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Real-time operation of multi-micro-grids using a multi-agent system

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  • Keshta, H.E.
  • Ali, A.A.
  • Saied, E.M.
  • Bendary, F.M.

Abstract

The inaccurate prediction of solar irradiance, wind speed and demand load may significantly influence the operation of micro-grids. This condition causes unbalances between generation and load that lead to variations in DC and AC bus voltages, and may affect system stability. A multi-agent system (MAS) is proposed in this study to achieve optimal energy management for voltage regulation and to enhance the stability of a system under different weather conditions and load perturbations for two connected micro-grids. Optimum operation is achieved through two stages. The first stage is the optimum day-ahead energy from each source based on historical data. The second stage is implemented to maintain the balance between generation and load by considering economic operation during real-time operation. This stage can be realized by controlling converters related to each source in both micro-grids. Different scenarios are presented to evaluate the effectiveness of the proposed MAS. The simulation results demonstrate that the performance of the proposed energy management system is efficient.

Suggested Citation

  • Keshta, H.E. & Ali, A.A. & Saied, E.M. & Bendary, F.M., 2019. "Real-time operation of multi-micro-grids using a multi-agent system," Energy, Elsevier, vol. 174(C), pages 576-590.
    • Handle: RePEc:eee:energy:v:174:y:2019:i:c:p:576-590
    DOI: 10.1016/j.energy.2019.02.145
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    References listed on IDEAS

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    1. Marzband, Mousa & Ghadimi, Majid & Sumper, Andreas & Domínguez-García, José Luis, 2014. "Experimental validation of a real-time energy management system using multi-period gravitational search algorithm for microgrids in islanded mode," Applied Energy, Elsevier, vol. 128(C), pages 164-174.
    2. Wei-Tzer Huang & Kai-Chao Yao & Chun-Ching Wu, 2014. "Using the Direct Search Method for Optimal Dispatch of Distributed Generation in a Medium-Voltage Microgrid," Energies, MDPI, vol. 7(12), pages 1-19, December.
    3. Nemati, Mohsen & Braun, Martin & Tenbohlen, Stefan, 2018. "Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming," Applied Energy, Elsevier, vol. 210(C), pages 944-963.
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    Cited by:

    1. Jiao, P.H. & Chen, J.J. & Peng, K. & Zhao, Y.L. & Xin, K.F., 2020. "Multi-objective mean-semi-entropy model for optimal standalone micro-grid planning with uncertain renewable energy resources," Energy, Elsevier, vol. 191(C).
    2. Md Mainul Islam & Mahmood Nagrial & Jamal Rizk & Ali Hellany, 2021. "General Aspects, Islanding Detection, and Energy Management in Microgrids: A Review," Sustainability, MDPI, vol. 13(16), pages 1-45, August.
    3. Cui, Shiting & Gao, Yao & Zhu, Ruijin, 2024. "A new integrated energy system cluster energy sharing framework adapted to high altitude areas," Applied Energy, Elsevier, vol. 366(C).
    4. Pan, Chenyun & Fan, Hongtao & Zhang, Ruixiang & Sun, Jie & Wang, Yu & Sun, Yaojie, 2023. "An improved multi-timescale coordinated control strategy for an integrated energy system with a hybrid energy storage system," Applied Energy, Elsevier, vol. 343(C).

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