IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v127y2018icp575-586.html
   My bibliography  Save this article

Optimal bidding strategy of wind power producers in pay-as-bid power markets

Author

Listed:
  • Afshar, Karim
  • Ghiasvand, Farshad Shamsini
  • Bigdeli, Nooshin

Abstract

This paper presents a method to determine the optimal bidding strategy of the wind power producers with market power for a strategic presence in the day-ahead market with pay as bid method. Since the wind power producer is not capable of exact prediction of his power production, he has to trade the difference between the amount won in the day-ahead market and the actual production value in the balancing market. Uncertainties related to power generation is modeled by likely scenarios. However in order to model the punitive effect of trade in balancing market, the balancing market price is considered as a factor of the day-ahead market's clearing price. In the proposed model, optimal bidding strategy is formulated via a bi-level problem including the upper-level and lower-level sub-problems. The purpose of the upper-level sub-problem is to maximize the wind power producer's earning while the purpose of the lower-level sub-problem is to clear the day-ahead market. To solve both upper-level and lower-level problems, particle swarm optimization algorithm is applied. The results of three-bus test system and IEEE 24-bus RTS shows the efficiency of the proposed method.

Suggested Citation

  • Afshar, Karim & Ghiasvand, Farshad Shamsini & Bigdeli, Nooshin, 2018. "Optimal bidding strategy of wind power producers in pay-as-bid power markets," Renewable Energy, Elsevier, vol. 127(C), pages 575-586.
    • Handle: RePEc:eee:renene:v:127:y:2018:i:c:p:575-586
    DOI: 10.1016/j.renene.2018.05.015
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148118305342
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2018.05.015?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Genc, Talat S. & Reynolds, Stanley S., 2011. "Supply function equilibria with capacity constraints and pivotal suppliers," International Journal of Industrial Organization, Elsevier, vol. 29(4), pages 432-442, July.
    2. 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.
    3. Li, Gong & Shi, Jing & Qu, Xiuli, 2011. "Modeling methods for GenCo bidding strategy optimization in the liberalized electricity spot market–A state-of-the-art review," Energy, Elsevier, vol. 36(8), pages 4686-4700.
    4. 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.
    5. Bunn, Derek W. & Martoccia, Maria & Ochoa, Patricia & Kim, Haein & Ahn, Nam-Sung & Yoon, Yong-Beom, 2010. "Vertical integration and market power: A model-based analysis of restructuring in the Korean electricity market," Energy Policy, Elsevier, vol. 38(7), pages 3710-3716, July.
    6. Ahn, Nam-sung & Niemeyer, Victor, 2007. "Modeling market power in Korea's emerging power market," Energy Policy, Elsevier, vol. 35(2), pages 899-906, February.
    7. Azadeh, A. & Skandari, M.R. & Maleki-Shoja, B., 2010. "An integrated ant colony optimization approach to compare strategies of clearing market in electricity markets: Agent-based simulation," Energy Policy, Elsevier, vol. 38(10), pages 6307-6319, October.
    8. Liu, Zhen & Zhang, Xiliang & Lieu, Jenny, 2010. "Design of the incentive mechanism in electricity auction market based on the signaling game theory," Energy, Elsevier, vol. 35(4), pages 1813-1819.
