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Synergizing Wind and Solar Power: An Advanced Control System for Grid Stability

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Listed:
  • Chaymae Boubii

    (Engineering Sciences Laboratory, ENSA, Ibn Tofail University, Kenitra 14000, Morocco)

  • Ismail El Kafazi

    (Laboratory SMARTILAB, Moroccan School Engineering Sciences, EMSI, Rabat 10150, Morocco)

  • Rachid Bannari

    (Engineering Sciences Laboratory, ENSA, Ibn Tofail University, Kenitra 14000, Morocco)

  • Brahim El Bhiri

    (Laboratory SMARTILAB, Moroccan School Engineering Sciences, EMSI, Rabat 10150, Morocco)

  • Badre Bossoufi

    (LIMAS Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco)

  • Hossam Kotb

    (Department of Electrical Power and Machines, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt)

  • Kareem M. AboRas

    (Department of Electrical Power and Machines, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt)

  • Ahmed Emara

    (Electrical Engineering Department, University of Business and Technology, Ar Rawdah, Jeddah 23435, Saudi Arabia
    Engineering Mathematics and Physics Department, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt)

  • Badr Nasiri

    (Laboratory of Optic of Information Processing, Mechanic, Energetic and Electronic, Faculty of Science, University Moulay Ismail, Meknes 50050, Morocco)

Abstract

In response to the escalating global energy crisis, the motivation for this research has been derived from the need for sustainable and efficient energy solutions. A gap in existing renewable energy systems, particularly in terms of stability and efficiency under variable environmental conditions, has been recognized, leading to the introduction of a novel hybrid system that combines photovoltaic (PV) and wind energy. The innovation of this study lies in the methodological approach that has been adopted, integrating dynamic modeling with a sophisticated control mechanism. This mechanism, a blend of model predictive control (MPC) and particle swarm optimization (PSO), has been specifically designed to address the fluctuations inherent in PV and wind power sources. The methodology involves a detailed stability analysis using Lyapunov’s theorem, a critical step distinguishing this system from conventional renewable energy solutions. The integration of MPC and PSO, pivotal in enhancing the system’s adaptability and optimizing the maximum power point tracking (MPPT) process, improves control efficiency across key components like the doubly fed induction generator (DFIG), rectifier-sourced converter (RSC), and grid-side converter (GSC). Through rigorous MATLAB simulations, the system’s robust response to changing solar irradiance and wind velocities has been demonstrated. The key findings confirm the system’s ability to maintain stable power generation, underscoring its practicality and efficiency in renewable energy integration. Not only has this study filled a crucial gap in renewable energy control systems, but it has also set a precedent for future research in sustainable energy technologies.

Suggested Citation

  • Chaymae Boubii & Ismail El Kafazi & Rachid Bannari & Brahim El Bhiri & Badre Bossoufi & Hossam Kotb & Kareem M. AboRas & Ahmed Emara & Badr Nasiri, 2024. "Synergizing Wind and Solar Power: An Advanced Control System for Grid Stability," Sustainability, MDPI, vol. 16(2), pages 1-47, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:815-:d:1321105
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    References listed on IDEAS

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    1. Mohamed A. Tolba & Hegazy Rezk & Vladimir Tulsky & Ahmed A. Zaki Diab & Almoataz Y. Abdelaziz & Artem Vanin, 2018. "Impact of Optimum Allocation of Renewable Distributed Generations on Distribution Networks Based on Different Optimization Algorithms," Energies, MDPI, vol. 11(1), pages 1-33, January.
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    Keywords

    PV; DFIG; MPC; PSO; Lyapunov;
    All these keywords.

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