摘要
大规模风电接入后,风电场的爬坡率控制对电网稳定运行有着重要意义。为此提出了基于概率预测的储能系统辅助风电场爬坡率控制方法。利用Gaussian过程回归预测对下一时段风电场输出功率进行预测,得到风电场的预测爬坡率,当预测爬坡率超出限定要求时,通过储能充放电做出补偿。基于某风电场的历史出力数据进行了案例计算,结果表明,基于Gaussian回归的单步预测精度可满足风电场爬坡率控制的要求。考虑储能充放电容量限制后,储能可减少风电场50%以上的爬坡事件。此工作为储能辅助风电场爬坡率控制提供了有效的策略,可在实践中加以应用。
It is significant to constrain the ramp rate of wind farms when large-scale wind generation is integrated in the grid. We proposed a novel method based on probability prediction of wind generation using energy storage system （ESS） for ramp rate control in the wind farm. The Gaussian process regression （GPR） model was used to predict the next step generation of the wind farm. When the predicted ramp rate exceeded the limited value, energy storage was used to com- pensate the variation. The historical data of a wind farm were used for case study. The calculation results show that the accuracy of the one-step GPR prediction can satisfy the requirements of ramp rate control. Considering the restriction of the ESS capacity, the amount of ramp events can decrease by 50% or more. The work of the paper can provides an effec- tive way for wind farm ramp rate control using energy storage which can be used in the practice.
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2015年第10期3233-3239,共7页
High Voltage Engineering
基金
国家高技术研究发展计划(863计划)(2012AA0502023)
国家电网公司科技项目(XT71-14-004)~~
关键词
爬坡率控制
概率预测
储能
Gaussian过程回归
风电场
自相关函数
相空间重构
ramp rate control
probability prediction
energy storage
Gaussian process regression
wind farm
auto-correlated function
phase space reconstruction
