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Minimization of reliability indices and cost of power distribution systems in urban areas using an efficient hybrid meta-heuristic algorithm

  • Avishek Banerjee
  • Samiran Chattopadhyay
  • Grigoras Gheorghe
  • Mihai Gavrilas
Methodologies and Application
  • 71 Downloads

Abstract

Power distribution systems (PDS) in urban areas suffer from different types of problems. One such major problem is accidental or scheduled interruption. In electrical networks, effects of interruptions are usually quantified using a set of reliability indices, namely the System Average Interruption Frequency Index and the System Average Interruption Duration Index. Installation cost (fixed cost) and cost due to temporary and/or permanent faults during interruptions (variable cost) are also major issues to be considered while achieving a cost efficient, fault-tolerant PDS. Formalization of an optimization problem that jointly minimizes the afore-mentioned reliability indices as well as the cost of a PDS by optimal allocation of different protective devices and switches has always been a challenging task. This paper presents a hybrid single as well as joint-objective function optimization technique to minimize different reliability indices (mixed integer minimization problems), as well as the operational cost of a PDS in urban areas. In the proposed technique, two well-known meta-heuristic search techniques, namely genetic algorithms (GA) and ant colony optimization (ACO), have been hybridized after modifying different participating operators. The effectiveness of the proposed algorithm is examined, and each PDS is tested in a different environment of constrained optimization. In addition, the presented simulation results are compared with existing approaches that solve this problem. The simulation results show the superiority of the proposed hybrid GA–ACO model, as compared to other established heuristic approaches.

Keywords

Power distribution system Hybrid algorithms Constrained optimization Genetic algorithm Ant colony optimization Reliability indices 

