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A Power Exchange Strategy for Multiple Areas with Hydro Power and Flexible Loads

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  • Jichun Liu

    (Department of Electrical Engineering, College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China)

  • Yangfang Yang

    (Department of Electrical Engineering, College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China)

  • Yue Xiang

    (Department of Electrical Engineering, College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China)

  • Junyong Liu

    (Department of Electrical Engineering, College of Electrical Engineering and Information Technology, Sichuan University, Chengdu 610065, China)

Abstract

Areas with hydro power may purchase extra power from the outside power market during dry seasons, which will cause a deviation between the actual and expected power purchase amount due to the inaccurate judgment of the market situation. Because of the uncertainty of price fluctuations, the risk of purchasing power in the real-time market to eliminate this deviation is very high. This paper proposes an innovative trade mode, where the power exchange strategy between multiple areas is adopted through forming an alliance, i.e., one area can use the controllable elements within others, and constructing a monthly and post day-ahead two phase optimization model. The objective function of the monthly stochastic robust optimization considers the power purchase cost to determine the controllable elements dispatch dates for every area in the alliance. Thus, areas can make reasonable dispatch schedules for controllable elements to avoid the resource waste that means more controllable elements are prepared before post day-ahead optimization but less are used after post day-ahead optimization. While the post day-ahead optimization model determines the internal controllable elements dispatch and power exchange amount after the day-ahead market clearing process, users’ satisfaction and dispatch schedule changes for energy storage device are also considered. In order to solve the proposed two phase model, the dual principle and linearization methods are used to convert them into mixed-integer linear programming problems that can be effectively solved by the Cplex solver. The study case verifies the power deviation cost decreases with the power exchange strategy and the important role of energy storage devices.

Suggested Citation

  • Jichun Liu & Yangfang Yang & Yue Xiang & Junyong Liu, 2019. "A Power Exchange Strategy for Multiple Areas with Hydro Power and Flexible Loads," Energies, MDPI, vol. 12(6), pages 1-17, March.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1160-:d:217088
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    References listed on IDEAS

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    1. Conejo, Antonio J. & Contreras, Javier & Espinola, Rosa & Plazas, Miguel A., 2005. "Forecasting electricity prices for a day-ahead pool-based electric energy market," International Journal of Forecasting, Elsevier, vol. 21(3), pages 435-462.
    2. Chen, Hung-Cheng, 2013. "Optimum capacity determination of stand-alone hybrid generation system considering cost and reliability," Applied Energy, Elsevier, vol. 103(C), pages 155-164.
    3. Hosseini, S. M. H. & Forouzbakhsh, F. & Rahimpoor, M., 2005. "Determination of the optimal installation capacity of small hydro-power plants through the use of technical, economic and reliability indices," Energy Policy, Elsevier, vol. 33(15), pages 1948-1956, October.
    4. Nojavan, Sayyad & Zare, Kazem & Mohammadi-Ivatloo, Behnam, 2017. "Robust bidding and offering strategies of electricity retailer under multi-tariff pricing," Energy Economics, Elsevier, vol. 68(C), pages 359-372.
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