IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v203y2020ics0360544220310021.html
   My bibliography  Save this article

Look-ahead bidding strategy for concentrating solar power plants with wind farms

Author

Listed:
  • Fang, Yuchen
  • Zhao, Shuqiang

Abstract

The concentrating solar power (CSP) plant with the thermal energy storage (TES) is one of the most effective methods to solve the intermittent characteristics of solar energy. CSP plants combined with wind farms could provide continuous, stable power generation and reduce the uncertainty of the wind power. In this paper, a look-ahead technique is proposed to optimize the bidding strategy for a joint system of CSP plants with wind farms considering both the day-ahead and the following day. It is assumed that the CSP plant participates in the ancillary service (AS) market bidding while providing the reserve capacity for the wind farm to counteract the output fluctuations. As the price-taker, the joint system has different levels of attention to the market price and renewable energy output. Therefore, the scenario set and the chance-constrained programming (CCP) is used to describe its uncertainty, respectively. Besides, in order to further enhance the flexibility and maximize the economic revenues of the system, an electric heater (EH) is added to the model. The joint operator can adjust the final heat storage level to balance profit opportunities during the current market window against potential opportunities in the subsequent market window. Finally, we compare the results of different bidding models to demonstrate the effectiveness of the proposed model and the advantages of the joint system.

Suggested Citation

  • Fang, Yuchen & Zhao, Shuqiang, 2020. "Look-ahead bidding strategy for concentrating solar power plants with wind farms," Energy, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:energy:v:203:y:2020:i:c:s0360544220310021
    DOI: 10.1016/j.energy.2020.117895
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220310021
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.117895?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. Gomes, I.L.R. & Pousinho, H.M.I. & Melício, R. & Mendes, V.M.F., 2017. "Stochastic coordination of joint wind and photovoltaic systems with energy storage in day-ahead market," Energy, Elsevier, vol. 124(C), pages 310-320.
    2. Martinek, Janna & Jorgenson, Jennie & Mehos, Mark & Denholm, Paul, 2018. "A comparison of price-taker and production cost models for determining system value, revenue, and scheduling of concentrating solar power plants," Applied Energy, Elsevier, vol. 231(C), pages 854-865.
    3. Pousinho, H.M.I. & Silva, H. & Mendes, V.M.F. & Collares-Pereira, M. & Pereira Cabrita, C., 2014. "Self-scheduling for energy and spinning reserve of wind/CSP plants by a MILP approach," Energy, Elsevier, vol. 78(C), pages 524-534.
    4. A. Charnes & W. W. Cooper, 1959. "Chance-Constrained Programming," Management Science, INFORMS, vol. 6(1), pages 73-79, October.
    5. Dominguez, R. & Baringo, L. & Conejo, A.J., 2012. "Optimal offering strategy for a concentrating solar power plant," Applied Energy, Elsevier, vol. 98(C), pages 316-325.
    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. Sun, Shitong & Kazemi-Razi, S. Mahdi & Kaigutha, Lisa G. & Marzband, Mousa & Nafisi, Hamed & Al-Sumaiti, Ameena Saad, 2022. "Day-ahead offering strategy in the market for concentrating solar power considering thermoelectric decoupling by a compressed air energy storage," Applied Energy, Elsevier, vol. 305(C).
    2. del Río, Pablo & Kiefer, Christoph P., 2023. "Academic research on renewable electricity auctions: Taking stock and looking forward," Energy Policy, Elsevier, vol. 173(C).
    3. Abiodun, Kehinde & Hood, Karoline & Cox, John L. & Newman, Alexandra M. & Zolan, Alex J., 2023. "The value of concentrating solar power in ancillary services markets," Applied Energy, Elsevier, vol. 334(C).
    4. Khaloie, Hooman & Anvari-Moghaddam, Amjad & Contreras, Javier & Siano, Pierluigi, 2021. "Risk-involved optimal operating strategy of a hybrid power generation company: A mixed interval-CVaR model," Energy, Elsevier, vol. 232(C).
    5. Feng, Chenjia & Shao, Chengcheng & Wang, Xifan, 2021. "CSP clustering in unit commitment for power system production cost modeling," Renewable Energy, Elsevier, vol. 168(C), pages 1217-1228.
    6. Xiao, Xiangsheng & Wang, JianXiao & Hill, David J., 2022. "Impact of Large-scale concentrated solar power on energy and auxiliary markets," Applied Energy, Elsevier, vol. 318(C).
    7. Lu, Xiaohui & Yang, Yang & Wang, Peifang & Fan, Yiming & Yu, Fangzhong & Zafetti, Nicholas, 2021. "A new converged Emperor Penguin Optimizer for biding strategy in a day-ahead deregulated market clearing price: A case study in China," Energy, Elsevier, vol. 227(C).
    8. Zhao, Yuxuan & Liu, Shengyuan & Lin, Zhenzhi & Wen, Fushuan & Ding, Yi, 2021. "Coordinated scheduling strategy for an integrated system with concentrating solar power plants and solar prosumers considering thermal interactions and demand flexibilities," Applied Energy, Elsevier, vol. 304(C).
    9. Jun Dong & Dongran Liu & Xihao Dou & Bo Li & Shiyao Lv & Yuzheng Jiang & Tongtao Ma, 2021. "Key Issues and Technical Applications in the Study of Power Markets as the System Adapts to the New Power System in China," Sustainability, MDPI, vol. 13(23), pages 1-29, December.

