基于混合储能减小平抑功率滞后性的风电平抑策略  

Wind-Power Smoothing Strategy Based on Hybrid Energy Storage to Reduce Smoothing Power Lag

作　　者：王镇林 陈麒宇 张雅静 李慧[1] 杨秀媛[1] WANG Zhenlin;CHEN Qiyu;ZHANG Yajing;LI Hui;YANG Xiuyuan(School of Automation,Beijing Information Science and Technology University,Beijing 100192,China;China Electric Power Research Institute,Beijing 100192,China)

机构地区：[1]北京信息科技大学自动化学院,北京市100192 [2]中国电力科学研究院有限公司,北京市100192

出　　处：《电力建设》2023年第9期149-159,共11页Electric Power Construction

基　　金：国家自然科学基金项目(52237008)。

摘　　要：随着储能技术的快速发展,为保障风电并网的安全、稳定,在风电并网处采用混合储能系统(hybrid energy storage system,HESS)进行风电平抑已经成为目前主流的风电平抑方式,但这些平抑策略的平抑功率大多存在较高的滞后性。为此提出一种基于混合储能的新型风电平抑策略,该策略通过灰色预测和自适应Savizkg-Golag(SG)算法相结合,获得风电并网参考功率和混合储能系统出力参考功率,再根据锂电池和超级电容的储能特性,通过变分模态分解(variational mode decomposition,VMD)算法对混合储能系统出力参考功率进行分解,实现混合储能系统出力的合理分配。最后,经过算例分析验证该策略具有良好的风电平抑效果,并可以解决传统平抑策略平抑功率存在滞后性的问题,从而减小储能系统荷电状态(state of charge,SOC)曲线的波动幅度,进而降低储能系统的容量配置需求。Energy-storage technology is currently under rapid development.Accordingly,to ensure the safety and stability of wind-power grid connections,the use of hybrid energy-storage systems(HESSs) for wind-power leveling at wind-power grid connections has become a mainstream wind-power leveling method.However,most leveling strategies have a high lag in leveling power.This strategy combines grey prediction and the adaptive Savizkg-Golag(SG) algorithm to obtain the grid-connected reference and output reference powers of the wind power and HESS,respectively.According to the energy-storage characteristics of lithium batteries and supercapacitors,the output reference power can be decomposed by the variational mode decomposition(VMD) algorithm to achieve a reasonable distribution of the HESS output power.Finally,an analysis verifies that the strategy has a good wind-power smoothing effect and can solve the problem of hysteresis in the traditional smoothing strategy to reduce fluctuations in the state-of-charge(SOC) curve of the HESS,thereby reducing its capacity allocation requirements.

关 键 词：混合储能系统(HESS) 风电功率平抑 灰色预测 变分模态分解(VMD) 自适应Savizkg-Golag(SG)算法 滞后性 

分 类 号：TM614[电气工程—电力系统及自动化]