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Hybrid particle swarm and sea horse optimization algorithm-based optimal reactive power dispatch of power systems comprising electric vehicles

Author

Listed:
  • Hasanien, Hany M.
  • Alsaleh, Ibrahim
  • Tostado-Véliz, Marcos
  • Zhang, Miao
  • Alateeq, Ayoob
  • Jurado, Francisco
  • Alassaf, Abdullah

Abstract

The reliability and effectiveness of today's electrical grids rely heavily on optimal reactive power dispatch (ORPD). The ORPD problem gets more challenging to resolve in the setting of ever-increasingly dynamic and complex systems. To handle the ORPD while also considering the existence of electric vehicles, this research introduces a novel technique: the Hybrid Particle Swarm Optimization and Sea Horse Optimization (PSOSHO) algorithm. Byreducing both active power loss and voltage variation, the proposed PSOSHO approach aims at improving the efficiency and reliability of the power grid. Simulation studies on reference power grids, including the IEEE 30-bus and IEEE 57-bus networks, verified its efficacy. Existing metaheuristic optimization techniques are compared using the same restrictions, governing parameters, and data. The results show that the PSOSHO method is trustworthy and effective in resolving the ORPD problem. One of the most pressing issues facing today's electricity grids is how to accommodate the growing number of electric vehicles. Real data on electric vehicles are incorporated in the analyses to obtain a realistic study. The suggested PSOSHO algorithm is a significant step forward in this area, providing a reliable and effective answer to the problem of optimum reactive power dispatch and helping to ensure the long-term viability of power systems in the age of electromobility.

Suggested Citation

  • Hasanien, Hany M. & Alsaleh, Ibrahim & Tostado-Véliz, Marcos & Zhang, Miao & Alateeq, Ayoob & Jurado, Francisco & Alassaf, Abdullah, 2024. "Hybrid particle swarm and sea horse optimization algorithm-based optimal reactive power dispatch of power systems comprising electric vehicles," Energy, Elsevier, vol. 286(C).
    • Handle: RePEc:eee:energy:v:286:y:2024:i:c:s0360544223029778
    DOI: 10.1016/j.energy.2023.129583
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    References listed on IDEAS

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