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Multi-Level Market Transaction Optimization Model for Electricity Sales Companies with Energy Storage Plant

Author

Listed:
  • Guan Wang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Zhongfu Tan

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Hongyu Lin

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Qingkun Tan

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Shenbo Yang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Liwei Ju

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Zhongrui Ren

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

Due to market price uncertainty and volatility, electricity sales companies today are facing greater risks in regard to the day-ahead market and the real-time market. Along with introducing the Time of Use (TOU) price for the customer as a type of balancing resource to avoid market risk, electricity sales companies should adopt the market risk-aversion method to reduce the high cost of ancillary services in the real-time market by using multi-level market transactions, as well as to provide a reference for the profits of power companies. In this paper, we establish a non-linear mathematical model based on stochastic programming by using conditional value-at-risk (CVaR) to measure transaction strategy risk. For the market price and consumer electricity load as the uncertain factors of multi-level market transactions of electricity sales companies, the optimal objective was to maximize the revenue of electricity sales companies and minimize the peak-valley differences in the system, which is solved by using mixed-integer linear programming (MILP). Finally, we provide an example to analyze the effect of the fluctuation degree of customer load and market price on the profit of electricity sales companies under different confidence coefficients.

Suggested Citation

  • Guan Wang & Zhongfu Tan & Hongyu Lin & Qingkun Tan & Shenbo Yang & Liwei Ju & Zhongrui Ren, 2019. "Multi-Level Market Transaction Optimization Model for Electricity Sales Companies with Energy Storage Plant," Energies, MDPI, vol. 12(1), pages 1-14, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:1:p:145-:d:194418
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    References listed on IDEAS

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    1. Muller, Moritz & Sensfu[ss], Frank & Wietschel, Martin, 2007. "Simulation of current pricing-tendencies in the German electricity market for private consumption," Energy Policy, Elsevier, vol. 35(8), pages 4283-4294, August.
    2. O'Mahoney, Amy & Denny, Eleanor, 2013. "Electricity prices and generator behaviour in gross pool electricity markets," Energy Policy, Elsevier, vol. 63(C), pages 628-637.
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    Cited by:

    1. Jerzy Andruszkiewicz & Józef Lorenc & Agnieszka Weychan, 2019. "Demand Price Elasticity of Residential Electricity Consumers with Zonal Tariff Settlement Based on Their Load Profiles," Energies, MDPI, vol. 12(22), pages 1-22, November.
    2. Yixin Huang & Xinyi Liu & Zhi Zhang & Li Yang & Zhenzhi Lin & Yangqing Dan & Ke Sun & Zhou Lan & Keping Zhu, 2020. "Multi-Stage Transmission Network Planning Considering Transmission Congestion in the Power Market," Energies, MDPI, vol. 13(18), pages 1-22, September.
    3. Marian Kampik & Marcin Fice & Adam Pilśniak & Krzysztof Bodzek & Anna Piaskowy, 2023. "An Analysis of Energy Consumption in Small- and Medium-Sized Buildings," Energies, MDPI, vol. 16(3), pages 1-21, February.

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