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Optimal Bidding Strategy for Renewable Microgrid with Active Network Management

Author

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  • Seung Wan Kim

    (Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea)

  • Jip Kim

    (Power System Research Division, Korea Electrical Engineering & Science Research Institute, Bldg. 130, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea)

  • Young Gyu Jin

    (Center for Advanced Power & Environmental Technology (APET), The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan)

  • Yong Tae Yoon

    (Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea)

Abstract

Active Network Management (ANM) enables a microgrid to optimally dispatch the active/reactive power of its Renewable Distributed Generation (RDG) and Battery Energy Storage System (BESS) units in real time. Thus, a microgrid with high penetration of RDGs can handle their uncertainties and variabilities to achieve the stable operation using ANM. However, the actual power flow in the line connecting the main grid and microgrid may deviate significantly from the day-ahead bids if the bids are determined without consideration of the real-time adjustment through ANM, which will lead to a substantial imbalance cost. Therefore, this study proposes a formulation for obtaining an optimal bidding which reflects the change of power flow in the connecting line by real-time adjustment using ANM. The proposed formulation maximizes the expected profit of the microgrid considering various network and physical constraints. The effectiveness of the proposed bidding strategy is verified through the simulations with a 33-bus test microgrid. The simulation results show that the proposed bidding strategy improves the expected operating profit by reducing the imbalance cost to a greater degree compared to the basic bidding strategy without consideration of ANM.

Suggested Citation

  • Seung Wan Kim & Jip Kim & Young Gyu Jin & Yong Tae Yoon, 2016. "Optimal Bidding Strategy for Renewable Microgrid with Active Network Management," Energies, MDPI, vol. 9(1), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:1:p:48-:d:62246
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    References listed on IDEAS

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    Cited by:

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    2. Kim, S. & Pollitt, M. & Jin, Y. & Yoon, Y., 2017. "Contractual Framework for the Devolution of System Balancing Responsibility from the Transmission System Operator to Distribution System Operators," Cambridge Working Papers in Economics 1738, Faculty of Economics, University of Cambridge.
    3. Aouss Gabash & Pu Li, 2016. "On Variable Reverse Power Flow-Part II: An Electricity Market Model Considering Wind Station Size and Location," Energies, MDPI, vol. 9(4), pages 1-13, March.
    4. Mohammed M. Alhaider & Ziad M. Ali & Mostafa H. Mostafa & Shady H. E. Abdel Aleem, 2023. "Economic Viability of NaS Batteries for Optimal Microgrid Operation and Hosting Capacity Enhancement under Uncertain Conditions," Sustainability, MDPI, vol. 15(20), pages 1-24, October.
    5. Kong, Xiangyu & Liu, Dehong & Xiao, Jie & Wang, Chengshan, 2019. "A multi-agent optimal bidding strategy in microgrids based on artificial immune system," Energy, Elsevier, vol. 189(C).
    6. Muhyaddin Rawa & Abdullah Abusorrah & Yusuf Al-Turki & Saad Mekhilef & Mostafa H. Mostafa & Ziad M. Ali & Shady H. E. Abdel Aleem, 2020. "Optimal Allocation and Economic Analysis of Battery Energy Storage Systems: Self-Consumption Rate and Hosting Capacity Enhancement for Microgrids with High Renewable Penetration," Sustainability, MDPI, vol. 12(23), pages 1-25, December.

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