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Day-Ahead Market Modeling for Strategic Wind Power Producers under Robust Market Clearing

Author

Listed:
  • Huiru Zhao

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

  • Yuwei Wang

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

  • Mingrui Zhao

    (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)

  • Sen Guo

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

Abstract

In this paper, considering real time wind power uncertainties, the strategic behaviors of wind power producers adopting two different bidding modes in day-ahead electricity market is modeled and experimentally compared. These two different bidding modes only provide a wind power output plan and a bidding curve consisting of bidding price and power output, respectively. On the one hand, to significantly improve wind power accommodation, a robust market clearing model is employed for day-ahead market clearing implemented by an independent system operator. On the other hand, since the Least Squares Continuous Actor-Critic algorithm is demonstrated as an effective method in dealing with Markov decision-making problems with continuous state and action sets, we propose the Least Squares Continuous Actor-Critic-based approaches to model and simulate the dynamic bidding interaction processes of many wind power producers adopting two different bidding modes in the day-head electricity market under robust market clearing conditions, respectively. Simulations are implemented on the IEEE 30-bus test system with five strategic wind power producers, which verify the rationality of our proposed approaches. Moreover, the quantitative analysis and comparisons conducted in our simulations put forward some suggestions about leading wind power producers to reasonably bid in market and bidding mode selections.

Suggested Citation

  • Huiru Zhao & Yuwei Wang & Mingrui Zhao & Qingkun Tan & Sen Guo, 2017. "Day-Ahead Market Modeling for Strategic Wind Power Producers under Robust Market Clearing," Energies, MDPI, vol. 10(7), pages 1-27, July.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:924-:d:103517
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    References listed on IDEAS

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    1. Xiao, Yunpeng & Wang, Xifan & Wang, Xiuli & Dang, Can & Lu, Ming, 2016. "Behavior analysis of wind power producer in electricity market," Applied Energy, Elsevier, vol. 171(C), pages 325-335.
    2. Huiru Zhao & Yuwei Wang & Sen Guo & Mingrui Zhao & Chao Zhang, 2016. "Application of a Gradient Descent Continuous Actor-Critic Algorithm for Double-Side Day-Ahead Electricity Market Modeling," Energies, MDPI, vol. 9(9), pages 1-20, September.
    3. Vilim, Michael & Botterud, Audun, 2014. "Wind power bidding in electricity markets with high wind penetration," Applied Energy, Elsevier, vol. 118(C), pages 141-155.
    4. Min, C.G. & Kim, M.K. & Park, J.K. & Yoon, Y.T., 2013. "Game-theory-based generation maintenance scheduling in electricity markets," Energy, Elsevier, vol. 55(C), pages 310-318.
    5. Salehizadeh, Mohammad Reza & Soltaniyan, Salman, 2016. "Application of fuzzy Q-learning for electricity market modeling by considering renewable power penetration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1172-1181.
    6. Wang, Jianhui & Zhou, Zhi & Botterud, Audun, 2011. "An evolutionary game approach to analyzing bidding strategies in electricity markets with elastic demand," Energy, Elsevier, vol. 36(5), pages 3459-3467.
    7. Laia, R. & Pousinho, H.M.I. & Melíco, R. & Mendes, V.M.F., 2016. "Bidding strategy of wind-thermal energy producers," Renewable Energy, Elsevier, vol. 99(C), pages 673-681.
    8. Shivaie, Mojtaba & Ameli, Mohammad T., 2015. "An environmental/techno-economic approach for bidding strategy in security-constrained electricity markets by a bi-level harmony search algorithm," Renewable Energy, Elsevier, vol. 83(C), pages 881-896.
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    Cited by:

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    5. Jun Dong & Peiwen Yang & Shilin Nie, 2019. "Day-Ahead Scheduling Model of the Distributed Small Hydro-Wind-Energy Storage Power System Based on Two-Stage Stochastic Robust Optimization," Sustainability, MDPI, vol. 11(10), pages 1-27, May.

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