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Equilibrium Interaction Strategies for Integrated Energy System Incorporating Demand-Side Management Based on Stackelberg Game Approach

Author

Listed:
  • Kangli Xiang

    (Power Economic Research Institute, State Grid Fujian Electric Power Company, Fuzhou 350001, China)

  • Jinyu Chen

    (Power Economic Research Institute, State Grid Fujian Electric Power Company, Fuzhou 350001, China)

  • Li Yang

    (State Grid Fujian Electric Power Company, Fuzhou 350001, China)

  • Jianfa Wu

    (Power Economic Research Institute, State Grid Fujian Electric Power Company, Fuzhou 350001, China)

  • Pengjia Shi

    (Power Economic Research Institute, State Grid Fujian Electric Power Company, Fuzhou 350001, China)

Abstract

This paper analyzes the balanced interaction strategy of an integrated energy system (IES) operator and an industrial user in the operation process of the IES under the demand-side management (DSM) based on game theory. Firstly, we establish an electric–thermal IES, which includes a power grid, a heat grid and a natural gas grid. Secondly, a two-stage Stackelberg dynamic game model is proposed to describe the game behavior of IES operators and industrial users in the process of participating in DSM. The interactions between the IES operator (leader) and the user (follower) are formulated into a one-leader–one-follower Stackelberg game, where optimization problems are formed for each player to help select the optimal strategy. A pricing function is adopted for regulating time-of-use (TOU), which acts as a coordinator, inducing users to join the game. Then, for the complex two-stage dynamic game model established, the lower user-side constraint optimization problem is replaced by its KKT condition, so that the two-stage hierarchical optimization problem is transformed into a single-stage mixed-integer nonlinear optimization model, and the branch-and-bound method is introduced to solve it. Finally, the equilibrium strategies and income values of both sides of the game are obtained through a case simulation, and the dynamic equilibrium strategy curves under different capacity configurations are obtained through the sensitivity analysis of key parameters. The equilibrium income of the IES is USD 93.859, while the equilibrium income of industrial users in the park is USD 92.720. The simulation results show that the proposed method and model are effective.

Suggested Citation

  • Kangli Xiang & Jinyu Chen & Li Yang & Jianfa Wu & Pengjia Shi, 2024. "Equilibrium Interaction Strategies for Integrated Energy System Incorporating Demand-Side Management Based on Stackelberg Game Approach," Energies, MDPI, vol. 17(14), pages 1-24, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:14:p:3603-:d:1440507
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    References listed on IDEAS

    as
    1. He, Liangce & Lu, Zhigang & Zhang, Jiangfeng & Geng, Lijun & Zhao, Hao & Li, Xueping, 2018. "Low-carbon economic dispatch for electricity and natural gas systems considering carbon capture systems and power-to-gas," Applied Energy, Elsevier, vol. 224(C), pages 357-370.
    2. Wang, Cheng & Wei, Wei & Wang, Jianhui & Bi, Tianshu, 2019. "Convex optimization based adjustable robust dispatch for integrated electric-gas systems considering gas delivery priority," Applied Energy, Elsevier, vol. 239(C), pages 70-82.
    3. Wang, Chengshan & Lv, Chaoxian & Li, Peng & Song, Guanyu & Li, Shuquan & Xu, Xiandong & Wu, Jianzhong, 2018. "Modeling and optimal operation of community integrated energy systems: A case study from China," Applied Energy, Elsevier, vol. 230(C), pages 1242-1254.
    4. Yu, Mengmeng & Hong, Seung Ho, 2016. "Supply–demand balancing for power management in smart grid: A Stackelberg game approach," Applied Energy, Elsevier, vol. 164(C), pages 702-710.
    5. Woon, Kok Sin & Phuang, Zhen Xin & Taler, Jan & Varbanov, Petar Sabev & Chong, Cheng Tung & Klemeš, Jiří Jaromír & Lee, Chew Tin, 2023. "Recent advances in urban green energy development towards carbon emissions neutrality," Energy, Elsevier, vol. 267(C).
    6. Zhang, Yachao & Le, Jian & Zheng, Feng & Zhang, Yi & Liu, Kaipei, 2019. "Two-stage distributionally robust coordinated scheduling for gas-electricity integrated energy system considering wind power uncertainty and reserve capacity configuration," Renewable Energy, Elsevier, vol. 135(C), pages 122-135.
    7. Tang, Rui & Wang, Shengwei & Li, Hangxin, 2019. "Game theory based interactive demand side management responding to dynamic pricing in price-based demand response of smart grids," Applied Energy, Elsevier, vol. 250(C), pages 118-130.
    8. Gu, Chenghong & Tang, Can & Xiang, Yue & Xie, Da, 2019. "Power-to-gas management using robust optimisation in integrated energy systems," Applied Energy, Elsevier, vol. 236(C), pages 681-689.
    9. Wu, Chenyu & Gu, Wei & Xu, Yinliang & Jiang, Ping & Lu, Shuai & Zhao, Bo, 2018. "Bi-level optimization model for integrated energy system considering the thermal comfort of heat customers," Applied Energy, Elsevier, vol. 232(C), pages 607-616.
    10. Wei, F. & Jing, Z.X. & Wu, Peter Z. & Wu, Q.H., 2017. "A Stackelberg game approach for multiple energies trading in integrated energy systems," Applied Energy, Elsevier, vol. 200(C), pages 315-329.
    11. Zhou, Zhe & Zhang, Jianyun & Liu, Pei & Li, Zheng & Georgiadis, Michael C. & Pistikopoulos, Efstratios N., 2013. "A two-stage stochastic programming model for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 103(C), pages 135-144.
    12. Jiang, Yibo & Xu, Jian & Sun, Yuanzhang & Wei, Congying & Wang, Jing & Liao, Siyang & Ke, Deping & Li, Xiong & Yang, Jun & Peng, Xiaotao, 2018. "Coordinated operation of gas-electricity integrated distribution system with multi-CCHP and distributed renewable energy sources," Applied Energy, Elsevier, vol. 211(C), pages 237-248.
    13. Li, Fan & Sun, Bo & Zhang, Chenghui & Zhang, Lizhi, 2018. "Operation optimization for combined cooling, heating, and power system with condensation heat recovery," Applied Energy, Elsevier, vol. 230(C), pages 305-316.
    14. Li, Guoqing & Zhang, Rufeng & Jiang, Tao & Chen, Houhe & Bai, Linquan & Li, Xiaojing, 2017. "Security-constrained bi-level economic dispatch model for integrated natural gas and electricity systems considering wind power and power-to-gas process," Applied Energy, Elsevier, vol. 194(C), pages 696-704.
    15. Yu, Jiah & Ryu, Jun-Hyung & Lee, In-beum, 2019. "A stochastic optimization approach to the design and operation planning of a hybrid renewable energy system," Applied Energy, Elsevier, vol. 247(C), pages 212-220.
    16. Zhou, Yizhou & Wei, Zhinong & Sun, Guoqiang & Cheung, Kwok W. & Zang, Haixiang & Chen, Sheng, 2018. "A robust optimization approach for integrated community energy system in energy and ancillary service markets," Energy, Elsevier, vol. 148(C), pages 1-15.
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