IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i12p2799-d1410566.html
   My bibliography  Save this article

Dispatch Optimization Scheme for High Renewable Energy Penetration Using an Artificial Intelligence Model

Author

Listed:
  • Mahmood Alharbi

    (Department of Electrical Engineering, Taibah University, Madinah 42353, Saudi Arabia
    These authors contributed equally to this work.)

  • Ibrahim Altarjami

    (Department of Electrical Engineering, Taibah University, Madinah 42353, Saudi Arabia
    These authors contributed equally to this work.)

Abstract

The scientific community widely recognizes that the broad use of renewable energy sources in clean energy systems will become a substantial and common trend in the next decades. The most urgent matter that has to be addressed is how to enhance the amount of renewable energy integration into the system while ensuring system stability in the presence of sudden fluctuations in generation and system faults. This study introduces a methodology that may be applied to any power system to optimize the level of renewable energy sources (RESs) integration. The methodology relies on using a trilayered neural network (TNN), which is a model utilized in the field of artificial intelligence. In order to apply and analyze the outcomes of the proposed optimization technique, the Kundur power system is employed as a case study. The objective of this methodology is to enhance the operation dispatches of a power system to attain a higher level of renewable energy output, specifically photovoltaic (PV) generation, while maintaining the stability of the system. This would enhance the stakeholders’ or utility providers’ capacity to make well-informed judgments on operation dispatch processes. The findings of this study suggest that it is generally recommended to raise the dispatchable power values for the generators in the loading region and lower the dispatchable power values for the generators in the generating area.

