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Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions

Author

Listed:
  • Latifa A. Yousef

    (Renewable and Sustainable Energy Research Center, Technology Innovation Institute, Masdar City, Abu Dhabi P.O. Box 9639, United Arab Emirates)

  • Hibba Yousef

    (Biotechnology Research Center, Technology Innovation Institute, Masdar City, Abu Dhabi P.O. Box 9639, United Arab Emirates)

  • Lisandra Rocha-Meneses

    (Renewable and Sustainable Energy Research Center, Technology Innovation Institute, Masdar City, Abu Dhabi P.O. Box 9639, United Arab Emirates)

Abstract

This review paper provides a summary of methods in which artificial intelligence (AI) techniques have been applied in the management of variable renewable energy (VRE) systems, and an outlook to future directions of research in the field. The VRE types included are namely solar, wind and marine varieties. AI techniques, and particularly machine learning (ML), have gained traction as a result of data explosion, and offer a method for integration of multimodal data for more accurate forecasting in energy applications. The VRE management aspects in which AI techniques have been applied include optimized power generation forecasting and integration of VRE into power grids, including the aspects of demand forecasting, energy storage, system optimization, performance monitoring, and cost management. Future directions of research in the applications of AI for VRE management are proposed and discussed, including the issue of data availability, types and quality, in addition to explainable artificial intelligence (XAI), quantum artificial intelligence (QAI), coupling AI with the emerging digital twins technology, and natural language processing.

Suggested Citation

  • Latifa A. Yousef & Hibba Yousef & Lisandra Rocha-Meneses, 2023. "Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions," Energies, MDPI, vol. 16(24), pages 1-27, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:8057-:d:1299919
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    References listed on IDEAS

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    1. Hirth, Lion & Ueckerdt, Falko & Edenhofer, Ottmar, 2015. "Integration costs revisited – An economic framework for wind and solar variability," Renewable Energy, Elsevier, vol. 74(C), pages 925-939.
    2. Sandra Minerva Valdivia-Bautista & José Antonio Domínguez-Navarro & Marco Pérez-Cisneros & Carlos Jesahel Vega-Gómez & Beatriz Castillo-Téllez, 2023. "Artificial Intelligence in Wind Speed Forecasting: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.
    3. Himeur, Yassine & Ghanem, Khalida & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2021. "Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives," Applied Energy, Elsevier, vol. 287(C).
    4. Fouzi Harrou & Ying Sun & Bilal Taghezouit & Abdelkader Dairi, 2023. "Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting," Energies, MDPI, vol. 16(18), pages 1-5, September.
    5. Lebotsa, Moshoko Emily & Sigauke, Caston & Bere, Alphonce & Fildes, Robert & Boylan, John E., 2018. "Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem," Applied Energy, Elsevier, vol. 222(C), pages 104-118.
    6. Peng Li & Chen Zhang & Huan Long, 2019. "Solar Power Interval Prediction via Lower and Upper Bound Estimation with a New Model Initialization Approach," Energies, MDPI, vol. 12(21), pages 1-17, October.
    7. Lyu, Wenjing & Liu, Jin, 2021. "Artificial Intelligence and emerging digital technologies in the energy sector," Applied Energy, Elsevier, vol. 303(C).
    8. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
    9. Mayer, Martin János & Gróf, Gyula, 2021. "Extensive comparison of physical models for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 283(C).
    10. Hodge, Bri-Mathias & Brancucci Martinez-Anido, Carlo & Wang, Qin & Chartan, Erol & Florita, Anthony & Kiviluoma, Juha, 2018. "The combined value of wind and solar power forecasting improvements and electricity storage," Applied Energy, Elsevier, vol. 214(C), pages 1-15.
    11. Tyler McCandless & Susan Dettling & Sue Ellen Haupt, 2020. "Comparison of Implicit vs. Explicit Regime Identification in Machine Learning Methods for Solar Irradiance Prediction," Energies, MDPI, vol. 13(3), pages 1-14, February.
    12. Köhler, Carmen & Steiner, Andrea & Saint-Drenan, Yves-Marie & Ernst, Dominique & Bergmann-Dick, Anja & Zirkelbach, Mathias & Ben Bouallègue, Zied & Metzinger, Isabel & Ritter, Bodo, 2017. "Critical weather situations for renewable energies – Part B: Low stratus risk for solar power," Renewable Energy, Elsevier, vol. 101(C), pages 794-803.
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