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A Comparative Study of MPC and Economic MPC of Wind Energy Conversion Systems

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  • Jinghan Cui

    (The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
    Department of Chemical & Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Su Liu

    (Department of Chemical & Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Jinfeng Liu

    (Department of Chemical & Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Xiangjie Liu

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

Abstract

In this work, we perform a comprehensive comparative study of two advanced control algorithms—the classical tracking model predictive control (MPC) and economic MPC (EMPC)—in the optimal operation of wind energy conversion systems (WECSs). A typical 5 MW wind turbine is considered in this work. The tracking MPC is designed to track steady-state optimal operating reference trajectories determined using a maximum power point tracking (MPPT) algorithm. In the design of the tracking MPC, the entire operating region of the wind turbine is divided into four subregions depending on the wind speed. The tracking MPC tracks different optimal reference trajectories determined by the MPPT algorithm in these subregions. In the designed EMPC, a uniform economic cost function is used for the entire operating region and the division of the operating region into subregions is not needed. Two common economic performance indices of WECSs are considered in the design of the economic cost function for EMPC. The relation between the two economic performance indices and the implications of the relation on EMPC performance are also investigated. Extensive simulations are performed to show the advantages and disadvantages of the two control algorithms under different conditions. It is found that when the near future wind speed can be predicted and used in control, EMPC can improve the energy utilization by about 2% and reduce the operating cost by about 30% compared to classical tracking MPC, especially when the wind speed varies such that the tracking MPC switches between operating subregions. It is also found that uncertainty in information (e.g., future wind speed, measurement noise in wind speed) may deteriorate the performance of EMPC.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3127-:d:182288
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    References listed on IDEAS

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    1. Song, Dongran & Yang, Jian & Dong, Mi & Joo, Young Hoon, 2017. "Model predictive control with finite control set for variable-speed wind turbines," Energy, Elsevier, vol. 126(C), pages 564-572.
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    5. Zhang, Xinan & Bao, Jie & Wang, Ruigang & Zheng, Chaoxu & Skyllas-Kazacos, Maria, 2017. "Dissipativity based distributed economic model predictive control for residential microgrids with renewable energy generation and battery energy storage," Renewable Energy, Elsevier, vol. 100(C), pages 18-34.
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    Cited by:

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