摘要
提出基于动力电池梯次利用的光储电站日前调度优化模型,通过回收状态不同的退役动力电池构建复合储能系统,结合可再生能源和负荷的预测值,进行调度优化。模型以总运行成本最低为目标函数,约束包括功率平衡、电池荷电状态、设备容量和充放电次数等。求解采用改进的粒子群算法,在最优解邻域内进行再搜索,既保证算法的收敛速度又避免陷入局部最优。通过算例仿真,对模型和算法进行验证:整个协调周期内4组电池的充放电转换次数仅5次,最大限度的降低了电池损耗并延长电池寿命,证明所建模型和算法的可行性和有效性。
A day-ahead dispatching optimization model of photovoltaic(PV)storage power station based on the echelon utilization of power battery was proposed.The composite energy storage system was built by recovering the retired power battery with different states,combined with the predicted value of renewable energy and load;the scheduling optimization was carried out.The objective function of the model was to minimize the total operating cost,the constraints including power balance,battery state of charge,equipment capacity and charge discharge times.The improved particle swarm optimization(PSO)algorithm was used to search in the neighborhood of the optimal solution to ensure the convergence speed of the algorithm and avoid falling into the local optimum.The model and algorithm were verified by example simulation:in the whole coordination cycle,the charge-discharge conversion times of four groups of batteries were only 5 times,which reduced the battery loss and prolonged the battery life to the maximum extent,which proved the feasibility and effectiveness of the model and algorithm.
作者
谢雨阳
熊焰
XIE Yu-yang;XIONG Yan(School of information Science and Engineering,Wuhan University of Science and Technology,Wuhan,Hubei 430081,China;EAST Group Co.,Ltd.,Dongguan,Guangdong 523722,China)
出处
《电池》
CAS
CSCD
北大核心
2020年第4期372-375,共4页
Battery Bimonthly
关键词
动力电池
梯次利用
日前调度
粒子群算法
power battery
echelon utilization
day-ahead scheduling
particle swarm optimization(PSO)algorithm
