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Medium- and Long-Term Trading Strategies for Large Electricity Retailers in China’s Electricity Market

Author

Listed:
  • Ting Lu

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
    New Energy Technology Center, National Institute of Clean-and-Low Carbon Energy, Beijing 102211, China)

  • Weige Zhang

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Yunjia Wang

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Hua Xie

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Xiaowei Ding

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Beijing Huashang Sanyou New Energy Technology Co., Ltd., Beijing 271000, China)

Abstract

In the rapid promotion of China’s electricity spot market, a large number of electricity retailers and large consumers participate in power trading, of which medium- and long-term power trading accounts for a large proportion. In the electricity spot market, the previous medium- and long-term transactions need to be closely combined with the current spot market transaction settlement rules. This paper analyzes the trading strategy of large retailers in the power market. In order to effectively reduce the total electricity cost, it is necessary to optimize the medium- and long-term transactions based on three aspects: electricity quantity and benchmark price decisions of medium- and long-term contracts, the daily electricity decomposition method in the day-ahead (DA) market, and the daily load curve decomposition strategy. According to load history characteristics that are extracted by the X12 method, daily electricity is decomposed from the medium- and long-term electricity quantity in the DA market. This paper introduces three methods of decomposing the daily load curve and proves that the particle swarm algorithm is the best method for effectively minimizing the cost in the DA market. Through analyzing the total electricity cost change pattern, we prove that the basic component of decision making is the relative relationship between the electricity price of medium- and long-term contracts and the equivalent kWh price of medium- and long-term electricity in the DA market, which is determined by the decomposition daily curve method. If the equivalent kilowatt-hour price obtained by the decomposition method in the DA market is greater than the electricity price of medium- and long-term contracts, the larger the electrical energy of medium- and long-term contracts, the lower the costs. Based on the above principles, electricity retailers can carry out planning for medium- and long-term transactions, as well as the decomposition and declaration of the daily electricity quantities and daily load curves.

Suggested Citation

  • Ting Lu & Weige Zhang & Yunjia Wang & Hua Xie & Xiaowei Ding, 2022. "Medium- and Long-Term Trading Strategies for Large Electricity Retailers in China’s Electricity Market," Energies, MDPI, vol. 15(9), pages 1-30, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3342-:d:808164
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    References listed on IDEAS

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    1. Paul J. Burke and Ashani Abayasekara, 2018. "The Price Elasticity of Electricity Demand in the United States: A Three-Dimensional Analysis," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    2. Lanot, Gauthier & Vesterberg, Mattias, 2021. "The price elasticity of electricity demand when marginal incentives are very large," Energy Economics, Elsevier, vol. 104(C).
    3. Feehan, James P., 2018. "The long-run price elasticity of residential demand for electricity: Results from a natural experiment," Utilities Policy, Elsevier, vol. 51(C), pages 12-17.
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