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Evolutionary Game Analysis of Co-Opetition Strategy in Energy Big Data Ecosystem under Government Intervention

Author

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  • A-Ru-Han Bao

    (Department of Economic Management, North China Electric Power University, Beijing 102206, China
    These authors contributed equally to this work.)

  • Yao Liu

    (Department of Economic Management, North China Electric Power University, Beijing 102206, China
    These authors contributed equally to this work.)

  • Jun Dong

    (Department of Economic Management, North China Electric Power University, Beijing 102206, China)

  • Zheng-Peng Chen

    (Department of Economic Management, North China Electric Power University, Beijing 102206, China)

  • Zhen-Jie Chen

    (Department of Economic Management, North China Electric Power University, Beijing 102206, China)

  • Chen Wu

    (Economic Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210008, China)

Abstract

This study discusses how to facilitate the barrier-free circulation of energy big data among multiple entities and how to balance the energy big data ecosystem under government supervision using dynamic game theory. First, we define the related concepts and summarize the recent studies and developments of energy big data. Second, evolutionary game theory is applied to examine the interaction mechanism of complex behaviors between power grid enterprises and third-party enterprises in the energy big data ecosystem, with and without the supervision of government. Finally, a sensitivity analysis is conducted on the main factors affecting co-opetition, such as the initial participation willingness, distribution of benefits, free-riding behavior, government funding, and punitive liquidated damages. The results show that both government supervision measures and the participants’ own will have an impact on the stable evolution of the energy big data ecosystem in the dynamic evolution process, and the effect of parameter changes on the evolution is more significant under the state of no government supervision. In addition, the effectiveness of the developed model in this work is verified by simulated analysis. The present model can provide an important reference for overall planning and efficient operation of the energy big data ecosystem.

Suggested Citation

  • A-Ru-Han Bao & Yao Liu & Jun Dong & Zheng-Peng Chen & Zhen-Jie Chen & Chen Wu, 2022. "Evolutionary Game Analysis of Co-Opetition Strategy in Energy Big Data Ecosystem under Government Intervention," Energies, MDPI, vol. 15(6), pages 1-24, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2066-:d:769334
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    References listed on IDEAS

