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Multi-layer game theory based operation optimisation of ICES considering improved independent market participant models and dedicated distributed algorithms

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  • Yan, Yi
  • Liu, Mingqi
  • Tian, Chongyi
  • Li, Ji
  • Li, Ke

Abstract

With the increase in distributed subjects such as renewable energy, the global energy industry is undergoing a market-based transformation, where energy subjects participate in the market in a competitive manner with evident conflicts of interest. However, current market participation methods are deficient in maintaining the balance of interests among the participants, safeguarding the interests of disadvantaged participants, and sounding the mechanism of independent market participation of the participants, which has resulted in long-term damage to the interests as well as low willingness to dispatch of certain participants under the market-based mode of operation; and there is a void in the solution tools for balancing the interests of multiple participants under the multi-constituent market participation scenario. Based on this, this paper constructs a market participation method for a multi-layer integrated community energy system that considers multiple types of independent energy participants, such as source-load-storage-station. Firstly, an independent energy storage market participation model is designed, and the integrated demand response mechanism is improved. Based on this, a hierarchical market participation framework considering multiple types of independent participants from source-load-storage-station is proposed. Secondly, a multi‑leader multi-follower four-dimensional collaborative multi-layer game model is constructed, which has a multi-layer Stackelberg game between the vertical layers and a two-level non-cooperative game between the horizontal layers. Meanwhile, an independent energy storage dedicated distributed algorithm is developed. Finally, a method for solving multi-layer optimization models is established based on the developed dedicated distributed algorithm coupled with theories such as KKT transformation. The equilibrium of the multi-layer game is shown to proof and is found by this method. Cases have shown that this method not only provided a reliable tool for solving the complex model of multiple participants, it also improved the status of market and enthusiasm of dispatching for energy storage, users etc., and broke the monopoly structure of energy saling side market participation. The effective utilization rate of energy storage increased by 21.61% on average. The time-to-time energy response of users increased by more than 10.13%. Where gains for users have increased by an average of 1.91%. And the total revenue of the energy purchasing side increased by 4%.

Suggested Citation

  • Yan, Yi & Liu, Mingqi & Tian, Chongyi & Li, Ji & Li, Ke, 2024. "Multi-layer game theory based operation optimisation of ICES considering improved independent market participant models and dedicated distributed algorithms," Applied Energy, Elsevier, vol. 373(C).
  • Handle: RePEc:eee:appene:v:373:y:2024:i:c:s0306261924010742
    DOI: 10.1016/j.apenergy.2024.123691
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    References listed on IDEAS

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    1. Lazzari, Florencia & Mor, Gerard & Cipriano, Jordi & Solsona, Francesc & Chemisana, Daniel & Guericke, Daniela, 2023. "Optimizing planning and operation of renewable energy communities with genetic algorithms," Applied Energy, Elsevier, vol. 338(C).
    2. Wang, Yongli & Liu, Zhen & Wang, Jingyan & Du, Boxin & Qin, Yumeng & Liu, Xiaoli & Liu, Lin, 2023. "A Stackelberg game-based approach to transaction optimization for distributed integrated energy system," Energy, Elsevier, vol. 283(C).
    3. Yu, Mengmeng & Hong, Seung Ho, 2017. "Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach," Applied Energy, Elsevier, vol. 203(C), pages 267-279.
    4. Gao, Mingfei & Han, Zhonghe & Zhang, Ce & Li, Peng & Wu, Di & Li, Peng, 2023. "Optimal configuration for regional integrated energy systems with multi-element hybrid energy storage," Energy, Elsevier, vol. 277(C).
    5. Tan, Jinjing & Li, Yang & Zhang, Xiaoping & Pan, Weiqi & Ruan, Wenjun, 2023. "Operation of a commercial district integrated energy system considering dynamic integrated demand response: A Stackelberg game approach," Energy, Elsevier, vol. 274(C).
    6. Gao, Yang & Ai, Qian & He, Xing & Fan, Songli, 2023. "Coordination for regional integrated energy system through target cascade optimization," Energy, Elsevier, vol. 276(C).
    7. Yu, Heyang & Zhang, Jingchen & Ma, Junchao & Chen, Changyu & Geng, Guangchao & Jiang, Quanyuan, 2023. "Privacy-preserving demand response of aggregated residential load," Applied Energy, Elsevier, vol. 339(C).
    8. Yao, Wenliang & Wang, Chengfu & Yang, Ming & Wang, Kang & Dong, Xiaoming & Zhang, Zhenwei, 2023. "A tri-layer decision-making framework for IES considering the interaction of integrated demand response and multi-energy market clearing," Applied Energy, Elsevier, vol. 342(C).
    9. Yan, Yi & Zhang, Chenghui & Li, Ke & Wang, Zhen, 2018. "An integrated design for hybrid combined cooling, heating and power system with compressed air energy storage," Applied Energy, Elsevier, vol. 210(C), pages 1151-1166.
    10. Li, Qi & Xiao, Xukang & Pu, Yuchen & Luo, Shuyu & Liu, Hong & Chen, Weirong, 2023. "Hierarchical optimal scheduling method for regional integrated energy systems considering electricity-hydrogen shared energy," Applied Energy, Elsevier, vol. 349(C).
    11. Li, Na & Hakvoort, Rudi A. & Lukszo, Zofia, 2021. "Cost allocation in integrated community energy systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    12. Wang, Lu & Gu, Wei & Wu, Zhi & Qiu, Haifeng & Pan, Guangsheng, 2020. "Non-cooperative game-based multilateral contract transactions in power-heating integrated systems," Applied Energy, Elsevier, vol. 268(C).
    13. Han, Fengwu & Zeng, Jianfeng & Lin, Junjie & Zhao, Yunlong & Gao, Chong, 2023. "A stochastic hierarchical optimization and revenue allocation approach for multi-regional integrated energy systems based on cooperative games," Applied Energy, Elsevier, vol. 350(C).
    14. Li, Ke & Ye, Ning & Li, Shuzhen & Wang, Haiyang & Zhang, Chenghui, 2023. "Distributed collaborative operation strategies in multi-agent integrated energy system considering integrated demand response based on game theory," Energy, Elsevier, vol. 273(C).
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