Research on ladder bidding strategy of thermal power enterprises according to evolutionary game in spot market

Erdong Zhao, Hao Wang, Hongyang Lin

    Research output: Contribution to journalArticlepeer-review

    10 Citations (Scopus)

    Abstract

    It is an inevitable trend for thermal power enterprises to enter the power spot market, and market mechanisms such as electricity bidding, green certificates, quotas and carbon trading are bound to increase the competitive pressure of thermal power enterprises. In order to make thermal power enterprises better adapt to market changes, this paper puts forward a ladder bidding strategy model based on improved genetic algorithm and evolutionary game according to the bidding and clearing rules in the spot market of electric power, so as to make auxiliary decision for thermal power enterprises in different power market supply and demand situations. By setting a classic example, we simulated the bidding strategies of thermal power enterprises with different technical levels and sizes under three supply and demand conditions; standard supply and demand, tight supply and demand and loose supply and demand. We found that the bidding strategy based on evolutionary game theory can assist the enterprises to find a ladder quotation suitable for the current background and gain advantages in market competition. It shows that the bidding strategy based on evolutionary game theory has strong applicability, which provides valuable strategic reference for thermal power enterprises to enter the spot market. This work is supported by National Natural Science Foundation of China (No. 71673086).

    Original languageEnglish
    Article number1000-7229(2020)08-0068-10
    Pages (from-to)68-77
    Number of pages10
    JournalDianli Jianshe/Electric Power Construction
    Volume41
    Issue number8
    DOIs
    Publication statusPublished - Aug 2020

    Keywords

    • Evolutionary game
    • Ladder quotation
    • Spot electricity market
    • Thermal power generation enterprise

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