Energy Storage Science and Technology ›› 2022, Vol. 11 ›› Issue (2): 615-622.doi: 10.19799/j.cnki.2095-4239.2021.0508

• Energy Storage System and Engineering • Previous Articles     Next Articles

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

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

CLC Number: