Skip to main content
SpringerLink
Log in

Optimal siting and sizing of distributed generation in radial distribution system using a novel student psychology-based optimization algorithm

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

Abstract

This paper propounds a new metaheuristic optimization algorithm to obtain the optimum site and size of distributed generation (DG) in radial distribution system (RDS). This problem is concomitant with reducing the active power loss, the total voltage deviation and the voltage stability index of the RDS considering different types of load models (such as constant power (CP), industrial (IL), residential (RES) as well as commercial (COM)). To solve this weighting factor-based multi-objective DG allocation problem, a novel metaheuristic optimization algorithm, student psychology-based optimization (SPBO), is suggested in this article. A multi-criteria approach (such as the analytic hierarchy process) is employed to optimize the weighting factors involved. To the best of the authors’ knowledge, this is the first time that this novel SPBO algorithm is being used for optimal siting and sizing of DGs in the RDS for different types of load models (like CP, IL, RES and COM) for IEEE 33-bus, IEEE 69-bus and practical Brazil 136-bus RDS. The simulation results attained by the proposed SPBO algorithm are compared with the results provided by recently surfaced Harris hawks optimization (HHO) algorithm and other state-of-the-art algorithms. The outcomes prove that the suggested SPBO algorithm is more efficient to solve the optimal multiple DG allocation problem with minimum real power loss, less computational time and prominent convergence rate.

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

Access this article

Log in via an institution

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Data availability

The data that support the findings of this study are openly available in https://ieeexplore.ieee.org/document/19265, DOI: https://doi.org/10.1109/61.19265 for IEEE 33-bus RDS) [49], https://www.Sciencedirect.com/science/article/abs/pii/S0142061507001342, DOI: https://doi.org/10.1016/j.ijepes.2007.08.004 (for IEEE 69-bus RDS) [50] and control & automation and https://www.sba.org.br/revista/vol11/v11a261.htm (for 136-bus RDS) [51].

Notes

  1. The used abbreviations are in line with the referred literatures.

  2. The used abbreviations are in line with the referred literatures.

  3. The used abbreviations are in line with the referred literatures.

Abbreviations

ABC:

Artificial bee colony

AM:

Analytical method

BFO:

Bacterial foraging optimization

BBO:

Biogeography-based optimization

BSA:

Backtracking search algorithm

COM:

Commercial

CABC:

Chaotic ABC

CP:

Constant power

CSA:

Cuckoo search algorithm

CSFS:

Chaotic SFS

CTLBO:

Comprehensive TLBO

DG:

Distributed generation

ECOA:

Enhanced coyote optimization algorithm

FPA:

Flower pollination algorithm

GA:

Genetic algorithm

HHO:

Harris hawks optimization

HSA-PABC:

Harmony search algorithm-particle ABC

IAM:

Improved AM

IL:

Industrial

IRRO:

Improved raven roosting optimization

ISCA:

Improved SCA

IWD:

Intelligent water drop

KHA:

Krill herd algorithm

LSA:

Lightning search algorithm

MOCDE:

Multi-objective opposition-based chaotic differential evolution

MOCSCA:

Multi-objective chaotic SCA

MOCSOS:

Multi-objective chaotic SOS

MOTM:

Multi-objective TM

PSO:

Particle swarm optimization

QOCSOS:

Quasi-oppositional chaotic SOS

QOSIMBO-Q:

Quasi-oppositional SIMBO-Q

QOTLBO:

Quasi-oppositional TLBO

RDS:

Radial distribution system

RES:

Residential

SIMBO-Q:

Swine influenza model-based optimization with quarantine

SCA:

Sine–cosine algorithm

SFS:

Stochastic factorial search

SOS:

Symbiotic search algorithm

SPBO:

Student psychology-based optimization

TLBO:

Teaching–learning-based optimization

TM:

Taguchi method

TVD:

Total voltage deviation

VSI:

Voltage stability index

VSMI:

Voltage stability margin index

WOA:

Whale optimization algorithm

\(K\) :

