Distributed power optimization of large wind farms using ADMM for real-time control
Distributed power optimization of large wind farms using ADMM for real-time control
In a wind farm, the interactions between turbines caused by wakes can significantly reduce the power output of the wind farm. Cooperative control among the turbines has the potential to improve the power output. However, existing centralized power optimization methods are computationally expensive and does not scale well for large wind farms, limiting their practical use in real-time control for time-varying wind conditions and turbine configuration (with adding or maintaining of turbines). To address this problem, this paper proposes a fully distributed power optimization method for wind farms using alternating direction method of multipliers (ADMM). The proposed method allows the wind farm power output to be optimized in fully distributed manner with turbine-toturbine message passing over a mesh network, guarantees the implemented control actions satisfy the control constraints of all turbines, and provably converges to a stationary point of the wind farm power optimization problem. Simulation results demonstrate that the proposed method can significantly reduce the computation time with hardly sacrificing the power gain compared with centralized method and thus is computationally efficient for real-time power optimization of large wind farms.
ADMM, Computational modeling, Convergence, Large wind farms, Optimization methods, Wind farms, Wind power generation, Wind speed, Wind turbines, distributed optimization, nonconvex optimization, power optimization
4832-4845
Xu, Zhiwei
1ba6215e-297a-4fe8-85b3-4ee57bd3d9b6
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Geng, Hua
4de0000a-a809-4b66-81bb-ea0befdad671
Nian, Xiaohong
30abba53-db3c-463c-93b6-718967a853f5
1 November 2022
Xu, Zhiwei
1ba6215e-297a-4fe8-85b3-4ee57bd3d9b6
Chu, Bing
555a86a5-0198-4242-8525-3492349d4f0f
Geng, Hua
4de0000a-a809-4b66-81bb-ea0befdad671
Nian, Xiaohong
30abba53-db3c-463c-93b6-718967a853f5
Xu, Zhiwei, Chu, Bing, Geng, Hua and Nian, Xiaohong
(2022)
Distributed power optimization of large wind farms using ADMM for real-time control.
IEEE Transactions on Power Systems, 37 (6), .
(doi:10.1109/TPWRS.2022.3149904).
Abstract
In a wind farm, the interactions between turbines caused by wakes can significantly reduce the power output of the wind farm. Cooperative control among the turbines has the potential to improve the power output. However, existing centralized power optimization methods are computationally expensive and does not scale well for large wind farms, limiting their practical use in real-time control for time-varying wind conditions and turbine configuration (with adding or maintaining of turbines). To address this problem, this paper proposes a fully distributed power optimization method for wind farms using alternating direction method of multipliers (ADMM). The proposed method allows the wind farm power output to be optimized in fully distributed manner with turbine-toturbine message passing over a mesh network, guarantees the implemented control actions satisfy the control constraints of all turbines, and provably converges to a stationary point of the wind farm power optimization problem. Simulation results demonstrate that the proposed method can significantly reduce the computation time with hardly sacrificing the power gain compared with centralized method and thus is computationally efficient for real-time power optimization of large wind farms.
Text
ADMM13012022
- Accepted Manuscript
More information
e-pub ahead of print date: 9 February 2022
Published date: 1 November 2022
Additional Information:
Funding Information:
This work was supported by the National Natural Science Foundation of China (NSFC) underGrantsU2166601, U2066602, and 52061635102.
Publisher Copyright:
© 1969-2012 IEEE.
Keywords:
ADMM, Computational modeling, Convergence, Large wind farms, Optimization methods, Wind farms, Wind power generation, Wind speed, Wind turbines, distributed optimization, nonconvex optimization, power optimization
Identifiers
Local EPrints ID: 469349
URI: http://eprints.soton.ac.uk/id/eprint/469349
ISSN: 0885-8950
PURE UUID: 45df65cf-5496-4346-a560-a0563dee046f
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Date deposited: 13 Sep 2022 16:53
Last modified: 17 Mar 2024 03:28
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Contributors
Author:
Zhiwei Xu
Author:
Bing Chu
Author:
Hua Geng
Author:
Xiaohong Nian
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