基于改进粒子群算法的储能系统优化运行

Optimal Operation of Energy Storage System Based on Improved Particle Swarm Optimization

针对储能系统运行经济性及储能电池运行损耗评估问题,建立计及电池损耗成本的储能系统运行收益优化模型;同时,针对标准粒子群算法全局寻优能力不足,罚函数难以选取等问题,提出一种双适应度混沌粒子群算法,采用该算法对电池损耗及储能系统收益情况进行优化计算,并与采用标准粒子群算法优化和未经运行优化所得结果进行对比分析。结果表明:储能电池损耗成本对系统收益影响明显,应建立合适的电池损耗模型进行评估计算;对储能系统进行运行优化后,合成负荷曲线方差减小,负荷峰、谷值削减,系统收益提高,损耗成本占比减小;通过与其他两种运行情况对比发现,双适应度混沌粒子群算法寻优所得储能系统运行收益值更高,储能系统经济性更佳。

This paper established an operational revenue optimal model to assess the cost of battery loss in battery energy storage system.Considering the fact that standard particle swarm optimization is weak in global optimization and penalty function selection,this paper came up with a chaotic particle swarm optimization algorithm with dual fitness to optimize the calculation of the loss and revenue of energy storage system.This paper compared the optimized results with the unoptimized ones and came to the conclusion that the battery loss cost obviously influences the energy storage system’s revenue.By optimizing the operation mode of the energy storage system,energy storage system sees decrease in combined load curve variance,load peaks and valleys and battery loss cost ratio,and increase in system revenue.Compared with the other two operating conditions,the improved particle swarm optimization brings higher energy efficiency and operation economy.