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Hierarchical Energy Management of Microgrids including Storage and Demand Response

Author

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  • Songli Fan

    (Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Qian Ai

    (Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Longjian Piao

    (Faculty of Technology, Policy and Management, Delft University of Technology, 2628 BX Delft, The Netherlands)

Abstract

Battery energy storage (BES) and demand response (DR) are considered to be promising technologies to cope with the uncertainty of renewable energy sources (RES) and the load in the microgrid (MG). Considering the distinct prediction accuracies of the RES and load at different timescales, it is essential to incorporate the multi-timescale characteristics of BES and DR in MG energy management. Under this background, a hierarchical energy management framework is put forward for an MG including multi-timescale BES and DR to optimize operation with the uncertainty of RES as well as load. This framework comprises three stages of scheduling: day-ahead scheduling (DAS), hour-ahead scheduling (HAS), and real-time scheduling (RTS). In DAS, a scenario-based stochastic optimization model is established to minimize the expected operating cost of MG, while ensuring its safe operation. The HAS is utilized to bridge DAS and RTS. In RTS, a control strategy is proposed to eliminate the imbalanced power owing to the fluctuations of RES and load. Then, a decomposition-based algorithm is adopted to settle the models in DAS and HAS. Simulation results on a seven-bus MG validate the effectiveness of the proposed methodology.

Suggested Citation

  • Songli Fan & Qian Ai & Longjian Piao, 2018. "Hierarchical Energy Management of Microgrids including Storage and Demand Response," Energies, MDPI, vol. 11(5), pages 1-23, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1111-:d:144070
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    References listed on IDEAS

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    1. Mayank Singh & Rakesh Chandra Jha, 2019. "Object-Oriented Usability Indices for Multi-Objective Demand Side Management Using Teaching-Learning Based Optimization," Energies, MDPI, vol. 12(3), pages 1-25, January.
    2. Anh-Duc Nguyen & Van-Hai Bui & Akhtar Hussain & Duc-Huy Nguyen & Hak-Man Kim, 2018. "Impact of Demand Response Programs on Optimal Operation of Multi-Microgrid System," Energies, MDPI, vol. 11(6), pages 1-18, June.
    3. Hyung-Joon Kim & Mun-Kyeom Kim, 2019. "Multi-Objective Based Optimal Energy Management of Grid-Connected Microgrid Considering Advanced Demand Response," Energies, MDPI, vol. 12(21), pages 1-28, October.
    4. Jingfeng Chen & Ping Yang & Jiajun Peng & Yuqi Huang & Yaosheng Chen & Zhiji Zeng, 2018. "An Improved Multi-Timescale Coordinated Control Strategy for Stand-Alone Microgrid with Hybrid Energy Storage System," Energies, MDPI, vol. 11(8), pages 1-23, August.
    5. Yongjie Zhong & Dongliang Xie & Suwei Zhai & Yonghui Sun, 2018. "Day-Ahead Hierarchical Steady State Optimal Operation for Integrated Energy System Based on Energy Hub," Energies, MDPI, vol. 11(10), pages 1-18, October.
    6. Mostafa Rezaeimozafar & Mohsen Eskandari & Mohammad Hadi Amini & Mohammad Hasan Moradi & Pierluigi Siano, 2020. "A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids," Energies, MDPI, vol. 13(7), pages 1-25, April.

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