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How dynamic renewable portfolio standards affect trading behavior of power generators? Considering green certificate and reward/penalty mechanism

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  • Hu, Fanshuai
  • Zhou, Dequn
  • Zhu, Qingyuan
  • Wang, Qunwei

Abstract

As the subsidy gap widens, China has announced that the renewable portfolio standards (RPS) will replace feed-in-tariff (FIT) as the primary policy for renewable energy. In order to resolve the conflicts of interest among electricity market entities during the policy evolution process, this paper constructs an evolutionary model combined with system dynamic to simulate the evolutionary strategy of three subjects (regulator, hybrid power generators and thermal power generators). Green certificate trading and the reward/penalty mechanism are considered. Our research findings highlight the following key points: (1) The initial strategy probability of regulator and hybrid power generators will not affect their willingness to pursue effective regulation and green electricity production. But when the initial strategy probability is low, thermal power generators may reduce enthusiasm to participate in green certificate trading. (2) When the incentive coefficient is not set or the penalty coefficient is <1, hybrid and thermal power generators will diminish their eagerness to engage in green certificate trading, the RPS policy will be ineffective. Interestingly, if the two coefficients are too high, the evolution speed of system will not accelerate either. (3) Reasonable regulation of critical parameters, such as quota obligation, green certificate prices and power generation will accelerate the pace of market entities evolving towards RPS. Surprisingly, the increase in subsidy will enhance power generators' enthusiasm for participating in green certificate transactions, and there is a certain upper limit.

Suggested Citation

  • Hu, Fanshuai & Zhou, Dequn & Zhu, Qingyuan & Wang, Qunwei, 2024. "How dynamic renewable portfolio standards affect trading behavior of power generators? Considering green certificate and reward/penalty mechanism," Applied Energy, Elsevier, vol. 375(C).
  • Handle: RePEc:eee:appene:v:375:y:2024:i:c:s0306261924014971
    DOI: 10.1016/j.apenergy.2024.124114
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    Cited by:

    1. Lefeng Cheng & Xin Wei & Manling Li & Can Tan & Meng Yin & Teng Shen & Tao Zou, 2024. "Integrating Evolutionary Game-Theoretical Methods and Deep Reinforcement Learning for Adaptive Strategy Optimization in User-Side Electricity Markets: A Comprehensive Review," Mathematics, MDPI, vol. 12(20), pages 1-56, October.

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