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

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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.

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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

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

    Korra Balu & V. Mukherjee

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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

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