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A Novel Hybrid GA-PSO Algorithm-Based Optimization of Transmission and Expansion Planning

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Abstract

In the power system environment, transmission and expansion planning (TNEP) is an essential and computationally very challenging problem in power systems. Competent and robust optimization techniques are required to get the optimal solution technically and economically. This paper aims to resolve the transmission and expansion planning problem in less computational time and investment costs using H1GAPSO and H2GAPSO algorithms. Two hybrid progressive algorithms based on the combination of PSO and GA methods are proposed and crossing over the PSO and GA have been implemented in this paper. The focus behind the proposed methods is to merge PSO and GA methods in a combination of parallel and series form, respectively. To validate the proposed hybrid algorithm and to test efficacy in comparison with other methods reported in the literature, it is tested on Garver’s-6 bus, IEEE-14 bus, and IEEE-24 bus test systems using MATLAB. For IEEE-14 and IEEE-24 bus systems, by applying the hybridization, the optimal investment costs are reduced to 520 US$ and 630 US$, respectively and the corresponding computational time in seconds are reduced to 4.3637 s and 4.3788 s. For Garver’s 6 bus system, the computational time are 1.4936 s and 1.1847 s for both hybridization. The results are compared with conventional GA and PSO methods. The simulation and observations of the outcome demonstrate the effectiveness of the proposed hybrid algorithms' time and have the better ability to find the global optimum solution.

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Data availability

The data that support the findings of this study are available in at https://www.academia.edu/13981064/Data_for_the_IEEE_24_bus_Reliability_Test_System, https://sps-lab.org/tag/garver-6-bus-test-systems/.

Abbreviations

TNEP:

Transmission and expansion planning

PSE:

Power system expansion

GA:

Genetic algorithm

PSO:

Particle swarm optimization

H1GAPSO:

Hybrid type I optimization

H2GAPSO:

Hybrid type II optimization

IC:

Incremental cost

AC:

Alternating current

Fit:

Fitness function

p.f:

Penalty factor

DC:

Direct current

TNEP:

Transmission and expansion planning

PSE:

Power system expansion

IP:

Interior point

GA:

Genetic algorithm

LP:

Linear programming

IPM:

Interior point method

MV:

Multi-verse

PSO:

Particle swarm optimization

B&B:

Branch and bound

MINLP:

Mixed integer non-linear programming

CS:

Cuckoo search

NSGA:

Non-sorted genetic algorithm

InvC :

Transmission cost

RP:

Vector with existing real power

Q :

Vector of node load demand

I ij :

Power flow in branch ij

\(I_{ij}^{\max }\) :

Maximum power flow in branch ij

B ij :

Additional branch

r ij :

Resistance of branch ij

n ij :

Original circuit number in the base system

X ij :

Reactance in branch ij

X ij max :

Maximum reactance in branch ij

G i min :

Minimum real power generation (ith node)

G i max :

Maximum real power generation (ith node)

M :

Susceptance matrix

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Authors and Affiliations

  1. Maulana Azad National Institute of Technology, Bhopal, India

    Shweta Mehroliya & Anoop Arya

  2. Samrat Ashok Technological Institute, Vidisha, India

    Shilpi Tomar

  3. Banaras Hindu University, Varanasi, India

    Anshul Verma

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Mehroliya, S., Tomar, S., Arya, A. et al. A Novel Hybrid GA-PSO Algorithm-Based Optimization of Transmission and Expansion Planning. SN COMPUT. SCI. 4, 690 (2023). https://doi.org/10.1007/s42979-023-02188-z

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