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Probabilistic Microgrid Energy Management with Interval Predictions

Author

Listed:
  • Jiayu Cheng

    (Shenzhen and Future Network of Intelligence Institute (FNii), The Chinese University of Hong Kong, Shenzhen 518172, China
    Shenzhen Research Institute of Big Data (SRIBD), Shenzhen 518172, China)

  • Dongliang Duan

    (Shenzhen Research Institute of Big Data (SRIBD), Shenzhen 518172, China
    Department of Electrical and Computer Engineering, University of Wyoming, Laramie, WY 82071, USA)

  • Xiang Cheng

    (Shenzhen Research Institute of Big Data (SRIBD), Shenzhen 518172, China
    State Key Laboratory of Advanced Optical Communication Systems and Networks, School of Electronics Engineering and Computing Sciences, Peking University, Beijing 100080, China)

  • Liuqing Yang

    (Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA)

  • Shuguang Cui

    (Shenzhen and Future Network of Intelligence Institute (FNii), The Chinese University of Hong Kong, Shenzhen 518172, China
    Shenzhen Research Institute of Big Data (SRIBD), Shenzhen 518172, China
    Department of Electrical and Computer Engineering, University of California at Davis, Davis, CA 95616, USA)

Abstract

In this paper, we consider a probabilistic microgrid dispatch problem where the predictions of the load and the Renewable Energy Source (RES) generation are given in the form of intervals. A hybrid method combining scenario-selected optimization and reserve strategy using the Model Predictive Control (MPC) framework is proposed. Specifically, first of all, an appropriate scenario is selected by the optimizer at each optimization stage, and then the optimal scheduling and reservation of system capacity are determined based on the selected scenario and possible variations in the future as provided by the predictors. In addition, a new reserve strategy is introduced to adaptively maintain system reliability and respond to variations in the hierarchical microgrid control. Simulations are conducted to compare our proposed method with the existing robust method and the deterministic dispatch with perfect information. Results show that our proposed method significantly improves the system efficiency while maintaining system reliability.

Suggested Citation

  • Jiayu Cheng & Dongliang Duan & Xiang Cheng & Liuqing Yang & Shuguang Cui, 2020. "Probabilistic Microgrid Energy Management with Interval Predictions," Energies, MDPI, vol. 13(12), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3116-:d:372339
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    References listed on IDEAS

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    1. Shams, Mohammad H. & Shahabi, Majid & Khodayar, Mohammad E., 2018. "Stochastic day-ahead scheduling of multiple energy Carrier microgrids with demand response," Energy, Elsevier, vol. 155(C), pages 326-338.
    2. Nicholas Moehle & Enzo Busseti & Stephen Boyd & Matt Wytock, 2019. "Dynamic Energy Management," Springer Optimization and Its Applications, in: Jesús M. Velásquez-Bermúdez & Marzieh Khakifirooz & Mahdi Fathi (ed.), Large Scale Optimization in Supply Chains and Smart Manufacturing, pages 69-126, Springer.
    3. Nicholas Moehle & Enzo Busseti & Stephen Boyd & Matt Wytock, 2019. "Dynamic Energy Management," Papers 1903.06230, arXiv.org.
    4. Sergio Bruno & Gabriella Dellino & Massimo La Scala & Carlo Meloni, 2019. "A Microforecasting Module for Energy Management in Residential and Tertiary Buildings †," Energies, MDPI, vol. 12(6), pages 1-20, March.
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    1. Elgamal, M. & Korovkin, Nikolay & Abdel Menaem, A. & Elmitwally, Akram, 2022. "Day-ahead complex power scheduling in a reconfigurable hybrid-energy islanded microgrid with responsive demand considering uncertainty and different load models," Applied Energy, Elsevier, vol. 309(C).
    2. Bingyin Lei & Yue Ren & Huiyu Luan & Ruonan Dong & Xiuyuan Wang & Junli Liao & Shu Fang & Kaiye Gao, 2023. "A Review of Optimization for System Reliability of Microgrid," Mathematics, MDPI, vol. 11(4), pages 1-30, February.
    3. Jiayu Cheng & Dongliang Duan & Xiang Cheng & Liuqing Yang & Shuguang Cui, 2021. "Adaptive Control for Energy Exchange with Probabilistic Interval Predictors in Isolated Microgrids," Energies, MDPI, vol. 14(2), pages 1-23, January.
    4. Cavus, Muhammed & Allahham, Adib & Adhikari, Kabita & Giaouris, Damian, 2024. "A hybrid method based on logic predictive controller for flexible hybrid microgrid with plug-and-play capabilities," Applied Energy, Elsevier, vol. 359(C).
    5. Amrutha Raju Battula & Sandeep Vuddanti & Surender Reddy Salkuti, 2021. "Review of Energy Management System Approaches in Microgrids," Energies, MDPI, vol. 14(17), pages 1-32, September.
    6. Dai Cui & Fei Xu & Weichun Ge & Pengxiang Huang & Yunhai Zhou, 2020. "A Coordinated Dispatching Model Considering Generation and Operation Reserve in Wind Power-Photovoltaic-Pumped Storage System," Energies, MDPI, vol. 13(18), pages 1-24, September.

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