基于元模型优化算法的混合储能系统双层优化配置方法

混合储能兼具能量型储能与功率型储能的优势,针对混合储能在风电平抑中的配置问题,提出了一种基于元模型优化算法的混合储能双层优化配置方法。首先,利用小波分解对风电功率的原始数据进行分解,得到混合储能需要平抑的功率。然后,针对功率分配策略对混合储能容量配置的影响问题,提出一种混合储能容量嵌套式双层优化配置方法。该方法的内层为混合储能功率优化分配策略,以荷电状态、充放电功率为约束条件,以蓄电池总体充放电功率最小为目标函数,以提高蓄电池的使用寿命;外层以最小容量、最小功率为约束条件,以混合储能的全寿命周期年均成本最小为目标函数。针对多变量、非线性、计算密集型双层优化方法具有求解复杂、计算时间长等问题,提出基于元模型优化算法的优化求解方法。算例分析结果表明,所提优化配置方法可以在保持混合储能经济性最优的同时,有效避免蓄电池频繁充放电,从而提高了其使用寿命;相比于传统的启发式求解方法,基于元模型优化算法的优化求解方法的计算速度更快,所得优化配置结果更精确。

Bi-level optimal configuration method of hybrid energy storage system based on meta model optimization algorithm

Hybrid energy storage has the advantages of both energy-type energy storage and power-type energy storage. Aiming at the configuration problem of hybrid energy storage in wind power smoothing, a bi-level optimal configuration method of hybrid energy storage system based on meta model optimization algorithm is proposed. Firstly, the original data of wind power are decomposed by wavelet transform to obtain the power needed to be smoothed by hybrid energy storage. Then, aiming at the influence of power allocation strategy on capacity configuration of hybrid energy storage, a nested bi-level optimal configuration method of hybrid energy storage is proposed. The inner layer of the method is the optimal strategy of power allocation, which takes the minimum overall charging and discharging power of the battery as the objective function, with the state of charge and charging/discharging power as the constraints, so as to improve the service life of the battery. The outer layer takes the minimum capacity and the minimum power as the constraints, and the minimum annual life cycle cost of hybrid energy storage as the objective function. The multi-varia-ble, nonlinear and compute-intensive bi-level optimization method has the problems of complex computation, long computation time and so on, so an optimization method based on meta model optimization algorithm is proposed. The example results show that the proposed method can not only maintain the optimal economy of hybrid energy storage, but also effectively avoid the frequent charging/discharging of the battery, thus improving its service life. Compared with the traditional heuristic method, the optimization method based on meta model optimization algorithm has faster calculation speed and more accurate optimal configuration results.