IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i14p5291-d868184.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/14/5291/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/14/5291/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Haiyang & Li, Ke & Zhang, Chenghui & Chen, Jianfei, 2024. "Capacity and operation joint optimization for integrated energy system based on Nash bargaining game," Energy, Elsevier, vol. 305(C).
    2. Mei, Jie & Chen, Chen & Wang, Jianhui & Kirtley, James L., 2019. "Coalitional game theory based local power exchange algorithm for networked microgrids," Applied Energy, Elsevier, vol. 239(C), pages 133-141.
    3. Li, Junkai & Ge, Shaoyun & Xu, Zhengyang & Liu, Hong & Li, Jifeng & Wang, Chengshan & Cheng, Xueying, 2023. "A network-secure peer-to-peer trading framework for electricity-carbon integrated market among local prosumers," Applied Energy, Elsevier, vol. 335(C).
    4. Fei Wang & Lidong Zhou & Hui Ren & Xiaoli Liu, 2017. "Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy and Improved Combined Cooling-Heating-Power Strategy Based Two-Time Scale Multi-Objective Optimizat," Energies, MDPI, vol. 10(12), pages 1-23, November.
    5. Romero-Quete, David & Garcia, Javier Rosero, 2019. "An affine arithmetic-model predictive control approach for optimal economic dispatch of combined heat and power microgrids," Applied Energy, Elsevier, vol. 242(C), pages 1436-1447.
    6. Zhong, Xiaoqing & Zhong, Weifeng & Lin, Zhenjia & Zhou, Guoxu & Lai, Loi Lei & Xie, Shengli & Yan, Jinyue, 2024. "Localized electricity and carbon allowance management for interconnected discrete manufacturing systems considering algorithmic and physical feasibility," Applied Energy, Elsevier, vol. 372(C).
    7. Zheng, Ling & Zhou, Bin & Cao, Yijia & Wing Or, Siu & Li, Yong & Wing Chan, Ka, 2022. "Hierarchical distributed multi-energy demand response for coordinated operation of building clusters," Applied Energy, Elsevier, vol. 308(C).
    8. Shen, Feifei & Zhao, Liang & Du, Wenli & Zhong, Weimin & Qian, Feng, 2020. "Large-scale industrial energy systems optimization under uncertainty: A data-driven robust optimization approach," Applied Energy, Elsevier, vol. 259(C).
    9. 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).
    10. Chen, Yang & Park, Byungkwon & Kou, Xiao & Hu, Mengqi & Dong, Jin & Li, Fangxing & Amasyali, Kadir & Olama, Mohammed, 2020. "A comparison study on trading behavior and profit distribution in local energy transaction games," Applied Energy, Elsevier, vol. 280(C).
    11. Xie, Xuehua & Qian, Tong & Li, Weiwei & Tang, Wenhu & Xu, Zhao, 2024. "An individualized adaptive distributed approach for fast energy-carbon coordination in transactive multi-community integrated energy systems considering power transformer loading capacity," Applied Energy, Elsevier, vol. 375(C).
    12. Seyfi, Mohammad & Mehdinejad, Mehdi & Mohammadi-Ivatloo, Behnam & Shayanfar, Heidarali, 2022. "Deep learning-based scheduling of virtual energy hubs with plug-in hybrid compressed natural gas-electric vehicles," Applied Energy, Elsevier, vol. 321(C).
    13. Xiaofeng Liu & Qi Wang & Wenting Wang, 2019. "Evolutionary Analysis for Residential Consumer Participating in Demand Response Considering Irrational Behavior," Energies, MDPI, vol. 12(19), pages 1-19, September.
    14. Vahid-Ghavidel, Morteza & Shafie-khah, Miadreza & Javadi, Mohammad S. & Santos, Sérgio F. & Gough, Matthew & Quijano, Darwin A. & Catalao, Joao P.S., 2023. "Hybrid IGDT-stochastic self-scheduling of a distributed energy resources aggregator in a multi-energy system," Energy, Elsevier, vol. 265(C).
    15. Xu, Jiazhu & Yi, Yuqin, 2023. "Multi-microgrid low-carbon economy operation strategy considering both source and load uncertainty: A Nash bargaining approach," Energy, Elsevier, vol. 263(PB).
    16. Mehdizadeh, Ali & Taghizadegan, Navid & Salehi, Javad, 2018. "Risk-based energy management of renewable-based microgrid using information gap decision theory in the presence of peak load management," Applied Energy, Elsevier, vol. 211(C), pages 617-630.
    17. Zhang, Y.Q. & Chen, J.J. & Wang, Y.X. & Feng, L., 2024. "Enhancing resilience of agricultural microgrid through electricity–heat–water based multi-energy hub considering irradiation intensity uncertainty," Renewable Energy, Elsevier, vol. 220(C).
    18. Zhai, Junyi & Wang, Sheng & Guo, Lei & Jiang, Yuning & Kang, Zhongjian & Jones, Colin N., 2022. "Data-driven distributionally robust joint chance-constrained energy management for multi-energy microgrid," Applied Energy, Elsevier, vol. 326(C).
    19. Mousavizadeh, Saeed & Haghifam, Mahmoud-Reza & Shariatkhah, Mohammad-Hossein, 2018. "A linear two-stage method for resiliency analysis in distribution systems considering renewable energy and demand response resources," Applied Energy, Elsevier, vol. 211(C), pages 443-460.
    20. Tang, Ruoli & Zhang, Shihan & Zhang, Shangyu & Lai, Jingang & Zhang, Yan, 2023. "Semi-online parameter identification methodology for maritime power lithium batteries," Applied Energy, Elsevier, vol. 339(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5291-:d:868184. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.