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Decision on Mixed Trading between Medium- and Long-Term Markets and Spot Markets for Electricity Sales Companies under New Electricity Reform Policies

Author

Listed:
  • Hui Wang

    (College of Economics and Management, Shanghai University of Electric Power, Shanghai 200090, China)

  • Congcong Wang

    (College of Economics and Management, Shanghai University of Electric Power, Shanghai 200090, China)

  • Wenhui Zhao

    (College of Economics and Management, Shanghai University of Electric Power, Shanghai 200090, China)

Abstract

The introduction of the new round of electricity reform policies has made the electricity sales companies’ trading environment increasingly complex. In the medium- and long-term market and spot market, following the new policy-oriented optimization of trading decisions is the focus of electricity sales companies. The main objective of this study is to consider the impact of the latest policies of China’s current electricity reform on each subject of electricity trading and to propose a method for electricity sales companies to make optimal decisions on renewable energy source (RES) power and conventional energy source (CES) power mixed with power trading in the medium- and long-term and spot markets to improve the efficiency of electricity market trading, promoting the consumption of renewable energy and helping the synergistic development of the electricity market and the tradable green certificate (TGC) market. This paper first discusses the impact of the new electricity reform policies on the transactions of various subjects in the electricity market and constructs the model of the consumer utility function, the profit model of an electricity sales company, and the profit model of power generators with energy storage. Considering the complex power supply and demand relationship among the various subjects of the electricity market, a game model is established for the decision on mixed trading between the medium- and long-term market, the spot market, and the tradable green certificate market to minimize the comprehensive power purchase cost of an electricity sales company. To reduce the decision-making risk caused by the uncertainty of spot price, the prophet model is used to predict the spot price; finally, through the analysis of the decision-making model of the electricity sales companies, the optimal transaction decisions of the electricity sales companies in different trading periods and different scenarios are solved. The test results show that the proposed model can significantly improve the profitability of the electricity sales companies and provide a decision-making reference for electricity sales companies to participate in the medium- and long-term market and spot market.

Suggested Citation

  • Hui Wang & Congcong Wang & Wenhui Zhao, 2022. "Decision on Mixed Trading between Medium- and Long-Term Markets and Spot Markets for Electricity Sales Companies under New Electricity Reform Policies," Energies, MDPI, vol. 15(24), pages 1-23, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9568-:d:1005870
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    as
    1. Wang, Ge & Zhang, Qi & Su, Bin & Shen, Bo & Li, Yan & Li, Zhengjun, 2021. "Coordination of tradable carbon emission permits market and renewable electricity certificates market in China," Energy Economics, Elsevier, vol. 93(C).
    2. Dagoumas, Athanasios S. & Polemis, Michael L., 2017. "An integrated model for assessing electricity retailer’s profitability with demand response," Applied Energy, Elsevier, vol. 198(C), pages 49-64.
    3. Kharrati, Saeed & Kazemi, Mostafa & Ehsan, Mehdi, 2016. "Equilibria in the competitive retail electricity market considering uncertainty and risk management," Energy, Elsevier, vol. 106(C), pages 315-328.
    4. Lin, Boqiang & Chen, Yufang, 2019. "Does electricity price matter for innovation in renewable energy technologies in China?," Energy Economics, Elsevier, vol. 78(C), pages 259-266.
    5. Ju, Liwei & Wu, Jing & Lin, Hongyu & Tan, Qinliang & Li, Gen & Tan, Zhongfu & Li, Jiayu, 2020. "Robust purchase and sale transactions optimization strategy for electricity retailers with energy storage system considering two-stage demand response," Applied Energy, Elsevier, vol. 271(C).
    6. Alexander J. M. Kell & A. Stephen McGough & Matthew Forshaw, 2021. "The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets," Papers 2103.04327, arXiv.org.
    7. Zhu, Chaoping & Fan, Ruguo & Lin, Jinchai, 2020. "The impact of renewable portfolio standard on retail electricity market: A system dynamics model of tripartite evolutionary game," Energy Policy, Elsevier, vol. 136(C).
    8. Cieplinski, A. & D'Alessandro, S. & Marghella, F., 2021. "Assessing the renewable energy policy paradox: A scenario analysis for the Italian electricity market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    9. Wu, Di & Ma, Xu & Balducci, Patrick & Bhatnagar, Dhruv, 2021. "An economic assessment of behind-the-meter photovoltaics paired with batteries on the Hawaiian Islands," Applied Energy, Elsevier, vol. 286(C).
    10. Li, Tianyu & Gao, Ciwei & Chen, Tao & Jiang, Yu & Feng, Yingchun, 2022. "Medium and long-term electricity market trading strategy considering renewable portfolio standard in the transitional period of electricity market reform in Jiangsu, China," Energy Economics, Elsevier, vol. 107(C).
    11. Lin, Jiang & Kahrl, Fredrich & Yuan, Jiahai & Liu, Xu & Zhang, Weirong, 2019. "Challenges and strategies for electricity market transition in China," Energy Policy, Elsevier, vol. 133(C).
    12. Guo, Hongye & Davidson, Michael R. & Chen, Qixin & Zhang, Da & Jiang, Nan & Xia, Qing & Kang, Chongqing & Zhang, Xiliang, 2020. "Power market reform in China: Motivations, progress, and recommendations," Energy Policy, Elsevier, vol. 145(C).
    13. Zugno, Marco & Morales, Juan Miguel & Pinson, Pierre & Madsen, Henrik, 2013. "A bilevel model for electricity retailers' participation in a demand response market environment," Energy Economics, Elsevier, vol. 36(C), pages 182-197.
    14. Wang, Ge & Zhang, Qi & Li, Yan & Mclellan, Benjamin C. & Pan, Xunzhang, 2019. "Corrective regulations on renewable energy certificates trading: Pursuing an equity-efficiency trade-off," Energy Economics, Elsevier, vol. 80(C), pages 970-982.
    15. Liu, Xiaoran & Ronn, Ehud I., 2020. "Using the binomial model for the valuation of real options in computing optimal subsidies for Chinese renewable energy investments," Energy Economics, Elsevier, vol. 87(C).
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    2. Maria Guadalupe Garcia-Garza & Jeyle Ortiz-Rodriguez & Esteban Picazzo-Palencia & Nora Munguia & Luis Velazquez, 2023. "The 2013 Mexican Energy Reform in the Context of Sustainable Development Goal 7," Energies, MDPI, vol. 16(19), pages 1-24, October.

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