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Two-stage stochastic programming model for optimal scheduling of the wind-thermal-hydropower-pumped storage system considering the flexibility assessment

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  • Daneshvar, Mohammadreza
  • Mohammadi-Ivatloo, Behnam
  • Zare, Kazem
  • Asadi, Somayeh

Abstract

This paper presents a two-stage stochastic programming model for optimal scheduling of the wind-thermal-hydropower-pumped storage system considering the competitive interactions between the electrical generation units. The first stage is focused on the day-ahead scheduling of the thermal power plants while the balancing market dispatch is considered in the second stage using the stochastic producers and quick dispatch units. To capture the uncertainties associated with electricity demand and wind speed, Latin hyperbolic sampling and fast forward selection methods are applied for scenario generation and reduction processes, respectively. The flexibility of the studied system is analyzed considering the variations of the key parameters of thermal units and the transmission line’s capacity. For this work, the modified IEEE 14-bus standard test system integrated with the components of the studied system is selected as the case study. After solving the problem, the maximum potential of clean energy production units is used in comparison with fossil fuel-based units through the optimal scheduling of the wind-thermal-hydropower-pumped storage system. Given the numerical results, reducing the flexibility of the system by reducing the ramp up/down parameters, increasing the minimum up/down parameters, and reducing the transmission line capacity has been led to increase of 6.47%, 7.3%, and 9.77% in the total energy cost, respectively.

Suggested Citation

  • Daneshvar, Mohammadreza & Mohammadi-Ivatloo, Behnam & Zare, Kazem & Asadi, Somayeh, 2020. "Two-stage stochastic programming model for optimal scheduling of the wind-thermal-hydropower-pumped storage system considering the flexibility assessment," Energy, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:energy:v:193:y:2020:i:c:s0360544219323527
    DOI: 10.1016/j.energy.2019.116657
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    5. Zhang, Yi & Cheng, Chuntian & Cao, Rui & Li, Gang & Shen, Jianjian & Wu, Xinyu, 2021. "Multivariate probabilistic forecasting and its performance’s impacts on long-term dispatch of hydro-wind hybrid systems," Applied Energy, Elsevier, vol. 283(C).
    6. Feng, Chen & Zheng, Yuan & Li, Chaoshun & Mai, Zijun & Wu, Wei & Chen, Huixiang, 2021. "Cost advantage of adjustable-speed pumped storage unit for daily operation in distributed hybrid system," Renewable Energy, Elsevier, vol. 176(C), pages 1-10.
    7. Xu, Jiuping & Liu, Tingting, 2020. "Technological paradigm-based approaches towards challenges and policy shifts for sustainable wind energy development," Energy Policy, Elsevier, vol. 142(C).
    8. Zhang, Gaohang & Li, Fengting & Wang, Sen & Yin, Chunya, 2023. "Robust low-carbon energy and reserve scheduling considering operational risk and flexibility improvement," Energy, Elsevier, vol. 284(C).
    9. Zheng, Lingwei & Zhou, Xingqiu & Qiu, Qi & Yang, Lan, 2020. "Day-ahead optimal dispatch of an integrated energy system considering time-frequency characteristics of renewable energy source output," Energy, Elsevier, vol. 209(C).
    10. Yahia, Zakaria & Pradhan, Anup, 2020. "Simultaneous and sequential stochastic optimization approaches for pumped storage plant scheduling with random breakdowns," Energy, Elsevier, vol. 204(C).
    11. de Oliveira, Glauber Cardoso & Bertone, Edoardo & Stewart, Rodney A., 2022. "Challenges, opportunities, and strategies for undertaking integrated precinct-scale energy–water system planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    12. Zhou, Yuekuan, 2023. "Sustainable energy sharing districts with electrochemical battery degradation in design, planning, operation and multi-objective optimisation," Renewable Energy, Elsevier, vol. 202(C), pages 1324-1341.
    13. Zhang, M.Y. & Chen, J.J. & Yang, Z.J. & Peng, K. & Zhao, Y.L. & Zhang, X.H., 2021. "Stochastic day-ahead scheduling of irrigation system integrated agricultural microgrid with pumped storage and uncertain wind power," Energy, Elsevier, vol. 237(C).

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