基于改进粒子群算法的储能调峰容量优化配置研究

Research on Optimal Configuration of Energy Storage Peak Shaving Capacity Based on Improved Particle Swarm Optimization Algorithm

配置储能系统是减少机组调峰负担、增加清洁能源接纳空间、降低新能源大规模并网给电网带来的压力的有效手段。首先,在分析储能调峰机理的基础上,综合考量储能调峰相关收益及成本,提出储能调峰的日效益模型及日成本模型;其次,以储能调峰日净收益最大为目标,综合考虑储能荷电状态约束、调峰后峰谷率、容量平衡等多种约束条件,提出考虑储能调峰效果的容量优化配置模型;然后,基于线性递减的惯性权重和异步学习的学习因子对标准粒子群优化算法进行改进,提出改进的粒子群算法,对储能容量进行优化配置;最后,综合考虑不同电池储能系统、不同季节典型日负荷间的差异,对某工业园区储能调峰容量进行优化配置,验证了所提模型和改进粒子群算法的有效性。算例结果表明,采用改进粒子群算法可以更快更好地得到优化结果。

The configuration of energy storage system is an effective means to reduce the burden of unit peaking,increase the space for clean energy acceptance and reduce the pressure on the grid brought by large-scale grid connection of new energy.Therefore,based on the analysis of the mechanism of energy storage peaking,this paper proposes a daily benefit model and a daily cost model for energy storage peaking,taking into account the benefits and costs associated with energy storage peaking.Secondly,to maximize the daily net benefit of energy storage peaking,the paper proposes a capacity optimal configuration model considering the effect of energy storage peaking,considering various constraints such as energy storage charge state constraints,peak-to-valley ratio after peaking,and capacity balance.Then,the standard particle swarm optimization algorithm is improved based on linear decreasing inertia weights and learning factors of asynchronous learning,and the improved particle swarm algorithm is proposed to optimize the configuration of energy storage capacity.Finally,on the basis of considering the differences between different battery energy storage systems and typical daily loads in different seasons,it verifies the the validity of the proposed model and the improved particle swarm algorithm through optimal allocation of energy storage and peaking capacity in an industrial park.The example results show that the improved particle swarm algorithm can be used to obtain faster and better optimization results.