Notes

Acknowledgements

We would like to thank all anonymous reviewers for their valuable comments and suggestions, which have enriched our paper significantly. Authors would also express their gratitude toward Prof. Asoke Kumar Bhunia for his valuable suggestions.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Andersson J (2000) A survey of multiobjective optimization in engineering design, pp 1–34. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.8.5638&rep=rep1&type=pdf
  2. Bäck T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, Oxford. doi: 10.1108/k.1998.27.8.979.4 MATHGoogle Scholar
  3. Barán B, Schaerer M (2003) A multiobjective ant colony system for vehicle routing problem with time windows. In: Applied informatics, pp 97–102. http://www.cnc.una.py/publicaciones/1_75.pdf
  4. Billinton R, Allan RN (1996a) Reliability evaluation of power systems. Springer, New York. doi: 10.1007/978-1-4899-1860-4 CrossRefMATHGoogle Scholar
  5. Billinton R, Allan RN (1996b) Distribution systems—basic techniques and radial networks. In: Reliability evaluation of power systems, pp 220–248: doi: 10.1007/978-1-4899-1860-4_7
  6. Blum C (2005) Beam-ACO—hybridizing ant colony optimization with beam search: an application to open shop scheduling. Comput Oper Res 32(6):1565–1591CrossRefMATHGoogle Scholar
  7. Blum C (2008) Beam-ACO for simple assembly line balancing. INFORMS J Comput 20(4):618–627CrossRefMATHGoogle Scholar
  8. Blum C, Sampels M (2004) An ant colony optimization algorithm for shop scheduling problems. J Math Model Algorithms 3(3):285–308. doi: 10.1023/b:jmma.0000038614.39977.6f MathSciNetCrossRefMATHGoogle Scholar
  9. Blum C, Vallès MY, Blesa MJ (2008) An ant colony optimization algorithm for DNA sequencing by hybridization. Comput Oper Res 35(11):3620–3635CrossRefMATHGoogle Scholar
  10. Brown R (2008) Electric power distribution reliability, 2nd edn. Power engineering (Willis). doi: 10.1201/9780849375682
  11. Cartina G, Grigoras G, Bobric EC, Comanescu D (2009) Improved fuzzy load models by clustering techniques in optimal planning of distribution networks. In: 2009 IEEE Bucharest PowerTech. doi: 10.1109/ptc.2009.5282025
  12. Ciornei I, Kyriakides E (2012) Hybrid ant colony-genetic algorithm (GAAPI) for global continuous optimization. IEEE Trans Syst Man Cybern B (Cybern) 1(42):234–245CrossRefGoogle Scholar
  13. Da Silva LGW, Pereira RAF, Abbad JR, Mantovani JRS (2008) Optimised placement of control and protective devices in electric distribution systems through reactive tabu search algorithm. Electr Power Syst Res 78(3):372–381CrossRefGoogle Scholar
  14. Di Caro G, Dorigo M (1998) Ant colonies for adaptive routing in packet-switched communications networks. In: Parallel problem solving from nature—PPSN V, pp 673–682. doi: 10.1007/bfb0056909
  15. Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the travelling salesman problem. IEEE Trans Evol Comput 1(1):53–66CrossRefGoogle Scholar
  16. Dorigo M, Stützle T (2010) Ant colony optimization: overview and recent advances. Int Ser Oper Res Manag Sci. doi: 10.1007/978-1-4419-1665-5_8 MATHGoogle Scholar
  17. Dorigo M, Di Caro G, Gambardella LM (1999) Ant algorithms for discrete optimization. Artif Life 5(2):137–172CrossRefGoogle Scholar
  18. Eberhart RC, Shi Y (1998) Comparison between genetic algorithms and particle swarm optimization. In: Evolutionary programming VII, pp 611–616: doi: 10.1007/bfb0040812
  19. Elhossini A, Areibi S, Dony R (2010) Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization. Evol Comput 18(1):127–156CrossRefGoogle Scholar
  20. Gambardella LM, Dorigo M (2000) An ant colony system hybridized with a new local search for the sequential ordering problem. INFORMS J Comput 12(3):237–255MathSciNetCrossRefMATHGoogle Scholar
  21. García-Martínez C, Cordón O, Herrera F (2007) A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP. Eur J Oper Res 180(1):116–148CrossRefMATHGoogle Scholar
  22. Grosan C, Abraham A (2007) Hybrid evolutionary algorithms: methodologies, architectures, and reviews. In: Studies in computational intelligence, pp 1–17. doi: 10.1007/978-3-540-73297-6_1
  23. Guo J, Wu Y, Liu W (2006) An ant colony optimization algorithm with evolutionary operator for traveling salesman problem. In: Sixth international conference on intelligent systems design and applications. doi: 10.1109/isda.2006.88
  24. Hilber P, Bertling L (2005) A method for extracting reliability importance indices from reliability simulations of electrical networks. PSCC’05 Liège 22–26 August 2005. Int J Electr Power Energy Syst 28(9):589. doi: 10.1016/j.ijepes.2006.05.001 Google Scholar
  25. Hoos HH, Stützle T (2004) Generalised local search machines. In: Stochastic local search, pp 113–147. doi: 10.1016/b978-155860872-6/50020-2
  26. IEEE 1366 (2012) IEEE guide for electric power distribution reliability indices. doi: 10.1109/ieeestd.2001.94438
