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Hybrid Game Optimization of Microgrid Cluster (MC) Based on Service Provider (SP) and Tiered Carbon Price

Author

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  • Fei Feng

    (School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
    Jiangsu Provincial Research and Development Center of Energy Internet and Large Data Integration Application Engineering Technology, Changzhou Vocational Institute of Engineering, Changzhou 213164, China)

  • Xin Du

    (School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China)

  • Qiang Si

    (Jiangsu Provincial Research and Development Center of Energy Internet and Large Data Integration Application Engineering Technology, Changzhou Vocational Institute of Engineering, Changzhou 213164, China)

  • Hao Cai

    (Jiangsu Provincial Research and Development Center of Energy Internet and Large Data Integration Application Engineering Technology, Changzhou Vocational Institute of Engineering, Changzhou 213164, China
    School of Electrical Engineering, Southeast University, Nanjing 210096, China)

Abstract

Carbon trading is a market-based mechanism towards low-carbon electric power systems. A hy-brid game optimization model is established for deriving the optimal trading price between mi-crogrids (MGs) as well as providing the optimal pricing scheme for trading between the microgrid cluster(MC) and the upper-layer service provider (SP). At first, we propose a robust optimization model of microgrid clusters from the perspective of risk aversion, in which the uncertainty of wind and photovoltaic (PV) output is modeled with resort to the information gap decision theo-ry(IGDT). Finally, based on the Nash bargaining theory, the electric power transaction payment model between MGs is established, and the alternating direction multiplier method (ADMM) is used to solve it, thus effectively protecting the privacy of each subject. It shows that the proposed strategy is able to quantify the uncertainty of wind and PV factors on dispatching operations. At the same time, carbon emission could be effectively reduced by following the tiered carbon price scheme.

Suggested Citation

  • Fei Feng & Xin Du & Qiang Si & Hao Cai, 2022. "Hybrid Game Optimization of Microgrid Cluster (MC) Based on Service Provider (SP) and Tiered Carbon Price," Energies, MDPI, vol. 15(14), pages 1-22, July.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5291-:d:868184
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    References listed on IDEAS

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    1. Zhong, Xiaoqing & Zhong, Weifeng & Liu, Yi & Yang, Chao & Xie, Shengli, 2022. "Optimal energy management for multi-energy multi-microgrid networks considering carbon emission limitations," Energy, Elsevier, vol. 246(C).
    2. Li, Bei & Li, Jiangchen, 2021. "Probabilistic sizing of a low-carbon emission power system considering HVDC transmission and microgrid clusters," Applied Energy, Elsevier, vol. 304(C).
    3. Craparo, Emily & Karatas, Mumtaz & Singham, Dashi I., 2017. "A robust optimization approach to hybrid microgrid operation using ensemble weather forecasts," Applied Energy, Elsevier, vol. 201(C), pages 135-147.
    4. Yang, Dongfeng & Jiang, Chao & Cai, Guowei & Yang, Deyou & Liu, Xiaojun, 2020. "Interval method based optimal planning of multi-energy microgrid with uncertain renewable generation and demand," Applied Energy, Elsevier, vol. 277(C).
    5. Fan, Songli & Ai, Qian & Piao, Longjian, 2018. "Bargaining-based cooperative energy trading for distribution company and demand response," Applied Energy, Elsevier, vol. 226(C), pages 469-482.
    6. Zhang, Xuehan & Son, Yongju & Cheong, Taesu & Choi, Sungyun, 2022. "Affine-arithmetic-based microgrid interval optimization considering uncertainty and battery energy storage system degradation," Energy, Elsevier, vol. 242(C).
    7. Chongxin Huang & Dong Yue & Song Deng & Jun Xie, 2017. "Optimal Scheduling of Microgrid with Multiple Distributed Resources Using Interval Optimization," Energies, MDPI, vol. 10(3), pages 1-23, March.
    8. Sinha, Rakesh Kumar & Chaturvedi, Nitin Dutt, 2019. "A review on carbon emission reduction in industries and planning emission limits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
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    Cited by:

    1. Lei Yao & Chongtao Bai & Hao Fu & Suhua Lou & Yan Fu, 2023. "Optimization of Expressway Microgrid Construction Mode and Capacity Configuration Considering Carbon Trading," Energies, MDPI, vol. 16(18), pages 1-17, September.

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