IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i11p3059-d561570.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/11/3059/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/11/3059/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Saddam Aziz & Cheung-Ming Lai & Ka Hong Loo, 2023. "Performance of an Adaptive Optimization Paradigm for Optimal Operation of a Mono-Switch Class E Induction Heating Application," Mathematics, MDPI, vol. 11(13), pages 1-18, July.
    2. Lingling Li & Jiarui Pei & Qiang Shen, 2023. "A Review of Research on Dynamic and Static Economic Dispatching of Hybrid Wind–Thermal Power Microgrids," Energies, MDPI, vol. 16(10), pages 1-23, May.
    3. Saddam Aziz & Muhammad Talib Faiz & Adegoke Muideen Adeniyi & Ka-Hong Loo & Kazi Nazmul Hasan & Linli Xu & Muhammad Irshad, 2022. "Anomaly Detection in the Internet of Vehicular Networks Using Explainable Neural Networks (xNN)," Mathematics, MDPI, vol. 10(8), pages 1-23, April.
    4. Abedrabboh, Khaled & Al-Fagih, Luluwah, 2023. "Applications of mechanism design in market-based demand-side management: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    5. Guo, Zhilong & Xu, Wei & Yan, Yue & Sun, Mei, 2023. "How to realize the power demand side actively matching the supply side? ——A virtual real-time electricity prices optimization model based on credit mechanism," Applied Energy, Elsevier, vol. 343(C).
    6. Ádám Sleisz & Dániel Divényi & Beáta Polgári & Péter Sőrés & Dávid Raisz, 2022. "A Novel Cost Allocation Mechanism for Local Flexibility in the Power System with Partial Disintermediation," Energies, MDPI, vol. 15(22), pages 1-18, November.
    7. Wisam Abed Kattea Al-Maliki & Hayder Q. A. Khafaji & Hasanain A. Abdul Wahhab & Hussein M. H. Al-Khafaji & Falah Alobaid & Bernd Epple, 2022. "Advances in Process Modelling and Simulation of Parabolic Trough Power Plants: A Review," Energies, MDPI, vol. 15(15), pages 1-15, July.
    8. Liu, Chunming & Wang, Chunling & Yin, Yujun & Yang, Peihong & Jiang, Hui, 2022. "Bi-level dispatch and control strategy based on model predictive control for community integrated energy system considering dynamic response performance," Applied Energy, Elsevier, vol. 310(C).
    9. Yao, Leyi & Liu, Zeyuan & Chang, Weiguang & Yang, Qiang, 2023. "Multi-level model predictive control based multi-objective optimal energy management of integrated energy systems considering uncertainty," Renewable Energy, Elsevier, vol. 212(C), pages 523-537.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Pinto, T. & Morais, H. & Oliveira, P. & Vale, Z. & Praça, I. & Ramos, C., 2011. "A new approach for multi-agent coalition formation and management in the scope of electricity markets," Energy, Elsevier, vol. 36(8), pages 5004-5015.
    3. Jiang, Sufan & Gao, Shan & Pan, Guangsheng & Zhao, Xin & Liu, Yu & Guo, Yasen & Wang, Sicheng, 2020. "A novel robust security constrained unit commitment model considering HVDC regulation," Applied Energy, Elsevier, vol. 278(C).
    4. Gao, Evelyn & Sowlati, Taraneh & Akhtari, Shaghaygh, 2019. "Profit allocation in collaborative bioenergy and biofuel supply chains," Energy, Elsevier, vol. 188(C).
    5. Liu, Yang & Jiang, Zhigao & Guo, Bowei, 2022. "Assessing China’s provincial electricity spot market pilot operations: Lessons from Guangdong province," Energy Policy, Elsevier, vol. 164(C).
    6. Zhang, Xiaochun & Ma, Chun & Song, Xia & Zhou, Yuyu & Chen, Weiping, 2016. "The impacts of wind technology advancement on future global energy," Applied Energy, Elsevier, vol. 184(C), pages 1033-1037.