    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. del Río, Pablo & Kiefer, Christoph P., 2023. "Academic research on renewable electricity auctions: Taking stock and looking forward," Energy Policy, Elsevier, vol. 173(C).
    2. Fang, Xichen & Guo, Hongye & Zhang, Xian & Wang, Xuanyuan & Chen, Qixin, 2022. "An efficient and incentive-compatible market design for energy storage participation," Applied Energy, Elsevier, vol. 311(C).
    3. Sadhan Gope & A. K. Goswami & P. K. Tiwari, 2020. "Transmission congestion management with integration of wind farm: a possible solution methodology for deregulated power market," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 287-296, April.
    4. Liu, Shuangquan & Yang, Qiang & Cai, Huaxiang & Yan, Minghui & Zhang, Maolin & Wu, Dianning & Xie, Mengfei, 2019. "Market reform of Yunnan electricity in southwestern China: Practice, challenges and implications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    5. Mojtaba Shivaie & Mohammad Kiani-Moghaddam & Philip D Weinsier, 2022. "Bilateral bidding strategy in joint day-ahead energy and reserve electricity markets considering techno-economic-environmental measures," Energy & Environment, , vol. 33(4), pages 696-727, June.
    6. Hans Ole Riddervold & Ellen Krohn Aasg{aa}rd & Lisa Haukaas & Magnus Korp{aa}s, 2021. "Internal hydro- and wind portfolio optimisation in real-time market operations," Papers 2102.10098, arXiv.org.
    7. Hao Zhen & Dongxiao Niu & Min Yu & Keke Wang & Yi Liang & Xiaomin Xu, 2020. "A Hybrid Deep Learning Model and Comparison for Wind Power Forecasting Considering Temporal-Spatial Feature Extraction," Sustainability, MDPI, vol. 12(22), pages 1-24, November.
    8. Riddervold, Hans Ole & Aasgård, Ellen Krohn & Haukaas, Lisa & Korpås, Magnus, 2021. "Internal hydro- and wind portfolio optimisation in real-time market operations," Renewable Energy, Elsevier, vol. 173(C), pages 675-687.
    9. Liao, Qi & Tu, Renfu & Zhang, Wan & Wang, Bohong & Liang, Yongtu & Zhang, Haoran, 2023. "Auction design for capacity allocation in the petroleum pipeline under fair opening," Energy, Elsevier, vol. 264(C).
    10. Jiménez, Alfredo Arcos & García Márquez, Fausto Pedro & Moraleda, Victoria Borja & Gómez Muñoz, Carlos Quiterio, 2019. "Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis," Renewable Energy, Elsevier, vol. 132(C), pages 1034-1048.
    11. Zhu, Xuehong & Zheng, Weihang & Zhang, Hongwei & Guo, Yaoqi, 2019. "Time-varying international market power for the Chinese iron ore markets," Resources Policy, Elsevier, vol. 64(C).
    12. Endemaño-Ventura, Lázaro & Serrano González, Javier & Roldán Fernández, Juan Manuel & Burgos Payán, Manuel & Riquelme Santos, Jesús Manuel, 2021. "Optimal energy bidding for renewable plants: A practical application to an actual wind farm in Spain," Renewable Energy, Elsevier, vol. 175(C), pages 1111-1126.
    13. Hosseini, Seyyed Ahmad & Toubeau, Jean-François & De Grève, Zacharie & Vallée, François, 2020. "An advanced day-ahead bidding strategy for wind power producers considering confidence level on the real-time reserve provision," Applied Energy, Elsevier, vol. 280(C).
    14. Khaloie, Hooman & Abdollahi, Amir & Shafie-khah, Miadreza & Anvari-Moghaddam, Amjad & Nojavan, Sayyad & Siano, Pierluigi & Catalão, João P.S., 2020. "Coordinated wind-thermal-energy storage offering strategy in energy and spinning reserve markets using a multi-stage model," Applied Energy, Elsevier, vol. 259(C).

