Advertisement

Soft Computing

, Volume 23, Issue 23, pp 12317–12330 | Cite as

A fuzzy pragmatic DE–CSA hybrid approach for unbalanced radial distribution system planning with distributed generation

  • Padarbinda SamalEmail author
  • Sanjib Ganguly
  • Sanjeeb Mohanty
Methodologies and Application
  • 82 Downloads

Abstract

This paper presents a multi-objective planning approach for the optimal placement of distributed generation (DG) units in unbalanced radial distribution systems using a hybrid differential evolution (DE) and cuckoo search algorithm (CSA). In this planning optimization, the objective functions formulated are the minimization of: (i) total real power loss, (ii) maximum average voltage deviation index, (iii) total neutral current, and (iv) total cost. The total cost includes the cost of energy purchased from the grid and the capital investment and operational cost of DG units. These objective functions are aggregated using max–max and max–min analogies. Fuzzy set theory is used to model the uncertainties in load and generation of renewable DG units. Hence, these objective functions are found to be fuzzy sets. An appropriate defuzzification approach is used so as to compare and rank different solutions. A modified three-phase forward–backward sweep-based load flow algorithm including the DG model is used as the support subroutine of the proposed solution algorithm using the hybrid DE–CSA. The simulation results show that significant improvements in power loss, maximum average voltage deviation, system unbalance, and total annual energy cost are obtained due to the DG integration in unbalanced distribution networks. The results obtained with fuzzy-based modeling of load and generation are found to be superior as compared to the deterministic load and generation.

Keywords

Unbalanced radial distribution system planning Fuzzy set Distributed generation Differential evolution algorithm Cuckoo search algorithm 

List of symbols

NBR

Total number of branches/lines/feeder segments

NB

Total number of buses

NG

Total number of DG units

RM(.)

Removal function

Superscript (woDG)

Without DG

Superscript (wDG)

With DG

Superscript ~

Fuzzy quantity

Superscript −

Phasor quantity

Superscript a, b, c

Phases a, b, c

\( IL(I) \)

Load (branch) current

V

Bus voltage

\( P\left( Q \right) \)

