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Title: Best Practices for Wake Model and Optimization Algorithm Selection in Wind Farm Layout Optimization: Preprint

Abstract

This paper presents the results of two case studies regarding the wind farm layout optimization problem. We asked a general audience to take part in the studies that we designed, and nine individuals participated. Case study 1 considered variations in optimization strategies for a given simple Gaussian wake model. Participants were provided with a wake model that outputs annual energy production (AEP) for an input set of wind turbine locations. Participants used an optimization method of their choosing to find an optimal wind farm layout. Case study 2 looked at trade-offs in performance resulting from variation in both physics model and optimization strategy. For case study 2, participants calculated AEP using a wake model of their choice while also implementing their chosen optimization method. Participants then used their wake model to calculate the AEP all other participant submitted turbine configurations produce for cross-comparison results. for optimized turbine locations were then cross-compared by recalculating the AEP using every other participant's wake model. Results for case study 1 show that the best optimal wind farm layouts in this study were achieved by participants who used gradient-based optimization methods. A front-runner emerged with the Sparse Nonlinear OPTimizer plus Wake Expansion Continuation (SNOPTplusWEC) optimizationmore » method, which consistently discovered a higher AEP for each scenario. Results for case study 2 show that for small wind farms with few turbines, turbine placement on the wind farm boundary is superior. Conclusions for case study 2 were drawn from participant cross-comparison of results.« less

Authors:
 [1];  [1];  [1];  [2];  [1]
  1. Brigham Young University
  2. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
OSTI Identifier:
1513186
Report Number(s):
NREL/CP-5000-72935
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at American Institute of Aeronautics and Astronautics SciTech Forum, 7-11 January 2019, San Diego, California
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; optimization; wind power plant layout; wake model; algorithm selection

Citation Formats

Baker, Nicholas F, Stanley, Andrew PJ, Thomas, Jared J, Dykes, Katherine L, and Ning, Andrew. Best Practices for Wake Model and Optimization Algorithm Selection in Wind Farm Layout Optimization: Preprint. United States: N. p., 2019. Web. doi:10.2514/6.2019-0540.c1.
Baker, Nicholas F, Stanley, Andrew PJ, Thomas, Jared J, Dykes, Katherine L, & Ning, Andrew. Best Practices for Wake Model and Optimization Algorithm Selection in Wind Farm Layout Optimization: Preprint. United States. doi:10.2514/6.2019-0540.c1.
Baker, Nicholas F, Stanley, Andrew PJ, Thomas, Jared J, Dykes, Katherine L, and Ning, Andrew. Thu . "Best Practices for Wake Model and Optimization Algorithm Selection in Wind Farm Layout Optimization: Preprint". United States. doi:10.2514/6.2019-0540.c1. https://www.osti.gov/servlets/purl/1513186.
@article{osti_1513186,
title = {Best Practices for Wake Model and Optimization Algorithm Selection in Wind Farm Layout Optimization: Preprint},
author = {Baker, Nicholas F and Stanley, Andrew PJ and Thomas, Jared J and Dykes, Katherine L and Ning, Andrew},
abstractNote = {This paper presents the results of two case studies regarding the wind farm layout optimization problem. We asked a general audience to take part in the studies that we designed, and nine individuals participated. Case study 1 considered variations in optimization strategies for a given simple Gaussian wake model. Participants were provided with a wake model that outputs annual energy production (AEP) for an input set of wind turbine locations. Participants used an optimization method of their choosing to find an optimal wind farm layout. Case study 2 looked at trade-offs in performance resulting from variation in both physics model and optimization strategy. For case study 2, participants calculated AEP using a wake model of their choice while also implementing their chosen optimization method. Participants then used their wake model to calculate the AEP all other participant submitted turbine configurations produce for cross-comparison results. for optimized turbine locations were then cross-compared by recalculating the AEP using every other participant's wake model. Results for case study 1 show that the best optimal wind farm layouts in this study were achieved by participants who used gradient-based optimization methods. A front-runner emerged with the Sparse Nonlinear OPTimizer plus Wake Expansion Continuation (SNOPTplusWEC) optimization method, which consistently discovered a higher AEP for each scenario. Results for case study 2 show that for small wind farms with few turbines, turbine placement on the wind farm boundary is superior. Conclusions for case study 2 were drawn from participant cross-comparison of results.},
doi = {10.2514/6.2019-0540.c1},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {5}
}

Conference:
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