基于滑动平均和模型预测控制的风储平抑策略  

Strategy of Smoothing Wind Power Fluctuation with Storage Battery Based on Moving Average and Model Predictive Control

作　　者：章竹耀 郭晓丽[2] 张新松[2] 马啸宇[2] ZHANG Zhu-yao;GUO Xiao-li;ZHANG Xin-song;MA Xiao-yu(Shanghai Railway Station,China Railway Shanghai Group Co.,Ltd.,Shanghai 200000,China;School of Electrical Engineering,Nantong University,Nantong 226019,China)

机构地区：[1]中国铁路上海局集团有限公司上海站,上海200000 [2]南通大学电气工程学院,江苏南通226019

出　　处：《控制工程》2019年第1期114-119,共6页Control Engineering of China

基　　金：国家自然科学基金项目批准号(51407097);江苏省"六大人才高峰"计划第12批资助项目(2015-ZNDW-009);江苏省高校自然科学基金(16KJB470014)

摘　　要：风电出力的随机波动给电网带来了大量的负面影响,电池储能系统(Battery Energy StorageSystem,BESS)的接入可以有效平抑风功率波动,提高风电出力稳定性。为了提高储能利用效率,BESS的荷电状态(SOC)需最大限度控制在一定区域内,以便拥有足够裕量进行下一时刻的充放电动作,从而带来更好的平滑效果。为此,提出了一种由滑动平均滤波法和模型预测控制法协调运行的控制策略。该策略利用风电出力预测曲线,综合考虑了BESS出力、BESS的SOC以及风储联合输出功率平滑效果来实现对BESS优化控制。仿真验证表明该策略与传统低通滤波相比,不仅平滑效果更为理想,而且BESS的SOC限制在一定区间内,从而降低了BESS的最大充放电深度,节约了储能投资成本。The random fluctuations of wind power outputs have brought a lot of negative impacts in the power grid.Using storage batteries to smooth the wind power fluctuation can optimize wind power output characteristics and improve the stability of the output power in the wind farm.Controlling the state of charge(SOC) of energy storage battery in a defined interval can extend the service life of the storage battery and bring a better smoothing effect because of sufficient capacity.So,a new control strategy consisting of the moving average filter method and model predictive control act was proposed.It is based on the wind power prediction curve,with consideration of the BESS output,the SOC of BESS and the combined output power of wind storage to achieve the optimization of BESS.Simulation results show that the new strategy not only does have a good stabilizing effect but also extends the storage battery life to save storage investment costs because the SOC of the energy storage battery is limited to a set interval when it compared with the traditional first-order low-pass filter algorithm.

关 键 词：风功率波动 储能电池 荷电状态 控制策略 

分 类 号：TM73[电气工程—电力系统及自动化]