Advertisement

An improved meta-heuristic method to maximize the penetration of distributed generation in radial distribution networks

  • Khoa Hoang Truong
  • Perumal NallagowndenEmail author
  • Irraivan Elamvazuthi
  • Dieu Ngoc Vo
Original Article
  • 19 Downloads

Abstract

This paper proposes a novel scheme based on an improved meta-heuristic method to determine the optimal number of distributed generation (DG) units to be installed in distribution networks for maximum DG penetration. The proposed meta-heuristic method is the quasi-oppositional chaotic symbiotic organisms search (QOCSOS) algorithm, which is the improved version of the original SOS algorithm. QOCSOS integrates two search strategies including quasi-opposition-based learning and chaotic local search into SOS to achieve better performance. In this study, QOCSOS was implemented to find the optimal number, location, size, and power factor of DG units considering different values of DG power factor (unity and non-unity), with the objective of maximum real power loss reduction. The effectiveness of the proposed method was validated on the standard IEEE radial distribution networks including 33, 69, and 118-bus test networks. The results obtained by QOCSOS were compared to those from other methods available in the literature and the standard SOS algorithm. Comparative results revealed that QOCSOS obtained better solutions than other compared methods, and performed greater than SOS. Accordingly, QOCSOS can be a very favourable method to cope with the optimal DG placement problem.

Keywords

Symbiotic organisms search Distributed generation Power loss reduction Optimal power factor Optimal placement Quasi-oppositional 

Notes

Acknowledgements

This research work is supported by Universiti Teknologi PETRONAS with the help of Department of Electrical and Electronic Engineering.

