基于哈里斯鹰优化算法的光伏储能容量配置优化

Optimization of photovoltaic energy storage capacity allocation based on Harris Hawk Optimization algorithm

在双碳目标下,高比例的光伏等新能源接入对电网造成了冲击。针对此问题,从电站侧出发,基于最低的光伏功率平均波动率和储能系统成本这两个优化目标,提出了一种基于哈里斯鹰优化算法的储能容量配置优化模型,通过聚类算法将天气类型分为四种并分别进行优化。结合算例分析,所提出的模型对储能配置问题在雨雪等较差的工况可将日均波动率从54.61%降低至13.60%,晴天等较好的工况日均波动率可下降约5%。通过双层优化,确定出最终储能配置方案为2.5 MW/4.59 MW·h,结合全年数据进行优化验证,此方案下全年平均波动率从19.41%下降至11.33%,全周期成本814.75万元。

Under the“3060”goal,a high proportion of new energy such as photovoltaics have made an impact on the grid.To deal with this problem,subject to the lowest average fluctuation rate of photovoltaic and the cost of the energy storage system from the power station side,proposes an energy storage capacity configuration optimization model based on the Harris Hawk Optimization(HHO)algorithm,divide whole⁃year⁃data into four typical weathers by k⁃means,and optimize each weather individually.Combined with the example,the model proposed can reduce the average daily volatility from 54.61%to 13.60%for lower conditions such as rain and snow,and about 5%for better conditions such as sunny.Through the double⁃layer opt⁃imization,the final energy storage allocation scheme was determined as 2.5 MW/4.59 MW·h.Combined with the annual data for optimization verification,the annual average volatility under this scheme decreased from 19.41%to 11.33%,and the full⁃cycle cost was 8147500 yuan.