摘要
针对传统风电平抑方法的滞后性,文中提出了根据功率预测,考虑储能容量配置及并网要求的目标功率平抑策略,以国家风电并网规定为参考指标,采用风功率预测技术在实现储能系统容量优化的同时获得所需的目标功率。在此基础上建立以目标功率和预测功率偏差最小为优化目标的数学模型,以并网要求的有功功率变化限值为约束条件,并在Matlab中利用粒子群优化算法(particle swam optimization algorithm,PSO)对其进行求解。以实际风电场数据为基础进行仿真优化计算,仿真结果表明该策略优于两个时间尺度下的国家并网要求,能够有效平滑风电输出、增加风电并网利用率,对风电大规模并网起到一定作用,并对未来的风电—储能系统发展提供了一定的理论指导。
In view of the lag of the traditional wind power stabilization method,in this paper,the target power stabilization strategy in accordance with power prediction and with consideration of energy storage capacity configuration and the grid connection requirement is proposed.The target power needed is obtained at the time of achieving capacity optimization of energy storage system with China National wind power grid connection specification as reference index and by the use of wind power predication technology.On this basis,a mathematical model with the minimum target power and predicted power deviation as optimal is established,which is constrained by the active power variation limit of the grid,and the particle swarm optimization(PSO)is used to solve it in the Matlab.The simulation optimal calculation is performed on the basis of the actual wind farm data.It is shown by the simulation result that the strategy is superior to the national grid connection requirements under two time scales,can effectively smooth the wind power output and increase the utilization rate of wind power grid.It also plays a definite role in the large?scale wind power grid connection and provides a definite theory instruction significance for the development of the wind power storage system in the future.
作者
热孜古丽·买买提
陈洁
田小壮
刘颢
方圆
Reziguli·MAIMAITI;CHEN Jie;TIAN Xiaozhuang;LIU Hao;FANG Yuan(School of Electric Engineering,Xinjiang University,Urumqi 830047,China;State Grid Xinjiang Electric Power Company Maintenance Company,Urumqi 830000,China)
出处
《电力电容器与无功补偿》
北大核心
2019年第4期188-192,共5页
Power Capacitor & Reactive Power Compensation
基金
国家自然科学基金项目(51467020)
关键词
风功率波动
目标功率
粒子群优化算法
波动量
wind power fluctuation
target power
particle swarm optimization algorithm
fluctuation