基于灰色预测和低通滤波的风电平抑策略
摘要：在风电并网处配置一定容量的储能系统对并网风功率进行平抑处理可以大幅提高风电并网的安全性、可靠性。传统的低通滤波等方法具有较高的滞后特性，风电跟随能力较差，这将导致储能系统的充放电深度变大，从而影响储能系统的寿命，同时也会使其配置容量需求增大。因此提出一种灰色预测和低通滤波相结合的储能系统平滑风电功率控制策略，该策略能够根据储能系统的荷电状态(state of charge，SOC)变化自动调整算法参数，保证SOC维持在安全范围内，同时实现风电功率的平滑和跟踪，并减少储能系统的容量配置需求及充放电深度。最后通过Matlab仿真验证了该策略的可行性和有效性。
Abstract:Equipping an energy storage system with a certain capacity at the site of grid-connection of wind power to stabilize the grid-connected wind power, the security and reliability of grid-connected wind power can be greatly improved. The long lag characteristic in low-pass filtering and other traditional means and the insufficient following ability of wind power system lead to larger charge and discharge depth of the energy storage system, so that the lifetime of the energy storage system is affected and the energy storage system with higher capacity has to be equipped. For this reason, by use of an energy storage system, in which the grey prediction and the low-pass filtering was combined, a wind power smoothing control strategy was proposed. The proposed strategy can automatically adjust the parameter of the algorithm according to the change of state of charge (abbr. SOC) of energy storage system to ensure the SOC was kept within the secure range, meanwhile, the smoothing and tracking of wind power was achieved, and the depth of charge and discharge and the demand on capacity configuration of energy storage system could be decreased. Finally, by means of simulation with MATLAB the feasibility and effectiveness of the proposed strategy are verified.
标题：基于灰色预测和低通滤波的风电平抑策略
英文标题：Wind Power Stabilization Strategy Based on Grey Prediction and Low-pass Filtering
