Exact solution approaches for a class of bilevel fractional programs

Yang, Jing; Shi, Xueyu; Prokopyev, Oleg A.

doi:10.1007/s11590-022-01869-7

Cited by 2 publications

(2 citation statements)

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“…Since all the leader's (upper-level's) variables are binary and the follower's (lowerlevel's) variables are continuous, the solution technique proposed in [37] to solve the bilevel linear FP can be applied. In this context, the compact form of the wind farm investment problem can be written as: It is worth mentioning that, unlike the current bi-level models where either KKT conditions or primal-dual formulation is used to transform the problem into a single level, these methods cannot be directly applied to the formulated bi-level model due to the non-convexity of the lower-level problem.…”

Section: Solution Strategymentioning

confidence: 99%

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Investing in Wind Energy Using Bi-Level Linear Fractional Programming

2023

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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.

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Section: Solution Strategymentioning

confidence: 99%

Section: Solution Strategymentioning

confidence: 99%