IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i20p5201-d1501963.html
   My bibliography  Save this article

Centralised Control and Energy Management of Multiple Interconnected Standalone AC Microgrids

Author

Listed:
  • Ezenwa Udoha

    (Department of Engineering, Faculty of Environment, Science and Economy, University of Exeter, Penryn Campus, Cornwall TR10 9FE, UK)

  • Saptarshi Das

    (Centre for Environmental Mathematics, Faculty of Environment, Science and Economy, University of Exeter, Penryn Campus, Cornwall TR10 9FE, UK
    Institute for Data Science and Artificial Intelligence, University of Exeter, Laver Building, North Park Road, Exeter, Devon EX4 4QE, UK)

  • Mohammad Abusara

    (Department of Engineering, Faculty of Environment, Science and Economy, University of Exeter, Penryn Campus, Cornwall TR10 9FE, UK)

Abstract

When microgrids operate autonomously, they must curtail the surplus of renewable energy sources (RES) while minimising reliance on gas. However, when interconnected, microgrids can collaboratively minimise RES curtailment and gas consumption due to the ability of exchanging power. This paper presents a centralised controller and energy management of multiple standalone AC microgrids interconnected to a common AC bus using back-to-back converters. Each microgrid consists of RES, a battery, a gas-powered auxiliary unit, and a load. The battery’s state of charge (SOC) is controlled and is used in the AC bus frequency to indicate whether the microgrid has a surplus or shortage of power. High-level global droop control exchanges power between the microgrids. The optimisation problem for this interconnected system is modelled cooperatively to determine the optimal dispatch solution that minimises the energy cost from the auxiliary unit. The optimal dispatch is solved in three cases using the Nelder–Mead simplex algorithm under different settings: one-variable optimisation, three-variable optimisation with the standard droop equation, and three-variable optimisation with a modified droop equation. The optimised performance results are compared with those of the non-optimised benchmark to determine the percentage of optimal performance. The simulation results show that the total energy cost from the auxiliary unit is minimised by 8.98%.

