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Intelligent Industrial Systems
Erschienen in: Intelligent Industrial Systems 4/2016

01.12.2016 | Original Paper

Multi-Objective Distribution Network Reconfiguration Based on Pareto Front Ranking

verfasst von: Andrea Mazza, Gianfranco Chicco, Angela Russo, Elena Otilia Virjoghe

Erschienen in: Intelligent Industrial Systems | Ausgabe 4/2016

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Abstract

Electrical distribution system reconfiguration is frequently addressed as a multi-objective problem, typically taking into account the system losses together with other objectives, among which reliability indicators are widely used. In the multi-objective context, Pareto front analysis enables the operator handling conflicting and even non-commensurable objectives without needing the use of additional hypotheses or weights. This paper provides advances on the application of Pareto front analysis to multi-objective distribution network reconfiguration. Starting from previous results in which genetic algorithms were effectively adopted to find the best-known Pareto front, a version of the multi-objective binary particle swarm optimization (MOBPSO) customized for distribution network reconfiguration has been developed by exploiting the internal ranking of the solutions (based on a multi-criteria decision making method in the selection of the local best) and the network topology. Furthermore, the Pareto front mismatch metric (already used by the authors to compare different methods for small networks for which the complete Pareto front can be calculated) has been generalized to be used with large systems for which only the best-known Pareto front is found. Applications to a test network and to a real urban distribution network are discussed, showing the consistent superiority of the customized MOBPSO version with respect to the application of genetic algorithms and of a more classical version of the particle swarm optimization method.
Vorheriger Artikel Journal of Intelligent Industrial Systems
Nächster Artikel Enabling Residential Demand Response Applications with a ZigBee-Based Load Controller System

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Fußnoten
1
For each approach, an early paper introducing the method for optimal distribution network reconfiguration is indicated. A complete review of multi-objective methods is outside the scope of this paper.
 
2
In addition, a last-resource large number of iterations \(N_S^{(max)}\) is included to avoid infinite loops (generally the convergence occurs by satisfying the adaptive stop criterion before reaching \(N_S^{(max)}\) iterations).
 
3
In order to adapt the offset \(\delta \) to the velocity values under analysis, the value of \(\delta \) is obtained by finding the minimum value of velocity \(v_{min}\) among the entries composing the set \(\tilde{\mathbf{Z}}^{(k)}\) and then computing the value \(\delta =v_{min} /2\).
 
4
In one of the variants adopted in [46], each time a global best has to be used, instead of extracting only one solution, two solutions are selected at random from the archive, and the one with the better hyper-volume is considered as the global best.
 
