基于自适应VMD的混合储能容量优化配置研究

刘仲民, 齐国愿, 高敬更, 王治国

太阳能学报 ›› 2022, Vol. 43 ›› Issue (4) : 75-81.

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太阳能学报 ›› 2022, Vol. 43 ›› Issue (4) : 75-81. DOI: 10.19912/j.0254-0096.tynxb.2020-0965
电化学储能安全性与退役动力电池梯次利用关键技术专题

基于自适应VMD的混合储能容量优化配置研究

  • 刘仲民1, 齐国愿1, 高敬更2, 王治国2
作者信息 +

RESEARCH ON OPTIMAL CONFIGURATION OF HYBRID ENERGY STORAGE CAPACITY BASED ON ADAPTIVE VMD

  • Liu Zhongmin1, Qi Guoyuan1, Gao Jinggeng2, Wang Zhiguo2
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文章历史 +

摘要

针对用户用电需求和可再生能源发电情况,提出一种由重力势能储能、蓄电池和超级电容组成的混合储能系统,建立其数学模型。针对其不同的特性,提出基于自适应变分模态分解的混合储能系统容量优化配置策略,对混合储能容量优化配置模型求解。以某风光互补电站典型日功率数据为例,对最佳储能系统的分解尺度K和高中、中低频分界点及其对应的储能配置进行优化分析,仿真结果验证所提方案的经济性和合理性。

Abstract

Based on power demand of users and renewable energy generation situation, in this paper, a hybrid energy storage system consisting of gravity energy storage, battery and supercapacitor is proposed, and the mathematical modelling of the hybrid energy storage system is established. According to different characteristics, a strategy of optimal capacity configuration of hybrid energy storage system based on adaptive variational mode decomposition is proposed and the optimal configuration model of hybrid energy storage capacity is sowed. The optimal energy storage system’s decomposition scale K, the frequency demarcation points and their corresponding energy storage configuration are verified on typical daily output data of a wind-solar generation station. It is indicated that the results reflect the economy and rationality of the proposed method.

关键词

风电功率 / 光伏功率 / 储能 / 自适应变分模态分解 / 容量优化配置

Key words

wind power / solar power / energy storage / adaptive variational mode decomposition / optimal capacity configuration

引用本文

导出引用
刘仲民, 齐国愿, 高敬更, 王治国. 基于自适应VMD的混合储能容量优化配置研究[J]. 太阳能学报. 2022, 43(4): 75-81 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0965
Liu Zhongmin, Qi Guoyuan, Gao Jinggeng, Wang Zhiguo. RESEARCH ON OPTIMAL CONFIGURATION OF HYBRID ENERGY STORAGE CAPACITY BASED ON ADAPTIVE VMD[J]. Acta Energiae Solaris Sinica. 2022, 43(4): 75-81 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0965
中图分类号: TM614    TM615   

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基金

基于用户用电行为大数据分析的智能异常诊断关键技术及深化应用研究(SGGSKY00JLJS1900271)

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