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Optimizing renewable energy utilization with high gain converters

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  • Tamilselvan, D.
  • T D, Sudhakar

Abstract

This research proposes a novel control technique for optimizing hybrid renewable energy sources (HRES) by using a high-gain DC-to-DC converter topology. The Hybrid RES system comprises a battery components, wind turbine (WT), and photovoltaic (PV). The proposed control methodology utilizes an enhanced jellyfish search (EJS) controller, which enhances the search behavior of the jellyfish search algorithm through the incorporation of mutation and crossover operators. The converter is designed to maximize efficiency, minimize switching losses, and effectively utilize renewable energy. To achieve optimal performance, a dataset with optimum gain values is constructed using a minimal error target function and proportional-integral (PI) controller gain parameters. The EJS algorithm determines the PI controller gain based on the error in reactive and active power. Randomly generated gain values are used as inputs to the system, and the algorithm adjusts the PI settings by minimizing system error and optimizing power flow management. The inclusion of batteries helps balance renewable power and enhances output consistency. The proposed system is implemented in MATLAB and is compared with existing systems. The novelty of this research lies in the combination of the EJS algorithm with converter efficiency, switching losses, and hybrid renewable energy sources. By utilizing the EJS algorithm, the proposed strategy has the potential to achieve superior outcomes based on cost savings, energy efficiency, and grid stability compared to conventional methods. Furthermore, the integration of this method contributes to the advancement of optimization algorithms and neural network applications.

Suggested Citation

  • Tamilselvan, D. & T D, Sudhakar, 2024. "Optimizing renewable energy utilization with high gain converters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:rensus:v:191:y:2024:i:c:s1364032123009632
    DOI: 10.1016/j.rser.2023.114105
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    References listed on IDEAS

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    1. Lorestani, A. & Ardehali, M.M., 2018. "Optimal integration of renewable energy sources for autonomous tri-generation combined cooling, heating and power system based on evolutionary particle swarm optimization algorithm," Energy, Elsevier, vol. 145(C), pages 839-855.
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    1. Hashemizadeh, Ali & Ju, Yanbing & Abadi, Faezeh Zareian Baghdad, 2024. "Policy design for renewable energy development based on government support: A system dynamics model," Applied Energy, Elsevier, vol. 376(PB).

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