Economic dispatch for wind farm using model predictive control method

Author(s):  
Yonghao Gui ◽  
Chunghun Kim ◽  
Chung Choo Chung
2020 ◽  
Vol 194 ◽  
pp. 02003
Author(s):  
Li Jianlin ◽  
Tan Yuliang

In a large-scale wind power generation system, active power fluctuation caused by random wind speed will have a serious impact on the power grid. In order to limit the power fluctuation that wind farm transmits to the power grid and protect the energy storage battery, this paper has proposed a model predictive control method of hybrid energy storage by optimizing the objective function and constraint conditions. Firstly, the mathematical model of predictive control method has been established in a wind power system with hybrid energy storage. Then, with the goal of minimum energy storage output and maximum charging-discharging capacity of the super-capacitor, the predictive control process has been optimized. Meanwhile, the constraint on the output power of the battery has been dynamically changed to reduce its charging-discharging switching frequency, and the model predictive control strategy of the hybrid energy storage has been formed. Finally, compared with the model prediction control method of single energy storage, based on a wind farm data, the simulation results show that the proposed method can smooth wind power fluctuation, reduce the time that the power does not satisfy the fluctuation requirements, ensure the capability of the super-capacitor, and reduce the charging-discharging switching frequency of the energy storage battery.


2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Shuyou Yu ◽  
Matthias Hirche ◽  
Yanjun Huang ◽  
Hong Chen ◽  
Frank Allgöwer

AbstractThis paper reviews model predictive control (MPC) and its wide applications to both single and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established optimal control method, which uses the predicted future information to optimize the control actions while explicitly considering constraints. On the other hand, AGVs are able to make forecasts and adapt their decisions in uncertain environments. Therefore, because of the nature of MPC and the requirements of AGVs, it is intuitive to apply MPC algorithms to AGVs. AGVs are interesting not only for considering them alone, which requires centralized control approaches, but also as groups of AGVs that interact and communicate with each other and have their own controller onboard. This calls for distributed control solutions. First, a short introduction into the basic theoretical background of centralized and distributed MPC is given. Then, it comprehensively reviews MPC applications for both single and multiple AGVs. Finally, the paper highlights existing issues and future research directions, which will promote the development of MPC schemes with high performance in AGVs.


Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3608
Author(s):  
Yang Yuan ◽  
Neng Zhu ◽  
Haizhu Zhou ◽  
Hai Wang

To enhance the energy performance of a central air-conditioning system, an effective control method for the chilled water system is always essential. However, it is a real challenge to distribute exact cooling energy to multiple terminal units in different floors via a complex chilled water network. To mitigate hydraulic imbalance in a complex chilled water system, many throttle valves and variable-speed pumps are installed, which are usually regulated by PID-based controllers. Due to the severe hydraulic coupling among the valves and pumps, the hydraulic oscillation phenomena often occur while using those feedback-based controllers. Based on a data-calibrated water distribution model which can accurately predict the hydraulic behaviors of a chilled water system, a new Model Predictive Control (MPC) method is proposed in this study. The proposed method is validated by a real-life chilled water system in a 22-floor hotel. By the proposed method, the valves and pumps can be regulated safely without any hydraulic oscillations. Simultaneously, the hydraulic imbalance among different floors is also eliminated, which can save 23.3% electricity consumption of the pumps.


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