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Energy Management of Combined Cooling, Heating and Power Micro Energy Grid Based on Leader-Follower Game Theory

Author

Listed:
  • Kaijun Lin

    (School of Electrical Engineering, Beijing Jiaotong University, Haidian District, Beijing 100044, China)

  • Junyong Wu

    (School of Electrical Engineering, Beijing Jiaotong University, Haidian District, Beijing 100044, China)

  • Di Liu

    (School of Electrical Engineering, Beijing Jiaotong University, Haidian District, Beijing 100044, China)

  • Dezhi Li

    (China Electric Power Research Institute Limited Company, Haidian District, Beijing 100192, China)

  • Taorong Gong

    (China Electric Power Research Institute Limited Company, Haidian District, Beijing 100192, China)

Abstract

In this paper, we consider a general model and solution algorithm for the energy management of combined cooling, heating, and power micro energy grid (MEG) under the game theory framework. An innovative dynamic leader-follower game strategy is proposed in this paper to balance the interactions between MEG and user. We show that such game between MEG and user has a unique Nash equilibrium (NE), and in order to quantify the user’s expenditure and dissatisfaction, we model them and adopt the fuzzy bi-objective algorithm. For more details in the proposed game model, the MEG leads the game by deciding energy sales prices and optimizing the power, cooling and heating outputs according to the user’s load plan to maximize its own profit. With the prices being released by MEG, user’s adjustment of energy consumption follows and is again fed to MEG. In practice, we initialize simulations with daily loads of a typical community. As the numerical results demonstrate, MEG is proficient in consumption capacity of renewable energy and energy optimization. It also shows that the user achieves his economic optimum with experience of energy usage taken into account.

Suggested Citation

  • Kaijun Lin & Junyong Wu & Di Liu & Dezhi Li & Taorong Gong, 2018. "Energy Management of Combined Cooling, Heating and Power Micro Energy Grid Based on Leader-Follower Game Theory," Energies, MDPI, vol. 11(3), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:647-:d:136244
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    References listed on IDEAS

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    1. 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.
    2. Liang, Yile & Liu, Feng & Wang, Cheng & Mei, Shengwei, 2017. "Distributed demand-side energy management scheme in residential smart grids: An ordinal state-based potential game approach," Applied Energy, Elsevier, vol. 206(C), pages 991-1008.
    3. Ying Yu & Tongdan Jin & Chunjie Zhong, 2015. "Designing an Incentive Contract Menu for Sustaining the Electricity Market," Energies, MDPI, vol. 8(12), pages 1-22, December.
    4. Kireem Han & Joohyung Lee & Junkyun Choi, 2017. "Evaluation of Demand-Side Management over Pricing Competition of Multiple Suppliers Having Heterogeneous Energy Sources," Energies, MDPI, vol. 10(9), pages 1-16, September.
    5. 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.
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    Cited by:

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    2. Wagner, Lukas Peter & Reinpold, Lasse Matthias & Kilthau, Maximilian & Fay, Alexander, 2023. "A systematic review of modeling approaches for flexible energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).

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