Literatur
1.
Kalambe, S., Agnihotri, G.: Loss minimization techniques used in distribution network: bibliographical survey. Renew. Sustain. Energy Rev. 29, 184–200 (2014)CrossRef
2.
Mazza, A., Chicco, G., Andrei, A., Rubino, M.: Determination of the relevant periods for intraday distribution system minimum loss reconfiguration. Int. Trans. Electr. Energy Syst. 25, 1992–2023 (2015)CrossRef
3.
Jordehi, A.R.: Optimisation of electric distribution systems: a review. Renew. Sustain. Energy Rev. 51, 1088–1100 (2015)CrossRef
4.
Roytelman, I., Melnik, V., Lee, S.S.H., Lugtu, R.L.: Multi-objective feeder reconfiguration by distribution management system. IEEE Trans. Power Syst. 11, 661–667 (1996)CrossRef
5.
Chiang, H.D., Jean-Jumeau, V.: Optimal network reconfigurations in distribution systems. II. Solution algorithms and numerical results. IEEE Trans. Power Deliv. 5, 1568–1574 (1990)CrossRef
6.
Das, I., Dennis, J.E.: A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems. Struct. Optim. 14, 63–69 (1997)CrossRef
7.
Hsiao, Y.T., Chien, C.Y.: Multiobjective optimal feeder reconfiguration. IET Gener. Transm. Distrib 148, 333–336 (2001)CrossRef
8.
Ahuja, A., Das, S., Pahwa, A.: An AIS-ACO hybrid approach for multi-objective distribution system reconfiguration. IEEE Trans. Power Syst. 22, 1101–1111 (2007)CrossRef
9.
Tsai, M.S., Hsu, F.Y.: Application of grey correlation analysis in evolutionary programming for distribution system feeder reconfiguration. IEEE Trans. Power Syst. 25, 1126–1133 (2010)CrossRef
10.
Toscano, R.: Stochastic methods for hard optimization: application to robust control and fault diagnosis of industrial systems, chapter 7. In: Rigatos, G.G. (ed.) Intelligent Industrial Systems: Modeling, Automation and Adaptive Behavior. Information Science Reference, Hershey (2010)
11.
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)CrossRef
12.
Mazza, A., Chicco, G., Russo, A.: Optimal multi-objective distribution system reconfiguration with multi criteria decision making-based solution ranking and enhanced genetic operators. Int. J. Electr. Power Energy Syst. 54, 255–267 (2014)CrossRef
13.
Campoccia, A., Riva Sanseverino, E., Zizzo, G.: Considering safety issues in minimum losses reconfiguration for MV distribution networks. Eur. Trans. Electr. Power 19, 642–654 (2009)CrossRefMATH
14.
Tomoiagă, B., Chindriş, M., Sumper, A., Sudria-Andreu, A., Villafafila-Robles, R.: Pareto optimal reconfiguration of power distribution systems using a genetic algorithm based on NSGA-II. Energies 6, 1439–1455 (2013)CrossRef
15.
Hsu, F.Y., Tsai, M.S.: A non-dominated sorting evolutionary programming algorithm for multi-objectives power distribution system feeder reconfiguration problems. Int. Trans. Electr. Energy Syst. 23, 191–213 (2013)CrossRef
16.
Mendoza, J.E., Lopez, M.E., Coello Coello, C.A., Lopez, E.A.: Microgenetic multiobjective reconfiguration algorithm considering power losses and reliability indices for medium voltage distribution network. IET Gener. Transm. Distrib. 3, 825–840 (2009)CrossRef
17.
Alonso, F.R., Oliveira, D.Q., Zambroni de Souza, A.C.: Artificial immune systems optimization approach for multiobjective distribution system reconfiguration. IEEE Trans. Power Syst. 30, 840–847 (2015)CrossRef
18.
Mazza, A., Chicco, G., Russo, A.: Comparison of multi-objective optimization approaches for distribution system reconfiguration. In: Proceedings of IEEE PowerTech, Grenoble (2013)
19.
Guedes, L.S.M., Lisboa, A.C., Vieira, D.A.G., Saldanha, R.R.: A multiobjective heuristic for reconfiguration of the electrical radial network. IEEE Trans. Power Deliv. 28, 311–319 (2013)CrossRef
20.
Rabiee, M., Zandieh, M., Ramezani, P.: Bi-objective partial flexible job scheduling problem: NSGA-II, NRGA, MOGA and PAES approaches. Int. J. Prod. Res. 50, 1–16 (2012)CrossRef
21.
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3, 257–271 (1999)CrossRef
22.
Andrei, H., Chicco, G.: Identification of the radial configurations extracted from the weakly meshed structures of electrical distribution systems. IEEE Trans. Circuits Syst. I 55, 1149–1158 (2008)MathSciNetCrossRef
23.
Shirmohammadi, D., Hong, H.W., Semlyen, A., Luo, G.X.: A compensation-based power flow method for weakly meshed distribution and transmission networks. IEEE Trans. Power Syst. 3, 753–762 (1988)CrossRef
24.
Carpaneto, E., Chicco, G.: Evaluation of the probability density functions of distribution system reliability indices with a characteristic functions-based approach. IEEE Trans. Power Syst. 19, 724–734 (2004)CrossRef