Priority matrix

\(k_{1}\) :

Weighting factor, 0.714

\(k_{2}\) :

Weighting factor, 0.143

\(k_{3}\) :

Weighting factor, 0.143

\({\text{loc}}_{{{\text{DG}}(i)}}\) :

Location of the ith DG

\({\text{loc}}_{{{\text{max}}}}\) :

Maximum location

\({\text{loc}}_{{{\text{min}}}}\) :

Minimum location

\(N_{b}\) :

Number of buses

\(N_{{{\text{DG}}}}\) :

Number of DGs

\(of_{1}\) :

System \(P_{{{\text{loss}}}}\) (p.u.)

\(of_{2}\) :

System TVD (p.u.)

\(of_{3}\) :

System VSI (p.u.)

\(P_{i}\) :

Real power of the ith bus

\(P_{Dj}\) :

Real power demand at the jth bus

\(P_{{{\text{DG}}}}^{i}\) :

Active power of the ith DG

\(P_{{{\text{DG}},{\text{max}}}}^{i}\) :

Maximum real power of the ith DG

\(P_{{{\text{DG}},\min }}^{i}\) :

Minimum active power of the ith DG

\(P_{{{\text{loss}}}}\) :

Real power loss

\(P_{{{\text{ss}}}}\) :

Active power fed from substation

\(Q_{i}\) :

Reactive power of the ith bus

\(Q_{Dj}\) :

Reactive power demand at the jth bus

\(Q_{{{\text{ss}}}}\) :

Reactive power fed from the substation

\(R{}_{ij}\) :

Resistance of the line connecting the ith and the jth bus

\(S_{{{\text{avg}}}}\) :

Average performance of the student

\(S_{ij}\) :

Apparent power flow between the ith and the jth bus

\(S_{{{\text{best}}}}\) :

Best student

\(S_{k}\) :

Randomly selected kth student

\(S_{{{\text{max}}}}\) :

Maximum marks of the student

\(S_{{{\text{min}}}}\) :

Minimum marks of the student

\(V^{i}\) :

Voltage (p.u.) at the ith bus

\(V_{{{\text{max}}}}^{i}\) :

Maximum voltage (1.05 p.u.) at the ith bus

\(V_{{{\text{nom}}}}\) :

Nominal voltage (1 p.u.)

\(V_{{{\text{min}}}}^{i}\) :

Minimum voltage (0.95 p.u.) at the ith bus

\(X_{ij}\) :

Reactance of the line connecting the ith and the jth bus

References

  1. Ackermann T, Anderson G, Söder L (2001) Distributed generation: definition. Electr Power Syst Res 57(3):195–204. https://doi.org/10.1016/S0378-7796(01)00101-8

    Article  Google Scholar 

  2. Priyanka P, Patidar NP, Nema RK (2014) Planning of grid integrated distributed generators: a review of technology, objectives and techniques. Renew Sustain Energy Rev 40:557–570. https://doi.org/10.1016/j.rser.2014.07.200

    Article  Google Scholar 

  3. Hung DQ, Mithulananthan N, Bansal RC (2010) Analytical expressions for DG allocation in primary distribution networks. IEEE Trans Energy Convers 25(3):814–820. https://doi.org/10.1109/TEC.2010.2044414

    Article  Google Scholar 

  4. García JAM, Mena AJG (2013) Optimal distributed generation location and size using a modified teaching learning based optimization algorithm. Int J Electr Power Energy Syst 50:65–75. https://doi.org/10.1016/j.ijepes.2013.02.023

    Article  Google Scholar 

  5. Lalitha PM, Veera Reddy VC, Sivarami Reddy N, Usha Reddy V (2011) DG source allocation by fuzzy and clonal selection algorithm for minimum loss in distribution system. Distrib Gener Altern Energy J 26(4):17–35. https://doi.org/10.1080/21563306.2011.10462202

    Article  Google Scholar 

  6. Duong Quoc H, Mithulananthan N (2011) Multiple distributed generator placement in primary distribution networks for loss reduction. IEEE Trans Ind Electron 60(4):1700–1708. https://doi.org/10.1109/TIE.2011.2112316

