摘要
将退役动力电池梯次利用于家庭智能用电系统的储能,可有效提升动力电池使用寿命,实现资源的高效利用。考虑到家用电动汽车的普及化,文章提出了一种家用电动汽车退役动力电池梯次利用于家庭储能的容量优化配置方案。从家庭常用负荷、家庭可调控负荷、梯次储能系统、电动汽车和家庭光伏等角度,建立了家庭能量系统模型;综合考虑梯次储能的投资成本,以用户用电投入最少为目标函数,以家庭能量系统中元件的运行条件为约束条件,构建了梯次储能容量优化模型;分析了基于改进粒子群算法和模糊控制算法的梯次储能容量优化步骤;结合算例,对所提模型和算法的可行性进行了验证。结果表明,与无储能的系统相比,梯次储能的优化配置可有效降低家庭日电费量,且效果显著。
Making second-use of retired power battery for energy storage in home smart power systems can increase the life of the power batteries effectively,and achieve the use of resources efficiently.Considering the popularization of household electric vehicles,a capacity optimized allocation scheme for the second-use of retired power battery as household energy storage is proposed in this paper.The model of household energy system is established based on household common load,household adjustable load,energy storage system,electric vehicle and household photovoltaic;Taking the cost of seconduse of retired battery energy storage system into account,the optimization model of energy storage capacity is constructed,with the minimum power input as the objective function and the operation conditions of the components in the household energy system as the constraints.The optimization steps are analyzed based on the improved particle swarm optimization and fuzzy control algorithm.The feasibility of the proposed model and algorithm is verified by an example.The results show that compared with the system without energy storage,the second-use of retired power battery as a household energy storage system can effectively reduce the daily cost with remarkable effect.
作者
王帅
尹忠东
田硕文
林挚
王银顺
谢呵呵
Wang Shuai;Yin Zhongdong;Tian Shuowen;Lin Zhi;Wang Yinshun;Xie Hehe(State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,North China Electrical Power University,Beijing 102206,China)
出处
《电测与仪表》
北大核心
2020年第9期58-64,88,共8页
Electrical Measurement & Instrumentation
基金
国家重点研发计划专项资助项目(2016YFB0101900)。
关键词
退役动力电池
家庭储能
家庭能量管理
模糊控制
粒子群算法
second-use of retired power battery
household energy storage
home energy management
fuzzy control
particle swarm optimization
