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The Impact of Participation Ratio and Bidding Strategies on New Energy’s Involvement in Electricity Spot Market Trading under Marketization Trends—An Empirical Analysis Based on Henan Province, China

Author

Listed:
  • Liqing Zhang

    (State Grid Henan Electric Power Company, Zhengzhou 450003, China)

  • Chunzheng Tian

    (Economics and Technology Research Institute of State Grid Henan Electric Power Company, Zhengzhou 450052, China)

  • Zhiheng Li

    (Henan Power Exchange Center, Zhengzhou 450003, China)

  • Shuo Yin

    (Economics and Technology Research Institute of State Grid Henan Electric Power Company, Zhengzhou 450052, China)

  • Anbang Xie

    (Economics and Technology Research Institute of State Grid Henan Electric Power Company, Zhengzhou 450052, China)

  • Peng Wang

    (National Institute of Energy Development Strategy, North China Electric Power University, Beijing 102206, China)

  • Yihong Ding

    (National Institute of Energy Development Strategy, North China Electric Power University, Beijing 102206, China)

Abstract

As new-energy electricity increasingly enters the post-subsidy era, traditional fixed feed-in tariffs and guaranteed purchase policies are not conducive to the optimal allocation of large-scale, high-proportion new-energy power due to the high pressure of subsidy funds and the fairness issues of power-generation grid connection. Encouraging new energy to participate in electricity market transactions is considered an effective solution. However, existing studies have presupposed the adverse effects of new energy in proposing market mechanism optimization designs for new-energy participation without quantitative results to support this, which is not conducive to a true assessment of the comprehensive impact of individual instances of new-energy participation in the market. To this end, this study, based on the actual experience and data cases of China’s electricity spot market pilot provinces, considers both unit commitment and economic dispatch in the electricity distribution process, and constructs a two-stage optimization model for electricity spot market clearing. According to the differences in grid connection time and the construction costs of new-energy power, differentiated proportions of new-energy participation in the market and bidding strategies are set. By analyzing the quantitative results of new energy participating in spot market transactions under multiple scenarios, using both typical daily data for normal loads and peak loads, the study provides theoretical support and a data basis for the optimized design of market mechanisms. The research results show that there is a non-linear relationship between the scale of new energy entering the market and its bidding strategies and market-clearing electricity prices. In the transition phase of the low-carbon transformation of the power sector, the impacts of thermal power technology with a certain generation capacity and changes in the relationship between power supply and demand on electricity prices are significant. From the perspective of the individual interests of new-energy providers, the analysis of their bidding strategies in the market is important.

Suggested Citation

  • Liqing Zhang & Chunzheng Tian & Zhiheng Li & Shuo Yin & Anbang Xie & Peng Wang & Yihong Ding, 2024. "The Impact of Participation Ratio and Bidding Strategies on New Energy’s Involvement in Electricity Spot Market Trading under Marketization Trends—An Empirical Analysis Based on Henan Province, China," Energies, MDPI, vol. 17(17), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4463-:d:1472202
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    References listed on IDEAS

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    1. Nemati, Mohsen & Braun, Martin & Tenbohlen, Stefan, 2018. "Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming," Applied Energy, Elsevier, vol. 210(C), pages 944-963.
    2. Tan, Qinliang & Ding, Yihong & Ye, Qi & Mei, Shufan & Zhang, Yimei & Wei, Yongmei, 2019. "Optimization and evaluation of a dispatch model for an integrated wind-photovoltaic-thermal power system based on dynamic carbon emissions trading," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. Ding, Yihong & Tan, Qinliang & Shan, Zijing & Han, Jian & Zhang, Yimei, 2023. "A two-stage dispatching optimization strategy for hybrid renewable energy system with low-carbon and sustainability in ancillary service market," Renewable Energy, Elsevier, vol. 207(C), pages 647-659.
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