Skip to main content

Advertisement

Springer Nature Link
Log in

Improved stochastic fractal search algorithm with chaos for optimal determination of location, size, and quantity of distributed generators in distribution systems

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

In the power system operation, the reduction of the power loss in distribution systems has significance in the reduction of operating cost. In this paper, a novel chaotic stochastic fractal search (CSFS) method is implemented for determining the optimal siting, sizing, and number of distributed generation (DG) units in distribution systems. The objective of the optimal DG placement problem is to minimize the power loss in distribution systems subject to the constraints such as power balance, bus voltage limits, DG capacity limits, current limits, and DG penetration limit. The proposed CSFS method improves the performance of the original SFS by integrating chaotic maps into it. On the other hand, ten chaotic maps are utilized to replace the random scheme of the original SFS to enhance its performance in terms of accuracy of solution and convergence speed, corresponding to ten chaotic variants of the SFS where variant being chosen is the best chaotic variant regarding search performance. For solving the problem, the CSFS is implemented to simultaneously find the optimal siting and sizing of DG units and the optimal number of DG units will be obtained via comparing optimal results from different numbers of DG in the problem. The proposed method is tested on the IEEE 33-bus, 69-bus, and 118-bus radial distribution systems. The obtained results from the CSFS are verified by comparing to those from the original SFS and other methods in the literature. The result comparisons indicate that the proposed CSFS method can obtain higher quality solutions than the original SFS version and many other methods in the literature for the considered cases of the test systems. Moreover, the incorporation of chaos theory allows performing the search process at higher speeds. Therefore, the proposed CSFS method can be a very promising method for solving the problem of optimal placement of DG units in distribution systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Pepermans G, Driesen J, Haeseldonckx D, Belmans R, D’Haeseleer W (2005) Distributed generation: definition, benefits and issues. Energy Policy 33(6):787–798

    Article  Google Scholar 

  2. Ackermann T, Andersson G, Söder L (2001) Distributed generation: a definition. Electr Power Syst Res 57(3):195–204

    Article  Google Scholar 

  3. Georgilakis PS, Hatziargyriou ND (2012) Optimal distributed generation placement in power distribution networks: models, methods, and future research. IEEE Trans Power Syst 28:3420–3428

    Article  Google Scholar 

  4. Rau NS, Wan YH (1994) Optimum location of resources in distributed planning. IEEE Trans Power Syst 9:2014–2020

    Google Scholar 

  5. Keane A, O’Malley M (2005) Optimal allocation of embedded generation on distribution networks. IEEE Trans Power Syst 20:1640–1646

    Article  Google Scholar 

  6. Atwa YM, El-Saadany EF, Salama MMA, Seethapathy P (2010) Optimal renewable resources mix for distribution system energy loss minimization. IEEE Trans Power Syst 25:360–370

    Article  Google Scholar 

  7. Al Hajri MF, Alrashidi MR, El-Hawary ME (2010) Improved sequential quadratic programming approach for optimal distribution generation deployments via stability and sensitivity analyses. Electr Power Compon Syst 38:1595–1614

    Article  Google Scholar 

  8. Khalesi N, Rezaei N, Haghifam MR (2011) DG allocation with application of dynamic programming for loss reduction and reliability improvement. Int J Electr Power Energy Syst 33:288–295

    Article  Google Scholar 

  9. Wang C, Nehrir MH (2004) Analytical approaches for optimal placement of distributed generation sources in power system. IEEE Power Energy Society 19:2068–2076

    Google Scholar 

  10. Hung DQ, Mithulananthan N, Bansal RC (2010) Analytical expressions for DG allocation in primary distribution networks. IEEE Trans Energy Convers 25:814–820

    Article  Google Scholar 

  11. Hung DQ, Mithulananthan N, Bansal RC (2013) Analytical strategies for renewable distributed generation integration considering energy loss minimization. Appl Energy 105:75–85

    Article  Google Scholar 

  12. Hung DQ, Mithulananthan N (2013) Multiple distributed generator placement in primary distribution networks for loss reduction. IEEE Trans Ind Electron 60:1700–1708

    Article  Google Scholar 

  13. Hassan AA, Fahmy FH, Nafeh AESA, Abu-elmagd MA (2017) Genetic single objective optimisation for sizing and allocation of renewable DG systems. Int J Sust Energy 36(6):545–562

    Article  Google Scholar 

  14. Reddy SS, Dey SHN, Paul S (2012) Optimal Size and location of distributed generation and KVAR support in unbalanced 3 phase distribution system using PSO. In: Proceedings of IEEE international conference on emerging trends in electrical engineering and energy management. Chennai, pp 77–83

  15. Jain N, Singh SN, Srivastava SC (2013) A generalized approach for DG planning and viability analysis under market scenario. IEEE Trans Ind Electron 60(11):5075–5085

