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Bilevel approach to wind-CSP day-ahead scheduling with spinning reserve under controllable degree of trust

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  • Pousinho, H.M.I.
  • Esteves, J.
  • Mendes, V.M.F.
  • Collares-Pereira, M.
  • Pereira Cabrita, C.

Abstract

This paper proposes a day-ahead schedule harmonization between wind power plants and concentrating solar thermal power plants having thermal energy storage. The negative correlation between wind power and solar power is computed and an artificial neural network method estimates the power. The schedule is carried out by a bilevel mathematical programming approach. The upper-level determines energy and spinning reserve schedule by the maximization of profit subject to all lower-level problems. Lower-level problems minimize the post-contingency power output. A controllable degree of trust on the schedule is introduced based on n – K security criterion for worst-case contingency. The approach uses duality theory and problem approximations for a conversion into an equivalent mixed-integer linear programming problem. A case study is presented to illustrate the effectiveness of the approach for power producers not only with transmission constraints, but also valuing safekeeping on the day-ahead schedule to ensure a degree trust on the satisfaction of compromises within electricity markets.

Suggested Citation

  • Pousinho, H.M.I. & Esteves, J. & Mendes, V.M.F. & Collares-Pereira, M. & Pereira Cabrita, C., 2016. "Bilevel approach to wind-CSP day-ahead scheduling with spinning reserve under controllable degree of trust," Renewable Energy, Elsevier, vol. 85(C), pages 917-927.
    • Handle: RePEc:eee:renene:v:85:y:2016:i:c:p:917-927
    DOI: 10.1016/j.renene.2015.07.022
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    Cited by:

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    2. Murphy, C.A. & Schleifer, A. & Eurek, K., 2021. "A taxonomy of systems that combine utility-scale renewable energy and energy storage technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    3. Li, Rong & Guo, Su & Yang, Yong & Liu, Deyou, 2020. "Optimal sizing of wind/ concentrated solar plant/ electric heater hybrid renewable energy system based on two-stage stochastic programming," Energy, Elsevier, vol. 209(C).
    4. Hoz, Jordi de la & Martín, Helena & Montalà, Montserrat & Matas, José & Guzman, Ramon, 2018. "Assessing the 2014 retroactive regulatory framework applied to the concentrating solar power systems in Spain," Applied Energy, Elsevier, vol. 212(C), pages 1377-1399.
    5. Keyif, Enes & Hornung, Michael & Zhu, Wanshan, 2020. "Optimal configurations and operations of concentrating solar power plants under new market trends," Applied Energy, Elsevier, vol. 270(C).
    6. Akbari, Ebrahim & Hooshmand, Rahmat-Allah & Gholipour, Mehdi & Parastegari, Moein, 2019. "Stochastic programming-based optimal bidding of compressed air energy storage with wind and thermal generation units in energy and reserve markets," Energy, Elsevier, vol. 171(C), pages 535-546.
    7. Sepasi, Saeed & Reihani, Ehsan & Howlader, Abdul M. & Roose, Leon R. & Matsuura, Marc M., 2017. "Very short term load forecasting of a distribution system with high PV penetration," Renewable Energy, Elsevier, vol. 106(C), pages 142-148.
    8. 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.
    9. Salkuti, Surender Reddy, 2019. "Day-ahead thermal and renewable power generation scheduling considering uncertainty," Renewable Energy, Elsevier, vol. 131(C), pages 956-965.
    10. 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.
    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.

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