IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i6p2066-d769334.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/6/2066/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/6/2066/
    Download Restriction: no
    ---><---

    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. Gao, Evelyn & Sowlati, Taraneh & Akhtari, Shaghaygh, 2019. "Profit allocation in collaborative bioenergy and biofuel supply chains," Energy, Elsevier, vol. 188(C).
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Vangelis Marinakis, 2020. "Big Data for Energy Management and Energy-Efficient Buildings," Energies, MDPI, vol. 13(7), pages 1-18, March.
    8. 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).
    9. 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).
    10. 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.
    11. 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).
    12. Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2016. "Energy Internet: The business perspective," Applied Energy, Elsevier, vol. 178(C), pages 212-222.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    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).
    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. Ning, Jiajun & Xiong, Lixin, 2024. "Analysis of the dynamic evolution process of the digital transformation of renewable energy enterprises based on the cooperative and evolutionary game model," Energy, Elsevier, vol. 288(C).

    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. Olga Pilipczuk, 2020. "Sustainable Smart Cities and Energy Management: The Labor Market Perspective," Energies, MDPI, vol. 13(22), pages 1-24, November.
    2. Wu, Ying & Wu, Yanpeng & Guerrero, Josep M. & Vasquez, Juan C., 2021. "A comprehensive overview of framework for developing sustainable energy internet: From things-based energy network to services-based management system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    3. Lyu, Wenjing & Liu, Jin, 2021. "Artificial Intelligence and emerging digital technologies in the energy sector," Applied Energy, Elsevier, vol. 303(C).
    4. Vinoth Kumar Ponnusamy & Padmanathan Kasinathan & Rajvikram Madurai Elavarasan & Vinoth Ramanathan & Ranjith Kumar Anandan & Umashankar Subramaniam & Aritra Ghosh & Eklas Hossain, 2021. "A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid," Sustainability, MDPI, vol. 13(23), pages 1-35, December.
    5. Liu, Changyu & Song, Yadong & Wang, Wei & Shi, Xunpeng, 2023. "The governance of manufacturers’ greenwashing behaviors: A tripartite evolutionary game analysis of electric vehicles," Applied Energy, Elsevier, vol. 333(C).
    6. Zhou, Kaile & Yang, Changhui & Shen, Jianxin, 2017. "Discovering residential electricity consumption patterns through smart-meter data mining: A case study from China," Utilities Policy, Elsevier, vol. 44(C), pages 73-84.
    7. Jia, Kunqi & Guo, Ge & Xiao, Jucheng & Zhou, Huan & Wang, Zhihua & He, Guangyu, 2019. "Data compression approach for the home energy management system," Applied Energy, Elsevier, vol. 247(C), pages 643-656.
    8. Liu, Bo & Hou, Yufan & Luan, Wenpeng & Liu, Zishuai & Chen, Sheng & Yu, Yixin, 2023. "A divide-and-conquer method for compression and reconstruction of smart meter data," Applied Energy, Elsevier, vol. 336(C).
    9. Na Yu & Chunfeng Zhao, 2021. "Chain Innovation Mechanism of the Manufacturing Industry in the Yangtze River Delta of China Based on Evolutionary Game," Sustainability, MDPI, vol. 13(17), pages 1-20, August.
    10. Akhil Joseph & Patil Balachandra, 2020. "Energy Internet, the Future Electricity System: Overview, Concept, Model Structure, and Mechanism," Energies, MDPI, vol. 13(16), pages 1-26, August.
    11. Emilio Ghiani & Alessandro Serpi & Virginia Pilloni & Giuliana Sias & Marco Simone & Gianluca Marcialis & Giuliano Armano & Paolo Attilio Pegoraro, 2018. "A Multidisciplinary Approach for the Development of Smart Distribution Networks," Energies, MDPI, vol. 11(10), pages 1-29, September.
    12. Chen, Wen & Zhou, Kaile & Yang, Shanlin & Wu, Cheng, 2017. "Data quality of electricity consumption data in a smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 98-105.
    13. Li, Francis G.N. & Bataille, Chris & Pye, Steve & O'Sullivan, Aidan, 2019. "Prospects for energy economy modelling with big data: Hype, eliminating blind spots, or revolutionising the state of the art?," Applied Energy, Elsevier, vol. 239(C), pages 991-1002.
    14. Zou, Juan & Yang, Xu & Liu, Zhongbing & Liu, Jiangyang & Zhang, Ling & Zheng, Jinhua, 2021. "Multiobjective bilevel optimization algorithm based on preference selection to solve energy hub system planning problems," Energy, Elsevier, vol. 232(C).
    15. Mir Hamid Taghavi & Peyman Akhavan & Rouhollah Ahmadi & Ali Bonyadi Naeini, 2022. "Identifying Key Components in Implementation of Internet of Energy (IoE) in Iran with a Combined Approach of Meta-Synthesis and Structural Analysis: A Systematic Review," Sustainability, MDPI, vol. 14(20), pages 1-23, October.
    16. Aleksander Jakimowicz, 2022. "The Energy Transition as a Super Wicked Problem: The Energy Sector in the Era of Prosumer Capitalism," Energies, MDPI, vol. 15(23), pages 1-31, December.
    17. Lu, Xinhui & Liu, Zhaoxi & Ma, Li & Wang, Lingfeng & Zhou, Kaile & Feng, Nanping, 2020. "A robust optimization approach for optimal load dispatch of community energy hub," Applied Energy, Elsevier, vol. 259(C).
    18. József Magyari & Krisztina Hegedüs & Botond Sinóros-Szabó, 2022. "Integration Opportunities of Power-to-Gas and Internet-of-Things Technical Advancements: A Systematic Literature Review," Energies, MDPI, vol. 15(19), pages 1-19, September.
    19. Ramsebner, J. & Haas, R. & Auer, H. & Ajanovic, A. & Gawlik, W. & Maier, C. & Nemec-Begluk, S. & Nacht, T. & Puchegger, M., 2021. "From single to multi-energy and hybrid grids: Historic growth and future vision," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    20. Wei Feng & Ling Zhao & Yue Chen, 2022. "Research on Collaborative Innovation Mode of Enterprise Group from the Perspective of Comprehensive Innovation Management," Sustainability, MDPI, vol. 14(9), pages 1-19, April.

    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:6:p:2066-:d:769334. 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.