储能科学与技术 ›› 2022, Vol. 11 ›› Issue (2): 615-622.doi: 10.19799/j.cnki.2095-4239.2021.0508

• 储能系统与工程 • 上一篇    下一篇

基于双层规划模型的用户侧混合储能优化配置

郭斌(), 邢洁(), 姚飞, 景小敏   

  1. 东华大学信息科学与技术学院,上海 201620
  • 收稿日期:2021-09-27 修回日期:2021-10-26 出版日期:2022-02-05 发布日期:2022-02-08
  • 通讯作者: 邢洁 E-mail:530714014@qq.com;xingj@dhu.edu.cn
  • 作者简介:郭斌(1997—),男,硕士研究生,研究方向为储能系统在电力系统中的应用,E-mail:530714014@qq.com
  • 基金资助:
    上海市自然科学基金项目(21ZR1402300)

Optimal configuration of user-side hybrid energy storage based on bi-level programming model

Bin GUO(), Jie XING(), Fei YAO, Xiaomin JING   

  1. School of Information Science and Technology Donghua University, Shanghai 201620, China
  • Received:2021-09-27 Revised:2021-10-26 Online:2022-02-05 Published:2022-02-08
  • Contact: Jie XING E-mail:530714014@qq.com;xingj@dhu.edu.cn

摘要:

利用用户侧峰谷电价差,优化配置储能系统并合理调度可降低用电成本。本文提出一种铅酸电池-超级电容混合储能系统的双层规划模型,以储能投资年回报率为外层目标函数,在目标函数中考虑了储能削峰填谷收益、需量防守收益、全生命周期成本等因素;以储能日调度收益为内层目标函数,研究了储能各时段最优充放电功率。在构建混合储能系统模型时,考虑到铅酸电池和超级电容在能量、功率特性上的不同,在约束条件中加入充放电频率约束对不同储能元件日运行充放电次数进行限制。基于某电力用户实际负荷,采用粒子群算法和CPLEX求解器对双层规划模型进行求解,得到混合储能系统的最优配置,验证了混合储能模型在月综合收益、年投资回报率上均优于单独储能系统。

关键词: 混合储能系统, 全生命周期成本, 双层规划, 粒子群算法

Abstract:

Utilizing the peak-to-valley price difference on the user side, optimizing the configuration of energy storage systems and adequate dispatching can reduce the cost of electricity. Herein, we propose a two-level planning model for lead-acid battery-supercapacitor hybrid energy storage systems to calculate the annual return on energy storage investment. The outer objective function accounts for factors, such as energy storage peak shaving and valley filling income, demand defense income, and full life-cycle costs. Taking energy storage daily dispatch income as the inner objective function, the optimal energy storage period was studied. Energy storage performance constraints were considered during the development of the model to limit the daily charge and discharge times for different energy storage components according to the various properties of different energy storage components. According to the actual load of users in a certain place, the two-level programming model is solved by particle swarm algorithm and CPLEX solvers. We compare the rated power and capacity configurations of hybrid and single energy storage systems and verify the monthly comprehensive income of the hybrid energy storage model. The annual return on investment for the hybrid energy storage model is better than that of the single energy storage model. Furthermore, we compare the annual return on investment of different types of batteries and give suggestions for energy storage configuration planning.

Key words: hybrid energy storage system, full life-cycle cost, two-level programming, particle swarm algorithm

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