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平抑联络线功率波动的微网混合储能容量优化 被引量:4

Capacity Optimization of Microgrid Hybrid Energy Storage for Smoothing Power Fluctuation of Tie-line
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摘要 为合理配置微网混合储能系统的容量,降低联络线功率的波动性,提出平抑联络线功率波动的混合储能系统容量配置方法。首先,以净负荷和联络线期望功率的偏差平方最小为目标,以联络线功率的上下限以及最小调节时间为约束,优化得到联络线期望功率。其次,依据净负荷功率以及优化得到的联络线期望功率计算得出混合储能系统功率。然后,建立了以混合储能系统全生命周期成本为目标,以蓄电池以及超级电容器工作频段的分界频率为优化变量的混合储能系统容量优化模型,并应用粒子群算法求解该模型获得最优的储能系统功率和容量。最后,采用实际微网运行功率数据,进行了案例验证,仿真结果证明了所提方法的经济性和有效性。 In order to configure appropriate capacity of microgrid hybrid energy storage system and stabilize the power of tie-line,this paper proposed a capacity allocation method for smoothing tie-line power fluctuation.Firstly,the minimized square of difference between net load and the expected power of tie-line was taken as a goal;the upper and lower limits of the tie-line power and the minimum adjustment time served as constraint,an optimal model was then established to obtain the expected power of tie-line.Next,the hybrid energy storage system power was calculated on the basis of the net load power and the expected power of tie-line.Then,taking the whole life cycle cost of hybrid energy storage system as the objective function and the boundary frequency of batteries and supercapacitors’compensation frequency as the optimization variable,a capacity optimization method for hybrid energy storage was modeled.This paper applied particle swarm optimization algorithm to calculate the most optimal power and capacity of energy storage system.Finally,the actual power data of microgrid verified this model.The results show that the proposed method is economical and effective.
作者 任建文 陈兴沛 焦瑞浩 REN Jianwen;CHEN Xingpei;JIAO Ruihao(State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,North China Electric Power University,Baoding 071003,China)
出处 《华北电力大学学报(自然科学版)》 CAS 北大核心 2019年第1期67-73,共7页 Journal of North China Electric Power University:Natural Science Edition
关键词 微网 混合储能系统 联络线期望功率 分界频率 粒子群算法 microgrid hybrid energy storage system expected power of tie-line boundary frequency particle swarm optimization
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