Active (reactive) power demand by the load

Notes

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. Abu-Mouti FS, El-Hawary ME (2011) Optimal distributed generation allocation and sizing in distribution systems via artificial bee colony algorithm. IEEE Trans Power Deliv 26:2090–2101CrossRefGoogle Scholar
  2. Adefarati T, Bansal RC (2016) Integration of renewable distributed generators into the distribution system: a review. IET Renew Power Gener 10(7):873–884CrossRefGoogle Scholar
  3. Al Abri RS, El-Saadany EF, Atwa YM (2013) Optimal placement and sizing method to improve the voltage stability margin in a distribution system using distributed generation. IEEE Trans Power Syst 28:326–334CrossRefGoogle Scholar
  4. Ali SH (2012) A novel tool (FP-KC) for handle the three main dimensions reduction and association rule mining. In: 2012 6th international conference on sciences of electronics, technologies of information and telecommunications (SETIT), Sousse, pp 951–961.  https://doi.org/10.1109/setit.2012.6482042
  5. Al-Janabi S (2017) Pragmatic miner to risk analysis for intrusion detection (PMRA-ID). In: Mohamed A, Berry M, Yap B (eds) Soft computing in data science. SCDS 2017. Communications in computer and information science, vol 788. Springer, BerlinGoogle Scholar
  6. Al-Janabi S (2018) Smart system to create an optimal higher education environment using IDA and IOTs. Int J Comput Appl.  https://doi.org/10.1080/1206212x.2018.1512460 CrossRefGoogle Scholar
  7. Al-Janabi S, Alwan E (2017) Soft mathematical system to solve black box problem through development the FARB based on hyperbolic and polynomial functions. In: 2017 10th international conference on Developments in eSystems Engineering (DeSE), Paris, pp 37–42.  https://doi.org/10.1109/dese.2017.23
  8. Al-Janabi S, Al-Shourbaji I, Salman MA (2018) Assessing the suitability of soft computing approaches for forest fires prediction. Appl Comput Inform 14(2):214–224Google Scholar
  9. Amin F, Fahmi A, Abdullah S, Ali A, Ahmed R, Ghani F (2017) Triangular cubic linguistic hesitant fuzzy aggregation operators and their application in group decision making. J Intell Fuzzy Syst 34:2401–2416CrossRefGoogle Scholar
  10. Bayod-Rújula AA (2009) Future development of the electricity systems with distributed generation. Energy 34:377–383CrossRefGoogle Scholar
  11. Coelho FCR, da Silva IC, Bruno J, Dias H (2018) Optimal distributed generation allocation using a new metaheuristic. J Control Autom Electr Syst 29(1):91–98CrossRefGoogle Scholar
  12. Doagou-Mojarrad H, Gharehpetian GB, Rastegar H, Olamaei J (2013) Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithm. Energy 54:129–138CrossRefGoogle Scholar
  13. El-Khattam W, Salama MMA (2004) Distributed generation technologies, definitions and benefits. Electr Power Syst Res 71:119–128CrossRefGoogle Scholar
  14. Fahmi A, Abdullah S, Amin F, Siddque N, Ali A (2017a) Aggregation operators on triangular cubic fuzzy numbers and its application to multi-criteria decision making problems. J Intell Fuzzy Syst 33:3323–3337CrossRefGoogle Scholar
  15. Fahmi A, Abdullah S, Amin F, Ali A (2017b) Precursor selection for sol–gel synthesis of titanium carbide nanopowders by a new cubic fuzzy multi-attribute group decision-making model. J Intell Syst.  https://doi.org/10.1515/jisys-2017-0083 CrossRefGoogle Scholar
  16. Fahmi A, Abdullah S, Amin F, Ali A (2018a) Weighted average rating (WAR) method for solving group decision making problem using triangular cubic fuzzy hybrid aggregation (TCFHA). Punjab Univ J Math 50(1):23–34MathSciNetGoogle Scholar
  17. Fahmi A, Abdullah S, Amin F, Ali A, Khan WA (2018b) Some geometric operators with triangular cubic linguistic hesitant fuzzy number and their application in group decision-making. J Intell Fuzzy Syst.  https://doi.org/10.3233/jifs-18125 CrossRefGoogle Scholar
  18. Fahmi A, Abdullah S, Amin F, Khan MSA (2018c) Trapezoidal cubic fuzzy number einstein hybrid weighted averaging operators and its application to decision making. Soft Comput.  https://doi.org/10.1007/s00500-018-3242-6 CrossRefzbMATHGoogle Scholar
  19. Fahmi A, Amin F, Abdullah S, Ali A (2018d) Cubic fuzzy einstein aggregation operators and its application to decision making. Int J Syst Sci.  https://doi.org/10.1080/00207721.2018.1503356 MathSciNetCrossRefzbMATHGoogle Scholar
  20. 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(3):688–697CrossRefGoogle Scholar
  21. Ganguly S, Sahoo NC, Das D (2013) Multi-objective particle swarm optimization based on fuzzy-Pareto-dominance for possibilistic planning of electrical distribution systems incorporating distributed generation. Fuzzy Sets Syst 213:47–73MathSciNetCrossRefGoogle Scholar
  22. Gkaidatzis PA, Bouhouras AS, Doukas DI, Sgouras KI, Labridis DP (2017) Load variations impact on optimal DG placement problem concerning energy loss reduction. Electr Power Syst Res 152:36–47CrossRefGoogle Scholar
  23. Haghifam M-R, Falaghi H, Malik OP (2008) Risk-based distributed generation placement. IET Gener Transm Distrib 2(2):252–260CrossRefGoogle Scholar
  24. Hassan AA, Fahmy FH, El-S A, Nafeh A, Abu-elmagd MA (2017) Genetic single objective optimisation for sizing and allocation of renewable DG systems. Int J Sustain Energy 36(6):545–562.  https://doi.org/10.1080/14786451.2015.1053393 CrossRefGoogle Scholar
