| Issue |
E3S Web Conf.
Volume 194, 2020
2020 5th International Conference on Advances in Energy and Environment Research (ICAEER 2020)
|
|
|---|---|---|
| Article Number | 02003 | |
| Number of page(s) | 12 | |
| Section | Renewable Energy and New Energy Technology | |
| DOI | https://doi.org/10.1051/e3sconf/202019402003 | |
| Published online | 15 October 2020 | |
Model Predictive Control Method of hybrid Battery energy storage system for Smoothing Wind Power Fluctuation
Energy Storage Technology Engineering Research Center (North China University of Technology), Shijingshan District, Beijing 100144, China
* Corresponding author: Li jianlin, dkyljl@163.com
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.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.