  27. Korb O, Stützle T, Exner TE (2007) An ant colony optimization approach to flexible protein-ligand docking. Swarm Intell 1(2):115–134CrossRefGoogle Scholar
  28. Lee ZJ, Su SF, Chuang CC, Liu KH (2008) Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment. Appl Soft Comput 8(1):55–78CrossRefGoogle Scholar
  29. Levitin G, Mazal-Tov S, Elmakis D (1995) Genetic algorithm for optimal sectionalizing in radial distribution systems with alternative supply. Electr Power Syst Res 35(3):149–155CrossRefGoogle Scholar
  30. Li F, Brown RE (2004) A cost-effective approach of prioritizing distribution maintenance based on system reliability. IEEE Trans Power Deliv 19(1):439–441CrossRefGoogle Scholar
  31. Li BB, Wang L (2007) A hybrid quantum-inspired genetic algorithm for multi-objective flow shop scheduling. IEEE Trans Syst Man Cybern B (Cybern) 3(37):576–591CrossRefGoogle Scholar
  32. Moscal D (2015) Dumitru Loşonşi, Certitudini şi ipoteze etimologice, Editura Academiei Române, Bucureşti, 2007. Diacronia, (3). doi: 10.17684/i3a44en
  33. Neagu BC, Georgescu G, Guşă MD (2011) Load curves characteristics of consumers supplied from electricity repartition and distribution public systems. Bull Polytech Inst Jassy 57(61):141–157.https://www.researchgate.net/profile/Bogdan_Neagu2/publication/265412134_LOAD_CURVES_CHARACTERISTICS_OF_CONSUMERS_SUPPLIED_FROM_ELECTRICITY_REPARTITION_AND_DISTRIBUTION_PUBLIC_SYSTEMS/links/540ddb6a0cf2df04e756a857.pdf
  34. Pastorino M (2007) Stochastic optimization methods applied to microwave imaging: a review. IEEE Trans Antennas Propag 55(3):538–548CrossRefGoogle Scholar
  35. Popović DH, Greatbanks JA, Begović M, Pregelj A (2005) Placement of distributed generators and reclosers for distribution network security and reliability. Int J Electr Power Energy Syst 27(5):398–408CrossRefGoogle Scholar
  36. Pregelj A, Begovic M, Rohatgi A (2006) Recloser allocation for improved reliability of DG-enhanced distribution networks. IEEE Trans Power Syst 21(3):1442–1449CrossRefGoogle Scholar
  37. Quiroga OA, Meléndez J, Herraiz S (2011) Fault causes analysis in feeders of power distribution networks. Renew Energy Power Qual J. doi: 10.24084/repqj09.619 Google Scholar
  38. Ramírez-Rosado IJ, Domínguez-Navarro JA (2006) New multi-objective tabu search algorithm for fuzzy optimal planning of power distribution systems. IEEE Trans Power Syst 21(1):224–233CrossRefGoogle Scholar
  39. Ray S, Bhattacharya A, Bhattacharjee S (2016) Optimal placement of switches in a radial distribution network for reliability improvement. Int J Electr Power Energy Syst 76(March):53–68CrossRefGoogle Scholar
  40. Sahoo L, Banerjee A, Bhunia AK, Chattopadhyay S (2014) An efficient GA-PSO approach for solving mixed-integer nonlinear programming problem in reliability optimization. Swarm Evol Comput 19:43–51CrossRefGoogle Scholar
  41. Shmygelska A, Hoos HH (2005) An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem. BMC Bioinform 6(1):1–30CrossRefGoogle Scholar
  42. Soudi F, Tomsovic K (1998) Optimized distribution protection using binary programming. IEEE Trans Power Deliv 13(1):218–224CrossRefGoogle Scholar
  43. Teng JH, Lu CN (2002) Feeder-switch relocation for customer interruption cost minimization. IEEE Trans Power Deliv 17(1):254–259CrossRefGoogle Scholar
  44. Teng JH, Liu YH (2003) A novel ACS-based optimum switch relocation method. IEEE Trans Power Syst 18(1):113–120CrossRefGoogle Scholar
  45. Tippachon W, Rerkpreedapong D (2009) Multi-objective optimal placement of switches and protective devices in electric power distribution systems using ant colony optimization. Electr Power Syst Res 79(7):1171–1178CrossRefGoogle Scholar
  46. Tirapong K, Titti S (2014) Reliability improvement of distribution system using Reliability Centered Maintenance. In: 2014 IEEE PES T&D conference and exposition. doi: 10.1109/tdc.2014.6863360
  47. Tripathi PK, Bandyopadhyay S, Pal SK (2007) Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients. Inf Sci 177(22):5033–5049MathSciNetCrossRefMATHGoogle Scholar
  48. Wang J, Huang W, Ma G, Chen S (2015) An improved partheno genetic algorithm for multi-objective economic dispatch in cascaded hydropower systems. Int J Electr Power Energy Syst 67:591–597CrossRefGoogle Scholar
  49. Yang D, Jiao L, Gong M (2009) Adaptive multi-objective optimization based on non-dominated solutions. Comput Intell 25(2):84–108MathSciNetCrossRefGoogle Scholar
  50. Zhao X, Gao XS, Hu ZC (2007) Evolutionary programming based on non-uniform mutation. Appl Math Comput 192(1):1–11MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Asansol Engineering CollegeAsansolIndia
  2. 2.Jadavpur UniversityKolkataIndia
  3. 3.Technical University of IASIIasiRomania

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