25.
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth, vol. 4, pp. 1942–1948 (1995)
26.
Kennedy, J.: Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of the 1999 Congress on Evolutionary Computation, vol. 3, pp. 1931–1938 (1999)
27.
Kennedy, J., Eberhart, R.: Swarm Intelligence. Morgan Kaufmann Publisher, San Francisco (2001)
28.
Chicco, G., Mazza, A.: An overview of the probability-based methods for optimal electrical distribution system reconfiguration. In: Proceedings of 4th International Symposium on Electrical and Electronics Engineering, Galati (2013)
29.
Kennedy, J., Eberhart, RC.: A discrete binary version of the particle swarm algorithm. In: Proceedings of Conference on Systems, Man, and Cybernetics, pp. 4104–4109 (1997)
30.
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. 23, 186–195 (2008)CrossRef
31.
Sivanagaraju, S., Rao, J.V., Raju, P.S.: Discrete particle swarm optimization to network reconfiguration for loss reduction and load balancing. Electr. Power Compon. Syst. 36, 513–524 (2008)CrossRef
32.
Wu, W.C., Tsai, M.S.: Application of enhanced integer coded particle swarm optimization for distribution system feeder reconfiguration. IEEE Trans. Power Syst. 26, 1591–1599 (2011)CrossRef
33.
Chang, R.F., Lu, C.N.: Feeder reconfiguration for load factor improvement. IEEE Power Eng. Soc. Winter Meet. 2, 980–984 (2002)CrossRef
34.
Amanulla, B., Chakrabarti, S., Singh, S.N.: Reconfiguration of power distribution systems considering reliability and power loss. IEEE Trans. Power Deliv. 27, 918–926 (2012)CrossRef
35.
Liu, Y., Gu, X.: Skeleton-network reconfiguration based on topological characteristics of scale-free networks and discrete particle swarm optimization. IEEE Trans. Power Syst. 22, 1267–1274 (2007)MathSciNetCrossRef
36.
Wu, W.C., Tsai, M.S., Hsu, F.Y.: A new binary coding particle swarm optimization for feeder reconfiguration. In: Proceeding of the International Conference on Intelligent Systems Applications to Power Systems (2007)
37.
Hong, Y.Y., Ho, S.Y.: Determination of network configuration considering multiobjective in distribution systems using genetic algorithms. IEEE Trans. Power Syst. 20, 1062–1069 (2005)CrossRef
38.
Batrinu, F., Carpaneto, E., Chicco, G.: A novel particle swarm method for distribution system optimal reconfiguration. In: Proceedings of IEEE PowerTech (2005)
39.
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1968)
40.
Civanlar, S., Grainger, J., Yin, H., Lee, S.: Distribution feeder reconfiguration for loss reduction. IEEE Trans. Power Deliv. 3, 1217–1223 (1988)
41.
Coello, C.C., Lechuga M.S.: MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation, pp. 1051–1056 (2002)
42.
Reyes-Sierra, M., Coello Coello, C.A.: Multi-objective particle swarm optimizers: a survey of the state of the art. Int. J. Comput. Intell. Res. 2, 287–308 (2006)MathSciNet
43.
Andervazh, M.R., Olamaei, J., Haghifam, M.R.: Adaptive multi-objective distribution network reconfiguration using multi-objective discrete particles swarm optimisation algorithm and graph theory. IET Gener. Transm. Distrib. 7, 1367–1382 (2013)CrossRef
44.
Del Valle, Y., Venayagamoorthy, G., Mohagheghi, S., Hernandez, J.C., Harley, R.: Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans. Evol. Comput. 12, 171–195 (2008)CrossRef
45.
Coello, C., Pulido, G., Lechuga, M.: Handling multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 8, 256–279 (2004)CrossRef
46.
Nebro, A.J., Durillo, J.J., Coello, C.C.: Analysis of leader selection strategies in a multi-objective particle swarm optimizer. In: Proceedings of IEEE Congress on Evolutionary Computation, pp. 3153–3160 (2013)
47.
Baran, M.E., Wu, F.F.: Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Trans. Power Deliv. 4, 1401–1407 (1989)CrossRef
48.
Hwang, C.L., Yoon, K.: Multiple Attribute Decision Making. Methods and Applications: A State-of-the-Art Survey. Springer, Berlin (1981)CrossRefMATH
Metadaten
Titel
Multi-Objective Distribution Network Reconfiguration Based on Pareto Front Ranking
verfasst von
Andrea Mazza
Gianfranco Chicco
Angela Russo
Elena Otilia Virjoghe
Publikationsdatum
01.12.2016
Verlag
Springer Singapore
Erschienen in
Intelligent Industrial Systems / Ausgabe 4/2016
Print ISSN: 2363-6912
Elektronische ISSN: 2199-854X
DOI
https://doi.org/10.1007/s40903-016-0065-6

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