    Article  Google Scholar 

  7. 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(1):66–74. https://doi.org/10.1016/j.ijepes.2011.08.023

    Article  Google Scholar 

  8. Injeti SK, Prema KN (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(1):142–151. https://doi.org/10.1016/j.ijepes.2012.08.043

    Article  Google Scholar 

  9. 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. https://doi.org/10.1016/j.ijepes.2014.06.031

    Article  Google Scholar 

  10. Quadri IA, Bhowmick S, Joshi D (2017) A comprehensive technique for optimal allocation of distributed energy resources in radial distribution systems. Appl Energy 21(1):1245–1260

    Google Scholar 

  11. Kowsalya M (2014) Optimal size and siting of multiple distributed generators in distribution system using bacterial foraging optimization. Swarm Evolut Comput 15:58–65. https://doi.org/10.1016/j.swevo.2013.12.001

    Article  Google Scholar 

  12. Rama PD, Jayabarathi T, Umamageswari R, Saranya S (2015) Optimal location and sizing of distributed generation unit using intelligent water drop algorithm. Sustain Energy Technol Assess 11:106–113. https://doi.org/10.1016/j.seta.2015.07.003

    Article  Google Scholar 

  13. El-Fergany A (2015) Study impact of various load models on DG placement and sizing using backtracking search algorithm. Appl Soft Comput 30:803–811. https://doi.org/10.1016/j.asoc.2015.02.028

    Article  Google Scholar 

  14. Subramanyam TC, Ram ST, Subrahmanyam JBV (2018) Dual stage approach for optimal sizing and siting of fuel cell in distributed generation systems. Comput Electr Eng 69:676–689. https://doi.org/10.1016/j.compeleceng.2018.02.003

    Article  Google Scholar 

  15. Mohandas N, Balamurugan R, Lakshminarasimman L (2015) Optimal location and sizing of real power DG units to improve the voltage stability in the distribution system using artificial bee colony algorithm united with chaos. Int J Electr Power Energy Syst 66:41–52. https://doi.org/10.1016/j.ijepes.2014.10.033

    Article  Google Scholar 

  16. Attia E-F (2015) Optimal allocation of multi-type distributed generators using backtracking search optimization algorithm. Int Electr Power Energy Syst 64:1197–1205. https://doi.org/10.1016/j.ijepes.2014.09.020

    Article  Google Scholar 

  17. Sultana S, Roy PK (2016) Krill herd algorithm for optimal location of distributed generator in radial distribution system. Appl Soft Comput 40:391–404. https://doi.org/10.1016/j.asoc.2015.11.036

    Article  Google Scholar 

  18. Muthukumar K, Jayalalitha S (2015) Optimal placement and sizing of distributed generators and shunt capacitors for power loss minimization in radial distribution networks using hybrid heuristic search optimization technique. Int J Electr Power Energy Syst 78:299–319. https://doi.org/10.1016/j.ijepes.2015.11.019

    Article  Google Scholar 

  19. Devabalaji KR, Ravi K (2016) Optimal size and siting of multiple DG and DSTATCOM in radial distribution system using bacterial foraging optimization algorithm. Ain Shams Eng J 7(3):959–971. https://doi.org/10.1016/j.asej.2015.07.002

    Article  Google Scholar 

  20. 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 74:348–373. https://doi.org/10.1016/j.ijepes.2015.07.034

    Article  Google Scholar 

  21. Kayal P, Sayonsom C, Chandan Kumar C (2017) An analytical approach for allocation and sizing of distributed generations in radial distribution network. Int Trans Electr Energy Syst 27(7):e2322. https://doi.org/10.1002/etep.2322

    Article  Google Scholar 

  22. Yuvaraj T, Ravi K (2018) Multi-objective simultaneous DG and DSTATCOM allocation in radial distribution networks using cuckoo searching algorithm. Alex Eng J 57(4):2729–2742. https://doi.org/10.1016/j.aej.2018.01.001

