IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v224y2024ipbp2-18.html
   My bibliography  Save this article

Tool for optimization of sale and storage of energy in wind farms

Author

Listed:
  • Celades, Eloy
  • Pérez, Emilio
  • Aparicio, Néstor
  • Peñarrocha-Alós, Ignacio

Abstract

In this work we address the problem of energy management in a wind farm supported by an Energy Storage System (ESS) that operates in an electricity market with six intraday sessions and with penalty policies for imbalances between commitments and the energy really injected. We face it through a cascade of model predictive controllers that also require the design of predictors for wind and electricity market price forecasts. The master controller is executed synchronously with the market sessions and decides the commitments. The slave controller is executed each hour and decides the energy that should be sold to minimize the economical penalties if the commitment is not achievable. Finally, a real-time controller decides how to manage the energy storage in the ESS to sell the desired energy when possible. We use historical real data for the design and validation of the approach and show its benefits. The results show that the cascade structure helps to adequately adapt the energy committed in the intraday market. We also obtain the necessary prices on batteries so that their use is profitable.

Suggested Citation

  • Celades, Eloy & Pérez, Emilio & Aparicio, Néstor & Peñarrocha-Alós, Ignacio, 2024. "Tool for optimization of sale and storage of energy in wind farms," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 224(PB), pages 2-18.
  • Handle: RePEc:eee:matcom:v:224:y:2024:i:pb:p:2-18
    DOI: 10.1016/j.matcom.2023.03.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475423001167
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.matcom.2023.03.010?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. Harun Or Rashid Howlader & Oludamilare Bode Adewuyi & Ying-Yi Hong & Paras Mandal & Ashraf Mohamed Hemeida & Tomonobu Senjyu, 2019. "Energy Storage System Analysis Review for Optimal Unit Commitment," Energies, MDPI, vol. 13(1), pages 1-21, December.
    2. Ayodele, T.R. & Ogunjuyigbe, A.S.O., 2015. "Mitigation of wind power intermittency: Storage technology approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 447-456.
    3. Ocker, Fabian & Jaenisch, Vincent, 2020. "The way towards European electricity intraday auctions – Status quo and future developments," Energy Policy, Elsevier, vol. 145(C).
    4. Katarzyna Maciejowska & Weronika Nitka & Tomasz Weron, 2019. "Day-Ahead vs. Intraday—Forecasting the Price Spread to Maximize Economic Benefits," Energies, MDPI, vol. 12(4), pages 1-15, February.
    5. Bourbon, R. & Ngueveu, S.U. & Roboam, X. & Sareni, B. & Turpin, C. & Hernandez-Torres, D., 2019. "Energy management optimization of a smart wind power plant comparing heuristic and linear programming methods," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 158(C), pages 418-431.
    Full references (including those not matched with items on IDEAS)

    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. Hakan Acaroğlu & Fausto Pedro García Márquez, 2021. "Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy," Energies, MDPI, vol. 14(22), pages 1-23, November.
    2. Ayodele, T.R. & Ogunjuyigbe, A.S.O. & Odigie, O. & Munda, J.L., 2018. "A multi-criteria GIS based model for wind farm site selection using interval type-2 fuzzy analytic hierarchy process: The case study of Nigeria," Applied Energy, Elsevier, vol. 228(C), pages 1853-1869.
    3. Uniejewski, Bartosz & Maciejowska, Katarzyna, 2023. "LASSO principal component averaging: A fully automated approach for point forecast pooling," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1839-1852.
    4. Shah Rukh Abbas & Syed Ali Abbas Kazmi & Muhammad Naqvi & Adeel Javed & Salman Raza Naqvi & Kafait Ullah & Tauseef-ur-Rehman Khan & Dong Ryeol Shin, 2020. "Impact Analysis of Large-Scale Wind Farms Integration in Weak Transmission Grid from Technical Perspectives," Energies, MDPI, vol. 13(20), pages 1-32, October.
    5. 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.
    6. Qin, Chao (Chris) & Loth, Eric, 2021. "Isothermal compressed wind energy storage using abandoned oil/gas wells or coal mines," Applied Energy, Elsevier, vol. 292(C).
    7. Tomasz Serafin & Bartosz Uniejewski & Rafał Weron, 2019. "Averaging Predictive Distributions Across Calibration Windows for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 12(13), pages 1-12, July.
    8. Ocker, Fabian & Jaenisch, Vincent, 2020. "The way towards European electricity intraday auctions – Status quo and future developments," Energy Policy, Elsevier, vol. 145(C).
    9. Thomas Kuppelwieser & David Wozabal, 2023. "Intraday power trading: toward an arms race in weather forecasting?," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 57-83, March.
    10. Ivana Kiprijanovska & Simon Stankoski & Igor Ilievski & Slobodan Jovanovski & Matjaž Gams & Hristijan Gjoreski, 2020. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning," Energies, MDPI, vol. 13(10), pages 1-29, May.
    11. Micha{l} Narajewski & Florian Ziel, 2020. "Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories," Papers 2005.01365, arXiv.org, revised Aug 2020.
    12. Jose R. Cedeño González & Juan J. Flores & Claudio R. Fuerte-Esquivel & Boris A. Moreno-Alcaide, 2020. "Nearest Neighbors Time Series Forecaster Based on Phase Space Reconstruction for Short-Term Load Forecasting," Energies, MDPI, vol. 13(20), pages 1-24, October.
    13. Katarzyna Maciejowska, 2022. "A portfolio management of a small RES utility with a Structural Vector Autoregressive model of German electricity markets," Papers 2205.00975, arXiv.org.
    14. Christopher Kath & Florian Ziel, 2020. "Optimal Order Execution in Intraday Markets: Minimizing Costs in Trade Trajectories," Papers 2009.07892, arXiv.org, revised Oct 2020.
    15. Li, Wei & Paraschiv, Florentina, 2022. "Modelling the evolution of wind and solar power infeed forecasts," Journal of Commodity Markets, Elsevier, vol. 25(C).
    16. Anuja Shaktawat & Shelly Vadhera, 2021. "Risk management of hydropower projects for sustainable development: a review," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(1), pages 45-76, January.
    17. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices," Energies, MDPI, vol. 13(14), pages 1-19, July.
    18. Bertsiou, M. & Feloni, E. & Karpouzos, D. & Baltas, E., 2018. "Water management and electricity output of a Hybrid Renewable Energy System (HRES) in Fournoi Island in Aegean Sea," Renewable Energy, Elsevier, vol. 118(C), pages 790-798.
    19. Joanna Janczura & Aleksandra Michalak, 2020. "Optimization of Electric Energy Sales Strategy Based on Probabilistic Forecasts," Energies, MDPI, vol. 13(5), pages 1-16, February.
    20. Grzegorz Marcjasz & Bartosz Uniejewski & Rafał Weron, 2020. "Beating the Naïve—Combining LASSO with Naïve Intraday Electricity Price Forecasts," Energies, MDPI, vol. 13(7), pages 1-16, April.

    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:matcom:v:224:y:2024:i:pb:p:2-18. 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/mathematics-and-computers-in-simulation/ .

    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.