Approximate Scenario-Based Economic Model Predictive Control With Application to Wind Energy Conversion System

Cui, Jinghan; Liu, Xiangjie; Chai, Tianyou

doi:10.1109/tii.2022.3189440

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…MPC usually acts as a set point tracking at the lower level, and its tracked steady state point is optimized by the upper level based on the economic indicators of the system. However, this hierarchical control strategy makes the control process delayed, and it cannot guarantee the economic efficiency and system constraints during the transient process [29]. Thus, an EMPC algorithm is proposed, which can combine the economic optimization of the system and the process control in order to realize the real−time optimization and control of the system, and improve the control performance and economic efficiency of the system.…”

Section: The Principles Of Empcmentioning

confidence: 99%

“…( ) Considering the output constraints to be satisfied by the system itself as well as the state constraints ( 30)- (32), the optimal control problem can be obtained by minimizing the objective function (29) as follows (33):…”

Section: Dc-ac Convertormentioning

confidence: 99%

“…The model predictive control (MPC) strategy has been used in PV applications due to its ability to handle process variable constraints as well as optimize performance metrics [27][28][29]. Sajadian et al [30] proposed a concept based on the combination of MPC and pole search optimization for tracking the maximum power point, with a simple control structure and a fast dynamic response.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MPPT Strategy of Waterborne Bifacial Photovoltaic Power Generation System Based on Economic Model Predictive Control

Tang,

Li,

Qiu

et al. 2023

Energies

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At present, the new energy industry represented by photovoltaics has become the main force to realize the optimization of China’s energy structure and the goal of “double carbon”; with the absence of land resources, the waterborne bifacial photovoltaic has ushered in a new opportunity. Therefore, in order to address the problem that the maximum power point tracking (MPPT) of photovoltaics (PV) could not take into account, the dynamic economic performance in the control process, an economic model predictive control (EMPC), is proposed in this work to realize the MPPT of the waterborne bifacial PV power generation system. Firstly, the model of the bifacial PV module is constructed by combining the ray-tracing irradiance model and considering the effect of water surface albedo on the irradiance absorbed by the module. Secondly, the EMPC controller is designed based on the state-space model of the system to maximize the power generation as the economic performance index, and to solve the optimal input variables time by time to achieve a rolling optimization with the operational requirements of the system itself as the constraints. Thirdly, the MATLAB/Simulink (R2022a) simulation experimental results verify that the EMPC strategy could be utilized to achieve MPPT of the waterborne bifacial PV power generation system, according to the changes of environment. Finally, it is also demonstrated that the bifacial PV power generation system that employed the EMPC strategy outperformed the traditional MPPT algorithm, with respect to both output power tracking velocity and accuracy, and the power generation could be improved by about 6% to 14.5%, which significantly enhances the system’s dynamic process economics.

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Section: The Principles Of Empcmentioning

confidence: 99%

Section: Dc-ac Convertormentioning

confidence: 99%