基于SMES/BESS混合储能抑制风电功率波动的控制策略  

Control strategy for suppressing wind power fluctuation based on SMES/BESS hybrid energy storage

作　　者：潘生雄 赵霞[1] 罗映红[1] 金洪涛 Pan Shengxiong;Zhao Xia;Luo Yinghong;Jin Hongtao(School of Automation and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学自动化与电气工程学院,兰州730070

出　　处：《电测与仪表》2020年第5期101-106,共6页Electrical Measurement & Instrumentation

基　　金：国家自然科学基金资助项目(51767015);甘肃省自然科学基金资助项目(2016GS07210)。

摘　　要：针对风能的随机性和波动性,风力发电系统易出现功率波动的问题,采用超导磁储能(SMES)和蓄电池(BESS)混合储能的方式来平抑功率波动,提出了一种改进型混合遗传算法的变参数荷电状态(SOC)分区控制优化策略。基于自适应学习的思想对算法进行了改进,使得算法的收敛速度和精确度得以提高。将储能系统荷电状态剩余量和荷电状态分区限值作为改进后混合遗传算法的目标函数和边界条件。所得目标结果作为滤波器滤波时间常数修正值对其进行修正,从而实现功率二次分配。在Matlab/Simulink中搭建仿真模型验证了该控制策略的有效性。所提控制策略可以对任意时刻SMES和BESS出力进行最优配合,同时能减小电池充放电深度和提高对风电功率波动的平抑效果,且能有效提高混合储能系统的使用寿命。In view of the randomness and volatility of wind energy,wind power generation systems are prone to power fluctuations,power fluctuations can be stabilized by using superconducting magnetic energy storage(SMES)and battery(BESS)hybrid energy storage methods.An improved hybrid genetic algorithm for variable parameter state-of-charge control strategy is proposed in this paper.The algorithm is improved by the idea of adaptive learning,and the convergence speed and accuracy of the algorithm are also improved.The residual value of energy storage system in charging state and state partition in charging state are used as the objective function and boundary conditions of the improved hybrid genetic algorithm.The obtained target result is used as a correction value to correct filter time constant to achieve power secondary distribution.The simulation model is built in MATLAB/Simulink to verify the effectiveness of the control strategy.The proposed control strategy can optimally match the SMES and BESS output at any time,meanwhile,it can reduce the depth of battery charging and discharging and improve the smoothing effect on wind power fluctuation,and then,it can also effectively improve the service life of the hybrid energy storage system.

关 键 词：混合储能 超导磁储能 混合遗传算法 变参数 充放电深度 

分 类 号：TM73[电气工程—电力系统及自动化]