A method for reducing multi-modality in the wind farm layout optimization problem

Keywords

wind farm optimization, gradient-based, gradient-free, multimodal, continuation method, wind farm wakes, local optimum, global optimum

Abstract

This paper presents a process using an approach related to continuation optimization methods for reducing multi-modality in the wind farm layout optimization problem, referred to as Wake Expansion Continuation (WEC). The reduction in multi-modality is achieved by starting with an increased wake spread, while maintaining normal velocity deficits at the center of the wakes, and then reducing the wake spread for each of a series of optimization runs until the standard wake spread is used. Two optimization cases were tested, one with 16 turbines and one with 38 turbines, by optimizing from 200 different starting positions with a gradient-based method, a gradient-free method, and a gradient-based method using WEC. Results using WEC show a 4% mean optimized annual energy production (AEP) improvement compared to gradient-based optimization without WEC for both test cases. A gradient-free algorithm had a mean optimized AEP that was 1% higher than WEC for the 16-turbine case, but WEC resulted in a 10% higher mean optimized AEP compared to a gradient-free optimization method for the 38-turbine problem. These results are specific to the test cases and may not be generally representative.

Original Publication Citation

Thomas, J. J., and Ning, A., “A Method for Reducing Multi-Modality in the Wind Farm Layout Optimization Problem,” Journal of Physics: Conference Series, Vol. 1037, No. 042012, Milano, Italy, The Science of Making Torque from Wind, Jun. 2018. doi:10.1088/1742-6596/1037/4/042012

Document Type

Conference Paper

Publication Date

2018-6

Permanent URL

http://hdl.lib.byu.edu/1877/5016

Publisher

IOP Publishing

Language

English

College

Ira A. Fulton College of Engineering and Technology

Department

Mechanical Engineering

University Standing at Time of Publication

Assistant Professor

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