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Risk-averse profit-based optimal operation strategy of a combined wind farm–cascade hydro system in an electricity market

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  • Moghaddam, Iman Gerami
  • Nick, Mostafa
  • Fallahi, Farhad
  • Sanei, Mohsen
  • Mortazavi, Saeid

Abstract

There is a trend toward direct participation of wind farms in electricity markets. However, wind power is inherently intermittent and cannot be accurately predicted even in short time; thus increasing the imbalance costs paid by wind farm owners. To cope with these problems, some techniques have been proposed in literature including wind farm coupling to hydro units, energy storage facilities, and constructing a virtual power plant (VPP). This paper presents a stochastic profit-based model for day-ahead operational planning of a combined wind farm–cascade hydro system. The generation company (GenCo) that owns the VPP considers a portion of its hydro plants capacity to compensate the wind power forecast errors. The proposed optimization problem is a mixed integer linear programming (MILP), formulated as a two-stage stochastic programming model. The day-ahead scheduling is a here and now decision and the optimal operations of facilities are resources variables. In order to protect the GenCo against low price scenarios and wind power variation, the conditional value at risk (CVaR) is used as the risk aversion criterion. The proposed model is successfully applied to a real case study and the results are presented and discussed. The results are illustrated varying in the risk aversion level and the penalty coefficients for negative/positive imbalances. It is shown that the bidding strategy of the GenCo varies significantly depending on the chosen penalty market mechanism.

Suggested Citation

  • Moghaddam, Iman Gerami & Nick, Mostafa & Fallahi, Farhad & Sanei, Mohsen & Mortazavi, Saeid, 2013. "Risk-averse profit-based optimal operation strategy of a combined wind farm–cascade hydro system in an electricity market," Renewable Energy, Elsevier, vol. 55(C), pages 252-259.
    • Handle: RePEc:eee:renene:v:55:y:2013:i:c:p:252-259
    DOI: 10.1016/j.renene.2012.12.023
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    5. Jin, Xiaoyu & Liu, Benxi & Liao, Shengli & Cheng, Chuntian & Li, Gang & Liu, Lingjun, 2022. "Impacts of different wind and solar power penetrations on cascade hydroplants operation," Renewable Energy, Elsevier, vol. 182(C), pages 227-244.
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    8. Naval, Natalia & Yusta, Jose M., 2021. "Virtual power plant models and electricity markets - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    9. Liu, Yangyang & Jiang, Chuanwen & Shen, Jingshuang & Hu, Jiakai & Luo, Yifan, 2015. "Coordination of hydro units with wind power generation based on RAROC," Renewable Energy, Elsevier, vol. 80(C), pages 783-792.
    10. Ghasemi, Ahmad & Mortazavi, Seyed Saeidollah & Mashhour, Elaheh, 2016. "Hourly demand response and battery energy storage for imbalance reduction of smart distribution company embedded with electric vehicles and wind farms," Renewable Energy, Elsevier, vol. 85(C), pages 124-136.
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    12. Yao Wang & Yan Lu & Liwei Ju & Ting Wang & Qingkun Tan & Jiawei Wang & Zhongfu Tan, 2019. "A Multi-objective Scheduling Optimization Model for Hybrid Energy System Connected with Wind-Photovoltaic-Conventional Gas Turbines, CHP Considering Heating Storage Mechanism," Energies, MDPI, vol. 12(3), pages 1-28, January.
    13. 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).
    14. Tajeddini, Mohammad Amin & Rahimi-Kian, Ashkan & Soroudi, Alireza, 2014. "Risk averse optimal operation of a virtual power plant using two stage stochastic programming," Energy, Elsevier, vol. 73(C), pages 958-967.
    15. Zamani, Ali Ghahgharaee & Zakariazadeh, Alireza & Jadid, Shahram, 2016. "Day-ahead resource scheduling of a renewable energy based virtual power plant," Applied Energy, Elsevier, vol. 169(C), pages 324-340.
    16. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2017. "A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 341-363.
    17. 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.
    18. Kim, Joon-Hyung & Cho, Bo-Min & Kim, Sung & Kim, Jin-Woo & Suh, Jun-Won & Choi, Young-Seok & Kanemoto, Toshiaki & Kim, Jin-Hyuk, 2017. "Design technique to improve the energy efficiency of a counter-rotating type pump-turbine," Renewable Energy, Elsevier, vol. 101(C), pages 647-659.
    19. Moreno, Blanca & Díaz, Guzmán, 2019. "The impact of virtual power plant technology composition on wholesale electricity prices: A comparative study of some European Union electricity markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 99(C), pages 100-108.
    20. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2016. "Stochastic profit-based scheduling of industrial virtual power plant using the best demand response strategy," Applied Energy, Elsevier, vol. 164(C), pages 590-606.
    21. Fan, Zhi-Ping & Cai, Siqin & Guo, Dongliang & Xu, Bo, 2022. "Facing the uncertainty of renewable energy production: Production decisions of a power plant with different risk attitudes," Renewable Energy, Elsevier, vol. 199(C), pages 1237-1247.

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