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Integrated Control and Optimization for Grid-Connected Photovoltaic Systems: A Model-Predictive and PSO Approach

Author

Listed:
  • Chaymae Boubii

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

  • Ismail El Kafazi

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

  • Rachid Bannari

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

  • Brahim El Bhiri

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

  • Saleh Mobayen

    (Graduate School of Intelligent Data Science, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 640301, Taiwan)

  • Anton Zhilenkov

    (Department of Cyber-Physical Systems, St. Petersburg State Marine Technical University, 190121 Saint-Petersburg, Russia)

  • Badre Bossoufi

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

Abstract

To propel us toward a greener and more resilient future, it is imperative that we adopt renewable sources and implement innovative sustainable solutions in response to the escalating energy crisis. Thus, renewable energies have emerged as a viable solution to the global energy crisis, with photovoltaic energy being one of the prominent sources in this regard. This paper represents a significant step in the desired direction by focusing on detailed, comprehensive dynamic modeling and efficient control of photovoltaic (PV) systems as grid-connected energy sources. The ultimate goal is to enhance system reliability and ensure high power quality. The behavior of the suggested photovoltaic system is tested under varying sun radiation conditions. The PV system is complemented by a boost converter and a three-phase pulse width modulation (PWM) inverter, with MATLAB software employed for system investigation. This research paper enhances photovoltaic (PV) system performance through the integration of model-predictive control (MPC) with a high-gain DC–DC converter. It improves maximum power point tracking (MPPT) efficiency in response to the variability of solar energy by combining MPC with the traditional incremental conductance (IN-C) method. Additionally, the system incorporates a DC–AC converter for three-phase pulse width modulation, which is also controlled by predictive control technology supported by Particle Swarm Optimization (PSO) to further enhance performance. PSO was selected due to its capability to optimize complex systems and its proficiency in handling nonlinear functions and multiple variables, making it an ideal choice for improving MPC control performance. The simulation results demonstrate the system’s ability to maintain stable energy production despite variations in solar irradiation levels, thus highlighting its effectiveness.

Suggested Citation

  • Chaymae Boubii & Ismail El Kafazi & Rachid Bannari & Brahim El Bhiri & Saleh Mobayen & Anton Zhilenkov & Badre Bossoufi, 2023. "Integrated Control and Optimization for Grid-Connected Photovoltaic Systems: A Model-Predictive and PSO Approach," Energies, MDPI, vol. 16(21), pages 1-22, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:21:p:7390-:d:1272308
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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.
    2. Sun, Bing & Yu, Yixin & Qin, Chao, 2017. "Should China focus on the distributed development of wind and solar photovoltaic power generation? A comparative study," Applied Energy, Elsevier, vol. 185(P1), pages 421-439.
    3. Singh, G.K., 2013. "Solar power generation by PV (photovoltaic) technology: A review," Energy, Elsevier, vol. 53(C), pages 1-13.
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