    Article  Google Scholar 

  16. Sultana S, Roy PK (2016) Krill herd algorithm for optimal location of distributed generator in radial distribution system. Appl Soft Comput 40:391–404

    Article  Google Scholar 

  17. Nara K, Hayashi Y, Ikeda K, Ashizawa T (2001) Application of Tabu search to optimal placement of distributed generators. In: Proceedings of IEEE power engineering society winter meeting. Columbus, pp 918–923

  18. Falaghi H, Haghifam MR (2007) ACO based algorithm for distributed generation sources allocation and sizing in distribution systems. In: Proceedings Power Tech, 2007 IEEE Lausanne, Lausanne, pp 555–560

  19. Moravej Z, Akhlaghi A (2013) A novel approach based on cuckoo search for DG allocation in distribution network. Int J Electr Power Energy Syst 44:672–679

    Article  Google Scholar 

  20. Kollu R, Rayapudi SR, Sadhu VLN (2012) A novel method for optimal placement of distributed generation in distribution systems using HSDO. Eur Trans Electr Power 24:547–561

    Google Scholar 

  21. Mohamed Imran A, Kowsalya M (2014) Optimal size and siting of multiple distributed generators in distribution system using bacterial foraging optimization. Swarm Evolut Comput 15:58–65

    Article  Google Scholar 

  22. Mistry K, Bhavsar V, Roy R (2012) GSA based optimal capacity and location determination of distributed generation in radial distribution system for loss minimization. In: Proceedings of the IEEE 11th international conference on environment and electrical engineering. Venice, pp 513–518

  23. Sultana U, Khairuddin AB, Mokhtar AS, Zareen N, Sultana B (2016) Grey wolf optimizer based placement and sizing of multiple distributed generation in the distribution system. Energy 111:525–536

    Article  Google Scholar 

  24. Ali ES, Elazim SA, Abdelaziz AY (2016) Ant lion optimization algorithm for renewable distributed generations. Energy 116:445–458

    Article  Google Scholar 

  25. Niknam T, Taheri SI, Aghaei J, Tabatabaei S, Nayeripour M (2011) A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources. Appl Energy 88(12):4817–4830

    Article  Google Scholar 

  26. Nekooei K, Farsangi MM, Nezamabadi-Pour H, Lee KY (2013) An improved multi-objective harmony search for optimal placement of DGs in distribution systems. IEEE Trans Smart Grid 4(1):557–567

    Article  Google Scholar 

  27. Meena N, Swankar A, Gupta N, Niazi K (2017) A multi-objective taguchi approach for optimal DG integration in distribution systems. IET Gener Transm Distrib 11(9):2418–2428

    Article  Google Scholar 

  28. Nguyen TP, Dieu VN, Vasant P (2017) Symbiotic organism search algorithm for optimal size and siting of distributed generators in distribution systems. Int J Energy Opt Eng 6(3):1–28

    Google Scholar 

  29. Moradi MH, Abedini N (2012) A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. Int J Electr Power Energy Syst 34:66–74

    Article  Google Scholar 

  30. Moradi MH, Zeinalzadeh A, Mohammadi Y, Abedini M (2014) An efficient hybrid method for solving the optimal sitting and sizing problem of DG and shunt capacitor banks simultaneously based on imperialist competitive algorithm and genetic algorithm. Int J Electr Power Energy Syst 54:101–111

    Article  Google Scholar 

  31. Gitizadeh M, Vahed AA, Aghaei J (2013) Multistage distribution system expansion planning considering distributed generation using hybrid evolutionary algorithms. Appl Energy 101:655–666

    Google Scholar 

  32. Kansal S, Kumar V, Tyagi B (2016) Hybrid approach for optimal placement of multiple DGs of multiple types in distribution networks. Int J Electr Power Energy Syst 75:226–235

    Article  Google Scholar 

  33. Salimi H (2015) Stochastic fractal search: a powerful metaheuristic algorithm. Knowl-Based Syst 75:1–18

    Article  Google Scholar 

  34. El-Fergany AA, Hasanien HM (2017) Optimized settings of directional overcurrent relays in meshed power networks using stochastic fractal search algorithm. Int Trans Electr Energy Syst 27:e2395

    Article  Google Scholar 

  35. Tejani GG, Bhensdadia VH, Bureerat S (2016) Examination of three meta-heuristic algorithms for optimal design of planar steel frames. Adv Comput Design 1(1):79–86

    Article  Google Scholar 

  36. Rahman TA (2016) Parameters optimization of an SVM-classifier using stochastic fractal search algorithm for monitoring an aerospace structure. Int J Fluids Heat Transfer 1:68–78

    Google Scholar 

  37. Rahman TA, Tokhi MO (2016) Enhanced stochastic fractal search algorithm with chaos. In: 2016 7th IEEE control and system graduate research colloquium (ICSGRC). Shah Alam, pp 22–27

