Open Access

ARTICLE

A Bi-Level Optimization Model and Hybrid Evolutionary Algorithm for Wind Farm Layout with Different Turbine Types

Erping Song^1,*, Zipin Yao²

1 School of Mathematics and Physics, Qinghai University, Xining, 810016, China
2 School of Energy and Electrical Engineering, Qinghai University, Xining, 810016, China

* Corresponding Author: Erping Song. Email:

Energy Engineering 2025, 122(12), 5129-5147. https://doi.org/10.32604/ee.2025.063827

Received 25 January 2025; Accepted 14 April 2025; Issue published 27 November 2025

Abstract

Wind farm layout optimization is a critical challenge in renewable energy development, especially in regions with complex terrain. Micro-siting of wind turbines has a significant impact on the overall efficiency and economic viability of wind farm, where the wake effect, wind speed, types of wind turbines, etc., have an impact on the output power of the wind farm. To solve the optimization problem of wind farm layout under complex terrain conditions, this paper proposes wind turbine layout optimization using different types of wind turbines, the aim is to reduce the influence of the wake effect and maximize economic benefits. The linear wake model is used for wake flow calculation over complex terrain. Minimizing the unit energy cost is taken as the objective function, considering that the objective function is affected by cost and output power, which influence each other. The cost function includes construction cost, installation cost, maintenance cost, etc. Therefore, a bi-level constrained optimization model is established, in which the upper-level objective function is to minimize the unit energy cost, and the lower-level objective function is to maximize the output power. Then, a hybrid evolutionary algorithm is designed according to the characteristics of the decision variables. The improved genetic algorithm and differential evolution are used to optimize the upper-level and lower-level objective functions, respectively, these evolutionary operations search for the optimal solution as much as possible. Finally, taking the roughness of different terrain, wind farms of different scales and different types of wind turbines as research scenarios, the optimal deployment is solved by using the algorithm in this paper, and four algorithms are compared to verify the effectiveness of the proposed algorithm.

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Keywords

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