Optimization of wind plant layouts using an adjoint approach
Using adjoint optimization and three-dimensional steady-state Reynolds-averaged Navier–Stokes (RANS) simulations, we present a new gradient-based approach for optimally siting wind turbines within utility-scale wind plants. By solving the adjoint equations of the flow model, the gradie...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-03-01
|
| Series: | Wind Energy Science |
| Online Access: | https://www.wind-energ-sci.net/2/115/2017/wes-2-115-2017.pdf |
| _version_ | 1819242249877192704 |
|---|---|
| author | R. N. King R. N. King K. Dykes P. Graf P. E. Hamlington |
| author_facet | R. N. King R. N. King K. Dykes P. Graf P. E. Hamlington |
| author_sort | R. N. King |
| collection | DOAJ |
| description | Using adjoint optimization and three-dimensional steady-state
Reynolds-averaged Navier–Stokes (RANS) simulations, we present a new
gradient-based approach for optimally siting wind turbines within
utility-scale wind plants. By solving the adjoint equations of the flow
model, the gradients needed for optimization are found at a cost that is
independent of the number of control variables, thereby permitting
optimization of large wind plants with many turbine locations. Moreover,
compared to the common approach of superimposing prescribed wake deficits
onto linearized flow models, the computational efficiency of the adjoint
approach allows the use of higher-fidelity RANS flow models which can capture
nonlinear turbulent flow physics within a wind plant. The steady-state RANS
flow model is implemented in the Python finite-element package
<tt>FEniCS</tt> and the derivation and solution of the discrete adjoint
equations are automated within the <tt>dolfin-adjoint</tt> framework.
Gradient-based optimization of wind turbine locations is demonstrated for
idealized test cases that reveal new optimization heuristics such as
rotational symmetry, local speedups, and nonlinear wake curvature effects.
Layout optimization is also demonstrated on more complex wind rose shapes,
including a full annual energy production (AEP) layout optimization over 36
inflow directions and 5 wind speed bins. |
| first_indexed | 2024-12-23T14:36:49Z |
| format | Article |
| id | doaj.art-ec1c72ed35194bb48698bc8e4b3e3948 |
| institution | Directory Open Access Journal |
| issn | 2366-7443 2366-7451 |
| language | English |
| last_indexed | 2024-12-23T14:36:49Z |
| publishDate | 2017-03-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Wind Energy Science |
| spelling | doaj.art-ec1c72ed35194bb48698bc8e4b3e39482022-12-21T17:43:21ZengCopernicus PublicationsWind Energy Science2366-74432366-74512017-03-01211513110.5194/wes-2-115-2017Optimization of wind plant layouts using an adjoint approachR. N. King0R. N. King1K. Dykes2P. Graf3P. E. Hamlington4University of Colorado, Boulder, Colorado, USANational Renewable Energy Laboratory, Golden, Colorado, USANational Renewable Energy Laboratory, Golden, Colorado, USANational Renewable Energy Laboratory, Golden, Colorado, USAUniversity of Colorado, Boulder, Colorado, USAUsing adjoint optimization and three-dimensional steady-state Reynolds-averaged Navier–Stokes (RANS) simulations, we present a new gradient-based approach for optimally siting wind turbines within utility-scale wind plants. By solving the adjoint equations of the flow model, the gradients needed for optimization are found at a cost that is independent of the number of control variables, thereby permitting optimization of large wind plants with many turbine locations. Moreover, compared to the common approach of superimposing prescribed wake deficits onto linearized flow models, the computational efficiency of the adjoint approach allows the use of higher-fidelity RANS flow models which can capture nonlinear turbulent flow physics within a wind plant. The steady-state RANS flow model is implemented in the Python finite-element package <tt>FEniCS</tt> and the derivation and solution of the discrete adjoint equations are automated within the <tt>dolfin-adjoint</tt> framework. Gradient-based optimization of wind turbine locations is demonstrated for idealized test cases that reveal new optimization heuristics such as rotational symmetry, local speedups, and nonlinear wake curvature effects. Layout optimization is also demonstrated on more complex wind rose shapes, including a full annual energy production (AEP) layout optimization over 36 inflow directions and 5 wind speed bins.https://www.wind-energ-sci.net/2/115/2017/wes-2-115-2017.pdf |
| spellingShingle | R. N. King R. N. King K. Dykes P. Graf P. E. Hamlington Optimization of wind plant layouts using an adjoint approach Wind Energy Science |
| title | Optimization of wind plant layouts using an adjoint approach |
| title_full | Optimization of wind plant layouts using an adjoint approach |
| title_fullStr | Optimization of wind plant layouts using an adjoint approach |
| title_full_unstemmed | Optimization of wind plant layouts using an adjoint approach |
| title_short | Optimization of wind plant layouts using an adjoint approach |
| title_sort | optimization of wind plant layouts using an adjoint approach |
| url | https://www.wind-energ-sci.net/2/115/2017/wes-2-115-2017.pdf |
| work_keys_str_mv | AT rnking optimizationofwindplantlayoutsusinganadjointapproach AT rnking optimizationofwindplantlayoutsusinganadjointapproach AT kdykes optimizationofwindplantlayoutsusinganadjointapproach AT pgraf optimizationofwindplantlayoutsusinganadjointapproach AT pehamlington optimizationofwindplantlayoutsusinganadjointapproach |