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Evaluation of a data driven stochastic approach to optimize the participation of a wind and storage power plant in day-ahead and reserve markets

Author

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  • Crespo-Vazquez, Jose L.
  • Carrillo, C.
  • Diaz-Dorado, E.
  • Martinez-Lorenzo, Jose A.
  • Noor-E-Alam, Md

Abstract

A more comprehensive participation of renewable generators in the power market is being practiced in many countries. To add storage capability to these generators is also a major trend nowadays. Decisions concerning the participation in the power market have to be made several hours in advance, which is a key challenge for the renewable energy-based generators. In this work, a decision making framework under uncertainty for a wind and storage power plant participating in day-ahead and reserve markets is developed. Available wind energy and regulation requirements by the system operator are considered as uncertain parameters. To maximize the net income of this system under uncertainty, a two-stage convex stochastic model is developed. In order to create meaningful scenarios to be used in our proposed stochastic model, at first, a Long Short-Term Memory Recurrent Neural Network is designed to generate forecasts for regulation requirements. Univariate and multivariate clustering based on k-means algorithms are also used to generate influential scenarios from historical data. Several simulation experiments are carried out to evaluate the quality of the proposed stochastic approach using real-world wind farm data. Simulation result shows the validity and usefulness of the proposed data-driven approaches to handle the uncertainty in regulation requirements.

Suggested Citation

  • Crespo-Vazquez, Jose L. & Carrillo, C. & Diaz-Dorado, E. & Martinez-Lorenzo, Jose A. & Noor-E-Alam, Md, 2018. "Evaluation of a data driven stochastic approach to optimize the participation of a wind and storage power plant in day-ahead and reserve markets," Energy, Elsevier, vol. 156(C), pages 278-291.
    • Handle: RePEc:eee:energy:v:156:y:2018:i:c:p:278-291
    DOI: 10.1016/j.energy.2018.04.185
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    Citations

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    Cited by:

    1. 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).
    2. Jun Dong & Anyuan Fu & Yao Liu & Shilin Nie & Peiwen Yang & Linpeng Nie, 2019. "Two-Stage Optimization Model for Two-Side Daily Reserve Capacity of a Power System Considering Demand Response and Wind Power Consumption," Sustainability, MDPI, vol. 11(24), pages 1-22, December.
    3. Lak, Omidreza & Rastegar, Mohammad & Mohammadi, Mohammad & Shafiee, Soroush & Zareipour, Hamidreza, 2021. "Risk-constrained stochastic market operation strategies for wind power producers and energy storage systems," Energy, Elsevier, vol. 215(PB).
    4. Loukatou, Angeliki & Johnson, Paul & Howell, Sydney & Duck, Peter, 2021. "Optimal valuation of wind energy projects co-located with battery storage," Applied Energy, Elsevier, vol. 283(C).
    5. Crespo-Vazquez, Jose L. & Carrillo, C. & Diaz-Dorado, E. & Martinez-Lorenzo, Jose A. & Noor-E-Alam, Md., 2018. "A machine learning based stochastic optimization framework for a wind and storage power plant participating in energy pool market," Applied Energy, Elsevier, vol. 232(C), pages 341-357.
    6. Jurasz, Jakub & Kies, Alexander & Zajac, Pawel, 2020. "Synergetic operation of photovoltaic and hydro power stations on a day-ahead energy market," Energy, Elsevier, vol. 212(C).
    7. Ochoa, Tomás & Gil, Esteban & Angulo, Alejandro & Valle, Carlos, 2022. "Multi-agent deep reinforcement learning for efficient multi-timescale bidding of a hybrid power plant in day-ahead and real-time markets," Applied Energy, Elsevier, vol. 317(C).
    8. Rujie Zhu & Kaushik Das & Poul Ejnar Sørensen & Anca Daniela Hansen, 2023. "Optimal Participation of Co-Located Wind–Battery Plants in Sequential Electricity Markets," Energies, MDPI, vol. 16(15), pages 1-17, July.
    9. Al-Lawati, Razan A.H. & Crespo-Vazquez, Jose L. & Faiz, Tasnim Ibn & Fang, Xin & Noor-E-Alam, Md., 2021. "Two-stage stochastic optimization frameworks to aid in decision-making under uncertainty for variable resource generators participating in a sequential energy market," Applied Energy, Elsevier, vol. 292(C).

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