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Flexible Smart Energy-Management Systems Using an Online Tendering Process Framework for Microgrids

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Listed:
  • Mansour Selseleh Jonban

    (MCIA Center, Electronic Engineering Department, Universitat Politecnica de Catalunya, 08222 Terrassa, Spain)

  • Luis Romeral

    (MCIA Center, Electronic Engineering Department, Universitat Politecnica de Catalunya, 08222 Terrassa, Spain)

  • Elyas Rakhshani

    (Technology Area, Control and Algorithm Group, Hybrid Energy-Storage Solutions (HESStec), 46980 Valencia, Spain)

  • Mousa Marzband

    (Electrical Power and Control Systems Research Group, Northumbria University, Ellison Place, Newcastle upon Tyne NE1 8ST, UK)

Abstract

Currently, modern power grids are evolving into complex cyber-physical systems integrated with distributed energy resources that can be controlled and monitored by computer-based algorithms. Given the increasing prevalence of artificial intelligence algorithms, it is essential to explore the possibility of energy management in microgrids by implementing control methodologies with advanced processing centers. This study proposes a novel smart multi-agent-based framework under a tendering process framework with a bottom-up approach to control and manage the flow of energy into a grid-connected microgrid (MG). The tendering organization in this structure as an upstream agent allocates demand among generators, creates a balance between supply and demand, and provides optimal energy cost for the MG. To optimize the electricity cost and decrease the use of grid power, the first-price sealed-bid (FPSB) algorithm is implemented over the tendering process. The proposed approach from one side optimally allocates energy among generators, and, from the other side, guarantees the system from blackouts. Theoretical analysis and results demonstrate that the proposed technique is easy to implement and provides a robust and stable control for MGs, which can guarantee energy management as well as flexible and online control. Furthermore, results show the proposed framework besides the real-time allocation of power among providers to optimize the injected power from the grid so that the total injected power by the grid is 146.92 kWh and the injected power to the grid is 214.34 kWh.

Suggested Citation

  • Mansour Selseleh Jonban & Luis Romeral & Elyas Rakhshani & Mousa Marzband, 2023. "Flexible Smart Energy-Management Systems Using an Online Tendering Process Framework for Microgrids," Energies, MDPI, vol. 16(13), pages 1-19, June.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:4914-:d:1178082
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    References listed on IDEAS

    as
    1. Anvari-Moghaddam, Amjad & Rahimi-Kian, Ashkan & Mirian, Maryam S. & Guerrero, Josep M., 2017. "A multi-agent based energy management solution for integrated buildings and microgrid system," Applied Energy, Elsevier, vol. 203(C), pages 41-56.
    2. 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).
    3. Chunxia Gao & Zhaoyan Zhang & Peiguang Wang, 2023. "Day-Ahead Scheduling Strategy Optimization of Electric–Thermal Integrated Energy System to Improve the Proportion of New Energy," Energies, MDPI, vol. 16(9), pages 1-30, April.
    4. Cátia Silva & Pedro Faria & Zita Vale, 2023. "Demand Response Implementation: Overview of Europe and United States Status," Energies, MDPI, vol. 16(10), pages 1-20, May.
    5. Necmi Altin & Süleyman Emre Eyimaya & Adel Nasiri, 2023. "Multi-Agent-Based Controller for Microgrids: An Overview and Case Study," Energies, MDPI, vol. 16(5), pages 1-18, March.
    6. Cheaitou, Ali & Larbi, Rim & Al Housani, Bashayer, 2019. "Decision making framework for tender evaluation and contractor selection in public organizations with risk considerations," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    7. Djamila Rekioua, 2023. "Energy Storage Systems for Photovoltaic and Wind Systems: A Review," Energies, MDPI, vol. 16(9), pages 1-26, May.
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