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A Comparative Study of Robust MPC and Stochastic MPC of Wind Power Generation System

Author

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  • Xiangjie Liu

    (The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Le Feng

    (The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Xiaobing Kong

    (The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

Abstract

In this paper, a complete comparison analysis of two advanced control algorithms, namely robust model predictive control (MPC) and stochastic MPC, is performed in order to optimize the operation of a wind power generation system (WPGS). The power maximization often conflicts with the mechanical load experienced by the turbine in the full-load region (i.e., the higher the power extracted, the higher the load) under the wind speed disturbance, thereby leading to high maintenance cost resulting from the fatigue damage. Thus, a typical 5 MW wind turbine operating in a high-speed region is considered to guarantee system security and economy. The robust MPC is designed by utilizing the min–max framework to track steady-state optimum operating reference trajectory with the deterministic constraint of output power, while the stochastic MPC is constructed by incorporating the invariant set theory to also ensure the system security subjecting to the probabilistic constraint of output power. The relation between the constraints and the implications on optimal performance are also studied. Comprehensive simulations on a mechanism model and FAST simulator are carried out to demonstrate the validation of the two control methods under various scenarios. It is discovered that when wind speed in the near future can be predicted and utilized in controller design, the stochastic MPC can effectively reduce the maintenance cost by suppressing the constraint violation rate compared to robust MPC with a similar energy utilization due to the incorporation of the stochastic characteristics of wind speed.

Suggested Citation

  • Xiangjie Liu & Le Feng & Xiaobing Kong, 2022. "A Comparative Study of Robust MPC and Stochastic MPC of Wind Power Generation System," Energies, MDPI, vol. 15(13), pages 1-22, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4814-:d:853139
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    References listed on IDEAS

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    1. Jinghan Cui & Su Liu & Jinfeng Liu & Xiangjie Liu, 2018. "A Comparative Study of MPC and Economic MPC of Wind Energy Conversion Systems," Energies, MDPI, vol. 11(11), pages 1-23, November.
    2. Kim, Dae-Young & Kim, Yeon-Hee & Kim, Bum-Suk, 2021. "Changes in wind turbine power characteristics and annual energy production due to atmospheric stability, turbulence intensity, and wind shear," Energy, Elsevier, vol. 214(C).
    3. Xiaobing Kong & Lele Ma & Xiangjie Liu & Mohamed Abdelkarim Abdelbaky & Qian Wu, 2020. "Wind Turbine Control Using Nonlinear Economic Model Predictive Control over All Operating Regions," Energies, MDPI, vol. 13(1), pages 1-21, January.
    4. Bohao Sun & Yong Tang & Lin Ye & Chaoyu Chen & Cihang Zhang & Wuzhi Zhong, 2018. "A Frequency Control Strategy Considering Large Scale Wind Power Cluster Integration Based on Distributed Model Predictive Control," Energies, MDPI, vol. 11(6), pages 1-19, June.
    5. Diego Calabrese & Gioacchino Tricarico & Elia Brescia & Giuseppe Leonardo Cascella & Vito Giuseppe Monopoli & Francesco Cupertino, 2020. "Variable Structure Control of a Small Ducted Wind Turbine in the Whole Wind Speed Range Using a Luenberger Observer," Energies, MDPI, vol. 13(18), pages 1-23, September.
    6. Abdelbaky, Mohamed Abdelkarim & Liu, Xiangjie & Jiang, Di, 2020. "Design and implementation of partial offline fuzzy model-predictive pitch controller for large-scale wind-turbines," Renewable Energy, Elsevier, vol. 145(C), pages 981-996.
    7. Moradi, Hamed & Vossoughi, Gholamreza, 2015. "Robust control of the variable speed wind turbines in the presence of uncertainties: A comparison between H∞ and PID controllers," Energy, Elsevier, vol. 90(P2), pages 1508-1521.
    8. Lasheen, Ahmed & Saad, Mohamed S. & Emara, Hassan M. & Elshafei, Abdel Latif, 2017. "Continuous-time tube-based explicit model predictive control for collective pitching of wind turbines," Energy, Elsevier, vol. 118(C), pages 1222-1233.
    9. Aitor Saenz-Aguirre & Ekaitz Zulueta & Unai Fernandez-Gamiz & Javier Lozano & Jose Manuel Lopez-Guede, 2019. "Artificial Neural Network Based Reinforcement Learning for Wind Turbine Yaw Control," Energies, MDPI, vol. 12(3), pages 1-17, January.
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    Cited by:

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    2. Velarde, Pablo & Gallego, Antonio J. & Bordons, Carlos & Camacho, Eduardo F., 2023. "Scenario-based model predictive control for energy scheduling in a parabolic trough concentrating solar plant with thermal storage," Renewable Energy, Elsevier, vol. 206(C), pages 1228-1238.
    3. Dongsen Li & Kang Qian & Ciwei Gao & Yiyue Xu & Qiang Xing & Zhangfan Wang, 2024. "Research on Electric Hydrogen Hybrid Storage Operation Strategy for Wind Power Fluctuation Suppression," Energies, MDPI, vol. 17(20), pages 1-15, October.
    4. Minan Tang & Wenjuan Wang & Jiandong Qiu & Detao Li & Linyuan Lei, 2022. "Active Power Cooperative Control for Wind Power Clusters with Multiple Temporal and Spatial Scales," Energies, MDPI, vol. 15(24), pages 1-21, December.

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