References

  1. 1.
    Ackermann T, Andersson G, Söder L (2001) Distributed generation: a definition. Electr Power Syst Res 57:195–204CrossRefGoogle Scholar
  2. 2.
    Pepermans G, Driesen J, Haeseldonckx D, Belmans R, Dhaeseleer W (2005) Distributed generation: definition, benefits and issues. Energy Policy 33:787–798CrossRefGoogle Scholar
  3. 3.
    Paliwal P, Patidar NP, Nema RK (2014) Planning of grid integrated distributed generators: a review of technology, objectives and techniques. Renew Sustain Energy Rev 40:557–570CrossRefGoogle Scholar
  4. 4.
    Rau NS, Yih-Heui W (1994) Optimum location of resources in distributed planning. IEEE Trans Power Syst 9:2014–2020CrossRefGoogle Scholar
  5. 5.
    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 33:288–295CrossRefGoogle Scholar
  6. 6.
    AlHajri 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 38:1595–1614CrossRefGoogle Scholar
  7. 7.
    Dent CJ, Ochoa LF, Harrison GP (2010) Network distributed generation capacity analysis using OPF with voltage step constraints. IEEE Trans Power Syst 25:296–304CrossRefGoogle Scholar
  8. 8.
    Ochoa LF, Harrison GP (2011) Minimizing energy losses: optimal accommodation and smart operation of renewable distributed generation. IEEE Trans Power Syst 26:198–205CrossRefGoogle Scholar
  9. 9.
    Georgilakis PS, Hatziargyriou ND (2015) A review of power distribution planning in the modern power systems era: models, methods and future research. Electr Power Syst Res 121:89–100CrossRefGoogle Scholar
  10. 10.
    Acharya N, Mahat P, Mithulananthan N (2006) An analytical approach for DG allocation in primary distribution network. Int J Electr Power Energy Syst 28:669–678CrossRefGoogle Scholar
  11. 11.
    Hung DQ, Mithulananthan N, Bansal RC (2010) Analytical expressions for DG allocation in primary distribution networks. IEEE Trans Energy Convers 25:814–820CrossRefGoogle Scholar
  12. 12.
    Hung DQ, Mithulananthan N, Bansal RC (2013) Analytical strategies for renewable distributed generation integration considering energy loss minimization. Appl Energy 105:75–85CrossRefGoogle Scholar
  13. 13.
    Prakash P, Khatod DK (2016) Optimal sizing and siting techniques for distributed generation in distribution systems: A review. Renew Sustain Energy Rev 57:111–130CrossRefGoogle Scholar
  14. 14.
    Li S (2016) The art of clustering bandits. Università degli Studi dell’Insubria, VareseGoogle Scholar
  15. 15.
    Singh D, Singh D, Verma KS (2009) Multiobjective optimization for DG planning with load models. IEEE Trans Power Syst 24:427–436CrossRefGoogle Scholar
  16. 16.
    Ganguly S, Samajpati D (2015) Distributed generation allocation on radial distribution networks under uncertainties of load and generation using genetic algorithm. IEEE Trans Sustain Energy 6:688–697CrossRefGoogle Scholar
  17. 17.
    El-Zonkoly AM (2011) Optimal placement of multi-distributed generation units including different load models using particle swarm optimization. Swarm Evolut Comput 1:50–59CrossRefGoogle Scholar
  18. 18.
    Soroudi A, Afrasiab M (2012) Binary PSO-based dynamic multi-objective model for distributed generation planning under uncertainty. IET Renew Power Gener 6:67–78CrossRefGoogle Scholar
  19. 19.
    Moradi MH, Abedini M (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 34:66–74CrossRefGoogle Scholar
  20. 20.
    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 63:534–545CrossRefGoogle Scholar
  21. 21.
    Sharma S, Bhattacharjee S, Bhattacharya A (2016) Quasi-oppositional swine influenza model based optimization with quarantine for optimal allocation of DG in radial distribution network. Int J Electr Power Energy Syst 74:348–373CrossRefGoogle Scholar
  22. 22.
    Quadri IA, Bhowmick S, Joshi D (2018) A comprehensive technique for optimal allocation of distributed energy resources in radial distribution systems. Appl Energy 211:1245–1260CrossRefGoogle Scholar
  23. 23.
    Moravej Z, Akhlaghi A (2013) A novel approach based on cuckoo search for DG allocation in distribution network. Int J Electr Power Energy Syst 44:672–679CrossRefGoogle Scholar
  24. 24.
    Mohamed Imran A, Kowsalya M (2014) Optimal size and siting of multiple distributed generators in distribution system using bacterial foraging optimization. Swarm Evolut Comput 15:58–65CrossRefGoogle Scholar
  25. 25.
    Sultana S, Roy PK (2016) Krill herd algorithm for optimal location of distributed generator in radial distribution system. Appl Soft Comput 40:391–404CrossRefGoogle Scholar
  26. 26.
    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 111:525–536CrossRefGoogle Scholar
  27. 27.
    Ali ES, Abd Elazim SM, Abdelaziz AY (2016) Ant lion optimization algorithm for renewable distributed generations. Energy 116(1):445–458CrossRefGoogle Scholar
  28. 28.
    Saha S, Mukherjee V (2016) Optimal placement and sizing of DGs in RDS using chaos embedded SOS algorithm. IET Gener Transm Distrib 10:3671–3680CrossRefGoogle Scholar
  29. 29.
    Meena NK, Swarnkar A, Gupta N, Niazi KR (2017) Multi-objective Taguchi approach for optimal DG integration in distribution systems. IET Gener Transm Distrib 11:2418–2428CrossRefGoogle Scholar
  30. 30.
    Kumar S, Mandal KK, Chakraborty N (2019) Optimal DG placement by multi-objective opposition based chaotic differential evolution for techno-economic analysis. Appl Soft Comput 78:70–83CrossRefGoogle Scholar
  31. 31.
    Gitizadeh M, Vahed AA, Aghaei J (2013) Multistage distribution system expansion planning considering distributed generation using hybrid evolutionary algorithms. Appl Energy 101:655–666CrossRefGoogle Scholar
  32. 32.
    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 54:101–111CrossRefGoogle Scholar
  33. 33.
    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 75:226–235CrossRefGoogle Scholar
  34. 34.
    Arabi Nowdeh S, Davoudkhani IF, Hadidian Moghaddam MJ, Najmi ES, Abdelaziz AY, Ahmadi A et al (2019) Fuzzy multi-objective placement of renewable energy sources in distribution system with objective of loss reduction and reliability improvement using a novel hybrid method. Appl Soft Comput 77:761–779CrossRefGoogle Scholar
  35. 35.
    Truong KH, Nallagownden P, Baharudin Z, Vo DN (2019) A quasi-oppositional-chaotic symbiotic organisms search algorithm for global optimization problems. Appl Soft Comput 77:567–583CrossRefGoogle Scholar
  36. 36.
    IEEE standard for interconnection and interoperability of distributed energy resources with associated electric power systems interfaces. In: IEEE Std 1547-2018 (Revision of IEEE Std 1547-2003), pp 1–138, 2018Google Scholar
  37. 37.
    Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: international conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC’06), pp 695–701Google Scholar
  38. 38.
    Rahnamayan S, Tizhoosh HR, Salama MMA (2007) Quasi-oppositional differential evolution. In: 2007 IEEE congress on evolutionary computation. pp 2229–2236Google Scholar
  39. 39.
    Ji J, Gao S, Wang S, Tang Y, Yu H, Todo Y (2017) Self-adaptive gravitational search algorithm with a modified chaotic local search. IEEE Access 5:17881–17895CrossRefGoogle Scholar
  40. 40.
    Jia D, Zheng G, Khurram Khan M (2011) An effective memetic differential evolution algorithm based on chaotic local search. Inf Sci 181:3175–3187CrossRefGoogle Scholar
  41. 41.
    Cheng M-Y, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112CrossRefGoogle Scholar
  42. 42.
    Zimmerman RD, Murillo-Sanchez CE (2011) Matpower 4.1 user’s manual. In: Power Systems Engineering Research Center, Cornell University, Ithaca, NYGoogle Scholar
  43. 43.
    Baran ME, Wu FF (1989) Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Trans Power Deliv 4:1401–1407CrossRefGoogle Scholar
  44. 44.
    Mahmoud K, Yorino N, Ahmed A (2016) Optimal distributed generation allocation in distribution systems for loss minimization. IEEE Trans Power Syst 31:960–969CrossRefGoogle Scholar
  45. 45.
    Baran ME, Wu FF (1989) Optimal capacitor placement on radial distribution systems. IEEE Trans Power Deliv 4:725–734CrossRefGoogle Scholar
  46. 46.
    Zhang D, Fu Z, Zhang L (2007) An improved TS algorithm for loss-minimum reconfiguration in large-scale distribution systems. Electr Power Syst Res 77:685–694CrossRefGoogle Scholar
  47. 47.
    Injeti SK, Prema Kumar N (2013) A novel approach to identify optimal access point and capacity of multiple DGs in a small, medium and large scale radial distribution systems. Int J Electr Power Energy Syst 45:142–151CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Khoa Hoang Truong
    • 1
  • Perumal Nallagownden
    • 1
    Email author
  • Irraivan Elamvazuthi
    • 1
  • Dieu Ngoc Vo
    • 2
  1. 1.Department of Electrical and Electronic EngineeringUniversiti Teknologi PETRONASSeri IskandarMalaysia
  2. 2.Department of Power SystemsHo Chi Minh City University of Technology, VNU-HCMHo Chi Minh CityVietnam

Personalised recommendations