摘要
在高比例风电主导的可再生能源电力系统中,配置储能系统是缓解风电功率波动、实现削峰填谷、提高风电可调度性的有效解决方案。文中从大规模储能出力水平以及风电未来输出对当前储能运行影响的角度出发,设计了风储联合运行的多目标优化仿真模型。采用马尔可夫预测模型形成风功率未来输出评估,同时利用粒子群优化算法实时滚动优化风储并网功率,获得储能电池最优运行策略。利用某百兆瓦级风电场的典型风功率数据进行仿真,仿真结果表明所提方法平滑效果良好,避免了储能电池过度充放电,防止了储能电池进入死区的情况,提高了风储一体化系统的可靠性和经济性。
In the renewable energy power system dominated by high proportion of wind power, configuring energy storage system(ESS) is an effective solution to suppress wind power fluctuations, achieve peak load shaving and improve wind power dispatchability. A multi-objective hybrid optimization simulation model for joint wind power and energy storage operation is designed, which integrates the impact of output level of the large-scale energy storage and the future output of wind power on the current energy storage operation. Markov prediction model is adopted to forecast the future output of wind power, and particle swarm optimization algorithm is used to optimize the grid-connected power of wind power and energy storage system in real time,then the optimal operation strategy of ESS can be obtained. The typical wind power data from a hundred-megawatt wind farm are used for simulation. Simulation results show that the proposed method has good smoothing performance, which avoids excessive charging and discharging of ESS and prevents it from entering dead zone, and further improves the reliability and the economy of the integrated system of wind power and energy storage.
作者
任永峰
薛宇
云平平
韩俊飞
贾伟青
REN Yongfeng;XUE Yu;YUN Pingping;HAN Junfei;JIA Weiqing(College of Energy and Power Engineering,Inner Mongolia University of Technology,Hohhot 010051,China;Inner Mongolia Electric Power Research Institute of Inner Mongolia Power(Group)Co.,Ltd.,Hohhot 010020,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2020年第6期67-74,共8页
Automation of Electric Power Systems
基金
国家自然科学基金资助项目(51967016,51567020)
内蒙古自治区科技计划资助项目(201802035)
内蒙古自治区自然科学基金资助项目(2015MS0532)。
关键词
马尔可夫预测
粒子群优化算法
功率波动平抑
储能
多目标优化
能量状态
Markov prediction
particle swarm optimization algorithm
power fluctuation suppression
energy storage
multi-objective optimization
state of energy
