摘要
通过建立数据中心负荷模型、电动汽车充电站负荷模型、储能站充放电模型和5G基站负荷模型,搭建了储能站投资回报率模型。采取两阶段优化方法、粒子群算法对目标函数进行优化,最终得出“多站融合”背景下储能站的最优运行策略。仿真结果表明,典型锂离子电池在合理区间内,峰谷电价差越大,最佳充放电深度越大,在此运行策略下,储能站的经济效益最高。
By establishing the data center load model,the electric vehicle charging station load model,the energy storage station charging and discharging model and the 5G base station load model,the return on investment model of the energy storage station is built.The two-stage optimization method is adopted,and the particle swarm optimization algorithm is used to optimize the objective function,and finally the optimal operation strategy of the energy storage station under the background of“multi-station integration”is obtained.The simulation results show that within a reasonable range,the greater the peak-valley price difference,the greater the optimal depth of charge and discharge.Under this operation strategy,the economic benefit of the energy storage station is the largest.
作者
田野
涂轶昀
TIAN Ye;TU Yiyun(School of Electrical Engineering,Shanghai University of Electric Power,Shanghai 200090,China)
出处
《上海电力大学学报》
CAS
2023年第4期357-363,共7页
Journal of Shanghai University of Electric Power
关键词
多站融合
运行策略
储能站
锂离子电池
充放电深度
粒子群算法
multi station integration
operation strategy
energy storage station
lithium-ion battery
depth of charge and discharge
particle swarm optimization
作者简介
通信作者:田野(1998-),女,在读硕士。主要研究方向为储能站运行策略。E-mail:1656606854@qq.com。
