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Synergistic Integration of Renewable Energy and HVDC Technology for Enhanced Multi-objective Economic Emission Dispatch Using the Salp Swarm Algorithm

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Neural Computing for Advanced Applications (NCAA 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2182))

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Abstract

This paper proposes a Salp Swarm Algorithm (SSA), a unique optimization technique for the synergistic integration of renewable energy and High Voltage Direct Current (HVDC) technology to enhance the performance of multi-objective economic emission dispatch (MODED). The primary aim is to optimize both the economic and environmental aspects of power systems. A mathematical model for MODED based on Wind-Solar-Thermal integrated energy has been carefully constructed, considering variables like the valve point effect, equality constraints, and inequality constraints. The study determines optimal generation levels and associated costs for six thermal generating units under various power demands, exploring diverse scenarios such as Economic Dispatch for High Voltage Alternating Current (HVAC) with Losses, Economic Dispatch for HVDC with Losses, Economic Dispatch for HVDC addressing challenges related to voltage instability, protection difficulties and losses in DC systems, Economic Dispatch HVAC & HVDC with Losses and Economic Dispatch for HVAC & HVDC with Renewable Energy (RE). To validate the model, tests have been conducted on the IEEE 30 Bus System with a substantial presence of renewable energy.

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Acknowledgments

This study was partly funded by a grant from the South African National Research Foundation (No. PSTD2204285206). This work was partly supported by the South African National Research Foundation under Grant 141951, Grants nos. 137951, and AJCR230704126719120106.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, 2006, South Africa

    Peter Anuoluwapo Gbadega & Yanxia Sun

Authors
  1. Peter Anuoluwapo Gbadega
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  2. Yanxia Sun
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Corresponding author

Correspondence to Peter Anuoluwapo Gbadega .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Haijun Zhang

  2. Guangxi Normal University, Guilin, China

    Xianxian Li

  3. South China Normal University, Guangzhou, China

    Tianyong Hao

  4. Technical University of Denmark, Kongens Lyngby, Denmark

    Weizhi Meng

  5. Chongqing University, Chongqing, China

    Zhou Wu

  6. Guilin University of Electronic Technology, Guilin, China

    Qian He

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Gbadega, P.A., Sun, Y. (2025). Synergistic Integration of Renewable Energy and HVDC Technology for Enhanced Multi-objective Economic Emission Dispatch Using the Salp Swarm Algorithm. In: Zhang, H., Li, X., Hao, T., Meng, W., Wu, Z., He, Q. (eds) Neural Computing for Advanced Applications. NCAA 2024. Communications in Computer and Information Science, vol 2182. Springer, Singapore. https://doi.org/10.1007/978-981-97-7004-5_17

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  • DOI: https://doi.org/10.1007/978-981-97-7004-5_17

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  • Online ISBN: 978-981-97-7004-5

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