摘要
电化学储能的循环寿命受到充放电次数和放电深度的影响,为了更加准确地在新能源电力系统中规划储能电站,提出基于Kullback-Leibler(KL)散度的储能电站分布鲁棒规划方法。根据电化学储能循环寿命的幂函数,建立基于等效全循环次数的储能电站寿命模型,考虑储能电站寿命模型约束和系统运行约束,以储能电站的全寿命周期成本和机组运行成本最小为目标来构建储能电站的规划模型。进一步,将基于KL散度的风电出力不确定性集嵌入到储能电站规划模型中,通过样本平均近似法将储能电站分布鲁棒规划模型转化混合整数线性规划模型求解。在含2个风电场的改进IEEE-30节点系统中验证了所提出的储能电站分布鲁棒规划方法的优势。
The cycle life of electrochemical energy storage is affected by the number of charges and discharg and the depth of discharge.In order to more accurately plan the energy storage plants in the renewable energy power system,this paper proposes a distributionally robust planning method based on Kullback-Leibler(KL)divergence.According to the power function of cycle life of electrochemical energy storage,the life model of energy storage plants with equivalent full cycles times is established.Considering the life model constraints of the energy storage plant and system operating constraints,the planning model of energy storage plants is constructed with the life cycle cost and units’operating cost minimum as the objective.Furthermore,the uncertainty set of wind power output based on KL divergence is embedded into the planning model of energy storage plants,and the distributionally robust planning model of energy storage plants is transformed into mixed integer linear programming model by sample average approximation method.In the modified IEEE-30 mode system with two wind farms,the advantages of the distributionally robust planning method proposed in this paper are verified.
作者
李旭霞
张琳娜
郑晓明
李佳
邓娇娇
梁燕
Li Xuxia;Zhang Linna;Zheng Xiaoming;Li Jia;Deng Jiaojiao;Liang Yan(State Grid Shanxi Economic and Technical Research Institute,Taiyuan 030021,China;State Grid Shanxi Electric Power Company,Taiyuan 030021,China)
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2022年第4期46-55,共10页
Acta Energiae Solaris Sinica
基金
国网山西省电力有限公司科技项目(520533200003)。
关键词
储能电站
电化学储能
变寿命特性
机会约束
分布鲁棒优化
Kullback-Leibler散度
energy storage plants
electrochemical energy storage
variable lifetime characteristic
chance constrained
distributionally robust optimization
Kullback-Leibler divergence