Suggested Citation

  • Mahmood Alharbi & Ibrahim Altarjami, 2024. "Dispatch Optimization Scheme for High Renewable Energy Penetration Using an Artificial Intelligence Model," Energies, MDPI, vol. 17(12), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:2799-:d:1410566
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/12/2799/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/12/2799/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rocha, Helder R.O. & Honorato, Icaro H. & Fiorotti, Rodrigo & Celeste, Wanderley C. & Silvestre, Leonardo J. & Silva, Jair A.L., 2021. "An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes," Applied Energy, Elsevier, vol. 282(PA).
    2. Brouwer, Anne Sjoerd & van den Broek, Machteld & Zappa, William & Turkenburg, Wim C. & Faaij, André, 2016. "Least-cost options for integrating intermittent renewables in low-carbon power systems," Applied Energy, Elsevier, vol. 161(C), pages 48-74.
    3. Luz, Thiago & Moura, Pedro & de Almeida, Aníbal, 2018. "Multi-objective power generation expansion planning with high penetration of renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2637-2643.
    4. Pallonetto, Fabiano & De Rosa, Mattia & Milano, Federico & Finn, Donal P., 2019. "Demand response algorithms for smart-grid ready residential buildings using machine learning models," Applied Energy, Elsevier, vol. 239(C), pages 1265-1282.
    5. Mahmood Alharbi & Ramzi Alahmadi & Ahmed Alahmadi, 2023. "Meteorological-Data-Based Modeling for PV Performance Optimization," Sustainability, MDPI, vol. 15(11), pages 1-16, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Kanakadhurga, Dharmaraj & Prabaharan, Natarajan, 2022. "Demand side management in microgrid: A critical review of key issues and recent trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    3. Tumiran & Lesnanto Multa Putranto & Sarjiya & Fransisco Danang Wijaya & Adi Priyanto & Ira Savitri, 2022. "Generation Expansion Planning Based on Local Renewable Energy Resources: A Case Study of the Isolated Ambon-Seram Power System," Sustainability, MDPI, vol. 14(5), pages 1-21, March.
    4. Ziad M. Ali & Martin Calasan & Shady H. E. Abdel Aleem & Francisco Jurado & Foad H. Gandoman, 2023. "Applications of Energy Storage Systems in Enhancing Energy Management and Access in Microgrids: A Review," Energies, MDPI, vol. 16(16), pages 1-41, August.
    5. Christian Gambardella & Michael Pahle & Wolf-Peter Schill, 2016. "Do Benefits from Dynamic Tariffing Rise? Welfare Effects of Real-Time Pricing under Carbon-Tax-Induced Variable Renewable Energy Supply," Discussion Papers of DIW Berlin 1621, DIW Berlin, German Institute for Economic Research.
    6. Carsten Helm & Mathias Mier, 2020. "Steering the Energy Transition in a World of Intermittent Electricity Supply: Optimal Subsidies and Taxes for Renewables Storage," ifo Working Paper Series 330, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    7. McPherson, Madeleine & Stoll, Brady, 2020. "Demand response for variable renewable energy integration: A proposed approach and its impacts," Energy, Elsevier, vol. 197(C).
    8. Nallapaneni Manoj Kumar & Aneesh A. Chand & Maria Malvoni & Kushal A. Prasad & Kabir A. Mamun & F.R. Islam & Shauhrat S. Chopra, 2020. "Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-42, November.
    9. Kruyt, Bert & Lehning, Michael & Kahl, Annelen, 2017. "Potential contributions of wind power to a stable and highly renewable Swiss power supply," Applied Energy, Elsevier, vol. 192(C), pages 1-11.
    10. Audoly, Richard & Vogt-Schilb, Adrien & Guivarch, Céline & Pfeiffer, Alexander, 2018. "Pathways toward zero-carbon electricity required for climate stabilization," Applied Energy, Elsevier, vol. 225(C), pages 884-901.
    11. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    12. Tong Koecklin, Manuel & Fitiwi, Desta & de Carolis, Joseph F. & Curtis, John, 2020. "Renewable electricity generation and transmission network developments in light of public opposition: Insights from Ireland," Papers WP653, Economic and Social Research Institute (ESRI).
    13. Helm, Carsten & Mier, Mathias, 2021. "Steering the energy transition in a world of intermittent electricity supply: Optimal subsidies and taxes for renewables and storage," Journal of Environmental Economics and Management, Elsevier, vol. 109(C).
    14. Gambardella, Christian & Pahle, Michael, 2018. "Time-varying electricity pricing and consumer heterogeneity: Welfare and distributional effects with variable renewable supply," Energy Economics, Elsevier, vol. 76(C), pages 257-273.
    15. Lenzen, Manfred & McBain, Bonnie & Trainer, Ted & Jütte, Silke & Rey-Lescure, Olivier & Huang, Jing, 2016. "Simulating low-carbon electricity supply for Australia," Applied Energy, Elsevier, vol. 179(C), pages 553-564.
    16. Xu, Jie & Lv, Tao & Hou, Xiaoran & Deng, Xu & Liu, Feng, 2021. "Provincial allocation of renewable portfolio standard in China based on efficiency and fairness principles," Renewable Energy, Elsevier, vol. 179(C), pages 1233-1245.
    17. Shirizadeh, Behrang & Quirion, Philippe, 2021. "Low-carbon options for the French power sector: What role for renewables, nuclear energy and carbon capture and storage?," Energy Economics, Elsevier, vol. 95(C).
    18. Constantino Dário Justo & José Eduardo Tafula & Pedro Moura, 2022. "Planning Sustainable Energy Systems in the Southern African Development Community: A Review of Power Systems Planning Approaches," Energies, MDPI, vol. 15(21), pages 1-28, October.
    19. Martina Ricci & Marcello Benvenuto & Stefano Gino Mosele & Roberto Pacciani & Michele Marconcini, 2022. "Predicting the Impact of Compressor Flexibility Improvements on Heavy-Duty Gas Turbines for Minimum and Base Load Conditions," Energies, MDPI, vol. 15(20), pages 1-14, October.
    20. Rodgers, Mark & Coit, David & Felder, Frank & Carlton, Annmarie, 2019. "Assessing the effects of power grid expansion on human health externalities," Socio-Economic Planning Sciences, Elsevier, vol. 66(C), pages 92-104.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:2799-:d:1410566. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.