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Double Layer Dynamic Game Bidding Mechanism Based on Multi-Agent Technology for Virtual Power Plant and Internal Distributed Energy Resource

Author

Listed:
  • Yajing Gao

    (China Electric Power Enterprise Association Power Construction Technology and Economic Consultation Center, Beijing 100000, China)

  • Xiaojie Zhou

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Baoding 071003, China)

  • Jiafeng Ren

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Baoding 071003, China)

  • Xiuna Wang

    (China Electric Power Enterprise Association Power Construction Technology and Economic Consultation Center, Beijing 100000, China)

  • Dongwei Li

    (China Electric Power Enterprise Association Power Construction Technology and Economic Consultation Center, Beijing 100000, China)

Abstract

As renewable energies become the main direction of global energy development in the future, Virtual Power Plant (VPP) becomes a regional multi-energy aggregation model for large-scale integration of distributed generation into the power grid. It also provides an important way for distributed energy resources (DER) to participate in electricity market transactions. Firstly, the basic concept of VPP is outlined, and various uncertainties within VPP are modeled. Secondly, using multi-agent technology and Stackelberg dynamic game theory, a double-layer nested dynamic game bidding model including VPP and its internal DERs is designed. The lower layer is a bidding game for VPP internal market including DER. VPP is the leader and each DER is a subagent that acts as a follower to maximize its profit. Each subagent uses the particle swarm algorithm (PSA) to determine the optimal offer coefficient, and VPP carries out internal market clearing with the minimum variance of unit profit according to the quoting results. Then, the subagents renew the game to update the bidding strategy based on the outcomes of the external and internal markets. The upper layer is the external market bidding game. The trading center (TC) is the leader and VPP is the agent and the follower. The game is played with the goal of maximum self-interest. The agent uses genetic algorithms to determine the optimal bid strategy, and the TC carries out market clearance with the goal of maximizing social benefits according to the quotation results. Each agent renews the game to update the bidding strategy based on the clearing result and the reporting of the subagents. The dynamic game is repeated until the optimal equilibrium solution is obtained. Finally, the effectiveness of the model is verified by taking the IEEE30-bus system as an example.

Suggested Citation

  • Yajing Gao & Xiaojie Zhou & Jiafeng Ren & Xiuna Wang & Dongwei Li, 2018. "Double Layer Dynamic Game Bidding Mechanism Based on Multi-Agent Technology for Virtual Power Plant and Internal Distributed Energy Resource," Energies, MDPI, vol. 11(11), pages 1-22, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3072-:d:181293
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    References listed on IDEAS

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    Cited by:

    1. Yerasyl Amanbek & Aidana Kalakova & Svetlana Zhakiyeva & Korhan Kayisli & Nurkhat Zhakiyev & Daniel Friedrich, 2022. "Distribution Locational Marginal Price Based Transactive Energy Management in Distribution Systems with Smart Prosumers—A Multi-Agent Approach," Energies, MDPI, vol. 15(7), pages 1-18, March.
    2. Yuqing Wang & Min Zhang & Jindi Ao & Zhaozhen Wang & Houqi Dong & Ming Zeng, 2022. "Profit Allocation Strategy of Virtual Power Plant Based on Multi-Objective Optimization in Electricity Market," Sustainability, MDPI, vol. 14(10), pages 1-22, May.
    3. Liang Tian & Yunlei Xie & Bo Hu & Xinping Liu & Tuoyu Deng & Huanhuan Luo & Fengqiang Li, 2019. "A Deep Peak Regulation Auxiliary Service Bidding Strategy for CHP Units Based on a Risk-Averse Model and District Heating Network Energy Storage," Energies, MDPI, vol. 12(17), pages 1-27, August.
    4. Pedro Faria & Zita Vale, 2019. "Distributed Energy Resources Management 2018," Energies, MDPI, vol. 13(1), pages 1-4, December.
    5. Naval, Natalia & Yusta, Jose M., 2021. "Virtual power plant models and electricity markets - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).

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