基于电池储能系统和双重扩展卡尔曼滤波的风能发电智能调度技术研究

风能等新能源发电系统在供电体系中的占比越来越大，但其随机性和波动性问题，将风力发电厂输出的电力直接向电网调度会造成安全隐患。为了解决这一问题，基于电池储能系统提出了一种风能发电智能调度技术，该技术以风力发电动力学模型和电池储能系统状态模型为基础，利用双重扩展卡尔曼滤波算法实现了风能发电系统的稳定输出。以某地风速实测数据和电网需求功率为参考，对不同算法的输出功率预测值进行了仿真分析和实验对比。结果表明：提出的改进算法预测的风速值误差相比于传感器观测值平均误差降低了28%以上，可以更准确地提供发电系统输出功率；提出的智能调度技术可以使电压波动幅度降低60%以上，系统整体输出功率稳定在参考功率附近，误差不超过2%,有一定的实用意义。

Intelligent Dispatching Technology Study of Wind Power Generation based on Battery Energy Storage System and Double Extended Kalman Filter

The proportion of new energy power generation systems such as wind power generation in the power supply system is increasing. However, due to its large randomness and volatility, dispatching the output power of wind power plants directly to the power grid will cause safety hazards. In order to solve this problem, this paper proposes an intelligent dispatching technology for wind power generation based on the battery energy storage system. Based on the dynamic model of wind power generation and the state model of the battery energy storage system, the double extended Kalman filter algorithm is used to realize the stable output of wind power generation system. Referenced by the measured data of wind speed in a certain place and the power demand of the power grid, simulation analysis and experimental comparison are carried out for the predicted value of output power using different algorithms. The experimental results show that the error of the wind speed value predicted by the proposed optimal algorithm is reduced by more than 28% compared with that of the sensor observation value, and the output power of the power generation system can be provided more accurately; the intelligent dispatching technology proposed in this paper can reduce the voltage fluctuation range by more than 60%. The overall output power of the system is stable near the reference power, and the error does not exceed 2%, which has a certain practical significance.