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Non-cooperative regulation coordination based on game theory for wind farm clusters during ramping events

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  • Qi, Yongzhi
  • Liu, Yutian
  • Wu, Qiuwei

Abstract

With increasing penetration of wind power in power systems, it is important to track scheduled wind power output as much as possible during ramping events to ensure security of the system. In this paper, a non-cooperative coordination strategy based on the game theory is proposed for the regulation of wind farm clusters (WFCs) in order to track scheduled wind power of the WFC during ramping events. In the proposed strategy, a non-cooperative game is formulated and wind farms compete to provide regulation to the WFC during ramping events. A regulation revenue function is proposed to evaluate the competition process of wind farms to provide regulation to the WFC which includes revenue of effective regulation (ER), power support regulation and punishment regulation. The multi-time-interval Nash equilibrium condition is derived for the regulation competition process of wind farms. By setting parameters of the regulation revenue function according to the derived Nash equilibrium condition, the ER strategy is the Nash equilibrium of the regulation competition. Case studies were conducted with the power output data of wind farms from State Grid Jibei Electric Power Company Limited of China to demonstrate the efficacy of the proposed coordination strategy during ramping events.

Suggested Citation

  • Qi, Yongzhi & Liu, Yutian & Wu, Qiuwei, 2017. "Non-cooperative regulation coordination based on game theory for wind farm clusters during ramping events," Energy, Elsevier, vol. 132(C), pages 136-146.
    • Handle: RePEc:eee:energy:v:132:y:2017:i:c:p:136-146
    DOI: 10.1016/j.energy.2017.05.060
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    References listed on IDEAS

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    1. Hansen, Anca D. & Sørensen, Poul & Iov, Florin & Blaabjerg, Frede, 2006. "Centralised power control of wind farm with doubly fed induction generators," Renewable Energy, Elsevier, vol. 31(7), pages 935-951.
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    2. Budi, Rizki Firmansyah Setya & Sarjiya, & Hadi, Sasongko Pramono, 2021. "Multi-level game theory model for partially deregulated generation expansion planning," Energy, Elsevier, vol. 237(C).
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    4. Ostadi, Bakhtiar & Motamedi Sedeh, Omid & Husseinzadeh Kashan, Ali, 2020. "Risk-based optimal bidding patterns in the deregulated power market using extended Markowitz model," Energy, Elsevier, vol. 191(C).
    5. Kavita Jain & Muhammed Basheer Jasser & Muzaffar Hamzah & Akash Saxena & Ali Wagdy Mohamed, 2022. "Harris Hawk Optimization-Based Deep Neural Networks Architecture for Optimal Bidding in the Electricity Market," Mathematics, MDPI, vol. 10(12), pages 1-19, June.

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