Multi-stage distributionally robust planning of energy storage capacity considering flexibility

被引：0
作者：
Zhu X. [1 ]
Shan Y. [1 ]
机构：
[1] State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2023年 / 43卷 / 06期
关键词：
capacity configuration; distributionally robust optimization; electric power systems; energy storage; flexibility; multi-time scale coordinated planning; uncertainty;
D O I：
10.16081/j.epae.202206007
中图分类号：
学科分类号：
摘要：
As a flexible resource，energy storage plays a role in promoting the consumption of new energy and the safe and stable operation of power system. However，limited by the investment cost of energy storage，it is difficult to meet the system flexibility requirements only relying on large-scale energy storage. Therefore，a capacity planning model of energy storage in the power system considering flexibility is proposed，which takes into account the influence of the existing adjustable traditional generating units on the flexibility，and uses the distributionally robust opportunity constraints to describe the uncertainty of new energy output. According to the operation characteristics of the system at different time scales，comprehensively considering the investment cost of energy storage，the uncertainty of new energy output and the system operation flexibility，a multi-time scale energy storage configuration model is established. The multi-stage iterative linear optimization method is used to improve the solution efficiency. Taking the IEEE-RTS 24-bus system as an example，the analysis results show that the proposed method has good economy and robustness in solving the configuration capacity of energy storage. © 2023 Electric Power Automation Equipment Press. All rights reserved.
引用
收藏
页码：152 / 159and167
相关论文
共 21 条
  • [1] LI Yunlong, LI Zhigang, ZHENG Jiehui, Distributionally robust economic dispatch of multi-regional power grid considering uncertainty and correlation of wind power［J］, Electric Power Automation Equipment, 41, 8, pp. 97-104, (2021)
  • [2] WEI Zhenbo, GUO Yi, WEI Ping'an, Et al., Distribution robust expansion planning model for integrated natural gas and electric power systems considering transmission switching［J］, Electric Power Automation Equipment, 41, 2, pp. 16-23, (2021)
  • [3] ZHU Rujie, Hua WEI, BAI Xiaoqing, Distributionally robust optimization of multi-energy dynamic optimal power flow［J］, Proceedings of the CSEE, 40, 11, pp. 3489-3498, (2020)
  • [4] LU Zhuoxin, XU Xiaoyuan, YAN Zheng, Et al., Overview on data-driven optimal scheduling methods of power system in uncertain environment［J］, Automation of Electric Power Systems, 44, 21, pp. 172-183, (2020)
  • [5] SINGH C., A distributionally robust optimization model for unit commitment considering uncertain wind power generation［J］, IEEE Transactions on Power Systems, 32, 1, pp. 39-49, (2017)
  • [6] RUAN Hebin, GAO Hongjun, LIU Junyong, Et al., A distributionally robust reactive power optimization model for active distribution network considering reactive power support of DG and switch reconfiguration［J］, Proceedings of the CSEE, 39, 3, pp. 685-695, (2019)
  • [7] YANG Libin, CAO Yang, WEI Wei, Et al., Configuration method of energy storage for wind farms considering wind power uncertainty and wind curtailment constraint［J］, Automation of Electric Power Systems, 44, 16, pp. 45-52, (2020)
  • [8] Zhongjie GUO, Wei WEI, Laijun CHEN, Et al., Sizing energy storage to reduce renewable power curtailment considering network power flows：a distributionally robust optimisation approach［J］, IET Renewable Power Generation, 14, 16, pp. 3273-3280, (2020)
  • [9] Yang SI, CHEN Laijun, CHEN Xiaotao, Et al., Optimal capacity allocation of hydrogen energy storage in wind-hydrogen hybrid system based on distributionally robust［J］, Electric Power Automation Equipment, 41, 10, pp. 3-10, (2021)
  • [10] LI Xiaozhu, WANG Weiqing, Application of distributed robust optimization based on Bayesian theory in allocation of energy storage［J］, Power System Technology, 46, 10, pp. 4001-4011, (2022)
123