为了平抑风电、光伏的出力波动,减少其带给微网的不利影响,以风光储系统中的混合储能系统(Hybrid Energy Storage System,HESS)为研究对象,提出一种基于变分模态分解(Variational Mode Decomposition,VMD)和神经网络的HESS容量优化配置方法。采用VMD分解并结合储能系统荷电状态(State of Charge,SOC)等约束,制定HESS中两类储能的充放电策略。通过构造神经网络模型拟合储能系统关键参数与风光储系统输出功率平滑度指标的对应关系。以平滑效果最优与投资成本最低为目标函数,构建HESS容量优化配置模型,采用遗传算法求解HESS的最优容量配置方案。最后结合实际算例分析验证了所提方法的有效性。
英文摘要:
In order to smooth power fluctuations of the wind-solar hybrid power generation and reduce their adverse effects on microgrid, an optimal design method of the hybrid energy storage system (HESS) based on variational mode decomposition (VMD) and neural network is proposed. The charging/discharging strategy of two types of energy storage in HESS is formulated by VMD combined with energy storage system constraints. The corresponding relationship between key parameters of HESS and smoothness indexes of output power of wind-solar-battery hybrid power system is fitted by constructing a neural network model. With objective functions of optimizing smoothing effect and lowest investment cost, an optimal capacity allocation model of HESS is constructed, which is solved by genetic algorithm. Finally, the effectiveness of the proposed method is verified by a practical example.