    Article  Google Scholar 

  23. 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(9):2418–2428. https://doi.org/10.1049/iet-gtd.2016.2126

    Article  Google Scholar 

  24. Oda ES, Abdelsalam AA, Abdel-Wahab MN, El-Saadawi MM (2017) Distributed generations planning using flower pollination algorithm for enhancing distribution system voltage stability. Ain Shams Eng J 8(4):593–603. https://doi.org/10.1016/j.asej.2015.12.001

    Article  Google Scholar 

  25. Yuvaraj T, Ravi K (2017) Multi-objective simultaneous placement of DG and DSTATCOM using novel lightning search algorithm. J Appl Res Technol 15(5):477–491. https://doi.org/10.1016/j.jart.2017.05.008

    Article  Google Scholar 

  26. Ravindran S, Victoire TAA (2018) A bio-geography-based algorithm for optimal siting and sizing of distributed generators with an effective power factor model. Comput Electr Eng 72:482–501. https://doi.org/10.1016/j.compeleceng.2018.10.010

    Article  Google Scholar 

  27. Nguyen TP, Vo DN (2018) A novel stochastic fractal search algorithm for optimal allocation of distributed generators in radial distribution systems. Appl Soft Comput 70:773–796. https://doi.org/10.1016/j.asoc.2018.06.020

    Article  Google Scholar 

  28. Nguyen TP, Vo DN (2019) Improved stochastic fractal search algorithm with chaos for optimal determination of location, size, and quantity of distributed generators in distribution systems. Neural Comput Appl 31(11):7707–7732. https://doi.org/10.1007/s00521-018-3603-1

    Article  Google Scholar 

  29. Essallah S, Khedher A, Bouallegue A (2019) Integration of distributed generation in electrical grid: optimal placement and sizing under different load conditions. Comput Electr Eng 79:106461. https://doi.org/10.1016/j.compeleceng.2019.106461

    Article  Google Scholar 

  30. Saha S, Mukherjee V (2019) A novel multi-objective chaotic symbiotic organisms search algorithm to solve optimal DG allocation problem in radial distribution system. Int Trans Electr Energy Syst 29(5):e2839. https://doi.org/10.1002/2050-7038.2839

    Article  Google Scholar 

  31. Truong KH, Nallagownden P, Elamvazuthi I, Vo DN (2020) A quasi-oppositional-chaotic symbiotic organisms search algorithm for optimal allocation of DG in radial distribution networks. Appl Soft Comput 88:106067. https://doi.org/10.1016/j.asoc.2020.106067

    Article  Google Scholar 

  32. 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 78:70–83. https://doi.org/10.1016/j.asoc.2019.02.013

    Article  Google Scholar 

  33. Hamid T, Behnam MI (2020) A three-dimensional group search optimization approach for simultaneous planning of distributed generation units and distribution network reconfiguration. Appl Soft Comput 88:106012. https://doi.org/10.1016/j.asoc.2019.106012

    Article  Google Scholar 

  34. Yuvaraj T, Devabalaji KR, Sudhakar BT (2020) Simultaneous allocation of DG and DSTATCOM using whale optimization algorithm, Iranian Journal of Science and Technology. Trans Electr Eng 44(2):879–896. https://doi.org/10.1007/s40998-019-00272-w

    Article  Google Scholar 

  35. Abdelsalam AA (2020) Optimal distributed energy resources allocation for enriching reliability and economic benefits using sine-cosine algorithm. Technol Econ Smart Grids Sustain Energy 5(1):1–18. https://doi.org/10.1007/s40866-020-00082-8

    Article  Google Scholar 

  36. Raut U, Mishra S (2020) An improved sine-cosine algorithm for simultaneous network reconfiguration and DG allocation in power distribution systems. Appl Soft Comput 92:106293. https://doi.org/10.1016/j.asoc.2020.106293

