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

Conference · Sun Jan 06 00:00:00 EST 2019

DOI:https://doi.org/10.2514/6.2019-0540· OSTI ID:1547238

Baker, Nicholas F. ^[1]; Stanley, Andrew P. J. ^[1]; Thomas, Jared J. ^[1]; Ning, Andrew ^[1]; Dykes, Katherine L ^[2]

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1. Brigham Young University
2. National Renewable Energy Laboratory (NREL), Golden, CO (United States)

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.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 1547238
• Report Number(s): NREL/CP-5000-74480
• Resource Relation: Conference: Presented at the AIAA SciTech 2019 Forum, 7-11 January 2019, San Diego, California
• Country of Publication: United States
• Language: English

Related Subjects

17 WIND ENERGY
optimization
wind power plant layout
wake model
algorithm selection