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Energy Efficiency in Modern Power Systems Utilizing Advanced Incremental Particle Swarm Optimization–Based OPF

Author

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  • Muhammad Bachtiar Nappu

    (Electricity Market and Power Systems Research Group, Department of Electrical Engineering, Faculty of Engineering, Hasanuddin University, Gowa 92171, Indonesia)

  • Ardiaty Arief

    (Power and Energy Systems Research Group, Department of Electrical Engineering, Faculty of Engineering, Hasanuddin University, Gowa 92171, Indonesia)

  • Willy Akbar Ajami

    (Electricity Market and Power Systems Research Group, Department of Electrical Engineering, Faculty of Engineering, Hasanuddin University, Gowa 92171, Indonesia)

Abstract

Since the power grid grows and the necessity for higher system efficiency is due to the increasing number of renewable energy penetrations, power system operators need a fast and efficient method of operating the power system. One of the main problems in a modern power system operation that needs to be resolved is optimal power flow (OPF). OPF is an efficient generator scheduling method to meet energy demands with the aim of minimizing the total production cost of power plants while maintaining system stability, security, and reliability. This paper proposes a new method to solve OPF by using incremental particle swarm optimization (IPSO). IPSO is a new algorithm of particle swarm optimization (PSO) that modifies the PSO structure by increasing the particle size, where each particle changes its position to determine its optimal position. The advantage of IPSO is that the population increases with each iteration so that the optimization process becomes faster. The results of the research on optimal power flow for energy generation costs, system voltage stability, and losses obtained by the IPSO method are superior to the conventional PSO method.

Suggested Citation

  • Muhammad Bachtiar Nappu & Ardiaty Arief & Willy Akbar Ajami, 2023. "Energy Efficiency in Modern Power Systems Utilizing Advanced Incremental Particle Swarm Optimization–Based OPF," Energies, MDPI, vol. 16(4), pages 1-13, February.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1706-:d:1062162
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    References listed on IDEAS

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    1. Mohamed A. M. Shaheen & Zia Ullah & Mohammed H. Qais & Hany M. Hasanien & Kian J. Chua & Marcos Tostado-Véliz & Rania A. Turky & Francisco Jurado & Mohamed R. Elkadeem, 2022. "Solution of Probabilistic Optimal Power Flow Incorporating Renewable Energy Uncertainty Using a Novel Circle Search Algorithm," Energies, MDPI, vol. 15(21), pages 1-19, November.
    2. Ghasemi, Mojtaba & Ghavidel, Sahand & Ghanbarian, Mohammad Mehdi & Gharibzadeh, Masihallah & Azizi Vahed, Ali, 2014. "Multi-objective optimal power flow considering the cost, emission, voltage deviation and power losses using multi-objective modified imperialist competitive algorithm," Energy, Elsevier, vol. 78(C), pages 276-289.
    3. Benedetto-Giuseppe Risi & Francesco Riganti-Fulginei & Antonino Laudani, 2022. "Modern Techniques for the Optimal Power Flow Problem: State of the Art," Energies, MDPI, vol. 15(17), pages 1-20, September.
    4. Bachtiar Nappu, Muhammad & Arief, Ardiaty & Bansal, Ramesh C., 2014. "Transmission management for congested power system: A review of concepts, technical challenges and development of a new methodology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 572-580.
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    Cited by:

    1. José R. Ortiz-Castrillón & Sergio D. Saldarriaga-Zuluaga & Nicolás Muñoz-Galeano & Jesús M. López-Lezama & Santiago Benavides-Córdoba & Juan B. Cano-Quintero, 2023. "Optimal Sliding-Mode Control of Semi-Bridgeless Boost Converters Considering Power Factor Corrections," Energies, MDPI, vol. 16(17), pages 1-24, August.

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