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Title: 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:
 [1];  [2];  [2];  [3]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States); Univ. of Colorado, Boulder, CO (United States)
  2. Univ. of Colorado, Boulder, CO (United States)
  3. National Renewable Energy Lab. (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:
1346445
Alternate Identifier(s):
OSTI ID: 1340643
Report Number(s):
NREL/JA-2C00-67162
Journal ID: ISSN 2366-7451
Grant/Contract Number:  
AC36-08GO28308; UGA-0-41026-70
Resource Type:
Journal Article: 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:
United States
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. United States: 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. United States. doi: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". United States. doi: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 = {United States},
year = {Fri Mar 10 00:00:00 EST 2017},
month = {Fri Mar 10 00:00:00 EST 2017}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.5194/wes-2-115-2017

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Works referenced in this record:

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