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Trading behavior strategy of power plants and the grid under renewable portfolio standards in China: A tripartite evolutionary game analysis

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  • Teng, Minmin
  • Lv, Kunfeng
  • Han, Chuanfeng
  • Liu, Pihui

Abstract

China is facing the challenge of selecting appropriate policies to implement tradable green certificate (TGC) under the renewable portfolio standards (RPS). This paper proposes a tripartite evolutionary game model among thermal power plants, green power plants and the grid under the RPS and TGC policy to analyze the evolutionary stability strategies (ESSs) of unilateral subjects and systems and use numerical simulation to discuss the influence of different types of TGC purchase ratios and different trading markets on the choice of tripartite strategy. The results are as follows. (1) When the renewable energy consumption quota ratio is 21%, thermal power plants are more and more active in choosing to trade TGC as the ratio of wind power TGC and photovoltaic TGC gradually increases from 1:5 to 5:1. (2) In the TGC market where the price of wind power and photovoltaic TGC is 50 yuan, green power plants are more inclined to hold TGC; in the subsidized TGC market, where the price of wind power TGC is 190 yuan and the price of photovoltaic TGC is 650 yuan, green power plants tend to sell TGC. (3) With the price of excess green electricity consumption gradually decreased from 700 yuan/MWh to 100 yuan/MWh, the activity of three parties participating in TGC trading gradually increased, effectively activating the TGC market. Finally, from the perspective of policy management, we provide suggestions for the long-term sustainable development of the TGC market.

Suggested Citation

  • Teng, Minmin & Lv, Kunfeng & Han, Chuanfeng & Liu, Pihui, 2023. "Trading behavior strategy of power plants and the grid under renewable portfolio standards in China: A tripartite evolutionary game analysis," Energy, Elsevier, vol. 284(C).
  • Handle: RePEc:eee:energy:v:284:y:2023:i:c:s0360544223017929
    DOI: 10.1016/j.energy.2023.128398
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