    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. Vasallo, Manuel Jesús & Cojocaru, Emilian Gelu & Gegúndez, Manuel Emilio & Marín, Diego, 2021. "Application of data-based solar field models to optimal generation scheduling in concentrating solar power plants," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 1130-1149.
    2. Xiao, Xiangsheng & Wang, JianXiao & Hill, David J., 2022. "Impact of Large-scale concentrated solar power on energy and auxiliary markets," Applied Energy, Elsevier, vol. 318(C).
    3. Abiodun, Kehinde & Hood, Karoline & Cox, John L. & Newman, Alexandra M. & Zolan, Alex J., 2023. "The value of concentrating solar power in ancillary services markets," Applied Energy, Elsevier, vol. 334(C).
    4. Coronas, Sergio & Martín, Helena & de la Hoz, Jordi & García de Vicuña, Luis & Castilla, Miguel, 2021. "MONTE-CARLO probabilistic valuation of concentrated solar power systems in Spain under the 2014 retroactive regulatory framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    5. Vasallo, Manuel Jesús & Bravo, José Manuel, 2016. "A MPC approach for optimal generation scheduling in CSP plants," Applied Energy, Elsevier, vol. 165(C), pages 357-370.
    6. Kahvecioğlu, Gökçe & Morton, David P. & Wagner, Michael J., 2022. "Dispatch optimization of a concentrating solar power system under uncertain solar irradiance and energy prices," Applied Energy, Elsevier, vol. 326(C).
    7. Xiong, Houbo & Yan, Mingyu & Guo, Chuangxin & Ding, Yi & Zhou, Yue, 2023. "DP based multi-stage ARO for coordinated scheduling of CSP and wind energy with tractable storage scheme: Tight formulation and solution technique," Applied Energy, Elsevier, vol. 333(C).
    8. Gao, Xian & Knueven, Bernard & Siirola, John D. & Miller, David C. & Dowling, Alexander W., 2022. "Multiscale simulation of integrated energy system and electricity market interactions," Applied Energy, Elsevier, vol. 316(C).
    9. Martinek, Janna & Jorgenson, Jennie & Mehos, Mark & Denholm, Paul, 2018. "A comparison of price-taker and production cost models for determining system value, revenue, and scheduling of concentrating solar power plants," Applied Energy, Elsevier, vol. 231(C), pages 854-865.
    10. Cojocaru, Emilian Gelu & Bravo, José Manuel & Vasallo, Manuel Jesús & Santos, Diego Marín, 2019. "Optimal scheduling in concentrating solar power plants oriented to low generation cycling," Renewable Energy, Elsevier, vol. 135(C), pages 789-799.
    11. Dowling, Alexander W. & Zheng, Tian & Zavala, Victor M., 2017. "Economic assessment of concentrated solar power technologies: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1019-1032.
    12. Rodríguez, I. & Pérez-Segarra, C.D. & Lehmkuhl, O. & Oliva, A., 2013. "Modular object-oriented methodology for the resolution of molten salt storage tanks for CSP plants," Applied Energy, Elsevier, vol. 109(C), pages 402-414.
    13. Azarnoosh Kafi & Behrouz Daneshian & Mohsen Rostamy-Malkhalifeh, 2021. "Forecasting the confidence interval of efficiency in fuzzy DEA," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(1), pages 41-59.
    14. Sander Claeys & Marta Vanin & Frederik Geth & Geert Deconinck, 2021. "Applications of optimization models for electricity distribution networks," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 10(5), September.
    15. Scott, James & Ho, William & Dey, Prasanta K. & Talluri, Srinivas, 2015. "A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments," International Journal of Production Economics, Elsevier, vol. 166(C), pages 226-237.
    16. Minjiao Zhang & Simge Küçükyavuz & Saumya Goel, 2014. "A Branch-and-Cut Method for Dynamic Decision Making Under Joint Chance Constraints," Management Science, INFORMS, vol. 60(5), pages 1317-1333, May.
    17. Hermann Held, 2019. "Cost Risk Analysis: Dynamically Consistent Decision-Making under Climate Targets," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 72(1), pages 247-261, January.
    18. Shen, Feifei & Zhao, Liang & Wang, Meihong & Du, Wenli & Qian, Feng, 2022. "Data-driven adaptive robust optimization for energy systems in ethylene plant under demand uncertainty," Applied Energy, Elsevier, vol. 307(C).
    19. Baringo, Luis & Boffino, Luigi & Oggioni, Giorgia, 2020. "Robust expansion planning of a distribution system with electric vehicles, storage and renewable units," Applied Energy, Elsevier, vol. 265(C).
    20. Wu, Desheng (Dash) & Lee, Chi-Guhn, 2010. "Stochastic DEA with ordinal data applied to a multi-attribute pricing problem," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1679-1688, December.

    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:energy:v:203:y:2020:i:c:s0360544220310021. 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/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.