  25. Hejazi HA, Araghi AR, Vahidi B, Hosseinian SH, Abedi M, Mohsenian-Rad H (2013) Independent distributed generation planning to profit both utility and DG investors. IEEE Trans Power Syst 28:1170–1178CrossRefGoogle Scholar
  26. Hien NC, Mithulananthan N, Bansal RC (2013) Location and sizing of distributed generation units for loadabilty enhancement in primary feeder. IEEE Syst J 7(4):797–806CrossRefGoogle Scholar
  27. Hung DQ, Mithulananthan N (2013) Multiple distributed generator placement in primary distribution networks for loss reduction. IEEE Trans Ind Electron 60(4):1700–1708CrossRefGoogle Scholar
  28. Jabr RA, Pal BC (2009) Ordinal optimisation approach for locating and sizing of distributed generation. IET Gener Transm Distrib 3(8):713–723CrossRefGoogle Scholar
  29. Jamian JJ, Mustafa MW, Mokhlis H, Baharudin MA, Abdilahi AM (2014) Gravitational search algorithm for optimal distributed generation operation in autonomous network. Arab J Sci Eng 39(10):7183–7188CrossRefGoogle Scholar
  30. Kansal S, Tyagi B, Kumar V (2017) Cost–benefit analysis for optimal distributed generation placement in distribution systems. Int J Ambient Energy 38(1):45–54.  https://doi.org/10.1080/01430750.2015.1031407 CrossRefGoogle Scholar
  31. Kim SY, Kim WW, Kim J-O (2014) Determining the optimal capacity of renewable distributed generation using restoration methods. IEEE Trans Power Syst 29(5):2001–2013CrossRefGoogle Scholar
  32. Kroposki B, Sen PK, Malmedal K (2013) Optimum sizing and placement of distributed and renewable energy sources in electric power distribution systems. IEEE Trans Ind Appl 49:2741–2752CrossRefGoogle Scholar
  33. Melgar Dominguez OD, Pourakbari Kasmaei M, Mantovani JRS (2018) Adaptive robust short-term planning of electrical distribution systems considering siting and sizing of renewable energy-based DG units. IEEE Trans Sustain Energy.  https://doi.org/10.1109/tste.2018.2828778 CrossRefGoogle Scholar
  34. Moreti W, Teixeira J, Belati EA (2018) New method for optimal allocation of distribution generation aimed at active losses reduction. Renew Energy.  https://doi.org/10.1016/j.renene.2018.02.065 CrossRefGoogle Scholar
  35. Nasiraghdam H, Jadid S (2012) Optimal hybrid PV/WT/FC sizing and distribution system reconfiguration using multi-objective artificial bee colony (MOABC) algorithm. Sol Energy 86(10):3057–3071CrossRefGoogle Scholar
  36. Nguyen TP, Vo DN (2018) A novel stochastic fractal search algorithm for optimal allocation of distributed generators in radial distribution systems. Appl Soft Comput 70(10):773–796.  https://doi.org/10.1016/j.asoc.2018.06.020 CrossRefGoogle Scholar
  37. Niknam T (2008) A new approach based on ant colony optimization for daily Volt/Var control in distribution networks considering distributed generators. Energy Convers Manag 49(12):3417–3424CrossRefGoogle Scholar
  38. Price K, Storn RM, Lampinen JA (2006) Differential evolution: a practical approach to global optimization. Springer, BerlinzbMATHGoogle Scholar
  39. Quadr IA, Bhowmick S, Joshi D (2018) A hybrid teaching–learning-based optimization technique for optimal DG sizing and placement in radial distribution systems. Soft Comput.  https://doi.org/10.1007/s00500-018-3544-8 CrossRefGoogle Scholar
  40. Ramana T, Ganesh V, Sivanagaraju S (2010) Distributed generator placement and sizing in unbalanced radial distribution system. Cogener Distrib Gener J 25:52–71CrossRefGoogle Scholar
  41. Samal P, Ganguly S (2015) A modified forward backward sweep load flow algorithm for unbalanced radial distribution systems. In: IEEE power and energy society general meeting, pp 1–5Google Scholar
  42. Samal P, Ganguly S, Mohanty S (2016) Planning of unbalanced radial distribution systems using differential evolution algorithm. J Energy Syst.  https://doi.org/10.1007/s12667-016-0202-z CrossRefGoogle Scholar
  43. Sanjay R, Jayabarathi T, Raghunathan T, Ramesh V, Mithulananthan N (2017) Optimal allocation of distributed generation using hybrid grey wolf optimizer. IEEE Access 5:14807–14818CrossRefGoogle Scholar
  44. Shaaban MF, Atwa YM, El-Saadany EF (2013) DG allocation for benefit maximization in distribution networks. IEEE Trans Power Syst 28:939–949CrossRefGoogle Scholar
  45. Sheng W, Liu K-Y, Liu Y, Meng X, Li Y (2015) Optimal placement and sizing of distributed generation via an improved nondominated sorting genetic algorithm II. IEEE Trans Power Deliv 30(2):569–578CrossRefGoogle Scholar
  46. Soroudi A, Ehsan M (2011) Application of a modified NSGA method for multi-objective static distributed generation planning. Arab J Sci Eng 36(5):809–825CrossRefGoogle Scholar
  47. Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: 2009 world congress on nature and biologically inspired computing, NABIC 2009—proceedings, pp 210–214Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Padarbinda Samal
    • 1
    Email author
  • Sanjib Ganguly
    • 2
  • Sanjeeb Mohanty
    • 3
  1. 1.Department of Electrical EngineeringKIIT Deemed to be UniversityBhubaneswarIndia
  2. 2.Department of Electronics and Electrical EngineeringIndian Institute of Technology GuwahatiGuwahatiIndia
  3. 3.Department of Electrical EngineeringNational Institute of TechnologyRourkelaIndia

Personalised recommendations