  38. Wang N, Liu L, Liu L (2001) Genetic algorithm in chaos. OR Trans 5:1–10

    Google Scholar 

  39. Yang L-J, Chen T-L (2002) Application of chaos in genetic algorithms. Commun Theor Phys 38:168–172

    Article  Google Scholar 

  40. Jothiprakash V, Arunkumar R (2013) Optimization of hydropower reservoir using evolutionary algorithms coupled with chaos. Water Resour Manag 27(7):1963–1979

    Article  Google Scholar 

  41. Zhenyu G, Bo C, Min Y, Binggang C (2006) Self-adaptive chaos differential evolution. In: Jiao L, Wang L, Gao X, Liu J, Wu F (eds) Advances in natural computation Lecture notes in computer science, vol 4221. Springer, Berlin, pp 972–975

    Chapter  Google Scholar 

  42. Saremi S, Mirjalili SM, Mirjalili S (2014) Chaotic krill herd optimization algorithm. Proc Technol 12:180–185

    Article  Google Scholar 

  43. Wang G-G, Guo L, Gandomi AH, Hao G-S, Wang H (2014) Chaotic krill herd algorithm. Inf Sci 274:17–34

    Article  MathSciNet  Google Scholar 

  44. Peitgen H-O, Jurgens H, Saupe D (1992) Chaos and fractals. Springer, New York

    Book  MATH  Google Scholar 

  45. Li Y, Deng S, Xiao D (2011) A novel Hash algorithm construction based on chaotic neural network. Neural Comput Appl 20:133–141

    Article  Google Scholar 

  46. Ott E (2002) Chaos in dynamical systems. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  47. Zimmerman RD, Murillo-Sánchez CE, Thomas RJ (2011) MATPOWER: steady-state operations, planning, and analysis tools for power systems research and education. IEEE Trans Power Syst 26(1):12–19

    Article  Google Scholar 

  48. Prakash K, Sydulu M (2007) Particle swarm optimization based capacitor placement on radial distribution systems. In: Proceedings power engineering society general meeting’07. IEEE, Tampa, pp 1–5

  49. Kashem MA, Ganapathy V, Jasmon GB, Buhari MI (2000) A novel method for loss minimization in distribution networks. In: Proceedings of IEEE international conference on electric utility deregulation and restructuring and power technologies. London, pp 251–256

  50. Baran ME, Wu FF (1989) Optimal capacitor placement on radial distribution systems. IEEE Trans Power Del 4(1):725–734

    Article  Google Scholar 

  51. Zhang D, Fu Z, Zhang L (2007) An improved TS algorithm for loss-minimum reconfiguration in large-scale distribution systems. Electr Power Syst Res 77:685–694

    Article  Google Scholar 

  52. Mahmoud K, Yorino N, Ahmed A (2016) Optimal distributed generation allocation in distribution systems for loss minimization. IEEE Trans Power Syst 31(2):960–969

    Article  Google Scholar 

  53. El-Fergany A (2015) Optimal allocation of multi-type distributed generators using backtracking search optimization algorithm. Int J Electr Power Energy Syst 64:1197–1205

    Article  Google Scholar 

  54. Jamian JJ, Mustafa MW, Mokhlis H (2015) Optimal multiple distributed generation output through rank evolutionary particle swarm optimization. Neurocomputing 152:190–198

    Article  Google Scholar 

  55. Injeti SK, Kumar NP (2013) A novel approach to identify optimal access point and capacity of multiple DGs in a small, medium and large scale radial distribution systems. Electr Power Energy Syst 45:142–151

    Article  Google Scholar 

  56. Prabha DR, Jayabarathi T (2016) Optimal placement and sizing of multiple distributed generating units in distribution networks by invasive weed optimization algorithm. Ain Shams Eng J 7(2):683–694

    Article  Google Scholar 

  57. Sultana S, Roy PK (2014) Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems. Int J Electr Power Energy Syst 63:534–545

    Article  Google Scholar 

  58. Muthukumar K, Jayalalitha S (2016) Optimal placement and sizing of distributed generators and shunt capacitors for power loss minimization in radial distribution networks using hybrid heuristic search optimization technique. Electr Power Energy Syst 78:299–319

    Article  Google Scholar 

  59. Ganguly S, Samajpati D (2017) Distributed generation allocation with on-load tap changer on radial distribution networks using adaptive genetic algorithm. Appl Soft Comput 59:45–67

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Power Systems, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Tri Phuoc Nguyen, Tung The Tran & Dieu Ngoc Vo

Authors
  1. Tri Phuoc Nguyen
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Tung The Tran
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Dieu Ngoc Vo
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Dieu Ngoc Vo.

Ethics declarations

Conflict of interest

All the authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nguyen, T.P., Tran, T.T. & Vo, D.N. Improved stochastic fractal search algorithm with chaos for optimal determination of location, size, and quantity of distributed generators in distribution systems. Neural Comput & Applic 31, 7707–7732 (2019). https://doi.org/10.1007/s00521-018-3603-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3603-1

Keywords