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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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