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A Wind Energy Supplier Bidding Strategy Using Combined EGA-Inspired HPSOIFA Optimizer and Deep Learning Predictor

Author

Listed:
  • Rongquan Zhang

    (College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518000, China)

  • Saddam Aziz

    (College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518000, China)

  • Muhammad Umar Farooq

    (Department of Business Studies, Namal Institute Mianwali, Mianwali 42201, Pakistan)

  • Kazi Nazmul Hasan

    (School of Engineering, RMIT University, Melbourne 3000, Australia)

  • Nabil Mohammed

    (School of Engineering, Macquarie University, Sydney 2019, Australia)

  • Sadiq Ahmad

    (Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Cantonment 47040, Pakistan)

  • Nisrine Ibadah

    (1 LRIT Laboratory, Faculty of Sciences, Mohammed V University, 10056 Rabat, Morocco)

Abstract

As the integration of large-scale wind energy is increasing into the electricity grids, the role of wind energy suppliers should be investigated as a price-maker as their participation would influence the locational marginal price (LMP) of electricity. The existing bidding strategies for a wind energy supplier faces limitations with respect to the potential cooperation, other competitors’ bidding behavior, network loss, and uncertainty of wind production (WP) and balancing market price (BMP). Hence, to solve these problems, a novel bidding strategy (BS) for a wind power supplier as a price-maker has been proposed in this paper. The new algorithm, called the evolutionary game approach (EGA) inspired hybrid particle swarm optimization and improved firefly algorithm (HPSOIFA), has been proposed to handle the bidding issue. The bidding behavior of power suppliers, including conventional power suppliers, has been encoded as one species to obtain the equilibrium where the EGA can explore dynamically reasonable behavior changes of the opponents. Each species of behavior change has been exploited by the HPSOIFA to improve the optimization solutions. Moreover, a deep learning algorithm, namely deep belief network, has been implemented for improving the accuracy of the forecasting results considering the WP and BMP, and the uncertainty revealed in the WP and BMP has been modeled by quantile regression (QR). Finally, the Shapley value (SV) has been calculated to estimate the benefits of cooperative power suppliers. The presented case studies have verified that the proposed algorithm and the established bidding strategy exhibit higher effectiveness.

Suggested Citation

  • Rongquan Zhang & Saddam Aziz & Muhammad Umar Farooq & Kazi Nazmul Hasan & Nabil Mohammed & Sadiq Ahmad & Nisrine Ibadah, 2021. "A Wind Energy Supplier Bidding Strategy Using Combined EGA-Inspired HPSOIFA Optimizer and Deep Learning Predictor," Energies, MDPI, vol. 14(11), pages 1-22, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3059-:d:561570
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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. Peng, Xu & Tao, Xiaoma, 2018. "Cooperative game of electricity retailers in China's spot electricity market," Energy, Elsevier, vol. 145(C), pages 152-170.
    3. Haibo Zhang & Xiaoming Liu & Honghai Ji & Zhongsheng Hou & Lingling Fan, 2019. "Multi-Agent-Based Data-Driven Distributed Adaptive Cooperative Control in Urban Traffic Signal Timing," Energies, MDPI, vol. 12(7), pages 1-19, April.
    4. Camal, S. & Teng, F. & Michiorri, A. & Kariniotakis, G. & Badesa, L., 2019. "Scenario generation of aggregated Wind, Photovoltaics and small Hydro production for power systems applications," Applied Energy, Elsevier, vol. 242(C), pages 1396-1406.
    5. Stern, Ari & Tettenhorst, Alexander, 2019. "Hodge decomposition and the Shapley value of a cooperative game," Games and Economic Behavior, Elsevier, vol. 113(C), pages 186-198.
    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. Li, Yunfeng & Xue, Wenli & Wu, Ting & Wang, Huaizhi & Zhou, Bin & Aziz, Saddam & He, Yang, 2021. "Intrusion detection of cyber physical energy system based on multivariate ensemble classification," Energy, Elsevier, vol. 218(C).
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    Cited by:

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