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投稿时间：2023-05-23
投稿时间：2023-05-23
中文摘要: 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。
Abstract: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.
keywords: multi station integration operation strategy energy storage station lithium-ion battery depth of charge and discharge particle swarm optimization
文章编号：20234008 中图分类号：TK01+8;TK02;TM732 文献标志码：
基金项目:
| 作者 | 单位 | |
| 田野 | 上海电力大学 电气工程学院 | 1656606854@qq.com |
| 涂轶昀 | 上海电力大学 电气工程学院 |
引用文本：
田野,涂轶昀.基于粒子群算法的“多站融合”背景下储能站运行策略研究[J].上海电力大学学报,2023,39(4):357-363.
TIAN Ye,TU Yiyun.Research on Operation Strategy of Energy Storage Station under the Background of Multi-station Fusion Based on Particle Swarm Optimization[J].Journal of Shanghai University of Electric Power,2023,39(4):357-363.
田野,涂轶昀.基于粒子群算法的“多站融合”背景下储能站运行策略研究[J].上海电力大学学报,2023,39(4):357-363.
TIAN Ye,TU Yiyun.Research on Operation Strategy of Energy Storage Station under the Background of Multi-station Fusion Based on Particle Swarm Optimization[J].Journal of Shanghai University of Electric Power,2023,39(4):357-363.