Suggested Citation

  • Ezenwa Udoha & Saptarshi Das & Mohammad Abusara, 2024. "Centralised Control and Energy Management of Multiple Interconnected Standalone AC Microgrids," Energies, MDPI, vol. 17(20), pages 1-26, October.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:20:p:5201-:d:1501963
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/20/5201/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/20/5201/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bullich-Massagué, Eduard & Díaz-González, Francisco & Aragüés-Peñalba, Mònica & Girbau-Llistuella, Francesc & Olivella-Rosell, Pol & Sumper, Andreas, 2018. "Microgrid clustering architectures," Applied Energy, Elsevier, vol. 212(C), pages 340-361.
    2. Xie, Min & Ji, Xiang & Hu, Xintong & Cheng, Peijun & Du, Yuxin & Liu, Mingbo, 2018. "Autonomous optimized economic dispatch of active distribution system with multi-microgrids," Energy, Elsevier, vol. 153(C), pages 479-489.
    3. Jafari, Amirreza & Ganjeh Ganjehlou, Hamed & Khalili, Tohid & Bidram, Ali, 2020. "A fair electricity market strategy for energy management and reliability enhancement of islanded multi-microgrids," Applied Energy, Elsevier, vol. 270(C).
    4. Nah-Oak Song & Ji-Hye Lee & Hak-Man Kim & Yong Hoon Im & Jae Yong Lee, 2015. "Optimal Energy Management of Multi-Microgrids with Sequentially Coordinated Operations," Energies, MDPI, vol. 8(8), pages 1-20, August.
    5. Nikmehr, Nima & Najafi-Ravadanegh, Sajad & Khodaei, Amin, 2017. "Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty," Applied Energy, Elsevier, vol. 198(C), pages 267-279.
    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. Diptish Saha & Najmeh Bazmohammadi & Juan C. Vasquez & Josep M. Guerrero, 2023. "Multiple Microgrids: A Review of Architectures and Operation and Control Strategies," Energies, MDPI, vol. 16(2), pages 1-32, January.
    2. Bandeiras, F. & Pinheiro, E. & Gomes, M. & Coelho, P. & Fernandes, J., 2020. "Review of the cooperation and operation of microgrid clusters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    3. Nawaz, Arshad & Zhou, Min & Wu, Jing & Long, Chengnian, 2022. "A comprehensive review on energy management, demand response, and coordination schemes utilization in multi-microgrids network," Applied Energy, Elsevier, vol. 323(C).
    4. Zhou, Xiaoqian & Ai, Qian & Yousif, Muhammad, 2019. "Two kinds of decentralized robust economic dispatch framework combined distribution network and multi-microgrids," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    5. Huang, Chunyi & Zhang, Mingzhi & Wang, Chengmin & Xie, Ning & Yuan, Zhao, 2022. "An interactive two-stage retail electricity market for microgrids with peer-to-peer flexibility trading," Applied Energy, Elsevier, vol. 320(C).
    6. Han, Dongho & Lee, Jay H., 2021. "Two-stage stochastic programming formulation for optimal design and operation of multi-microgrid system using data-based modeling of renewable energy sources," Applied Energy, Elsevier, vol. 291(C).
    7. Janko, Samantha A. & Johnson, Nathan G., 2018. "Scalable multi-agent microgrid negotiations for a transactive energy market," Applied Energy, Elsevier, vol. 229(C), pages 715-727.
    8. Yang, Yanhong & Pei, Wei & Huo, Qunhai & Sun, Jianjun & Xu, Feng, 2018. "Coordinated planning method of multiple micro-grids and distribution network with flexible interconnection," Applied Energy, Elsevier, vol. 228(C), pages 2361-2374.
    9. Du, Yan & Wang, Zhiwei & Liu, Guangyi & Chen, Xi & Yuan, Haoyu & Wei, Yanli & Li, Fangxing, 2018. "A cooperative game approach for coordinating multi-microgrid operation within distribution systems," Applied Energy, Elsevier, vol. 222(C), pages 383-395.
    10. Akhtar Hussain & Van-Hai Bui & Hak-Man Kim, 2016. "Robust Optimization-Based Scheduling of Multi-Microgrids Considering Uncertainties," Energies, MDPI, vol. 9(4), pages 1-21, April.
    11. 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.
    12. Ahmadi, Seyed Ehsan & Sadeghi, Delnia & Marzband, Mousa & Abusorrah, Abdullah & Sedraoui, Khaled, 2022. "Decentralized bi-level stochastic optimization approach for multi-agent multi-energy networked micro-grids with multi-energy storage technologies," Energy, Elsevier, vol. 245(C).
    13. Antoine Boche & Clément Foucher & Luiz Fernando Lavado Villa, 2022. "Understanding Microgrid Sustainability: A Systemic and Comprehensive Review," Energies, MDPI, vol. 15(8), pages 1-29, April.
    14. Grover-Silva, Etta & Heleno, Miguel & Mashayekh, Salman & Cardoso, Gonçalo & Girard, Robin & Kariniotakis, George, 2018. "A stochastic optimal power flow for scheduling flexible resources in microgrids operation," Applied Energy, Elsevier, vol. 229(C), pages 201-208.
    15. 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.
    16. Li, Qiang & Gao, Mengkai & Lin, Houfei & Chen, Ziyu & Chen, Minyou, 2019. "MAS-based distributed control method for multi-microgrids with high-penetration renewable energy," Energy, Elsevier, vol. 171(C), pages 284-295.
    17. Omaji Samuel & Nadeem Javaid & Mahmood Ashraf & Farruh Ishmanov & Muhammad Khalil Afzal & Zahoor Ali Khan, 2018. "Jaya based Optimization Method with High Dispatchable Distributed Generation for Residential Microgrid," Energies, MDPI, vol. 11(6), pages 1-29, June.
    18. Àlex Alonso & Jordi de la Hoz & Helena Martín & Sergio Coronas & Pep Salas & José Matas, 2020. "A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization," Energies, MDPI, vol. 13(21), pages 1-26, October.
    19. Jinyeong Lee & Kyungcheol Shin & Young-Min Wi, 2024. "Decentralized Operations of Industrial Complex Microgrids Considering Corporate Power Purchase Agreements for Renewable Energy 100% Initiatives in South Korea," Sustainability, MDPI, vol. 16(13), pages 1-23, June.
    20. Ahsan, Syed M. & Khan, Hassan A. & Hassan, Naveed-ul & Arif, Syed M. & Lie, Tek-Tjing, 2020. "Optimized power dispatch for solar photovoltaic-storage system with multiple buildings in bilateral contracts," Applied Energy, Elsevier, vol. 273(C).

    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:20:p:5201-:d:1501963. 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.