IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i16p2537-d1458036.html
   My bibliography  Save this article

Study of Flexibility Transformation in Thermal Power Enterprises under Multi-Factor Drivers: Application of Complex-Network Evolutionary Game Theory

Author

Listed:
  • Lefeng Cheng

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • Pan Peng

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • Wentian Lu

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • Pengrong Huang

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • Yang Chen

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

Abstract

With the increasing share of renewable energy in the grid and the enhanced flexibility of the future power system, it is imperative for thermal power companies to explore alternative strategies. The flexible transformation of thermal power units is an effective strategy to address the previously mentioned challenges; however, the factors influencing the diffusion of this technology merit further investigation, yet they have been seldom examined by scholars. To address this gap, this issue is examined using an evolutionary game model of multi-agent complex networks, and a more realistic group structure is established through heterogeneous group differentiation. With factors such as group relationships, diffusion paths, compensation electricity prices, and subsidy intensities as variables, several diffusion scenarios are developed for research purposes. The results indicate that when upper-level enterprises influence the decision-making of lower-level enterprises, technology diffusion is significantly accelerated, and enhanced communication among thermal power enterprises further promotes diffusion. Among thermal power enterprises, leveraging large and medium-sized enterprises to promote the flexibility transformation of units proves to be an effective strategy. With regard to factors like the compensation price for depth peak shaving, the initial application ratio of groups, and the intensity of government subsidies, the compensation price emerges as the key factor. Only with a high compensation price can the other two factors effectively contribute to promoting technology diffusion.

Suggested Citation

  • Lefeng Cheng & Pan Peng & Wentian Lu & Pengrong Huang & Yang Chen, 2024. "Study of Flexibility Transformation in Thermal Power Enterprises under Multi-Factor Drivers: Application of Complex-Network Evolutionary Game Theory," Mathematics, MDPI, vol. 12(16), pages 1-23, August.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:16:p:2537-:d:1458036
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/16/2537/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/16/2537/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sun, Li & Hua, Qingsong & Shen, Jiong & Xue, Yali & Li, Donghai & Lee, Kwang Y., 2017. "Multi-objective optimization for advanced superheater steam temperature control in a 300MW power plant," Applied Energy, Elsevier, vol. 208(C), pages 592-606.
    2. Huang, Chenchen & Lin, Boqiang, 2023. "Promoting decarbonization in the power sector: How important is digital transformation?," Energy Policy, Elsevier, vol. 182(C).
    3. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    4. Yong, Qingqing & Jin, Kaiyuan & Li, Xiaobo & Yang, Ronggui, 2023. "Thermo-economic analysis for a novel grid-scale pumped thermal electricity storage system coupled with a coal-fired power plant," Energy, Elsevier, vol. 280(C).
    5. Garðarsdóttir, Stefanía Ó. & Göransson, Lisa & Normann, Fredrik & Johnsson, Filip, 2018. "Improving the flexibility of coal-fired power generators: Impact on the composition of a cost-optimal electricity system," Applied Energy, Elsevier, vol. 209(C), pages 277-289.
    6. Christoph Hauert & Michael Doebeli, 2004. "Spatial structure often inhibits the evolution of cooperation in the snowdrift game," Nature, Nature, vol. 428(6983), pages 643-646, April.
    7. Zhi, Zhang & Ming, Zhou & Bo, Yuan & Zun, Guo & Zhaoyuan, Wu & Gengyin, Li, 2023. "Multipath retrofit planning approach for coal-fired power plants in low-carbon power system transitions: Shanxi Province case in China," Energy, Elsevier, vol. 275(C).
    8. Du, Wen-Bo & Cao, Xian-Bin & Hu, Mao-Bin & Yang, Han-Xin & Zhou, Hong, 2009. "Effects of expectation and noise on evolutionary games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(11), pages 2215-2220.
    9. Hisashi Ohtsuki & Christoph Hauert & Erez Lieberman & Martin A. Nowak, 2006. "A simple rule for the evolution of cooperation on graphs and social networks," Nature, Nature, vol. 441(7092), pages 502-505, May.
    10. Li, Xiaozhu & Chen, Laijun & Sun, Fan & Hao, Yibo & Du, Xili & Mei, Shenwei, 2023. "Share or not share, the analysis of energy storage interaction of multiple renewable energy stations based on the evolution game," Renewable Energy, Elsevier, vol. 208(C), pages 679-692.
    11. Friedman, Daniel, 1991. "Evolutionary Games in Economics," Econometrica, Econometric Society, vol. 59(3), pages 637-666, May.
    12. Zhao, Dan & Ji, Shou-feng & Wang, He-ping & Jiang, Li-wen, 2021. "How do government subsidies promote new energy vehicle diffusion in the complex network context? A three-stage evolutionary game model," Energy, Elsevier, vol. 230(C).
    13. Zhao, Tian & Liu, Zhixin, 2019. "A novel analysis of carbon capture and storage (CCS) technology adoption: An evolutionary game model between stakeholders," Energy, Elsevier, vol. 189(C).
    Full references (including those not matched with items on IDEAS)

