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Design and Implementation of a Microgrid Energy Management System

Author

Listed:
  • Eun-Kyu Lee

    (Information and Telecommunication Engineering, Incheon National University, Incheon 22012, Korea
    UCLA Smart Grid Energy Research Center, Los Angeles, CA 90095, USA)

  • Wenbo Shi

    (UCLA Smart Grid Energy Research Center, Los Angeles, CA 90095, USA)

  • Rajit Gadh

    (UCLA Smart Grid Energy Research Center, Los Angeles, CA 90095, USA)

  • Wooseong Kim

    (Computer Engineering, Gachon University, Seongnam 13120, Korea)

Abstract

A microgrid is characterized by the integration of distributed energy resources and controllable loads in a power distribution network. Such integration introduces new, unique challenges to microgrid management that have never been exposed to traditional power systems. To accommodate these challenges, it is necessary to redesign a conventional Energy Management System (EMS) so that it can cope with intrinsic characteristics of microgrids. While many projects have shown excellent research outcomes, they have either tackled portions of the characteristics or validated their EMSs only via simulations. This paper proposes a Microgrid Platform (MP), an advanced EMS for efficient microgrid operations. We design the MP by taking into consideration (i) all the functional requirements of a microgrid EMS (i.e., optimization, forecast, human–machine interface, and data analysis) and (ii) engineering challenges (i.e., interoperability, extensibility, and flexibility). Moreover, a prototype system is developed and deployed in two smart grid testbeds: UCLA Smart Grid Energy Research Center and Korea Institute of Energy Research. We then conduct experiments to verify the feasibility of the MP design in real-world settings. Our testbeds and experiments demonstrate that the MP is able to communicate with various energy devices and to perform an energy management task efficiently.

Suggested Citation

  • Eun-Kyu Lee & Wenbo Shi & Rajit Gadh & Wooseong Kim, 2016. "Design and Implementation of a Microgrid Energy Management System," Sustainability, MDPI, vol. 8(11), pages 1-19, November.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:11:p:1143-:d:82276
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    Citations

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    Cited by:

    1. Yuxin Wen & Peixiao Fan & Jia Hu & Song Ke & Fuzhang Wu & Xu Zhu, 2022. "An Optimal Scheduling Strategy of a Microgrid with V2G Based on Deep Q-Learning," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    2. Giovanni Mercurio Casolino & Mario Russo & Pietro Varilone & Daniele Pescosolido, 2018. "Hardware-in-the-Loop Validation of Energy Management Systems for Microgrids: A Short Overview and a Case Study," Energies, MDPI, vol. 11(11), pages 1-17, November.
    3. Komsan Hongesombut & Suphicha Punyakunlaset & Sillawat Romphochai, 2021. "Under Frequency Protection Enhancement of an Islanded Active Distribution Network Using a Virtual Inertia-Controlled-Battery Energy Storage System," Sustainability, MDPI, vol. 13(2), pages 1-39, January.
    4. Restrepo, Mauricio & Cañizares, Claudio A. & Simpson-Porco, John W. & Su, Peter & Taruc, John, 2021. "Optimization- and Rule-based Energy Management Systems at the Canadian Renewable Energy Laboratory microgrid facility," Applied Energy, Elsevier, vol. 290(C).
    5. Akram Qashou & Sufian Yousef & Firas Hazzaa & Kahtan Aziz, 2024. "Temporal forecasting by converting stochastic behaviour into a stable pattern in electric grid," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(9), pages 4426-4442, September.
    6. Sharma, Pavitra & Dutt Mathur, Hitesh & Mishra, Puneet & Bansal, Ramesh C., 2022. "A critical and comparative review of energy management strategies for microgrids," Applied Energy, Elsevier, vol. 327(C).
    7. Byeong-Cheol Jeong & Dong-Hwan Shin & Jae-Beom Im & Jae-Young Park & Young-Jin Kim, 2019. "Implementation of Optimal Two-Stage Scheduling of Energy Storage System Based on Big-Data-Driven Forecasting—An Actual Case Study in a Campus Microgrid," Energies, MDPI, vol. 12(6), pages 1-20, March.
    8. Fatma Yaprakdal & M. Berkay Yılmaz & Mustafa Baysal & Amjad Anvari-Moghaddam, 2020. "A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid," Sustainability, MDPI, vol. 12(4), pages 1-27, February.
    9. Tayab, Usman Bashir & Zia, Ali & Yang, Fuwen & Lu, Junwei & Kashif, Muhammad, 2020. "Short-term load forecasting for microgrid energy management system using hybrid HHO-FNN model with best-basis stationary wavelet packet transform," Energy, Elsevier, vol. 203(C).
    10. Uikyun Na & Eun-Kyu Lee, 2020. "Fog BEMS: An Agent-Based Hierarchical Fog Layer Architecture for Improving Scalability in a Building Energy Management System," Sustainability, MDPI, vol. 12(7), pages 1-28, April.
    11. Mageswaran Rengasamy & Sivasankar Gangatharan & Rajvikram Madurai Elavarasan & Lucian Mihet-Popa, 2021. "Incorporation of Microgrid Technology Solutions to Reduce Power Loss in a Distribution Network with Elimination of Inefficient Power Conversion Strategies," Sustainability, MDPI, vol. 13(24), pages 1-25, December.
    12. Rishang Long & Jian Liu & Chunliang Lu & Jiaqi Shi & Jianhua Zhang, 2017. "Coordinated Optimal Operation Method of the Regional Energy Internet," Sustainability, MDPI, vol. 9(5), pages 1-14, May.
    13. Akram Qashou & Sufian Yousef & Erika Sanchez-Velazquez, 2022. "Mining sensor data in a smart environment: a study of control algorithms and microgrid testbed for temporal forecasting and patterns of failure," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2371-2390, October.
    14. Tayab, Usman Bashir & Lu, Junwei & Yang, Fuwen & AlGarni, Tahani Saad & Kashif, Muhammad, 2021. "Energy management system for microgrids using weighted salp swarm algorithm and hybrid forecasting approach," Renewable Energy, Elsevier, vol. 180(C), pages 467-481.

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