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Investing in Wind Energy Using Bi-Level Linear Fractional Programming

Author

Listed:
  • Adel F. Alrasheedi

    (Statistics and Operations Research Department, College of Science, King Saud University, Riyadh 11451, Saudi Arabia)

  • Ahmad M. Alshamrani

    (Statistics and Operations Research Department, College of Science, King Saud University, Riyadh 11451, Saudi Arabia)

  • Khalid A. Alnowibet

    (Statistics and Operations Research Department, College of Science, King Saud University, Riyadh 11451, Saudi Arabia)

Abstract

Investing in wind energy is a tool to reduce greenhouse gas emissions without negatively impacting the environment to accelerate progress towards global net zero. The objective of this study is to present a methodology for efficiently solving the wind energy investment problem, which aims to identify an optimal wind farm placement and capacity based on fractional programming (FP). This study adopts a bi-level approach whereby a private price-taker investor seeks to maximize its profit at the upper level. Given the optimal placement and capacity of the wind farm, the lower level aims to optimize a fractional objective function defined as the ratio of total generation cost to total wind power output. To solve this problem, the Charnes-Cooper transformation is applied to reformulate the initial bi-level problem with a fractional objective function in the lower-level problem as a bi-level problem with a fractional objective function in the upper-level problem. Afterward, using the primal-dual formulation, a single-level linear FP model is created, which can be solved via a sequence of mixed-integer linear programming (MILP). The presented technique is implemented on the IEEE 118-bus power system, where the results show the model can achieve the best performance in terms of wind power output.

Suggested Citation

  • Adel F. Alrasheedi & Ahmad M. Alshamrani & Khalid A. Alnowibet, 2023. "Investing in Wind Energy Using Bi-Level Linear Fractional Programming," Energies, MDPI, vol. 16(13), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:4952-:d:1179649
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    References listed on IDEAS

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    1. Lam, J.C.K. & Woo, C.K. & Kahrl, F. & Yu, W.K., 2013. "What moves wind energy development in China? Show me the money!," Applied Energy, Elsevier, vol. 105(C), pages 423-429.
    2. Zhou, Yuzhou & Zhai, Qiaozhu & Yuan, Wei & Wu, Jiang, 2021. "Capacity expansion planning for wind power and energy storage considering hourly robust transmission constrained unit commitment," Applied Energy, Elsevier, vol. 302(C).
    3. Zolfaghari, Saeed & Akbari, Tohid, 2018. "Bilevel transmission expansion planning using second-order cone programming considering wind investment," Energy, Elsevier, vol. 154(C), pages 455-465.
    4. Alishahi, E. & Moghaddam, M. Parsa & Sheikh-El-Eslami, M.K., 2012. "A system dynamics approach for investigating impacts of incentive mechanisms on wind power investment," Renewable Energy, Elsevier, vol. 37(1), pages 310-317.
    5. Sofia Spyridonidou & Dimitra G. Vagiona & Eva Loukogeorgaki, 2020. "Strategic Planning of Offshore Wind Farms in Greece," Sustainability, MDPI, vol. 12(3), pages 1-20, January.
    6. Li, Cun-bin & Lu, Gong-shu & Wu, Si, 2013. "The investment risk analysis of wind power project in China," Renewable Energy, Elsevier, vol. 50(C), pages 481-487.
    7. Zhu, Mengye & Qi, Ye & Belis, David & Lu, Jiaqi & Kerremans, Bart, 2019. "The China wind paradox: The role of state-owned enterprises in wind power investment versus wind curtailment," Energy Policy, Elsevier, vol. 127(C), pages 200-212.
    8. Baringo, L. & Conejo, A.J., 2013. "Correlated wind-power production and electric load scenarios for investment decisions," Applied Energy, Elsevier, vol. 101(C), pages 475-482.
    9. Xu, M. & Zhuan, X., 2013. "Optimal planning for wind power capacity in an electric power system," Renewable Energy, Elsevier, vol. 53(C), pages 280-286.
    10. Nikkhah, Saman & Rabiee, Abbas, 2018. "Optimal wind power generation investment, considering voltage stability of power systems," Renewable Energy, Elsevier, vol. 115(C), pages 308-325.
    11. Walter Gil-González & Oscar Danilo Montoya & Luis Fernando Grisales-Noreña & Alberto-Jesus Perea-Moreno & Quetzalcoatl Hernandez-Escobedo, 2020. "Optimal Placement and Sizing of Wind Generators in AC Grids Considering Reactive Power Capability and Wind Speed Curves," Sustainability, MDPI, vol. 12(7), pages 1-20, April.
    12. Baringo, L. & Conejo, A.J., 2011. "Wind power investment within a market environment," Applied Energy, Elsevier, vol. 88(9), pages 3239-3247.
    13. Werner Dinkelbach, 1967. "On Nonlinear Fractional Programming," Management Science, INFORMS, vol. 13(7), pages 492-498, March.
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