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Title: Optimization Under Uncertainty for Wake Steering Strategies

Abstract

Offsetting turbines' yaw orientations from incoming wind is a powerful tool that may be leveraged to reduce undesirable wake effects on downstream turbines. First, we examine a simple two-turbine case to gain intuition as to how inflow direction uncertainty affects the optimal solution. The turbines are modeled with unidirectional inflow such that one turbine directly wakes the other, using ten rotor diameter spacing. We perform optimization under uncertainty (OUU) via a parameter sweep of the front turbine. The OUU solution generally prefers less steering. We then do this optimization for a 60-turbine wind farm with unidirectional inflow, varying the degree of inflow uncertainty and approaching this OUU problem by nesting a polynomial chaos expansion uncertainty quantification routine within an outer optimization. We examined how different levels of uncertainty in the inflow direction effect the ratio of the expected values of deterministic and OUU solutions for steering strategies in the large wind farm, assuming the directional uncertainty used to reach said OUU solution (this ratio is defined as the value of the stochastic solution or VSS).

Authors:
 [1];  [1];  [1];  [1]; ORCiD logo [1];  [2]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. Brigham Young University
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:
1374526
Report Number(s):
NREL/PR-5000-68865
Journal ID: ISSN 1742--6588
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Journal Volume: 854; Conference: Presented at the Wind Energy Science Conference (WESC) 2017, 26-29 June 2017, Lyngby, Denmark
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; systems engineering; wind energy; optimization; optimization under uncertainty; wake steering

Citation Formats

Quick, Julian, Annoni, Jennifer, King, Ryan N, Dykes, Katherine L, Fleming, Paul A, and Ning, Andrew. Optimization Under Uncertainty for Wake Steering Strategies. United States: N. p., 2017. Web. doi:10.1088/1742-6596/854/1/012036.
Quick, Julian, Annoni, Jennifer, King, Ryan N, Dykes, Katherine L, Fleming, Paul A, & Ning, Andrew. Optimization Under Uncertainty for Wake Steering Strategies. United States. doi:10.1088/1742-6596/854/1/012036.
Quick, Julian, Annoni, Jennifer, King, Ryan N, Dykes, Katherine L, Fleming, Paul A, and Ning, Andrew. Thu . "Optimization Under Uncertainty for Wake Steering Strategies". United States. doi:10.1088/1742-6596/854/1/012036. https://www.osti.gov/servlets/purl/1374526.
@article{osti_1374526,
title = {Optimization Under Uncertainty for Wake Steering Strategies},
author = {Quick, Julian and Annoni, Jennifer and King, Ryan N and Dykes, Katherine L and Fleming, Paul A and Ning, Andrew},
abstractNote = {Offsetting turbines' yaw orientations from incoming wind is a powerful tool that may be leveraged to reduce undesirable wake effects on downstream turbines. First, we examine a simple two-turbine case to gain intuition as to how inflow direction uncertainty affects the optimal solution. The turbines are modeled with unidirectional inflow such that one turbine directly wakes the other, using ten rotor diameter spacing. We perform optimization under uncertainty (OUU) via a parameter sweep of the front turbine. The OUU solution generally prefers less steering. We then do this optimization for a 60-turbine wind farm with unidirectional inflow, varying the degree of inflow uncertainty and approaching this OUU problem by nesting a polynomial chaos expansion uncertainty quantification routine within an outer optimization. We examined how different levels of uncertainty in the inflow direction effect the ratio of the expected values of deterministic and OUU solutions for steering strategies in the large wind farm, assuming the directional uncertainty used to reach said OUU solution (this ratio is defined as the value of the stochastic solution or VSS).},
doi = {10.1088/1742-6596/854/1/012036},
journal = {},
number = ,
volume = 854,
place = {United States},
year = {Thu Aug 03 00:00:00 EDT 2017},
month = {Thu Aug 03 00:00:00 EDT 2017}
}

Conference:
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  • Wind turbines in a wind power plant experience significant power losses because of aerodynamic interactions between turbines. One control strategy to reduce these losses is known as 'wake steering,' in which upstream turbines are yawed to direct wakes away from downstream turbines. Previous wake steering research has assumed perfect information, however, there can be significant uncertainty in many aspects of the problem, including wind inflow and various turbine measurements. Uncertainty has significant implications for performance of wake steering strategies. Consequently, the authors formulate and solve an optimization under uncertainty (OUU) problem for finding optimal wake steering strategies in the presencemore » of yaw angle uncertainty. The OUU wake steering strategy is demonstrated on a two-turbine test case and on the utility-scale, offshore Princess Amalia Wind Farm. When we accounted for yaw angle uncertainty in the Princess Amalia Wind Farm case, inflow-direction-specific OUU solutions produced between 0% and 1.4% more power than the deterministically optimized steering strategies, resulting in an overall annual average improvement of 0.2%. More importantly, the deterministic optimization is expected to perform worse and with more downside risk than the OUU result when realistic uncertainty is taken into account. Additionally, the OUU solution produces fewer extreme yaw situations than the deterministic solution.« less
  • This presentation covers the motivation for this research, optimization under the uncertainty problem formulation, a two-turbine case, the Princess Amalia Wind Farm case, and conclusions and next steps.
  • Here, wind turbines in a wind power plant experience significant power losses because of aerodynamic interactions between turbines. One control strategy to reduce these losses is known as 'wake steering,' in which upstream turbines are yawed to direct wakes away from downstream turbines. Previous wake steering research has assumed perfect information, however, there can be significant uncertainty in many aspects of the problem, including wind inflow and various turbine measurements. Uncertainty has significant implications for performance of wake steering strategies. Consequently, the authors formulate and solve an optimization under uncertainty (OUU) problem for finding optimal wake steering strategies in themore » presence of yaw angle uncertainty. The OUU wake steering strategy is demonstrated on a two-turbine test case and on the utility-scale, offshore Princess Amalia Wind Farm. When we accounted for yaw angle uncertainty in the Princess Amalia Wind Farm case, inflow-direction-specific OUU solutions produced between 0% and 1.4% more power than the deterministically optimized steering strategies, resulting in an overall annual average improvement of 0.2%. More importantly, the deterministic optimization is expected to perform worse and with more downside risk than the OUU result when realistic uncertainty is taken into account. Additionally, the OUU solution produces fewer extreme yaw situations than the deterministic solution.« less
  • Abstract not provided.
  • Incomplete convergence in numerical simulation such as computational physics simulations and/or Monte Carlo simulations can enter into the calculation of the objective function in an optimization problem, producing noise, bias, and topo- graphical inaccuracy in the objective function. These affect accuracy and convergence rate in the optimization problem. This paper is concerned with global searching of a diverse parameter space, graduating to accelerated local convergence to a (hopefully) global optimum, in a framework that acknowledges convergence uncertainty and manages model resolu- tion to efficiently reduce uncertainty in the final optimum. In its own right, the global-to-local optimization engine employed heremore » (devised for noise tolerance) performs better than other classical and contemporary optimization approaches tried individually and in combination on the "industrial" test problem to be presented.« less