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Optimal reactive power management through a hybrid BOA–GWO–PSO algorithm for alleviating congestion

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Abstract

In today's deregulated energy market, improving grid management in generating power with load optimization is a critical challenge. It is also perilous that the system does not have any problems owing to transmission line clogs. The Butterfly Optimization Algorithm is used for load balancing and load optimization in electricity markets. The proposed approach integrates Particle Swarm Optimization and Grey Wolf Optimizer, merging them with Butterfly Optimization Algorithm as a hybridised form to enhance exploration and exploitation skills. The benefit of the Butterfly Optimization Algorithm in general, as well as when it is employed to address difficult optimization issues, is validated using the New England 39 bus test system. The amalgamated algorithm approach was compared to other established meta-heuristic algorithms for the reactive power management under variable loading conditions. Using the realistic New England 39 bus system, the suggested algorithm minimizes transmission losses by 6.344% and operating costs by 6.347% with respect to the base case, respectively. The research work reveals that proposed amalgamated algorithm employing Butterfly Optimization Algorithm, Grey Wolf Optimizer, and Particle Swarm Optimization performs better and offers more potential in a range of situations. The proposed technique mathematical validation indicated that it has the capacity to tackle complex optimization issues and compete with contemporary peer-reviewed literature solutions.

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Abbreviations

RPM:

Reactive power management

BOA:

Butterfly Optimization Algorithm

PSO:

Particle Swarm Optimization

FVSI:

Fast voltage stability index

ANOVA:

Analysis of variance

ES:

Evolutionary strategy

HS:

Harmony search

CS-ALO:

Cuckoo search-ant lion optimizer

BOA-GWO-PSO:

Amalgamation algorithm

BB-BC:

Big bang-big crunch algorithm

CSA:

Crow search algorithm

WOA-SCA:

Whale optimization-sine cosine algorithm

GWO:

Grey Wolf Optimization

TLBO:

Teaching learning based optimization

Cp :

Overall real power cost

r:

Random number [0,1]

Xi :

Solution vector for ith butterfly

g* :

Best solution vector for ith butterfly

Gxy :

Conductance between xth and yth branch

Vx :

Voltage magnitudes at sending end voltage

Vy :

Voltage magnitudes at receiving end voltage

\({P}_{Gx}\) :

Active power generation at xth branch bus

\({P}_{Dx}\) :

Reactive power demand at xth branch bus

\({Q}_{Gy}\) :

Active power generation at yth branch bus

\({Q}_{Dy}\) :

Reactive power demand at yth branch bus

\({Q}_{cn}^{min}\) :

Minimum shunt capacitor at nth dimension

\({Q}_{cn}^{max}\) :

Maximum shunt capacitor at nth dimension

\({T}_{n}^{min}\) :

Minimum transformer tap settings at nth dimension

\({T}_{n}^{max}\) :

Maximum transformer tap settings at nth dimension

δ1 :

Phase angle of sending end voltage

δ2 :

Phase angle of receiving end voltage

Sy :

Apparent power at yth bus

Py :

Active power at yth bus

Qy :

Reactive power at yth bus

G best (P g ) :

Particle in the entire swarm of global position

Xi :

Position vector of ith particle up to nth dimension

Vi :

Velocity vector of ith particle up to nth dimension

c1 :

Cognitive learning factor

c2 :

Social learning factor

w1 :

Higher value of inertia weights

w2 :

Lower value of inertia weights

Fk :

Food source

P best (P i ) :

Particle at best position

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  1. EEE Department, ACED, Alliance University, Bangalore, India

    Manjulata Badi & Sheila Mahapatra

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  1. Manjulata Badi
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  2. Sheila Mahapatra
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Correspondence to Sheila Mahapatra.

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Badi, M., Mahapatra, S. Optimal reactive power management through a hybrid BOA–GWO–PSO algorithm for alleviating congestion. Int J Syst Assur Eng Manag 14, 1437–1456 (2023). https://doi.org/10.1007/s13198-023-01946-9

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