Investing in Wind Energy Using Bi-Level Linear Fractional Programming

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 identif...

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Bibliographic Details
Published in:Energies (Basel) 2023-07, Vol.16 (13), p.4952
Main Authors: Alrasheedi, Adel F., Alshamrani, Ahmad M., Alnowibet, Khalid A.
Format: Article
Language:English
Subjects:
Air pollution
Alternative energy sources
bi-level optimization
Buildings and facilities
Electricity
Emissions
Emissions control
Energy resources
Energy storage
Farm buildings
fractional programming
Green technology
Greenhouse effect
Greenhouse gases
Industrial plant emissions
Integer programming
Mathematical programming
Objective function
Planning
Power plants
primal-dual formulation
Profits
Renewable resources
stochastic programming
Systems stability
wind energy investment
Wind farms
Wind power
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en16134952