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Title: Efficient Distributed Optimization of Wind Farms Using Proximal Primal-Dual Algorithms

Abstract

This paper presents a distributed approach to performing real-time optimization of large wind farms. Wind turbines in a wind farm typically operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. This paper optimizes the overall power produced by a wind farm by formulating and solving a nonconvex optimization problem where the yaw angles are optimized to allow some turbines to operate in misaligned conditions and shape the aerodynamic interactions in a favorable way. The solution of the nonconvex smooth problem is tackled using a proximal primal-dual gradient method, which provably identifies a first-order stationary solution in a global sublinear manner. By adding auxiliary optimization variables for every pair of turbines that are coupled aerodynamically, and properly adding consensus constraints into the underlying problem, a distributed algorithm with turbine-to-turbine message passing is obtained; this allows for turbines to be optimized in parallel using local information rather than information from the whole wind farm. This algorithm is computationally light, as it involves closed-form updates. This approach is demonstrated on a large wind farm with 60 turbines. The results indicate that similar performance can be achieved as with finite-difference gradient-based optimization at a fraction ofmore » the computational time and thus approaching real-time control/optimization.« less

Authors:
ORCiD logo [1];  [2];  [3]; ORCiD logo [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. University of Colorado
  3. University of Minnesota
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:
1569437
Report Number(s):
NREL/CP-5000-75051
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2019 American Control Conference (ACC), 10-12 July 2019, Philadelphia, Pennsylvania
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; wind energy; wind farm control; distributed optimization; performance; optimization

Citation Formats

Annoni, Jennifer R, Dall'Anese, Emiliano, Hong, Mingyi, and Bay, Christopher. Efficient Distributed Optimization of Wind Farms Using Proximal Primal-Dual Algorithms. United States: N. p., 2019. Web.
Annoni, Jennifer R, Dall'Anese, Emiliano, Hong, Mingyi, & Bay, Christopher. Efficient Distributed Optimization of Wind Farms Using Proximal Primal-Dual Algorithms. United States.
Annoni, Jennifer R, Dall'Anese, Emiliano, Hong, Mingyi, and Bay, Christopher. Thu . "Efficient Distributed Optimization of Wind Farms Using Proximal Primal-Dual Algorithms". United States.
@article{osti_1569437,
title = {Efficient Distributed Optimization of Wind Farms Using Proximal Primal-Dual Algorithms},
author = {Annoni, Jennifer R and Dall'Anese, Emiliano and Hong, Mingyi and Bay, Christopher},
abstractNote = {This paper presents a distributed approach to performing real-time optimization of large wind farms. Wind turbines in a wind farm typically operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. This paper optimizes the overall power produced by a wind farm by formulating and solving a nonconvex optimization problem where the yaw angles are optimized to allow some turbines to operate in misaligned conditions and shape the aerodynamic interactions in a favorable way. The solution of the nonconvex smooth problem is tackled using a proximal primal-dual gradient method, which provably identifies a first-order stationary solution in a global sublinear manner. By adding auxiliary optimization variables for every pair of turbines that are coupled aerodynamically, and properly adding consensus constraints into the underlying problem, a distributed algorithm with turbine-to-turbine message passing is obtained; this allows for turbines to be optimized in parallel using local information rather than information from the whole wind farm. This algorithm is computationally light, as it involves closed-form updates. This approach is demonstrated on a large wind farm with 60 turbines. The results indicate that similar performance can be achieved as with finite-difference gradient-based optimization at a fraction of the computational time and thus approaching real-time control/optimization.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {8}
}

Conference:
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