    7. Jun Dong & Yuanyuan Wang & Xihao Dou & Zhengpeng Chen & Yaoyu Zhang & Yao Liu, 2021. "Research on Decision Optimization Model of Microgrid Participating in Spot Market Transaction," Sustainability, MDPI, vol. 13(12), pages 1-26, June.
    8. Yanbin Li & Feng Zhang & Yun Li & Bingkang Li & Zhen Li, 2019. "Evaluating the Power Grid Investment Behavior in China: From the Perspective of Government Supervision," Energies, MDPI, vol. 12(21), pages 1-23, November.
    9. Bo Sun & Siyuan Cheng & Jingdong Xie & Xin Sun, 2022. "Identification of Generators’ Economic Withholding Behavior Based on a SCAD-Logit Model in Electricity Spot Market," Energies, MDPI, vol. 15(11), pages 1-23, June.
    10. Lu, Qing & Lü, Shuaikang & Leng, Yajun, 2019. "A Nash-Stackelberg game approach in regional energy market considering users’ integrated demand response," Energy, Elsevier, vol. 175(C), pages 456-470.
    11. Zhang, Yi & Cheng, Chuntian & Yang, Tiantian & Jin, Xiaoyu & Jia, Zebin & Shen, Jianjian & Wu, Xinyu, 2022. "Assessment of climate change impacts on the hydro-wind-solar energy supply system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    12. Peng, Feixiang & Hu, Shubo & Fan, Xuanxuan & Sun, Hui & Zhou, Wei & Guo, Furan & Song, Wenzhuo, 2021. "Sequential coalition formation for wind-thermal combined bidding," Energy, Elsevier, vol. 236(C).
    13. González-Garrido, A. & Gaztañaga, H. & Saez-de-Ibarra, A. & Milo, A. & Eguia, P., 2020. "Electricity and reserve market bidding strategy including sizing evaluation and a novel renewable complementarity-based centralized control for storage lifetime enhancement," Applied Energy, Elsevier, vol. 262(C).
    14. Zhang, Yuanyuan & Zhao, Huiru & Li, Bingkang & Zhao, Yihang & Qi, Ze, 2022. "Research on credit rating and risk measurement of electricity retailers based on Bayesian Best Worst Method-Cloud Model and improved Credit Metrics model in China's power market," Energy, Elsevier, vol. 252(C).
    15. Li, Jinghua & Zhou, Jiasheng & Chen, Bo, 2020. "Review of wind power scenario generation methods for optimal operation of renewable energy systems," Applied Energy, Elsevier, vol. 280(C).
    16. Tongseok Lim, 2021. "Hodge theoretic reward allocation for generalized cooperative games on graphs," Papers 2107.10510, arXiv.org, revised Jan 2022.
    17. Yue Zhou & Hussein Obeid & Salah Laghrouche & Mickael Hilairet & Abdesslem Djerdir, 2020. "A Disturbance Rejection Control Strategy of a Single Converter Hybrid Electrical System Integrating Battery Degradation," Energies, MDPI, vol. 13(11), pages 1-19, June.
    18. Setya Budi, Rizki Firmansyah & Sarjiya, & Hadi, Sasongko Pramono, 2022. "Indonesia's deregulated generation expansion planning model based on mixed strategy game theory model for determining the optimal power purchase agreement," Energy, Elsevier, vol. 260(C).
    19. Li, Jiamei & Ai, Qian & Yin, Shuangrui & Hao, Ran, 2022. "An aggregator-oriented hierarchical market mechanism for multi-type ancillary service provision based on the two-loop Stackelberg game," Applied Energy, Elsevier, vol. 323(C).
    20. Sen Guo & Wenyue Zhang & Xiao Gao, 2020. "Business Risk Evaluation of Electricity Retail Company in China Using a Hybrid MCDM Method," Sustainability, MDPI, vol. 12(5), pages 1-21, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3059-:d:561570. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.