2nd Use Battery Energy Storage System Power Reduction Operation

  • Jinlei SunEmail author
  • Ruihang Liu
  • Qian Ma
  • Tianru Wang
  • Chuanyu Tang
Original Article


The battery energy storage system (BESS) is an ideal field of batteries retired from Electric Vehicle (EV)/Hybrid Electric Vehicle (HEV). The operation cost and service life is important for BESS operation. In order to solve these problems, this paper proposes a 2nd use BESS power reduction operation method. The BESS power allocation is optimized using Particle Swarm Optimization (PSO) algorithm to search the global optimization result. The state of health (SOH) caused cost increase is taken into consideration to minimize operating cost. Besides, in order to further improve the fault-tolerant operation ability and prolong the service life of 2nd use BESS, a power reduction operation method is proposed. Battery Systems (BSs) with different SOHs could work together, and avoid accelerated aging. Besides, BESS could continue working in power reduction mode when there exists BS that could not work anymore. This enhances system reliability and supplies more power throughput. Simulation results demonstrate that both scheduling time and power throughput are increased using the proposed power reduction operation method.


Battery energy storage system Particle swarm optimization State of health Power reduction operation 

List of symbols


Operation cost (CNY)


Maintenance cost (CNY)


Loss cost (CNY)


Fixed cost(CNY)


Number of BSs in the BESS


Time period per operation cycle (hour)


Operation and maintenance cost coefficient


Allocated power of the ith BS (kW)


Power loss coefficient (CNY/kWh)


Charge efficiency


Discharge efficiency


SOH variation of the ith BS


SOH lower limit of the ith BS


Construction cost of the ith BS (CNY)



This work was supported in part by the Fundamental Research Funds for the Central Universities under Project 30918011328.


  1. 1.
    Xiong R, Cao J, Yu Q, He H, Sun F (2018) Critical review on the battery state of charge estimation methods for electric vehicles. IEEE Access 6:1832–1843CrossRefGoogle Scholar
  2. 2.
    Feng F, Hu X, Hu L, Hu F, Li Y, Zhang L (2019) Propagation mechanisms and diagnosis of parameter inconsistency within Li-Ion battery packs. Renew Sustain Energy Rev 112:102–113CrossRefGoogle Scholar
  3. 3.
    Yan L, Baek MK, Park JB, Park YG, Roh JH (2017) An optimal energy storage operation scheduling algorithm for a smart home considering life cost of energy storage system. J Electr Eng Technol 12:1369–1375CrossRefGoogle Scholar
  4. 4.
    I.O. Office (2003) FreedomCAR Battery Test Manual For Power-Assist Hybrid Electric Vehicles, DOE:2003, 10: DOE/ID-11069Google Scholar
  5. 5.
    Won IK, Choo KM, Lee SR, Lee JH, Won CY (2018) Lifetime management method of lithium-ion battery for energy storage system. J Electr Eng Technol 13:1173–1184Google Scholar
  6. 6.
    Jiang Y, Jiang J, Zhang C, Zhang W, Gao Y, Guo Q (2017) Recognition of battery aging variations for LiFePO4 batteries in 2nd use applications combining incremental capacity analysis and statistical approaches. J Power Sour 360:180–188CrossRefGoogle Scholar
  7. 7.
    Kim W, Shin J, Kim S, Kim J (2017) Operation scheduling for an energy storage system considering reliability and aging. Energy 141:389–397CrossRefGoogle Scholar
  8. 8.
    Liu C, Wang X, Wu X, Guo J (2017) Economic scheduling model of microgrid considering the lifetime of batteries. IET Gener Transm Distrib 11:759–767CrossRefGoogle Scholar
  9. 9.
    Liu M, Li W, Wang C, Polis MP, Wang LY, Li J (2017) Reliability evaluation of large scale battery energy storage systems. IEEE T Smart Grid 8:2733–2743CrossRefGoogle Scholar
  10. 10.
    Adefarati T, Bansal RC (2019) Reliability, economic and environmental analysis of a microgrid system in the presence of renewable energy resources. Appl Energ 236:1089–1114CrossRefGoogle Scholar
  11. 11.
    Abu Abdullah M, Muttaqi KM, Sutanto D, Agalgaonkar AP (2015) An effective power dispatch control strategy to improve generation schedulability and supply reliability of a wind farm using a battery energy storage system. IEEE Trans Sustain Energ 6:1093–1102CrossRefGoogle Scholar
  12. 12.
    Liu W, Niu S, Xu H (2017) Optimal planning of battery energy storage considering reliability benefit and operation strategy in active distribution system. J Mod Power Syst Clean 5:177–186CrossRefGoogle Scholar
  13. 13.
    Debnath UK, Ahmad I, Habibi D, Saber AY (2015) Improving battery lifetime of gridable vehicles and system reliability in the smart grid. IEEE Syst J 9:989–999CrossRefGoogle Scholar
  14. 14.
    Nikolovski S, Reza Baghaee H, Mlakić D (2018) ANFIS-based peak power shaving/curtailment in microgrids including PV units and BESSs, EnergiesGoogle Scholar
  15. 15.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: 1995 IEEE international conference on neural networks proceedings (Cat. No. 95CH35828), pp 1942–1948Google Scholar
  16. 16.
    Jinlei S, Lie P, Ruihang L, Qian M, Chuanyu T, Tianru W (2019) Economic operation optimization for 2nd use batteries in battery energy storage systems. IEEE Access 7:41852–41859CrossRefGoogle Scholar
  17. 17.
    Qiu J, Zhao J, Zheng Y, Dong Z, Dong ZY (2018) Optimal allocation of BESS and MT in a microgrid. IET Gener Transm Distrib 12:1988–1997CrossRefGoogle Scholar

Copyright information

© The Korean Institute of Electrical Engineers 2019

Authors and Affiliations

  1. 1.School of AutomationUniversity of Science and TechnologyNanjingChina

Personalised recommendations