Optimizing resilience and sustainability: a tri-level energy management framework for multi-energy microgrids with power-to-hydrogen technology
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Mingyu Li
Abstract
This paper proposes a novel tri-layer energy management system (EMS) architecture for community microgrids, consisting of a Primary Community Microgrid (PCMG) layer, multiple Microgrid (MG) layers, and individual prosumer layers. The framework is designed to enhance coordination, autonomy, and efficiency in distributed energy systems. A dual-level optimization strategy is adopted: day-ahead planning at the PCMG level and real-time scheduling at the MG and prosumer levels. Each layer operates under a distinct objective function – cost minimization, profit maximization, and utility satisfaction while maintaining inter-layer communication and decision consistency. A simulation-based case study is conducted using realistic parameters to evaluate the framework’s performance under various operating scenarios. The validation demonstrates the framework’s effectiveness in improving energy distribution, enhancing prosumer participation, and reducing overall operational costs. Comparative results with a conventional single-layer EMS model are presented using graphical and numerical analyses, confirming the proposed framework’s advantages in flexibility, scalability, and energy efficiency.
Acknowledgments
I would like to take this opportunity to acknowledge that there are no individuals or organizations that require acknowledgment for their contributions to this work.
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Research ethics: Research involving Human Participants and Animals: The observational study conducted on medical staff needs no ethical code. Therefore, the above study was not required to acquire ethical code.
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Informed consent: This option is not neccessary due to that the data were collected from the references.
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Author contributions: The author contributed to the study’s conception and design. Data collection, simulation and analysis were performed by “Mingyu Li”. Also the first draft of the manuscript was written by Mingyu Li commented on previous versions of the manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: During the preparation of this work, the authors used Large Language Models, AI, and Machine Learning tools for tasks such as language refinement, data analysis, or figure generation, with all outputs being reviewed and validated by the authors to ensure accuracy and originality. After using these tools/services, the authors reviewed and edited the content and take full responsibility for the content of the published article.
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Conflict of interest: The author declare no competing of interests.
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Research funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
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Data availability: The authors do not have permissions to share data.
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