N.: Radial network reconfiguration using genetic algorithm based on the matroid theory. IEEE Trans. Power Syst. 1(23), 186–195 (2008)

    Article  Google Scholar 

  • Faria Jr., H., Binato, S., Resende, M., Falcão, D.M.: Transmission network design by a greedy randomized adaptive path relinking approach. IEEE Trans. Power Syst. 20, 43–49 (2005)

    Article  Google Scholar 

  • Festa, P., Pardalos, P., Pitsoulis, L., Resende, M.: GRASP with path-relinking for the weighted MAXSAT problem. ACM J. Exp Algorithmics 11, 1–16 (2006)

    MATH  MathSciNet  Google Scholar 

  • Gonçalves, J., Resende, M.: Biased random-key genetic algorithms for combinatorial optimization. J. Heuristics 17(5), 487–525 (2010)

    Article  Google Scholar 

  • Gonçalves, J.F., Resende, M., Toso, R.F.: An experimental comparison of biased and unbiased random-key genetic algorithms. Pesquisa Oper. 34, 143–164 (2014)

    Article  Google Scholar 

  • Lavorato, M., Franco, J.F., Rider, M.J., Romero, R.: Imposing radiality constraints in distribution system optimization problems. IEEE Trans. Power Syst. 1(27), 172–180 (2012)

    Article  Google Scholar 

  • Li, Z., Chen, X., Yu, K., Sun, Y., Liu, H.: A hybrid particle swarm optimization approach for distribution network reconfiguration problem. IEEE Power and Energy Society General Meeting—Conversion and Delivery of Electrical Energy in the 21st Century, pp 20–24 (2008)

  • Nara, K., Shiose, A., Kitagawa, M., Ishihara, T.: Implementation of genetic algorithm for distribution systems loss minimum re-configuration. IEEE Trans. Power Syst. 3(7), 1044–1051 (1992)

    Article  Google Scholar 

  • Rama Rao, P.V.V., Sivanagaraju, S.: Radial distribution network reconfiguration for loss reduction and load balancing using plant growth simulation algorithm. Int. J. Electron. Eng. Inform 2(4), 266–277 (2010)

    Article  Google Scholar 

  • Ramos, E.R., Expósito, A.G., Santos, J.R., Iborra, F.L.: Path-based distribution network modeling: application to reconfiguration for loss reduction. IEEE Trans. Power Syst. 2(20), 556–564 (2005)

    Article  Google Scholar 

  • Roque, L.A.C., Fontes, D.B.M.M., Fontes, F.A.C.C.: A hybrid biased random key genetic algorithm approach for the unit commitment problem. J. Comb. Optim. 28, 140–166 (2014)

    Article  MATH  MathSciNet  Google Scholar 

  • Santos, A.C., Delbem, A.C.B., London, J.B.A.Jr, Bretas, N.G.: Node-Depth encoding and multiobjective evolutionary algorithm applied to large-scale distribution system reconfiguration. IEEE Trans. Power Syst. 3(25), 1254–1265 (2010)

    Article  Google Scholar 

  • Savier, J.S., Das, D.: Impact of network reconfiguration on loss allocation of radial distribution systems. IEEE Trans. Power Deliv. 4(22), 2473–2480 (2007)

    Article  Google Scholar 

  • Schmidt, H.P., Ida, N., Kagan, N., Guaraldo, J.C.: Fast reconfiguration of distribution systems considering loss minimization. IEEE Trans. Power Syst. 3(20), 1311–1319 (2005)

    Article  Google Scholar 

  • Spears, W.M., DeJong K.A.: On the virtues of parameterized uniform crossover. In: Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 230–236 (1991)

  • Swarnkar, A., Gupta, N., Niazi, K.R.: A novel codification for meta-heuristic techniques used in distribution network reconfiguration. Electr. Power Syst. Res. 7(81), 1619–1626 (2011)

    Article  Google Scholar 

  • Wang, C., Zhao, A., Dong, H., Li, Z.: An improved immune genetic algorithm for distribution network reconfiguration. In: IEEE International Conference on Information Management (2009)

  • Wenchuan, M., Jiaju, Q.: An artificial immune algorithm to distribution network reconfiguration. Proc. CSEE 26(17), 25–29 (2006)

    Google Scholar 

  • Yang, X.S.: Engineering Optimization: An Introduction with Metaheuristic Applications. Wiley, Hoboken (2010)

    Book  Google Scholar 

  • Zhang, C.Q., Zhang, J.J., Gu X.H.: The application of hybrid genetic particle swarm optimization algorithm in the distribution network reconfigurations multi-objective optimization. In: Third Int. Conf. on Natural Computation Proceeding, vol. 2, p. 455–459 (2007)

  • Zhu, J.Z.: Optimal reconfiguration of distribution network using the refined genetic algorithm. Elsevier Elect. Power Syst. Res. 62, 37–42 (2002)

    Article  Google Scholar 

  • Zifa, L., Shaoyun, G., Yixin, Y.: A hybrid intelligent algorithm for loss minimum reconfiguration in distribution networks. Proc. CSEE 5(15), 73–78 (2005)

    Google Scholar 

  • Zimmerman, R.D., Murillo-Sánchez, C.E., Thomas, R.J.: Matpower: steady-state operations, planning and analysis tools for power systems research and education. IEEE Trans. Power Syst. 1(26), 12–19 (2011)

    Article  Google Scholar 

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Authors and Affiliations

  1. CECS, UFABC – Universidade Federal do ABC, Rua Santa Adélia, No 166, Santo André, SP, 09.210-170, Brazil

    H. de Faria Jr.

  2. Montefiore Institute, Quartier Polytech 1, 10, Université de Liège, Allée de la découverte 4000, B28, Liege, Belgium

    H. de Faria Jr. & D. Ernst

  3. Mathematical Optimization and Planning, Amazon.com, 333 Boren Ave. N, Seattle, WA, 98109, USA

    M. G. C. Resende

Authors
  1. H. de Faria Jr.
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  2. M. G. C. Resende
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  3. D. Ernst
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Correspondence to H. de Faria Jr..

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de Faria, H., Resende, M.G.C. & Ernst, D. A biased random key genetic algorithm applied to the electric distribution network reconfiguration problem. J Heuristics 23, 533–550 (2017). https://doi.org/10.1007/s10732-017-9355-8

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  • DOI: https://doi.org/10.1007/s10732-017-9355-8

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