    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. Li, Gong & Shi, Jing & Qu, Xiuli, 2011. "Modeling methods for GenCo bidding strategy optimization in the liberalized electricity spot market–A state-of-the-art review," Energy, Elsevier, vol. 36(8), pages 4686-4700.
    2. Jorge Barrientos Marin & Hector Gomez Marin, 2022. "Oligopoly and Collusion in the Colombian Electricity Market," International Journal of Energy Economics and Policy, Econjournals, vol. 12(3), pages 125-134, May.
    3. 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.
    4. Bubak, Baran, 2020. "تخمین نرخ بهینه عوارض تراکم برای آزادراه‏های ایران با استفاده از مدل قیمت‏گذاری ارزش [Estimation of Optimal Rate for Compact Tariffs in Highways across Iran applying the Value Pricing Methodology]," MPRA Paper 105490, University Library of Munich, Germany.
    5. Majid Al-Gwaiz & Xiuli Chao & Owen Q. Wu, 2017. "Understanding How Generation Flexibility and Renewable Energy Affect Power Market Competition," Manufacturing & Service Operations Management, INFORMS, vol. 19(1), pages 114-131, February.
    6. 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.
    7. Motamedi Sedeh, Omid & Ostadi, Bakhtiar, 2020. "Optimization of bidding strategy in the day-ahead market by consideration of seasonality trend of the market spot price," Energy Policy, Elsevier, vol. 145(C).
    8. Sarıca, Kemal & Kumbaroğlu, Gürkan & Or, Ilhan, 2012. "Modeling and analysis of a decentralized electricity market: An integrated simulation/optimization approach," Energy, Elsevier, vol. 44(1), pages 830-852.
    9. 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.
    10. Zou, Peng & Chen, Qixin & Xia, Qing & He, Chang & Kang, Chongqing, 2015. "Incentive compatible pool-based electricity market design and implementation: A Bayesian mechanism design approach," Applied Energy, Elsevier, vol. 158(C), pages 508-518.
    11. Dzikri Firmansyah Hakam, 2018. "Market Power Modelling in Electricity Market: A Critical Review," International Journal of Energy Economics and Policy, Econjournals, vol. 8(5), pages 347-356.
    12. Ghaninejad, Mousa, 2020. "عرضه، تقاضا، و پیشنهاد قیمت در بازار برق ایران [Supply, Demand, and Bidding in Iran’s Electricity Market]," MPRA Paper 105340, University Library of Munich, Germany.
    13. Silva, Ana R. & Pousinho, H.M.I. & Estanqueiro, Ana, 2022. "A multistage stochastic approach for the optimal bidding of variable renewable energy in the day-ahead, intraday and balancing markets," Energy, Elsevier, vol. 258(C).
    14. Holmberg, Pär & Newbery, David & Ralph, Daniel, 2013. "Supply function equilibria: Step functions and continuous representations," Journal of Economic Theory, Elsevier, vol. 148(4), pages 1509-1551.
    15. Wang, Meng & Infante Ferreira, Carlos A., 2017. "Absorption heat pump cycles with NH3 – ionic liquid working pairs," Applied Energy, Elsevier, vol. 204(C), pages 819-830.
    16. Rubin, Ofir D. & Babcock, Bruce A., 2013. "The impact of expansion of wind power capacity and pricing methods on the efficiency of deregulated electricity markets," Energy, Elsevier, vol. 59(C), pages 676-688.
    17. Yang, Yunpeng & Yang, Weixin & Chen, Hongmin & Li, Yin, 2020. "China’s energy whistleblowing and energy supervision policy: An evolutionary game perspective," Energy, Elsevier, vol. 213(C).
    18. Weimer-Jehle, Wolfgang & Buchgeister, Jens & Hauser, Wolfgang & Kosow, Hannah & Naegler, Tobias & Poganietz, Witold-Roger & Pregger, Thomas & Prehofer, Sigrid & von Recklinghausen, Andreas & Schippl, , 2016. "Context scenarios and their usage for the construction of socio-technical energy scenarios," Energy, Elsevier, vol. 111(C), pages 956-970.
    19. Banaei, Mohsen & Oloomi-Buygi, Majid & Zabetian-Hosseini, Seyed-Mahdi, 2018. "Strategic gaming of wind power producers joined with thermal units in electricity markets," Renewable Energy, Elsevier, vol. 115(C), pages 1067-1074.
    20. Bolle, Friedel & Grimm, Veronika & Ockenfels, Axel & del Pozo, Xavier, 2013. "An experiment on supply function competition," European Economic Review, Elsevier, vol. 63(C), pages 170-185.

    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:eee:renene:v:127:y:2018:i:c:p:575-586. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.