摘要
大规模虚拟储能是指在电网中通过虚拟化技术将多个离散的储能设备组成一个大型的储能系统,以实现对电网功率进行平衡调节。由于新能源发电具有随机性、波动性和间歇性等特点,新能源功率预测误差控制难度较大。为提高新能源就地消纳水平,降低新能源功率预测误差,提出大规模虚拟储能平抑新能源功率预测误差优化调度方法。通过设置新能源功率预测时间分辨率,统计大规模虚拟储能平抑新能源功率分布特性,确定新能源功率预测误差分布特点,估计新能源功率预测误差置信区间,将新能源预测功率按照一定置信度纳入发电计划,设计大规模虚拟储能平抑新能源功率预测误差优化调度约束条件,构建新能源功率预测误差优化调度模型,并利用粒子群算法求解模型最优解。选择某地区实际数据设计试验,试验结果表明:所提方法对大规模虚拟储能平抑新能源功率预测误差灵敏度更高,高载能负荷调节量变化更小,且成本更低,具有显著的经济性和有效性。
Large scale virtual energy storage is a large-scale energy storage system composed of multiple discrete energy storage devices through virtualization technology in the power grid,in order to achieve power balance regulation of the power grid.Because of the randomness,fluctuation and intermittence features of new energy power generation,it is difficult to control the prediction error of new energy power.In order to improve the local consumption level of new energy and reduce the prediction error of new energy power,an optimal scheduling method of large-scale virtual energy storage to suppress the prediction error of new energy power is proposed.By setting the time resolution of new energy power prediction,the new energy power stabilizing distribution characteristics of large-scale virtual energy storage are counted,the distribution characteristics of new energy power prediction error are determined,the confidence interval of new energy power prediction error is estimated,the new energy prediction power is included in the power generation plan according to certain confidence degree,the constraint conditions of large-scale virtual energy storage to stabilize new energy power prediction error are designed,the optimal scheduling model of new energy power prediction error is constructed,and the optimal solution of the model is solved by using particle swarm optimization algorithm.The experimental results show that the proposed method is more sensitive to large-scale virtual energy storage to stabilize the prediction error of new energy power,with less change in high-energy load regulation and lower cost,and has remarkable economy and effectiveness.
作者
沙伟燕
胡伟
何宁辉
张涛
谢海滨
SHA Weiyan;HU Wei;HE Ninghui;ZHANG Tao;XIE Haibin(Electric Power Research Institute,State Grid Ningxia Electric Power Co.,Ltd.,Yinchuan 750011,China;State Grid Ningxia Electric Power Co.,Ltd.,Yinchuan 750001,China)
出处
《电力科学与技术学报》
CAS
CSCD
北大核心
2023年第6期167-174,共8页
Journal of Electric Power Science And Technology
基金
国网宁夏电力有限公司科技攻关项目(NX039000021)。
关键词
大规模虚拟储能
新能源
功率
预测误差
置信度
调度
粒子群算法
large-scale virtual energy storage
new energy
power
prediction error
confidence
scheduling
particle swarm optimization algorithm
