针对光伏发电输出功率的波动性以及混合储能容量优化,以光储联合的发电系统为研究对象,提出一种基于变分模态分解（VMD）的混合储能容量优化配置策略。该策略采用VMD对光伏输出功率进行处理,利用欧氏距离方法将相关模态和非相关模态进行区分,利用滑动平均法提取非相关模态中的持续分量信号,将其与相关模态进行重构作为满足国家标准的并网功率,并利用混合储能系统平抑非相关模态中的波动分量信号。建立以储能系统年均配置成本最小为目标函数的混合储能系统容量优化模型,采用改进的布谷鸟算法求解模型,得到满足系统要求的储能容量配置方案。在Matlab/Simulink上进行仿真和分析,验证所提策略的有效性与经济性。
Abstract
Focusing on the photovoltaic power output fluctuation and the hybrid energy storage capacity optimization, taking the photovoltaic system as research object, an optimal allocation strategy of hybrid energy storage capacity is put forward based on variational mode decomposition （VMD）. In this strategy, VMD is used to deal with the photovoltaic power output and Euclidean distance is employed to distinguish the relevant modes from the irrelevant modes. Meanwhile, persistent component signals in the irrelevant modes are extracted with the moving average method, which are reconstructed with the relevant modes to satisfy the national standard as the grid-connected power, and the fluctuation component signals in the irrelevant modes are suppressed with the hybrid energy storage system. As the result, the capacity optimization model of the hybrid energy storage system is established taking the minimum annual average allocation cost of the energy storage system as the objective function and the energy storage capacity allocation solution meeting the system requirements is obtained by using the improved cuckoo search algorithm to solve the model. The effectiveness and the economical efficiency are verified with the simulation and analysis on MATLAB/Simulink.
关键词
光伏组件 /
储能 /
滑动平均法 /
容量配置 /
变分模态分解 /
布谷鸟算法
Key words
photovoltaic modules /
energy storage /
moving average /
capacity allocation /
variational mode decomposition /
cuckoo search algorithm
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基金
国家重点研发计划（2017YFB0903504）; 安徽省科技重大专项（17030701041）
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