基于状态量预测的风储联合并网储能优化控制方法  

An Optimal Energy Storage Control Scheme for Wind Power and Energy Storage System Based on State Forecast

作　　者：柴炜[1] 曹云峰[1] 李征[1] 蔡旭[1,2] 

机构地区：[1]上海交通大学电子信息与电气工程学院风力发电研究中心,上海市200240 [2]海洋工程国家重点实验室、上海交通大学船舶海洋与建筑工程学院,上海市200240

出　　处：《电力系统自动化》2015年第2期13-20,共8页Automation of Electric Power Systems

基　　金：国家高技术研究发展计划(863计划)资助项目(2012AA050203);上海市科委项目(11dz1210300)~~

摘　　要：为了使风电场兼具可调度性和输出功率平稳性,提出了基于状态量预测的电池储能系统(BESS)优化控制方法。该方法在BESS配合风电场短时调度的基础上增加了预测控制模块,该模块根据超短期风功率预测结果对电池极限状态进行预判,并综合考虑预测误差、波动尺度限定值和运行约束条件,实时调节BESS输出功率,以降低极端状态下的并网尖峰波动。为了提高预测精度,提出了结合混沌法和一般线性法的神经网络组合预测模型。算例结果表明,所提控制方法能使风储并网功率很好地跟踪调度指令,在实现了可调性的同时降低了并网尖峰波动。In order to make wind farms dispatchable and smooth its output power, an optimal control scheme of battery energy storage system （BESS） based on state forecast is proposed. This method introduces a predictive control model based on the short-term dispatch of wind farms combined with BESS. The model forecasts the ultimate battery state according to the wind power forecast results. In consideration of forecast errors, fluctuation limits and operating constraints, the output power of BESS is adjusted in real time to reduce the peaks of power fluctuation under extreme conditions. In order to improve the forecast accuracy, a neural network combination forecasting model based on the chaos theory and the general linear method is developed. Case study results show that the proposed control scheme enables the grid power of the wind power and energy storage system to track the dispatch instructions and reduces the peaks of power fluctuation while enabling the wind farm dispatchable.

关 键 词：电池储能系统 风电场 短时调度 预测控制 混沌预测 神经网络 

分 类 号：TM614[电气工程—电力系统及自动化]