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Stochastic optimal reactive power dispatch at varying time of load demand and renewable energsy resources using an efficient modified jellyfish optimizer

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Abstract

Solving the optimal reactive dispatch (ORPD) is a strenuous task to assign the best operating point of the electrical system components to obtain the most secure and stable state of system. This problem became more complex problem due the variation of the load demand or inclusion the renewable energy resources (RERs). The aim of this paper is solving the ORPD problem using a modified jellyfish search optimizer (MJSO) under deterministic and probabilistic states of the load demand and the RERs. The MJSO is based on boosting the exploration and exploitation phases of the standard jellyfish search optimizer (JSO) using two strategies. The first strategy is enhancing the exploration process by using a chaotic mutation while the second strategy is implemented for the exploitation process using a spiral orientation motion of the populations around the sorted jellyfish. Three uncertain parameters are considered including the load demand, the solar irradiance, and the wind speed which are represented using the Weibull, the Beta, and the normal probability density functions, respectively. The Monte Carlo simulation along with scenario-based reduction to generate a set of scenarios for the stochastic ORPD. To verify the effectiveness of the MJSO for solving the ORPD problem, it is tested on IEEE 30-bus system and the obtained results are compared with other well-known optimization techniques. The obtained results and the comparison with other techniques indicate that the proposed MJSO algorithm provides effective and robust high-quality solution when solving the ORPD at deterministic state. In addition of that the expected power loss is decreased considerably with application the proposed technique for solving the ORPD at stochastic state.

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Abbreviations

ALO:

Ant Lion Optimizer

BBO:

Biogeography-Based Optimization

CSA:

Cuckoo Search Algorithm

PSO:

Particle Swarm Optimization

CLPSO:

Comprehensive Learning PSO

DE:

Differential Evolution

FA:

Firefly algorithm

GSA:

Gravitational Search Algorithm

GSA-CSS:

Gravitational Search Algorithm Conditional Selection Strategies

HSA:

Harmony Search Algorithm

TS:

Tabu Search

HPSO–TS:

Hybrid PSO with the TS

SA:

Simulated annealing

SSA:

Salp Swarm Algorithm

HSSSA:

Hybrid SSA and SA

IPG-PSO:

Improved Pseudo-Gradient PSO

ISSO:

Improved Social Spider Optimization

IDE:

Improved DE

JA:

Jaya Algorithm

IGSA-CSS:

Improved GSA-CSS

IALO:

Improved Antlion Optimization

MSSA:

Modified Salp Swarm Algorithm

QOTLBO:

Teaching Learning-based Optimization

PSO-TAC:

PSO and Time Acceleration Coefficients

PSO-TIW:

PSO and Time Inertia Weight

PG-PSO:

PSO and Pseudo-Gradient Search

PSO-CF:

PSO and Constriction Factor

SPSO-TAC:

PSO and Time Acceleration Coefficients

PSO-TAC:

PSO and Time Acceleration Coefficients

SGA:

Specialized Genetic Algorithm

SCA:

Sine Cosine Algorithm

SSO:

Social Spider Optimization

STGA:

Standard Genetic Algorithm

QTLBO:

Quasi-oppositional TLBO

WOA:

Whale optimization algorithm

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Acknowledgements

The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this work through the projects DSR2020-02-493

Author information

Authors and Affiliations

  1. Department of Physics, College of Science and Arts, Jouf University, Al-Qurayat Branch, P.O. Box 756, Sakaka, Saudi Arabia

    Fatma Gami

  2. Physics Department, South Valley University, Qena, 83523, Egypt

    Fatma Gami

  3. Department of Physics, College of Science, Jouf University, P.O. Box 2014, Sakaka, Saudi Arabia

    Ziyad A. Alrowaili

  4. Department of Basic Sciences, Common First Year Deanship, Jouf University, P.O. Box 2014, Sakaka, Saudi Arabia

    Mohammed Ezzeldien

  5. Metallurgy & Material Science Tests (MMST) Lab, Department of Physics, Faculty of Science, South Valley University, Qena, 83523, Egypt

    Mohammed Ezzeldien

  6. Department of Electrical Engineering, Faculty of Engineering, Sohag University, Sohag, 82524, Egypt

    Mohamed Ebeed

  7. Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan, 81542, Egypt

    Salah kamel

  8. Department of Electrical Engineering, Faculty of Engineering, Suez Canal University, Ismailia, 41522, Egypt

    Eyad S. Oda

  9. Department of Electrical Engineering, Faculty of Engineering, South Valley University, Qena, 83523, Egypt

    Shazly A. Mohamed

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  1. Fatma Gami
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Correspondence to Eyad S. Oda.

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Gami, F., Alrowaili, Z.A., Ezzeldien, M. et al. Stochastic optimal reactive power dispatch at varying time of load demand and renewable energsy resources using an efficient modified jellyfish optimizer. Neural Comput & Applic 34, 20395–20410 (2022). https://doi.org/10.1007/s00521-022-07526-5

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