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Title: Active Power Control for Wind Farms Using Distributed Model Predictive Control and Nearest Neighbor Communication: Preprint

Abstract

Wind plant control strategies, including axial induction and wake steering control, aim to improve the performance of wind farms, including increasing energy production and decreasing turbine loads. This paper presents a linear model of wake characteristics for use with a distributed model predictive control method for the purpose of optimizing axial induction and yaw misalignment setpoints. In particular, we use an iterative, distributed control method with nearest neighbor communication to coordinate turbine control actions that account for wake interactions between turbines. Simulations of the model and controller are performed on a 2x3 array of turbines using a modified version of the FLOw Redirection and Induction in Steady-state (FLORIS) model to dynamically track the relevant wake parameters. Preliminary results show the controller's ability to follow an arbitrary wind farm power reference signal for the purpose of providing active power control (APC) ancillary services for power grid stability. This efficient distributed control strategy can enable real-time wind farm optimization and control, even for very large scale farms.

Authors:
 [1];  [1];  [2];  [3];  [2]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. Colorado School of Mines
  3. University of Colorado
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:
1465640
Report Number(s):
NREL/CP-5000-70936
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the American Control Conference, 27-29 June 2018, Milwaukee, Wisconsin
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; wind farm control; distributed control; optimization

Citation Formats

Annoni, Jennifer, Johnson, Kathryn E, Bay, Christopher, Pao, Lucy, and Taylor, Timothy. Active Power Control for Wind Farms Using Distributed Model Predictive Control and Nearest Neighbor Communication: Preprint. United States: N. p., 2018. Web.
Annoni, Jennifer, Johnson, Kathryn E, Bay, Christopher, Pao, Lucy, & Taylor, Timothy. Active Power Control for Wind Farms Using Distributed Model Predictive Control and Nearest Neighbor Communication: Preprint. United States.
Annoni, Jennifer, Johnson, Kathryn E, Bay, Christopher, Pao, Lucy, and Taylor, Timothy. Wed . "Active Power Control for Wind Farms Using Distributed Model Predictive Control and Nearest Neighbor Communication: Preprint". United States. https://www.osti.gov/servlets/purl/1465640.
@article{osti_1465640,
title = {Active Power Control for Wind Farms Using Distributed Model Predictive Control and Nearest Neighbor Communication: Preprint},
author = {Annoni, Jennifer and Johnson, Kathryn E and Bay, Christopher and Pao, Lucy and Taylor, Timothy},
abstractNote = {Wind plant control strategies, including axial induction and wake steering control, aim to improve the performance of wind farms, including increasing energy production and decreasing turbine loads. This paper presents a linear model of wake characteristics for use with a distributed model predictive control method for the purpose of optimizing axial induction and yaw misalignment setpoints. In particular, we use an iterative, distributed control method with nearest neighbor communication to coordinate turbine control actions that account for wake interactions between turbines. Simulations of the model and controller are performed on a 2x3 array of turbines using a modified version of the FLOw Redirection and Induction in Steady-state (FLORIS) model to dynamically track the relevant wake parameters. Preliminary results show the controller's ability to follow an arbitrary wind farm power reference signal for the purpose of providing active power control (APC) ancillary services for power grid stability. This efficient distributed control strategy can enable real-time wind farm optimization and control, even for very large scale farms.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {8}
}

Conference:
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