    as
    1. Lingling Wang & Tsunemi Watanabe, 2016. "A Stackelberg Game Theoretic Analysis of Incentive Effects under Perceived Risk for China’s Straw-Based Power Plant Supply Chain," Energies, MDPI, vol. 9(6), pages 1-20, June.
    2. Zhang, Yue-Jun & Wang, Ao-Dong & Da, Ya-Bin, 2014. "Regional allocation of carbon emission quotas in China: Evidence from the Shapley value method," Energy Policy, Elsevier, vol. 74(C), pages 454-464.
    3. Gao, Evelyn & Sowlati, Taraneh & Akhtari, Shaghaygh, 2019. "Profit allocation in collaborative bioenergy and biofuel supply chains," Energy, Elsevier, vol. 188(C).
    4. Hongmei Shan & Ying Li & Jing Shi, 2020. "Influence of Supply Chain Collaborative Innovation on Sustainable Development of Supply Chain: A Study on Chinese Enterprises," Sustainability, MDPI, vol. 12(7), pages 1-19, April.
    5. Wang, Changfeng & Hu, Qiying, 2020. "Knowledge sharing in supply chain networks: Effects of collaborative innovation activities and capability on innovation performance," Technovation, Elsevier, vol. 94.
    6. Xinxin Liu & Xiaosheng Wang & Haiying Guo & Xiaojie An, 2021. "Benefit Allocation in Shared Water-Saving Management Contract Projects Based on Modified Expected Shapley Value," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 39-62, January.
    7. Weiwei Liu & Jianing Yang, 2018. "The Evolutionary Game Theoretic Analysis for Sustainable Cooperation Relationship of Collaborative Innovation Network in Strategic Emerging Industries," Sustainability, MDPI, vol. 10(12), pages 1-16, December.
    8. Adrian Vasile & Carmen Eugenia Costea & Tania Georgia Viciu, 2012. "An Evolutionary Game Theory Approach To Market Competition And Cooperation," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 15(supp0), pages 1-15.
    9. Naser Hossein Motlagh & Mahsa Mohammadrezaei & Julian Hunt & Behnam Zakeri, 2020. "Internet of Things (IoT) and the Energy Sector," Energies, MDPI, vol. 13(2), pages 1-27, January.
    10. Gnyawali, Devi R. & Park, Byung-Jin (Robert), 2011. "Co-opetition between giants: Collaboration with competitors for technological innovation," Research Policy, Elsevier, vol. 40(5), pages 650-663, June.
    11. Giest, Sarah & Mukherjee, Ishani, 2018. "Behavioral instruments in renewable energy and the role of big data: A policy perspective," Energy Policy, Elsevier, vol. 123(C), pages 360-366.
    12. Jie Hou & Baizhou Li, 2020. "The Evolutionary Game for Collaborative Innovation of the IoT Industry under Government Leadership in China: An IoT Infrastructure Perspective," Sustainability, MDPI, vol. 12(9), pages 1-21, May.
    13. Vangelis Marinakis, 2020. "Big Data for Energy Management and Energy-Efficient Buildings," Energies, MDPI, vol. 13(7), pages 1-18, March.
    14. Mohebbi, Shima & Li, Xueping, 2015. "Coalitional game theory approach to modeling suppliers' collaboration in supply networks," International Journal of Production Economics, Elsevier, vol. 169(C), pages 333-342.
    15. Liu, Weihua & Long, Shangsong & Xie, Dong & Liang, Yanjie & Wang, Jinkun, 2021. "How to govern the big data discriminatory pricing behavior in the platform service supply chain?An examination with a three-party evolutionary game model," International Journal of Production Economics, Elsevier, vol. 231(C).
    16. Noah Kittner & Felix Lill & Daniel M. Kammen, 2017. "Energy storage deployment and innovation for the clean energy transition," Nature Energy, Nature, vol. 2(9), pages 1-6, September.
    17. Yang, Shenbo & Tan, Zhongfu & Lin, Hongyu & Li, Peng & De, Gejirifu & Zhou, Feng’ao & Ju, Liwei, 2020. "A two-stage optimization model for Park Integrated Energy System operation and benefit allocation considering the effect of Time-Of-Use energy price," Energy, Elsevier, vol. 195(C).
    18. Ma, Tengfei & Wu, Junyong & Hao, Liangliang & Lee, Wei-Jen & Yan, Huaguang & Li, Dezhi, 2018. "The optimal structure planning and energy management strategies of smart multi energy systems," Energy, Elsevier, vol. 160(C), pages 122-141.
    19. Gao, Yixiang & Li, Shuhui & Fu, Xingang & Dong, Weizhen & Lu, Bing & Li, Zhongwen, 2020. "Energy management and demand response with intelligent learning for multi-thermal-zone buildings," Energy, Elsevier, vol. 210(C).
    20. Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2016. "Energy Internet: The business perspective," Applied Energy, Elsevier, vol. 178(C), pages 212-222.
    21. Zhuozhuo Gou & Yansong Deng, 2021. "Dynamic Model of Collaboration in Multi-Agent System Based on Evolutionary Game Theory," Games, MDPI, vol. 12(4), pages 1-19, October.
    22. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
    23. Fawcett, Stanley E. & Jones, Stephen L. & Fawcett, Amydee M., 2012. "Supply chain trust: The catalyst for collaborative innovation," Business Horizons, Elsevier, vol. 55(2), pages 163-178.
    24. Ma, Jun & Cheng, Jack C.P., 2016. "Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technology," Applied Energy, Elsevier, vol. 183(C), pages 182-192.
    25. Philip E. Bourne & Jon R. Lorsch & Eric D. Green, 2015. "Perspective: Sustaining the big-data ecosystem," Nature, Nature, vol. 527(7576), pages 16-17, November.
    26. Bowen Da & Chuanzhe Liu & Nana Liu & Sidun Fan, 2021. "Strategies of Two-Level Green Technology Investments for Coal Supply Chain under Different Dominant Modes," Sustainability, MDPI, vol. 13(7), pages 1-24, March.
    27. Ji, Shou-feng & Zhao, Dan & Luo, Rong-juan, 2019. "Evolutionary game analysis on local governments and manufacturers' behavioral strategies: Impact of phasing out subsidies for new energy vehicles," Energy, Elsevier, vol. 189(C).
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