IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i6p5453-d1102177.html
   My bibliography  Save this article

An Artificial-Intelligence-Based Renewable Energy Prediction Program for Demand-Side Management in Smart Grids

Author

Listed:
  • Vinothini Arumugham

    (School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India)

  • Hayder M. A. Ghanimi

    (Biomedical Engineering Department, College of Engineering, University of Warith Al-Anbiyaa, Karbala 56001, Iraq)

  • Denis A. Pustokhin

    (Department of Logistics, State University of Management, 109542 Moscow, Russia)

  • Irina V. Pustokhina

    (Department of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, 117997 Moscow, Russia)

  • Vidya Sagar Ponnam

    (Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, Andhra Pradesh, India)

  • Meshal Alharbi

    (Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia)

  • Parkavi Krishnamoorthy

    (School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India)

  • Sudhakar Sengan

    (Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli 627152, Tamil Nadu, India)

Abstract

Technology advancements have enabled the capture of Renewable Energy Sources (RES) on a massive scale. Smart Grids (SGs) that combine conventional and RES are predicted as a sustainable method of power generation. Moreover, environmental conditions impact all RES, causing changes in the amount of electricity produced by these sources. Furthermore, availability is dependent on daily or annual cycles. Although smart meters allow real-time demand prediction, precise models that predict the electricity produced by RES are also required. Prediction Models (PMs) accurately guarantee grid stability, efficient scheduling, and energy management. For example, the SG must be smoothly transformed into the conventional energy source for that time and guarantee that the electricity generated meets the predicted demand if the model predicts a period of Renewable Energy (RE) loss. The literature also suggests scheduling methods for demand-supply matching and different learning-based PMs for sources of RE using open data sources. This paper developed a model that accurately replicates a microgrid, predicts demand and supply, seamlessly schedules power delivery to meet demand, and gives actionable insights into the SG system’s operation. Furthermore, this work develops the Demand Response Program (DRP) using improved incentive-based payment as cost suggestion packages. The test results are valued in different cases for optimizing operating costs through the multi-objective ant colony optimization algorithm (MOACO) with and without the input of the DRP.

Suggested Citation

  • Vinothini Arumugham & Hayder M. A. Ghanimi & Denis A. Pustokhin & Irina V. Pustokhina & Vidya Sagar Ponnam & Meshal Alharbi & Parkavi Krishnamoorthy & Sudhakar Sengan, 2023. "An Artificial-Intelligence-Based Renewable Energy Prediction Program for Demand-Side Management in Smart Grids," Sustainability, MDPI, vol. 15(6), pages 1-26, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5453-:d:1102177
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/6/5453/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/6/5453/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Aghajani, G.R. & Shayanfar, H.A. & Shayeghi, H., 2017. "Demand side management in a smart micro-grid in the presence of renewable generation and demand response," Energy, Elsevier, vol. 126(C), pages 622-637.
    2. Mazzola, Simone & Astolfi, Marco & Macchi, Ennio, 2015. "A detailed model for the optimal management of a multigood microgrid," Applied Energy, Elsevier, vol. 154(C), pages 862-873.
    3. Josue Campos do Prado & Wei Qiao & Liyan Qu & Julio Romero Agüero, 2019. "The Next-Generation Retail Electricity Market in the Context of Distributed Energy Resources: Vision and Integrating Framework," Energies, MDPI, vol. 12(3), pages 1-24, February.
    4. Khan, Aftab Ahmed & Razzaq, Sohail & Khan, Asadullah & Khursheed, Fatima & Owais,, 2015. "HEMSs and enabled demand response in electricity market: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 773-785.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, Chengying & Wu, Zhixin & Li, Xuetao & Fars, Ashk, 2024. "Risk-constrained stochastic scheduling for energy hub: Integrating renewables, demand response, and electric vehicles," Energy, Elsevier, vol. 288(C).
    2. Te Li & Mengze Zhang & Yan Zhou, 2024. "LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting," Papers 2410.15286, arXiv.org.

