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A Novel Internet of Energy Based Optimal Multi-Agent Control Scheme for Microgrid including Renewable Energy Resources

Author

Listed:
  • Bilal Naji Alhasnawi

    (Electrical Engineering Department, University of Basrah, Basrah 61001, Iraq)

  • Basil H. Jasim

    (Electrical Engineering Department, University of Basrah, Basrah 61001, Iraq)

  • Zain-Aldeen S. A. Rahman

    (Electrical Engineering Department, University of Basrah, Basrah 61001, Iraq
    Department of Electrical Techniques, Qurna Technique Institute, Southern Technical University, Basra 61016, Iraq)

  • Josep M. Guerrero

    (Center for Research on Microgrids (CROM), Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

  • M. Dolores Esteban

    (Civil Engineering Department, Hydraulics, Energy and Environment/Profesor Aranguren 3, Universidad Politécnica de Madrid (UPM), 28040 Madrid, Spain)

Abstract

The increasing integration of Renewable Energy Resources (RERs) in distribution networks forms the Networked Renewable Energy Resources (NRERs). The cooperative Peer-to-Peer (P2P) control architecture is able to fully exploit the resilience and flexibility of NRERs. This study proposes a multi-agent system to achieve P2P control of NRERs based Internet of Things (IoT). The control system is fully distributed and contains two control layers operated in the agent of each RER. For primary control, a droop control is adopted by each RER-agent for localized power sharing. For secondary control, a distributed diffusion algorithm is proposed for arbitrary power sharing among RERs. The proposed levels communication system is implemented to explain the data exchange between the distribution network system and the cloud server. The local communication level utilizes the Internet Protocol (IP)/Transmission Control Protocol (TCP), and Message Queuing Telemetry Transport (MQTT) is used as the protocol for the global communication level. The effectiveness of the proposed system is validated by numerical simulation with the modified IEEE 9 node test feeder. The controller proposed in this paper achieved savings of 20.65% for the system, 25.99% for photovoltaic, 35.52 for diesel generator, 24.59 for batteries, and 52.34% for power loss.

Suggested Citation

  • Bilal Naji Alhasnawi & Basil H. Jasim & Zain-Aldeen S. A. Rahman & Josep M. Guerrero & M. Dolores Esteban, 2021. "A Novel Internet of Energy Based Optimal Multi-Agent Control Scheme for Microgrid including Renewable Energy Resources," IJERPH, MDPI, vol. 18(15), pages 1-24, July.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:15:p:8146-:d:606517
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    References listed on IDEAS

    as
    1. Bilal Naji Alhasnawi & Basil H. Jasim & Pierluigi Siano & Josep M. Guerrero, 2021. "A Novel Real-Time Electricity Scheduling for Home Energy Management System Using the Internet of Energy," Energies, MDPI, vol. 14(11), pages 1-29, May.
    2. Bilal Naji Alhasnawi & Basil H. Jasim & Bishoy E. Sedhom & Eklas Hossain & Josep M. Guerrero, 2021. "A New Decentralized Control Strategy of Microgrids in the Internet of Energy Paradigm," Energies, MDPI, vol. 14(8), pages 1-34, April.
    3. Bilal Naji Alhasnawi & Basil H. Jasim & Walid Issa & Amjad Anvari-Moghaddam & Frede Blaabjerg, 2020. "A New Robust Control Strategy for Parallel Operated Inverters in Green Energy Applications," Energies, MDPI, vol. 13(13), pages 1-31, July.
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    Cited by:

    1. Ming Li & Anqing Chen & Peixiong Liu & Wenbo Ren & Chenghao Zheng, 2024. "Multivariable Algorithm Using Signal-Processing Techniques to Identify Islanding Events in Utility Grid with Renewable Energy Penetration," Energies, MDPI, vol. 17(4), pages 1-23, February.
    2. Fahim Muntasir & Anusheel Chapagain & Kishan Maharjan & Mirza Jabbar Aziz Baig & Mohsin Jamil & Ashraf Ali Khan, 2023. "Developing an Appropriate Energy Trading Algorithm and Techno-Economic Analysis between Peer-to-Peer within a Partly Independent Microgrid," Energies, MDPI, vol. 16(3), pages 1-21, February.
    3. Ali M. Jasim & Basil H. Jasim & Soheil Mohseni & Alan C. Brent, 2023. "Energy Internet-Based Load Shifting in Smart Microgrids: An Experimental Study," Energies, MDPI, vol. 16(13), pages 1-26, June.

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