    Article  Google Scholar 

  37. Selim A, Kamel S, Jurado F (2020) Efficient optimization technique for multiple DG allocation in distribution networks. Appl Soft Comput 86:105938. https://doi.org/10.1016/j.asoc.2019.105938

    Article  Google Scholar 

  38. Nagaballi S, Kale VS (2020) Pareto optimality and game theory approach for optimal deployment of DG in radial distribution system to improve techno-economic benefits. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2020.106234

    Article  Google Scholar 

  39. Thai DP, Nguyen TT, Dinh BH (2020) Find optimal capacity and location of distributed generation units in radial distribution networks by using enhanced coyote optimization algorithm. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05239-1

    Article  Google Scholar 

  40. Rodger JA (2014) A fuzzy nearest neighbor neural network statistical model for predicting demand for natural gas and energy cost savings in public buildings. Expert Syst Appl 41(4):1813–1829. https://doi.org/10.1016/j.eswa.2013.08.080

    Article  Google Scholar 

  41. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849–872. https://doi.org/10.1016/j.future.2019.02.028

    Article  Google Scholar 

  42. Das B, Mukherjee V, Das D (2020) Student psychology based optimization algorithm: a new population based optimization algorithm for solving optimization problems. Adv Eng Softw 146:102804. https://doi.org/10.1016/j.advengsoft.2020.102804

    Article  Google Scholar 

  43. Teng J-H (2003) A direct approach for distribution system load flow solutions. IEEE Trans Power Deliv 18(3):882–887. https://doi.org/10.1109/TPWRD.2003.813818

    Article  Google Scholar 

  44. Dogan A, Mustafa A (2019) Simultaneous optimization of network reconfiguration and DG installation using heuristic algorithms. Elektron Ir Elektrotech. https://doi.org/10.5755/j01.eie.25.1.22729

    Article  Google Scholar 

  45. Deependra S, Devender S, Verma KS (2009) Multi-objective optimization for DG planning with load models. IEEE Trans Power Syst 24(1):427–436. https://doi.org/10.1109/TPWRS.2008.2009483

    Article  Google Scholar 

  46. Chakravorty M, Das D (2001) Voltage stability analysis of radial distribution networks. Int J Electr Power Energy Syst 23(2):129–135. https://doi.org/10.1016/S0142-0615(00)00040-5

    Article  Google Scholar 

  47. Jin J, Rothrock L, McDermott L, Barnes M (2010) Using the analytic hierarchy process to examine judgment consistency in a complex multi-attribute task. IEEE Trans Syst Man Cybern Part A Syst Hum 40(5):1105–1115. https://doi.org/10.1109/TSCA.2010.2045119

    Article  Google Scholar 

  48. Murthy VVSN, Kumar A (2013) Comparison of optimal DG allocation methods in radial distribution systems based on sensitivity approaches. Electr Power Energy Syst 53:450–467. https://doi.org/10.1016/j.ijepes.2013.05.018

    Article  Google Scholar 

  49. Baran ME, Wu FF (1989) Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Trans Power Deliv 4(2):1401–1407. https://doi.org/10.1109/61.25627

    Article  Google Scholar 

  50. Das D (2008) Optimal placement of capacitors in radial distribution system using a fuzzy-GA method. Int J Electr Power Energy Syst 30(6–7):361–367. https://doi.org/10.1016/j.ijepes.2007.08.004

    Article  Google Scholar 

  51. Mantovani JRS, Casari F, Romero RA (2000) Reconfiguration of radial distribution systems using the voltage drop criterion. Control Autom SBA 11(3):150–159

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India

    Korra Balu & V. Mukherjee

Authors
  1. Korra Balu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Mukherjee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. Mukherjee.

Ethics declarations

Conflict of interest

All the authors declare that they have no conflict of interest. We have also not received any funding support for this work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Balu, K., Mukherjee, V. Optimal siting and sizing of distributed generation in radial distribution system using a novel student psychology-based optimization algorithm. Neural Comput & Applic 33, 15639–15667 (2021). https://doi.org/10.1007/s00521-021-06185-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06185-2

Keywords

Search

Navigation