    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. Aghajani, G.R. & Shayanfar, H.A. & Shayeghi, H., 2017. "Demand side management in a smart micro-grid in the presence of renewable generation and demand response," Energy, Elsevier, vol. 126(C), pages 622-637.
    2. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    3. de la Hoz, Jordi & Martín, Helena & Alonso, Alex & Carolina Luna, Adriana & Matas, José & Vasquez, Juan C. & Guerrero, Josep M., 2019. "Regulatory-framework-embedded energy management system for microgrids: The case study of the Spanish self-consumption scheme," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    4. Christoforos Menos-Aikateriniadis & Ilias Lamprinos & Pavlos S. Georgilakis, 2022. "Particle Swarm Optimization in Residential Demand-Side Management: A Review on Scheduling and Control Algorithms for Demand Response Provision," Energies, MDPI, vol. 15(6), pages 1-26, March.
    5. Ullah, Kalim & Hafeez, Ghulam & Khan, Imran & Jan, Sadaqat & Javaid, Nadeem, 2021. "A multi-objective energy optimization in smart grid with high penetration of renewable energy sources," Applied Energy, Elsevier, vol. 299(C).
    6. 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.
    7. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
    8. Simone Di Leo & Marta Chicca & Cinzia Daraio & Andrea Guerrini & Stefano Scarcella, 2022. "A Framework for the Analysis of the Sustainability of the Energy Retail Market," Sustainability, MDPI, vol. 14(12), pages 1-28, June.
    9. Ceran, Bartosz, 2019. "The concept of use of PV/WT/FC hybrid power generation system for smoothing the energy profile of the consumer," Energy, Elsevier, vol. 167(C), pages 853-865.
    10. Mughees, Neelam & Jaffery, Mujtaba Hussain & Mughees, Anam & Ansari, Ejaz Ahmad & Mughees, Abdullah, 2023. "Reinforcement learning-based composite differential evolution for integrated demand response scheme in industrial microgrids," Applied Energy, Elsevier, vol. 342(C).
    11. Sulaiman A. Almohaimeed & Siddharth Suryanarayanan & Peter O’Neill, 2021. "Simulation Studies to Quantify the Impact of Demand Side Management on Environmental Footprint," Sustainability, MDPI, vol. 13(17), pages 1-24, August.
    12. Elma, Onur & Selamogullari, Ugur Savas, 2015. "A new home energy management algorithm with voltage control in a smart home environment," Energy, Elsevier, vol. 91(C), pages 720-731.
    13. Lu, Qing & Yu, Hao & Zhao, Kangli & Leng, Yajun & Hou, Jianchao & Xie, Pinjie, 2019. "Residential demand response considering distributed PV consumption: A model based on China's PV policy," Energy, Elsevier, vol. 172(C), pages 443-456.
    14. Shakeri, Mohammad & Shayestegan, Mohsen & Reza, S.M. Salim & Yahya, Iskandar & Bais, Badariah & Akhtaruzzaman, Md & Sopian, Kamaruzzaman & Amin, Nowshad, 2018. "Implementation of a novel home energy management system (HEMS) architecture with solar photovoltaic system as supplementary source," Renewable Energy, Elsevier, vol. 125(C), pages 108-120.
    15. Ahmad Khan, Aftab & Naeem, Muhammad & Iqbal, Muhammad & Qaisar, Saad & Anpalagan, Alagan, 2016. "A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1664-1683.
    16. Lisardo Prieto González & Anna Fensel & Juan Miguel Gómez Berbís & Angela Popa & Antonio de Amescua Seco, 2021. "A Survey on Energy Efficiency in Smart Homes and Smart Grids," Energies, MDPI, vol. 14(21), pages 1-16, November.
    17. Mehrjerdi, Hasan & Bornapour, Mosayeb & Hemmati, Reza & Ghiasi, Seyyed Mohammad Sadegh, 2019. "Unified energy management and load control in building equipped with wind-solar-battery incorporating electric and hydrogen vehicles under both connected to the grid and islanding modes," Energy, Elsevier, vol. 168(C), pages 919-930.
    18. Huang, Pei & Wu, Hunjun & Huang, Gongsheng & Sun, Yongjun, 2018. "A top-down control method of nZEBs for performance optimization at nZEB-cluster-level," Energy, Elsevier, vol. 159(C), pages 891-904.
    19. Jurasz, Jakub & Dąbek, Paweł B. & Kaźmierczak, Bartosz & Kies, Alexander & Wdowikowski, Marcin, 2018. "Large scale complementary solar and wind energy sources coupled with pumped-storage hydroelectricity for Lower Silesia (Poland)," Energy, Elsevier, vol. 161(C), pages 183-192.
    20. Masoud Dashtdar & Aymen Flah & Seyed Mohammad Sadegh Hosseinimoghadam & Hossam Kotb & Elżbieta Jasińska & Radomir Gono & Zbigniew Leonowicz & Michał Jasiński, 2022. "Optimal Operation of Microgrids with Demand-Side Management Based on a Combination of Genetic Algorithm and Artificial Bee Colony," Sustainability, MDPI, vol. 14(11), pages 1-26, May.

    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:jsusta:v:15:y:2023:i:6:p:5453-:d:1102177. 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.