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Title: Wake steering optimization under uncertainty

Abstract

Turbines in wind power plants experience significant power losses when wakes from upstream turbines affect the energy production of downstream turbines. A promising plant-level control strategy to reduce these losses is wake steering, where upstream turbines are yawed to direct wakes away from downstream turbines. However, there are significant uncertainties in many aspects of the wake steering problem. For example, infield sensors do not give perfect information, and inflow to the plant is complex and difficult to forecast with available information, even over short time periods. Here, we formulate and solve an optimization under uncertainty (OUU) problem for determining optimal plant-level wake steering strategies in the presence of independent uncertainties in the direction, speed, turbulence intensity, and shear of the incoming wind, as well as in turbine yaw positions. The OUU wake steering strategy is first examined for a two-turbine test case to explore the impacts of different types of inflow uncertainties, and it is then demonstrated for a more realistic 11-turbine wind power plant. Of the sources of uncertainty considered, we find that wake steering strategies are most sensitive to uncertainties in the wind speed and direction. When maximizing expected power production, the OUU strategy also tends to favormore » smaller yaw angles, which have been shown in previous work to reduce turbine loading. Ultimately, the plant-level wake steering strategy formulated using an OUU approach yields 0.48?% more expected annual energy production for the 11-turbine wind plant than a strategy that neglects uncertainty when considering stochastic inputs. Thus, not only does the present OUU strategy produce more power in realistic conditions, but it also reduces risk by prescribing strategies that call for less extreme yaw angles.« less

Authors:
 [1]; ORCiD logo [2];  [2];  [3]; ORCiD logo [2]
  1. Univ. of Colorado, Boulder, CO (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  3. Univ. of Colorado, Boulder, 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), Renewable Power Office. Wind Energy Technologies Office
OSTI Identifier:
1659960
Report Number(s):
NREL/JA-2C00-77181
Journal ID: ISSN 2366-7443; MainId:26127;UUID:b6ef6bef-4f38-4cef-9e45-6fa074687cf4;MainAdminID:13695
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Wind Energy Science
Additional Journal Information:
Journal Volume: 5; Journal Issue: 1; Journal ID: ISSN 2366-7443
Publisher:
European Wind Energy Association - Copernicus
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; optimization under uncertainty; wake steering

Citation Formats

Quick, Julian, King, Jennifer, King, Ryan N., Hamlington, Peter E., and Dykes, Katherine. Wake steering optimization under uncertainty. United States: N. p., 2020. Web. https://doi.org/10.5194/wes-5-413-2020.
Quick, Julian, King, Jennifer, King, Ryan N., Hamlington, Peter E., & Dykes, Katherine. Wake steering optimization under uncertainty. United States. https://doi.org/10.5194/wes-5-413-2020
Quick, Julian, King, Jennifer, King, Ryan N., Hamlington, Peter E., and Dykes, Katherine. Mon . "Wake steering optimization under uncertainty". United States. https://doi.org/10.5194/wes-5-413-2020. https://www.osti.gov/servlets/purl/1659960.
@article{osti_1659960,
title = {Wake steering optimization under uncertainty},
author = {Quick, Julian and King, Jennifer and King, Ryan N. and Hamlington, Peter E. and Dykes, Katherine},
abstractNote = {Turbines in wind power plants experience significant power losses when wakes from upstream turbines affect the energy production of downstream turbines. A promising plant-level control strategy to reduce these losses is wake steering, where upstream turbines are yawed to direct wakes away from downstream turbines. However, there are significant uncertainties in many aspects of the wake steering problem. For example, infield sensors do not give perfect information, and inflow to the plant is complex and difficult to forecast with available information, even over short time periods. Here, we formulate and solve an optimization under uncertainty (OUU) problem for determining optimal plant-level wake steering strategies in the presence of independent uncertainties in the direction, speed, turbulence intensity, and shear of the incoming wind, as well as in turbine yaw positions. The OUU wake steering strategy is first examined for a two-turbine test case to explore the impacts of different types of inflow uncertainties, and it is then demonstrated for a more realistic 11-turbine wind power plant. Of the sources of uncertainty considered, we find that wake steering strategies are most sensitive to uncertainties in the wind speed and direction. When maximizing expected power production, the OUU strategy also tends to favor smaller yaw angles, which have been shown in previous work to reduce turbine loading. Ultimately, the plant-level wake steering strategy formulated using an OUU approach yields 0.48?% more expected annual energy production for the 11-turbine wind plant than a strategy that neglects uncertainty when considering stochastic inputs. Thus, not only does the present OUU strategy produce more power in realistic conditions, but it also reduces risk by prescribing strategies that call for less extreme yaw angles.},
doi = {10.5194/wes-5-413-2020},
journal = {Wind Energy Science},
number = 1,
volume = 5,
place = {United States},
year = {2020},
month = {3}
}

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