    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. Yang, Xiaomeng & Kong, Lingkai & Qu, Sen, 2024. "Evolution of technology cooperation networks based on networked evolutionary games model: An industrial heterogeneity perspective," Technology in Society, Elsevier, vol. 78(C).
    2. Liming Zhang & Ming Cai & Yingxin Zhang & Shuai Wang & Yao Xiao, 2024. "Two-layer network evolutionary game model applied to complex systems," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(11), pages 1-17, November.
    3. Yao Meng & Sean P. Cornelius & Yang-Yu Liu & Aming Li, 2024. "Dynamics of collective cooperation under personalised strategy updates," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    4. Jorge Peña & Yannick Rochat, 2012. "Bipartite Graphs as Models of Population Structures in Evolutionary Multiplayer Games," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-13, September.
    5. Ping Zhu & Guiyi Wei, 2014. "Stochastic Heterogeneous Interaction Promotes Cooperation in Spatial Prisoner's Dilemma Game," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-10, April.
    6. Qinghu Liao & Wenwen Dong & Boxin Zhao, 2023. "A New Strategy to Solve “the Tragedy of the Commons” in Sustainable Grassland Ecological Compensation: Experience from Inner Mongolia, China," Sustainability, MDPI, vol. 15(12), pages 1-24, June.
    7. Zhao, Zhengwu & Zhang, Chunyan, 2023. "The mechanisms of labor division from the perspective of task urgency and game theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    8. Chen, Feng & Wu, Bin & Lou, Wen-qian & Zhu, Bo-wen, 2024. "Impact of dual-credit policy on diffusion of technology R & D among automakers: Based on an evolutionary game model with technology-spillover in complex network," Energy, Elsevier, vol. 303(C).
    9. Han, Jia-Xu & Wang, Rui-Wu, 2023. "Complex interactions promote the frequency of cooperation in snowdrift game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    10. Qiguang An & Hongfeng Guo & Yating Zheng, 2022. "On Robust Stability and Stabilization of Networked Evolutionary Games with Time Delays," Mathematics, MDPI, vol. 10(15), pages 1-12, July.
    11. Sabin Lessard, 2011. "Effective Game Matrix and Inclusive Payoff in Group-Structured Populations," Dynamic Games and Applications, Springer, vol. 1(2), pages 301-318, June.
    12. Chen, Zhuo & Gao, Jianxi & Cai, Yunze & Xu, Xiaoming, 2011. "Evolutionary prisoner’s dilemma game in flocks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(1), pages 50-56.
    13. Silveira, Douglas & Vasconcelos, Silvinha, 2020. "Essays on duopoly competition with asymmetric firms: Is profit maximization always an evolutionary stable strategy?," International Journal of Production Economics, Elsevier, vol. 225(C).
    14. Zhao, Dan & Ji, Shou-feng & Wang, He-ping & Jiang, Li-wen, 2021. "How do government subsidies promote new energy vehicle diffusion in the complex network context? A three-stage evolutionary game model," Energy, Elsevier, vol. 230(C).
    15. Wang, Yitong & Fan, Ruguo & Du, Kang & Bao, Xuguang, 2023. "Exploring incentives to promote electric vehicles diffusion under subsidy abolition: An evolutionary analysis on multiplex consumer social networks," Energy, Elsevier, vol. 276(C).
    16. Yan, Zeyuan & Zhao, Hui & Liang, Shu & Li, Li & Song, Yanjie, 2024. "Inter-layer feedback mechanism with reinforcement learning boosts the evolution of cooperation in multilayer network," Chaos, Solitons & Fractals, Elsevier, vol. 185(C).
    17. Wang, Yitong & Fan, Ruguo & Chen, Rongkai & Xie, Xiao & Ke, Can, 2025. "Exploring the coevolution dynamics of residents and recyclers in electric vehicle battery recycling decisions on the two-layer heterogeneous complex networks," Applied Energy, Elsevier, vol. 382(C).
    18. Fabio Della Rossa & Fabio Dercole & Anna Di Meglio, 2020. "Direct Reciprocity and Model-Predictive Strategy Update Explain the Network Reciprocity Observed in Socioeconomic Networks," Games, MDPI, vol. 11(1), pages 1-28, March.
    19. Sarkar, Bijan, 2021. "The cooperation–defection evolution on social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    20. Li, Bin-Quan & Wu, Zhi-Xi & Guan, Jian-Yue, 2022. "Critical thresholds of benefit distribution in an extended snowdrift game model," Chaos, Solitons & Fractals, Elsevier, vol. 157(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:jmathe:v:12:y:2024:i:16:p:2537-:d:1458036. 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.