IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i19p6086-d642333.html
   My bibliography  Save this article

DC Microgrid Utilizing Artificial Intelligence and Phasor Measurement Unit Assisted Inverter

Author

Listed:
  • Raziq Yaqub

    (Department of Electrical Engineering and Computer Science, Alabama A&M University, Huntsville, AL 35762, USA)

  • Mohamed Ali

    (Department of Electrical Engineering, The City College of New York, New York, NY 10031, USA)

  • Hassan Ali

    (Department of Electrical Engineering, College of the North Atlantic-Qatar, P.O. Box Doha 24449, Qatar)

Abstract

Community microgrids are set to change the landscape of future energy markets. The technology is being deployed in many cities around the globe. However, a wide-scale deployment faces three major issues: initial synchronization of microgrids with the utility grids, slip management during its operation, and mitigation of distortions produced by the inverter. This paper proposes a Phasor Measurement Unit (PMU) Assisted Inverter (PAI) that addresses these three issues in a single solution. The proposed PAI continually receives real-time data from a Phasor Measurement Unit installed in the distribution system of a utility company and keeps constructing a real-time reference signal for the inverter. To validate the concept, a unique intelligent DC microgrid architecture that employs the proposed Phasor Measurement Unit (PMU) Assisted Inverter (PAI) is also presented, alongside the cloud-based Artificial Intelligence (AI), which harnesses energy from community shared resources, such as batteries and the community’s rooftop solar resources. The results show that the proposed system produces quality output and is 98.5% efficient.

Suggested Citation

  • Raziq Yaqub & Mohamed Ali & Hassan Ali, 2021. "DC Microgrid Utilizing Artificial Intelligence and Phasor Measurement Unit Assisted Inverter," Energies, MDPI, vol. 14(19), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6086-:d:642333
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/19/6086/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/19/6086/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Unamuno, Eneko & Barrena, Jon Andoni, 2015. "Hybrid ac/dc microgrids—Part I: Review and classification of topologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1251-1259.
    2. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    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. Alexandra Blanch-Fortuna & David Zambrano-Prada & Oswaldo López-Santos & Abdelali El Aroudi & Luis Vázquez-Seisdedos & Luis Martinez-Salamero, 2024. "Hierarchical Control of Power Distribution in the Hybrid Energy Storage System of an Ultrafast Charging Station for Electric Vehicles," Energies, MDPI, vol. 17(6), pages 1-20, March.
    2. Fuster-Palop, Enrique & Prades-Gil, Carlos & Masip, X. & Viana-Fons, Joan D. & Payá, Jorge, 2021. "Innovative regression-based methodology to assess the techno-economic performance of photovoltaic installations in urban areas," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    3. Davide Coraci & Silvio Brandi & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "Online Implementation of a Soft Actor-Critic Agent to Enhance Indoor Temperature Control and Energy Efficiency in Buildings," Energies, MDPI, vol. 14(4), pages 1-26, February.
    4. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    5. Bustos, Cristian & Watts, David, 2017. "Novel methodology for microgrids in isolated communities: Electricity cost-coverage trade-off with 3-stage technology mix, dispatch & configuration optimizations," Applied Energy, Elsevier, vol. 195(C), pages 204-221.
    6. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    7. Sohail Sarwar & Desen Kirli & Michael M. C. Merlin & Aristides E. Kiprakis, 2022. "Major Challenges towards Energy Management and Power Sharing in a Hybrid AC/DC Microgrid: A Review," Energies, MDPI, vol. 15(23), pages 1-30, November.
    8. Zeyue Sun & Mohsen Eskandari & Chaoran Zheng & Ming Li, 2022. "Handling Computation Hardness and Time Complexity Issue of Battery Energy Storage Scheduling in Microgrids by Deep Reinforcement Learning," Energies, MDPI, vol. 16(1), pages 1-20, December.
    9. Sijia Li & Arman Oshnoei & Frede Blaabjerg & Amjad Anvari-Moghaddam, 2023. "Hierarchical Control for Microgrids: A Survey on Classical and Machine Learning-Based Methods," Sustainability, MDPI, vol. 15(11), pages 1-22, June.
    10. Patrick Sunday Onen & Geev Mokryani & Rana H. A. Zubo, 2022. "Planning of Multi-Vector Energy Systems with High Penetration of Renewable Energy Source: A Comprehensive Review," Energies, MDPI, vol. 15(15), pages 1-25, August.
    11. Davarzani, Sima & Pisica, Ioana & Taylor, Gareth A. & Munisami, Kevin J., 2021. "Residential Demand Response Strategies and Applications in Active Distribution Network Management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    12. Vitor Monteiro & Julio S. Martins & João Carlos Aparício Fernandes & Joao L. Afonso, 2021. "Review of a Disruptive Vision of Future Power Grids: A New Path Based on Hybrid AC/DC Grids and Solid-State Transformers," Sustainability, MDPI, vol. 13(16), pages 1-25, August.
    13. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    14. Vladimir Franki & Darin Majnarić & Alfredo Višković, 2023. "A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector," Energies, MDPI, vol. 16(3), pages 1-35, January.
    15. Lu, Hsi-Peng & Cheng, Hsiang-Ling & Tzou, Jen-Chuen & Chen, Chiao-Shan, 2023. "Technology roadmap of AI applications in the retail industry," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    16. Hossein Shayeghi & Elnaz Shahryari & Mohammad Moradzadeh & Pierluigi Siano, 2019. "A Survey on Microgrid Energy Management Considering Flexible Energy Sources," Energies, MDPI, vol. 12(11), pages 1-26, June.
    17. Isaías Gomes & Rui Melicio & Victor M. F. Mendes, 2021. "Assessing the Value of Demand Response in Microgrids," Sustainability, MDPI, vol. 13(11), pages 1-16, May.
    18. Nebiyu Kedir & Phuong H. D. Nguyen & Citlaly Pérez & Pedro Ponce & Aminah Robinson Fayek, 2023. "Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation," Energies, MDPI, vol. 16(9), pages 1-38, April.
    19. 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).
    20. Houssem Rafik Al-Hana Bouchekara & Mohammad Shoaib Shahriar & Muhammad Sharjeel Javaid & Yusuf Abubakar Sha’aban & Makbul Anwari Muhammad Ramli, 2021. "Multi-Objective Optimization of a Hybrid Nanogrid/Microgrid: Application to Desert Camps in Hafr Al-Batin," Energies, MDPI, vol. 14(5), pages 1-24, February.

    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:14:y:2021:i:19:p:6086-:d:642333. 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.