An Economic Model Predictive Control Approach for Wind Power Smoothing and Tower Load Mitigation

Author(s):  
Mohamed M. Alhneaish ◽  
Mohamed L. Shaltout ◽  
Sayed M. Metwalli

An economic model predictive control framework is presented in this study for an integrated wind turbine and flywheel energy storage system. The control objective is to smooth wind power output and mitigate tower fatigue load. The optimal control problem within the model predictive control framework has been formulated as a convex optimal control problem with linear dynamics and convex constraints that can be solved globally. The performance of the proposed control algorithm is compared to that of a standard wind turbine controller. The effect of the proposed control actions on the fatigue loads acting on the tower and blades is studied. The simulation results, with various wind scenarios, showed the ability of the proposed control algorithm to achieve the aforementioned objectives in terms of smoothing output power and mitigating tower fatigue load at the cost of a minimal reduction of the wind energy harvested.

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Mohamed L. Shaltout ◽  
Mohamed M. Alhneaish ◽  
Sayed M. Metwalli

Abstract Wind power intermittency represents one of the major challenges facing the future growth of grid-connected wind energy projects. The integration of wind turbines and energy storage systems (ESS) provides a viable approach to mitigate the unfavorable impact on grid stability and power quality. In this study, an economic model predictive control (MPC) framework is presented for an integrated wind turbine and flywheel energy storage system (FESS). The control objective is to smooth wind power output and mitigate tower fatigue load. The optimal control problem within the model predictive control framework has been formulated as a convex optimal control problem with linear dynamics and convex constraints that can be solved globally. The performance of the proposed control algorithm is compared to that of a baseline wind turbine controller. The effect of the proposed control actions on the fatigue loads acting on the tower and blades is investigated. The simulation results, with various wind scenarios, showed the ability of the proposed control algorithm to achieve the aforementioned objectives in terms of smoothing output power and mitigating tower fatigue load with negligible effect on the wind energy harvested.


Author(s):  
Mohamed L. Shaltout ◽  
Zheren Ma ◽  
Dongmei Chen

Motivated by the reduction of overall wind power cost, considerable research effort has been focused on enhancing both efficiency and reliability of wind turbines. Maximizing wind energy capture while mitigating fatigue loads has been one of the main goals for control design. Recent developments in remote wind speed measurement systems (e.g., light detection and ranging (LIDAR)) have paved the way for implementing advanced control algorithms in the wind energy industry. In this paper, an LIDAR-assisted economic model predictive control (MPC) framework with a real-time adaptive approach is presented to achieve the aforementioned goal. First, the formulation of a convex optimal control problem is introduced, with linear dynamics and convex constraints that can be solved globally. Then, an adaptive approach is proposed to reject the effects of model-plant mismatches. The performance of the developed control algorithm is compared to that of a standard wind turbine controller, which is widely used as a benchmark for evaluating new control designs. Simulation results show that the developed controller can reduce the tower fatigue load with minimal impact on energy capture. For model-plant mismatches, the adaptive controller can drive the wind turbine to its optimal operating conditions while satisfying the optimal control objectives.


2019 ◽  
Vol 85 ◽  
pp. 100-109 ◽  
Author(s):  
Hui Tian ◽  
Qiugang Lu ◽  
R. Bhushan Gopaluni ◽  
Victor M. Zavala ◽  
James A. Olson

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