Optimization of wind plant layouts using an adjoint approach
Abstract
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 FEniCS and the derivation and solution of the discrete adjoint equations are automated within the dolfin-adjoint 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.
- Authors:
- Publication Date:
- Research Org.:
- National Renewable Energy Laboratory (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:
- 1346445
- Alternate Identifier(s):
- OSTI ID: 1340643
- Report Number(s):
- NREL/JA-2C00-67162
Journal ID: ISSN 2366-7451
- Grant/Contract Number:
- UGA-0-41026-70; AC36-08GO28308
- Resource Type:
- Published Article
- Journal Name:
- Wind Energy Science (Online)
- Additional Journal Information:
- Journal Name: Wind Energy Science (Online) Journal Volume: 2 Journal Issue: 1; Journal ID: ISSN 2366-7451
- Publisher:
- European Wind Energy Association - Copernicus
- Country of Publication:
- Germany
- Language:
- English
- Subject:
- 17 WIND ENERGY; wind turbines; siting; utility-scale; adjoint optimization; RANS simulations
Citation Formats
King, Ryan N., Dykes, Katherine, Graf, Peter, and Hamlington, Peter E. Optimization of wind plant layouts using an adjoint approach. Germany: N. p., 2017.
Web. doi:10.5194/wes-2-115-2017.
King, Ryan N., Dykes, Katherine, Graf, Peter, & Hamlington, Peter E. Optimization of wind plant layouts using an adjoint approach. Germany. https://doi.org/10.5194/wes-2-115-2017
King, Ryan N., Dykes, Katherine, Graf, Peter, and Hamlington, Peter E. Fri .
"Optimization of wind plant layouts using an adjoint approach". Germany. https://doi.org/10.5194/wes-2-115-2017.
@article{osti_1346445,
title = {Optimization of wind plant layouts using an adjoint approach},
author = {King, Ryan N. and Dykes, Katherine and Graf, Peter and Hamlington, Peter E.},
abstractNote = {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 FEniCS and the derivation and solution of the discrete adjoint equations are automated within the dolfin-adjoint 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.},
doi = {10.5194/wes-2-115-2017},
journal = {Wind Energy Science (Online)},
number = 1,
volume = 2,
place = {Germany},
year = {Fri Mar 10 00:00:00 EST 2017},
month = {Fri Mar 10 00:00:00 EST 2017}
}
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