Two-Stage Optimal Allocation Model of User-Side Energy Storage Based on Generalized Benders Decomposition

  • Yuanxing XiaEmail author
  • Minglei Qin
  • Enlin Cheng
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 585)


To cater for the commercial application of energy storage on the user side, a two-stage optimal configuration model of energy storage on the user side based on generalized Benders Decomposition algorithm is proposed. Firstly, according to the collected historical load data, the user can judge whether it is suitable to install the energy storage device. Then, aiming at maximizing the benefit of energy storage, a two-stage optimal energy storage allocation model with monthly scale and daily scale is established for users who are suitable for installing energy storage devices. Considering that the model is a nonlinear mixed integer optimization, the generalized Benders Decomposition algorithm is used to optimize the model. Finally, the load data of three typical large industrial users are collected, and the Benders Decomposition algorithm is compiled with MATLAB2017a to optimize the simulation, which verifies the economic effectiveness of the proposed model.


User-side energy storage Two-stage optimization Generalized benders decomposition Life cycle Demand management 


  1. 1.
    Latif U, Javaid N, Zarin SS et al (2018) Cost optimization in home energy management system using genetic algorithm, bat algorithm and hybrid bat genetic algorithm. In: 2018 IEEE 32nd international conference on advanced information networking and applications (AINA), pp 667–677Google Scholar
  2. 2.
    Zakeri B, Syri S (2015) Electrical energy storage systems: a comparative life cycle cost analysis. Renew Sustain Energy Rev 42:569–596CrossRefGoogle Scholar
  3. 3.
    Luo X, Wang J, Dooner M et al (2015) Overview of current development in electrical energy storage technologies and the application potential in power system operation. Appl Energy 137:511–536CrossRefGoogle Scholar
  4. 4.
    Bonaccorso F, Colombo L, Yu G et al (2015) Graphene, related two-dimensional crystals, and hybrid systems for energy conversion and storage. Science 347(6217):1246501CrossRefGoogle Scholar
  5. 5.
    Ma H, Wang B, Gao W, Zhu C, Zheng Z, Siyang, Yang Y (2019) Energy storage equipment in regional integrated energy system participates in the operation optimization of auxiliary services. Power Syst Autom 43(08):34–46+68Google Scholar
  6. 6.
    Liang Z, Song Z, Wang J et al (2018) Optimal configuration of liquid metal battery energy storage system in photohydrogen coupled microgrid. Power Syst Autom 42(4):64–69Google Scholar
  7. 7.
    Han X, Zhang Z, Xiu X et al (2016) Economic evaluation of fast charging station equipped with step battery energy storage system. Energy Storage Sci Technol 5(4):514–521Google Scholar
  8. 8.
    Chen L, Wu T, Liu H et al (2019) Two-stage large-user energy storage optimization model based on demand management. Power Syst Autom 43(01):262–271 Google Scholar
  9. 9.
    Amrouche SO, Rekioua D, Rekioua T et al (2016) Overview of energy storage in renewable energy systems. Int J Hydrogen Energy 41(45):20914–20927Google Scholar
  10. 10.
    Ross M, Abbey C, Bouffard F et al (2018) Microgrid economic dispatch with energy storage systems. IEEE Trans Smart Grid 9(4):3039–3047CrossRefGoogle Scholar
  11. 11.
    Hu W, Xu G, Shangze, Wang L, Wen F, Cheng H (2019) Joint planning of battery energy storage and demand response for peak shaving in Industrial Park [J/OL]. Power Syst Autom:1–8Google Scholar
  12. 12.
    Cheng L, Qining, Tian L (2019) Joint planning of generalized energy storage resources and distributed generation considering operation control strategies. Power Syst Autom 43(10):27–40+43Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.College of Energy and Electrical EngineeringHohai UniversityNanjingChina

Personalised recommendations